system

Abstract

The system according to this embodiment aims to allow business professionals to easily receive coaching tailored to their needs. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, a data processing unit, and an adjustment unit. The reception unit receives input of goals and tasks. The analysis unit analyzes the information received by the reception unit. The provision unit provides feedback and advice based on the analysis results obtained by the analysis unit. The data processing unit processes multiple types of data. The adjustment unit adjusts the communication style based on the user's personality and preferences.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is costly for a businessperson to receive coaching suitable for themselves and it is difficult to find a suitable coach.

[0005] The system according to the embodiment aims to enable a businessperson to easily receive coaching suitable for themselves.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, a data processing unit, and an adjustment unit. The reception unit receives input of goals and tasks. The analysis unit analyzes the information received by the reception unit. The provision unit provides feedback and advice based on the analysis results obtained by the analysis unit. The data processing unit processes multiple types of data. The adjustment unit adjusts the communication style based on the user's personality and preferences. [Effects of the Invention]

[0007] The system according to this embodiment allows business professionals to easily receive coaching tailored to their needs. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The coaching system according to an embodiment of the present invention is a system for improving the performance and career of business professionals. This coaching system allows users to input their goals and challenges, and the AI ​​analyzes this information to provide optimal feedback and advice. Furthermore, the multimodal AI can process multiple types of data, including not only text but also images, audio, and video, maximizing user performance. This allows for a more affordable, 24 / 7 coaching service than employing a human coach with specialized knowledge. Additionally, the AI ​​is unbiased and objective, protecting personal information. Moreover, the communication style can be adjusted to suit the user's personality and preferences. This allows users to obtain a truly personalized coach. For example, a user inputs their goals and challenges. The user only needs to input specific goals and challenges. For instance, they might input "I want to improve my project management skills" or "I want to improve my presentation skills." This information is input into the AI. Next, the AI ​​analyzes the input information. Based on the user's goals and challenges, the AI ​​provides optimal feedback and advice. For example, for the goal of improving project management skills, the AI ​​provides best practices and specific action plans for project management. Furthermore, multimodal AI can process multiple types of data, including not only text but also images, audio, and video. For example, if a user has the goal of improving their presentation skills, the AI ​​can analyze a video of the user's presentation and provide specific feedback on areas for improvement. This maximizes the user's performance. Additionally, the AI ​​is available 24 / 7 and is less expensive than hiring a human coach with specialized knowledge. Moreover, the AI ​​is unbiased and objective, and can protect personal information. This allows users to use the coaching service with peace of mind. Furthermore, the AI ​​can adjust its communication style to suit the user's personality and preferences.For example, if a user is introverted, the AI ​​will provide feedback in a calm tone. Conversely, if a user is extroverted, the AI ​​will provide feedback in an energetic tone. This allows the user to have a truly personalized coach. As a result, the coaching system can provide optimal feedback and advice for the user's goals and challenges, maximizing their performance.

[0029] The coaching system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, a data processing unit, and an adjustment unit. The reception unit receives goals and tasks entered by the user. Goals and tasks entered by the user include, but are not limited to, business goals, learning tasks, and personal goals. The reception unit accepts, for example, goals and tasks entered by the user in text format. The reception unit can also accept multiple input formats, such as voice input and image input. For example, if the user enters goals and tasks by voice, the reception unit converts the voice data into text data and accepts it. Furthermore, if the user enters goals and tasks using images or videos, the reception unit can also analyze and accept the image or video data. The analysis unit analyzes the information received by the reception unit. The analysis unit performs analysis to provide optimal feedback and advice based on the user's goals and tasks. For example, the analysis unit performs analysis to provide optimal feedback and advice based on the user's goal achievement level and past feedback results. The analysis unit can also perform analysis to adjust the content of feedback and advice based on the user's personality and preferences. For example, the analysis department identifies the user's personality and preferences based on personality assessment tests and past behavioral history, and adjusts the content of feedback and advice accordingly. The service department provides feedback and advice based on the analysis results obtained by the analysis department. For example, the service department provides specific feedback and advice regarding the user's goals and challenges. For example, if the user's goal is to improve their project management skills, the service department will provide best practices and specific action plans for project management. Also, if the user's goal is to improve their presentation skills, the service department can provide feedback on specific areas for improvement in presentations. The data processing unit processes multiple types of data. For example, the data processing unit processes text data, image data, audio data, and video data. For example, the data processing unit analyzes text data entered by the user and generates feedback and advice.Furthermore, the data processing unit can analyze image and video data entered by the user and provide specific feedback on areas for improvement. It can also analyze audio data entered by the user and generate feedback and advice. The adjustment unit adjusts the communication style based on the user's personality and preferences. For example, if the user is introverted, the adjustment unit provides feedback in a calm tone. If the user is extroverted, the adjustment unit can provide feedback in an energetic tone. Furthermore, the adjustment unit can adjust the content of the feedback and advice based on the user's preferences. For example, if the user prefers visual information, the adjustment unit provides feedback using images and videos. As a result, the coaching system according to this embodiment can provide optimal feedback and advice for the user's goals and challenges, maximizing performance.

[0030] The reception desk receives goals and tasks entered by users. These goals and tasks include, but are not limited to, business goals, learning tasks, and personal goals. The reception desk accepts user input of goals and tasks in text format, for example. It can also accept multiple input formats, such as voice input and image input. For example, if a user enters goals or tasks by voice, the reception desk converts the voice data into text data for acceptance. Furthermore, if a user enters goals or tasks using images or videos, the reception desk can analyze and accept the image or video data. Specifically, in the case of voice input, speech recognition technology is used to convert the voice data into text data, accurately understanding the user's intent. In the case of image or video input, image recognition technology and video analysis technology are used to extract the goals or tasks indicated by the user and save them as text data. This allows the reception desk to accurately receive and pass on goals and tasks to the next processing stage, regardless of the format in which the user enters them. The reception desk also provides a function to review and correct the goals and tasks entered by the user through the user interface. For example, if a user makes a mistake in their input, the system will be made easy to correct, improving user convenience. Furthermore, the reception desk will save the user's input history, allowing them to refer to past goals and tasks. This will enable users to review past goals and tasks and check their progress.

[0031] The Analysis Department analyzes information received by the Reception Department. For example, the Analysis Department conducts analysis to provide optimal feedback and advice based on the user's goals and challenges. For instance, it analyzes the user's goal achievement level and past feedback results to provide optimal feedback and advice. The Analysis Department can also analyze the content of feedback and advice based on the user's personality and preferences. For example, it identifies the user's personality and preferences based on personality tests and past behavioral history, and adjusts the content of feedback and advice accordingly. Specifically, it uses AI to analyze user input data and natural language processing technology to understand the user's intentions and emotions. Furthermore, it uses machine learning algorithms to analyze the user's past behavioral data and feedback history, extracting patterns. This allows the Analysis Department to identify user behavioral tendencies and obstacles to goal achievement, generating foundational data for providing optimal advice. Additionally, the Analysis Department identifies the user's personality traits based on the results of personality tests and adjusts the style of feedback and advice accordingly. For example, advice can be provided in a calm tone to introverted users and in an energetic tone to extroverted users. This allows the analytics department to provide optimal feedback and advice tailored to each user's individual needs.

[0032] The service provider provides feedback and advice based on the analysis results obtained by the analysis provider. For example, the service provider provides specific feedback and advice regarding the user's goals and challenges. For instance, if the user's goal is to improve their project management skills, the service provider will provide best practices and specific action plans for project management. Similarly, if the user's goal is to improve their presentation skills, the service provider can provide feedback on specific areas for improvement in presentations. Specifically, the service provider generates customized feedback tailored to the user's goals and delivers it through the user interface. For example, feedback for improving project management skills might include specific suggestions on task prioritization, progress management methods, and improvements to team communication. Feedback for improving presentation skills might include specific suggestions on improving slide structure and visual elements, as well as speaking style and gesture usage. Furthermore, the service provider monitors the user's response to the feedback and provides additional advice and support as needed. For example, if the user has questions or concerns about the feedback provided, the service provider will respond in real time and provide additional information and explanations. This allows the service provider to help users develop concrete action plans to achieve their goals and provide continuous support.

[0033] The data processing unit processes multiple types of data. For example, it processes text data, image data, audio data, and video data. For instance, it analyzes text data entered by users to generate feedback and advice. It can also analyze image and video data entered by users to provide specific areas for improvement. Furthermore, it can analyze audio data entered by users to generate feedback and advice. Specifically, it uses natural language processing technology to analyze text data and understand the user's intentions and emotions. It uses image recognition technology to analyze image data and extract the user's goals and challenges. It uses speech recognition technology to convert audio data into text data and analyze it. It uses video analysis technology to analyze video data and analyze the user's actions and facial expressions. This allows the data processing unit to accurately analyze data regardless of the format in which it is entered by the user and generate feedback and advice. The data processing unit also centrally manages the analysis results and can share data with other departments. For example, it can save the analysis results to a cloud server, allowing the analysis and provision departments to access them. This allows the data processing unit to process data efficiently and effectively, improving the overall performance of the system.

[0034] The adjustment unit adjusts the communication style based on the user's personality and preferences. For example, if the user is introverted, the adjustment unit will provide feedback in a calm tone. If the user is extroverted, the adjustment unit can also provide feedback in an energetic tone. Furthermore, the adjustment unit can adjust the content of the feedback and advice based on the user's preferences. For example, if the user prefers visual information, the adjustment unit will provide feedback using images and videos. Specifically, the unit identifies the user's personality traits and preferences based on personality assessment tests and past behavioral history, and selects a communication style accordingly. For example, it provides feedback in a calm and composed tone to introverted users, and in a lively and energetic tone to extroverted users. Also, if the user prefers visual information, it will make extensive use of images and videos in the feedback, providing information in a visually easy-to-understand format. In this way, the adjustment unit can provide the optimal communication style tailored to the user's individual needs and maximize the effectiveness of the feedback and advice. Furthermore, the adjustment unit monitors the user's response to the feedback and adjusts the communication style as needed. For example, if a user responds positively to the feedback provided, the style is maintained; if they respond negatively, the style is changed. This allows the adjustment unit to optimize communication with the user and enhance the effectiveness of feedback and advice.

[0035] The data processing unit can process data such as text, images, audio, and video. For example, the data processing unit can analyze text data entered by a user and generate feedback and advice. For instance, it can analyze text data using natural language processing technology to generate specific feedback and advice regarding the user's goals and challenges. The data processing unit can also analyze image and video data entered by a user and provide feedback on specific areas for improvement. For example, it can analyze image data using image recognition technology and provide feedback on areas for improvement in the user's presentation. Furthermore, the data processing unit can analyze audio data entered by a user and generate feedback and advice. For example, it can convert audio data into text data using speech recognition technology and generate feedback and advice based on that text data. By processing multiple types of data in this way, the user's performance can be maximized. Some or all of the above-described processing in the data processing unit may be performed using AI, for example, or without AI. For example, the data processing unit can input text data entered by a user into a generating AI, which can then analyze the text data to generate feedback and advice.

[0036] The adjustment unit can adjust the communication style based on the user's personality and preferences. For example, if the user is introverted, the adjustment unit can provide feedback in a gentle tone. The adjustment unit can also provide feedback in an energetic tone if the user is extroverted. Furthermore, the adjustment unit can adjust the content of the feedback and advice based on the user's preferences. For example, if the user prefers visual information, the adjustment unit can provide feedback using images or videos. This enables effective feedback and advice by providing the user with the most suitable communication style. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the results of the user's personality test into a generating AI, which can analyze the user's personality and preferences to generate the optimal communication style.

[0037] The service provider can provide specific feedback and advice based on the user's goals and challenges. For example, if the user's goal is to improve their project management skills, the service provider can provide best practices and specific action plans for project management. For instance, the service provider can provide best practices for project management and specific action plans for the user to effectively manage projects. Furthermore, if the user's goal is to improve their presentation skills, the service provider can provide feedback on specific areas for improvement in their presentations. For example, the service provider can analyze a video of the user's presentation and provide feedback on specific areas for improvement. This allows the service provider to improve the user's performance by providing specific feedback and advice regarding their goals and challenges. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's goals and challenges into a generating AI, which can then generate optimal feedback and advice.

[0038] The analysis department can analyze users' goals and challenges and provide optimal feedback and advice. For example, the analysis department can perform analysis to provide optimal feedback and advice based on the user's goal achievement level and past feedback results. For example, the analysis department can evaluate the user's goal achievement level and provide specific feedback and advice based on the evaluation results. The analysis department can also perform analysis to adjust the content of feedback and advice based on the user's personality and preferences. For example, the analysis department can identify the user's personality and preferences based on the user's personality diagnostic test and past behavioral history, and adjust the content of feedback and advice based on that. In this way, by analyzing the user's goals and challenges, optimal feedback and advice can be provided. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input the user's goals and challenges into a generating AI and perform analysis so that the generating AI can generate optimal feedback and advice.

[0039] The reception unit can receive goals and tasks entered by users. For example, the reception unit can accept goals and tasks entered by users in text format. The reception unit can also accept multiple input formats, such as voice input and image input. For example, if a user enters goals or tasks by voice, the reception unit can convert that voice data into text data and accept it. Furthermore, if a user enters goals or tasks using images or videos, the reception unit can analyze and accept that image or video data. This allows the system to function properly by accepting the goals and tasks entered by users. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the goals and tasks entered by the user into a generating AI, which can then analyze and accept the goals and tasks.

[0040] The service provider is available 24 hours a day, 365 days a year. The service provider can operate 24 hours a day, 365 days a year, for example, by utilizing cloud services. Furthermore, the service provider can maintain 24 / 7 availability by implementing system redundancy. This allows users to receive feedback and advice at any time, as it is available 24 / 7. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can build an AI system that operates 24 / 7 using cloud services to provide feedback and advice to users.

[0041] The service provider is unbiased and objective. The service provider eliminates bias, for example, by ensuring the transparency of the algorithm. The service provider can also ensure objectivity by having a third party evaluate the service. This allows users to obtain reliable information by providing unbiased and objective feedback and advice. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can build an AI system that ensures the transparency of the algorithm and provides unbiased and objective feedback and advice.

[0042] The service provider can protect personal information. For example, the service provider can protect personal information using data encryption technology. The service provider can also protect personal information by implementing access restrictions. For example, the service provider can protect personal information by implementing access restrictions. Furthermore, the service provider can protect personal information by complying with a privacy policy. For example, the service provider can protect personal information by complying with a privacy policy. This allows users to use the system with peace of mind by protecting personal information. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can build an AI system that uses data encryption technology to protect personal information.

[0043] The reception desk can analyze the user's past goal and task input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also analyze patterns of goals and tasks that the user has entered in the past and suggest similar input methods. Furthermore, the reception desk can suggest the optimal input method for a specific time period based on the user's past input history. In this way, by analyzing past input history, the reception desk can provide the user with the most suitable input method. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's past goal and task input history into a generating AI, which can then select the optimal input method.

[0044] The reception desk can filter the input of goals and tasks based on the user's current business situation and areas of interest. For example, the reception desk can suggest relevant goals and tasks based on the user's current project status. The reception desk can also prioritize the display of relevant goals and tasks based on the user's areas of interest. Furthermore, the reception desk can filter and display appropriate goals and tasks according to the user's business situation. This allows for the provision of more relevant goals and tasks by filtering based on the user's business situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's current business situation and areas of interest into a generating AI, which can then filter and suggest the most suitable goals and tasks.

[0045] The reception desk can prioritize the input of highly relevant goals and tasks by considering the user's geographical location when users input goals and tasks. For example, if the user is in a specific region, the reception desk can prioritize the input of goals and tasks related to that region. For example, if the user is on a business trip, the reception desk can prioritize the input of goals and tasks related to the destination of the business trip. For example, if the user is on a business trip, the reception desk can prioritize the input of goals and tasks related to the destination of the business trip. Furthermore, if the user is at home, the reception desk can prioritize the input of goals and tasks that can be performed at home. For example, if the user is at home, the reception desk can prioritize the input of goals and tasks that can be performed at home. In this way, by considering the user's geographical location, highly relevant goals and tasks can be provided. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location information into a generating AI, which can then select the most appropriate goals and tasks.

[0046] The reception desk can analyze the user's social media activity when they input goals and challenges, and input relevant goals and challenges. For example, the reception desk can automatically input goals and challenges that the user has mentioned on social media. For example, the reception desk can automatically input goals and challenges that the user has mentioned on social media. The reception desk can also suggest goals and challenges related to the user's areas of interest based on their social media activity. For example, the reception desk can suggest goals and challenges related to the user's areas of interest based on their social media activity. Furthermore, the reception desk can input goals and challenges shared by the user's social media followers and friends as a reference. For example, the reception desk can input goals and challenges shared by the user's social media followers and friends as a reference. This allows the reception desk to provide relevant goals and challenges by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's social media activity into a generating AI, which can then select the most appropriate goals and challenges.

[0047] The analysis unit can adjust the level of detail of its analysis based on the importance of the goals and issues. For example, the analysis unit can perform a detailed analysis for important goals and issues. For example, the analysis unit can perform a detailed analysis for important goals and issues. The analysis unit can also perform a concise analysis for low-priority goals and issues. For example, the analysis unit can perform a concise analysis for low-priority goals and issues. Furthermore, the analysis unit can perform a rapid analysis for urgent goals and issues. For example, the analysis unit can perform a rapid analysis for urgent goals and issues. By adjusting the level of detail of the analysis based on the importance of the goals and issues, more effective analysis becomes possible. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the importance of goals and issues into a generating AI, which can then select the optimal level of detail for the analysis.

[0048] The analysis unit can apply different analysis algorithms depending on the category of goals and issues during analysis. For example, the analysis unit can apply a project management-specific analysis algorithm to goals and issues related to project management. For example, the analysis unit can apply a presentation-specific analysis algorithm to goals and issues related to presentations. For example, the analysis unit can apply a presentation-specific analysis algorithm to goals and issues related to presentations. Furthermore, the analysis unit can apply a communication-specific analysis algorithm to goals and issues related to communication skills. For example, the analysis unit can apply a communication-specific analysis algorithm to goals and issues related to communication skills. By applying different analysis algorithms depending on the category of goals and issues, more effective analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of goals and issues into a generating AI, which can then select the optimal analysis algorithm.

[0049] The analysis department can prioritize analyses based on the submission deadlines for goals and tasks. For example, the analysis department can prioritize analyses for urgent goals and tasks. The analysis department can also quickly analyze goals and tasks with approaching deadlines. Furthermore, the analysis department can postpone analyses for goals and tasks with distant deadlines. This allows for more effective analyses by prioritizing analyses based on the submission deadlines for goals and tasks. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department can input the submission deadlines for goals and tasks into a generating AI, which can then determine the optimal analysis priority.

[0050] The analysis unit can adjust the order of analysis based on the relevance of goals and issues during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant goals and issues. The analysis unit can also postpone the analysis of less relevant goals and issues. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of goals and issues. This allows for more effective analysis by adjusting the order of analysis based on the relevance of goals and issues. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the relevance of goals and issues into a generating AI, which can then determine the optimal order of analysis.

[0051] The service provider can adjust the level of detail when providing feedback and advice based on the importance of the goal or issue. For example, the service provider can provide detailed feedback for important goals and issues. For example, the service provider can provide detailed feedback for important goals and issues. The service provider can also provide concise feedback for low-priority goals and issues. Furthermore, the service provider can provide rapid feedback for urgent goals and issues. For example, the service provider can provide rapid feedback for urgent goals and issues. By adjusting the level of detail based on the importance of the goal or issue, more effective feedback and advice can be provided. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input the importance of the goal or issue into a generating AI, which can then select the optimal level of detail for the feedback and advice.

[0052] The service provider can apply different service algorithms depending on the category of the goal or issue when providing feedback or advice. For example, the service provider can apply a service algorithm specifically for project management to goals and issues related to project management. For example, the service provider can apply a service algorithm specifically for presentations to goals and issues related to presentations. For example, the service provider can apply a service algorithm specifically for presentations to goals and issues related to presentations. Furthermore, the service provider can apply a service algorithm specifically for communication to goals and issues related to communication skills. For example, the service provider can apply a service algorithm specifically for communication to goals and issues related to communication skills. By applying different service algorithms depending on the category of the goal or issue, more effective feedback and advice can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the category of the goal or issue into a generating AI, and the generating AI can select the optimal service algorithm.

[0053] The service provider can prioritize feedback and advice based on the submission deadlines for goals and tasks. For example, the service provider can prioritize feedback for urgent goals and tasks. The service provider can also provide prompt feedback for goals and tasks with approaching deadlines. Furthermore, the service provider can postpone providing feedback for goals and tasks with distant deadlines. This allows for more effective feedback and advice by prioritizing based on the submission deadlines of goals and tasks. Some or all of the above processes in the service provider may be performed using AI, or not. For example, the service provider can input the submission deadlines for goals and tasks into a generating AI, which can then determine the optimal priority for feedback and advice.

[0054] The service provider can adjust the order of feedback and advice based on the relevance of goals and issues. For example, the service provider can prioritize providing feedback on highly relevant goals and issues. The service provider can also postpone providing feedback on less relevant goals and issues. Furthermore, the service provider can dynamically adjust the order of feedback based on the relevance of goals and issues. This allows for more effective feedback and advice to be provided by adjusting the order based on the relevance of goals and issues. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the relevance of goals and issues into a generating AI, which can then determine the optimal order of feedback and advice.

[0055] The data processing unit can optimize its processing algorithm based on the type of goal or issue during data processing. For example, the data processing unit can apply a processing algorithm specifically for project management to data related to project management. Similarly, the data processing unit can apply a processing algorithm specifically for presentations to data related to presentations. Furthermore, the data processing unit can apply a processing algorithm specifically for communication skills to data related to communication skills. This allows for more effective data processing by optimizing the processing algorithm based on the type of goal or issue. Some or all of the processing described above in the data processing unit may be performed using AI, or without AI. For example, the data processing unit can input the type of goal or issue into a generating AI, which can then select the optimal processing algorithm.

[0056] The data processing unit can select the optimal processing method by referring to the user's past data processing history during data processing. For example, the data processing unit can prioritize applying data processing methods previously used by the user. The data processing unit can also propose the optimal processing method based on the user's past data processing history. Furthermore, the data processing unit can analyze the user's past data processing patterns and select the optimal processing method. This allows the system to provide the optimal processing method by referring to the user's past data processing history. Some or all of the above processing in the data processing unit may be performed using AI, for example, or without AI. For example, the data processing unit can input the user's past data processing history into a generating AI, which can then select the optimal processing method.

[0057] The data processing unit can select the optimal processing method by considering the user's geographical location information during data processing. For example, if the user is in a specific region, the data processing unit can apply a data processing method relevant to that region. Furthermore, if the user is on a business trip, the data processing unit can apply a data processing method relevant to their destination. Additionally, if the user is at home, the data processing unit can apply a data processing method that can be executed at home. This allows the system to provide the optimal data processing method by considering the user's geographical location information. Some or all of the processing described above in the data processing unit may be performed using AI, or without AI. For example, the data processing unit can input the user's geographical location information into a generating AI, which can then select the optimal data processing method.

[0058] The data processing unit can analyze the user's social media activity and process relevant data during data processing. For example, the data processing unit can prioritize processing data mentioned by the user on social media. The data processing unit can also process data related to the user's areas of interest from the user's social media activity. Furthermore, the data processing unit can process data shared by the user's social media followers and friends as a reference. In this way, relevant data can be provided by analyzing the user's social media activity. Some or all of the processing described above in the data processing unit may be performed using AI, for example, or without AI. For example, the data processing unit can input the user's social media activity into a generating AI, which can then select the optimal data processing method.

[0059] The adjustment unit can select the optimal style by referring to the user's past feedback history when adjusting the communication style. For example, the adjustment unit can prioritize applying communication styles that the user has preferred in the past. The adjustment unit can also suggest the optimal communication style based on the user's past feedback history. Furthermore, the adjustment unit can analyze the user's past feedback patterns and select the optimal communication style. In this way, the optimal communication style can be provided by referring to the user's past feedback history. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's past feedback history into a generating AI, which can then select the optimal communication style.

[0060] The adjustment unit can customize the communication style based on the user's current business situation when adjusting the communication style. For example, if the user is working on a project, the adjustment unit can apply a communication style appropriate for the project. For example, if the user is preparing a presentation, the adjustment unit can apply a communication style appropriate for the presentation. For example, if the user is in a meeting, the adjustment unit can apply a communication style appropriate for the meeting. For example, if the user is in a meeting, the adjustment unit can apply a communication style appropriate for the meeting. By customizing the style based on the user's current business situation, more effective communication becomes possible. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input the user's current business situation into a generating AI, which can then select the optimal communication style.

[0061] The adjustment unit can select the optimal communication style by considering the user's geographical location information when adjusting the communication style. For example, if the user is in a specific region, the adjustment unit can apply a communication style appropriate for that region. For example, if the user is on a business trip, the adjustment unit can apply a communication style appropriate for the destination. For example, if the user is on a business trip, the adjustment unit can apply a communication style appropriate for the destination. For example, if the user is at home, the adjustment unit can apply a communication style appropriate for home. In this way, the optimal communication style can be provided by considering the user's geographical location information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's geographical location information into a generating AI, which can then select the optimal communication style.

[0062] The adjustment unit can analyze the user's social media activity and apply relevant styles when adjusting communication styles. For example, the adjustment unit can prioritize the application of communication styles mentioned by the user on social media. The adjustment unit can also apply styles of interest from the user's social media activity. Furthermore, the adjustment unit can refer to and apply styles shared by the user's social media followers and friends. In this way, by analyzing the user's social media activity, relevant communication styles can be provided. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's social media activity into a generating AI, which can then select the optimal communication style.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The coaching system can also include a health management unit that monitors the user's health status. This unit can, for example, monitor the user's heart rate and sleep patterns, and evaluate their health based on this data. For instance, if a user has a high heart rate, they may be experiencing stress, and the system can provide advice on relaxation. Similarly, if a user's sleep patterns are disrupted, the system can provide advice on improving sleep quality. Furthermore, the health management unit can collect data on the user's diet and exercise, and provide advice on maintaining healthy lifestyle habits. This allows for comprehensive management of the user's health status and supports performance improvement.

[0065] The coaching system can also include a learning management unit to manage the user's learning progress. This unit can, for example, monitor the user's set learning goals and progress, and provide appropriate feedback. For instance, if a user creates a learning plan to acquire a new skill, the unit can regularly check their progress and provide advice to help them achieve their goals. The learning management unit can also suggest learning materials and resources tailored to the user's learning style. For example, if a user prefers visual learning, it can suggest video materials. Furthermore, the learning management unit can analyze the user's learning history and suggest effective learning methods. This maximizes the user's learning efficiency and supports them in achieving their goals.

[0066] The coaching system can further leverage the user's social network to provide feedback. For example, it can collect feedback from the user's friends and colleagues and use that as a basis for providing advice. It can also gather information from online communities and forums the user participates in and provide relevant advice. Furthermore, it can analyze the user's social media activity and provide feedback related to their areas of interest. This allows for more personalized feedback by leveraging the user's social network.

[0067] The coaching system can further leverage the user's geographical location to provide feedback. For example, if the user is in a specific region, it can provide information and advice relevant to that region. Similarly, if the user is on a business trip, it can provide information and advice relevant to their destination. Furthermore, if the user is at home, it can provide advice that can be implemented at home. This allows for the provision of more relevant feedback by utilizing the user's geographical location.

[0068] The coaching system can further analyze the user's past feedback history to provide optimal feedback. For example, it can analyze the content and effectiveness of feedback the user has received in the past and provide optimal feedback for similar situations. It can also identify effective feedback patterns from the user's past feedback history and provide feedback based on those patterns. Furthermore, it can adjust the timing and format of feedback based on the user's past feedback history. This allows for the provision of more effective feedback by leveraging the user's past feedback history.

[0069] The coaching system can also provide feedback that takes into account the user's current business situation. For example, if the user is working on a project, it can provide feedback on project management. Similarly, if the user is preparing a presentation, it can provide feedback on presentation skills. Furthermore, if the user is in a meeting, it can provide feedback on meeting management. This allows for more relevant feedback that considers the user's current business situation.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The reception desk receives the goals and tasks entered by the user. These goals and tasks include business objectives, learning objectives, and personal goals. The reception desk can accept multiple input formats, such as text, voice input, and image input. For example, it can convert voice data into text data for acceptance, or analyze and accept image and video data. Step 2: The analysis department analyzes the information received by the reception department. Based on the user's goals and challenges, they conduct analysis to provide optimal feedback and advice. For example, they analyze the user's goal achievement level and past feedback results, and adjust the content of the feedback and advice based on the user's personality and preferences. Step 3: The service provider provides feedback and advice based on the analysis results obtained by the analysis provider. For example, they provide specific feedback and advice regarding the user's goals and challenges, and offer concrete action plans and improvement suggestions to help improve project management and presentation skills. Step 4: The data processing unit processes multiple types of data. For example, it processes text data, image data, audio data, video data, etc., and analyzes the data entered by the user to generate feedback and advice. Step 5: The adjustment unit adjusts the communication style based on the user's personality and preferences. For example, it provides feedback in a calm tone to introverted users and in an energetic tone to extroverted users. It also provides feedback using images or videos if the user prefers visual information, based on their preferences.

[0072] (Example of form 2) The coaching system according to an embodiment of the present invention is a system for improving the performance and career of business professionals. This coaching system allows users to input their goals and challenges, and the AI ​​analyzes this information to provide optimal feedback and advice. Furthermore, the multimodal AI can process multiple types of data, including not only text but also images, audio, and video, maximizing user performance. This allows for a more affordable, 24 / 7 coaching service than employing a human coach with specialized knowledge. Additionally, the AI ​​is unbiased and objective, protecting personal information. Moreover, the communication style can be adjusted to suit the user's personality and preferences. This allows users to obtain a truly personalized coach. For example, a user inputs their goals and challenges. The user only needs to input specific goals and challenges. For instance, they might input "I want to improve my project management skills" or "I want to improve my presentation skills." This information is input into the AI. Next, the AI ​​analyzes the input information. Based on the user's goals and challenges, the AI ​​provides optimal feedback and advice. For example, for the goal of improving project management skills, the AI ​​provides best practices and specific action plans for project management. Furthermore, multimodal AI can process multiple types of data, including not only text but also images, audio, and video. For example, if a user has the goal of improving their presentation skills, the AI ​​can analyze a video of the user's presentation and provide specific feedback on areas for improvement. This maximizes the user's performance. Additionally, the AI ​​is available 24 / 7 and is less expensive than hiring a human coach with specialized knowledge. Moreover, the AI ​​is unbiased and objective, and can protect personal information. This allows users to use the coaching service with peace of mind. Furthermore, the AI ​​can adjust its communication style to suit the user's personality and preferences.For example, if a user is introverted, the AI ​​will provide feedback in a calm tone. Conversely, if a user is extroverted, the AI ​​will provide feedback in an energetic tone. This allows the user to have a truly personalized coach. As a result, the coaching system can provide optimal feedback and advice for the user's goals and challenges, maximizing their performance.

[0073] The coaching system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, a data processing unit, and an adjustment unit. The reception unit receives goals and tasks entered by the user. Goals and tasks entered by the user include, but are not limited to, business goals, learning tasks, and personal goals. The reception unit accepts, for example, goals and tasks entered by the user in text format. The reception unit can also accept multiple input formats, such as voice input and image input. For example, if the user enters goals and tasks by voice, the reception unit converts the voice data into text data and accepts it. Furthermore, if the user enters goals and tasks using images or videos, the reception unit can also analyze and accept the image or video data. The analysis unit analyzes the information received by the reception unit. The analysis unit performs analysis to provide optimal feedback and advice based on the user's goals and tasks. For example, the analysis unit performs analysis to provide optimal feedback and advice based on the user's goal achievement level and past feedback results. The analysis unit can also perform analysis to adjust the content of feedback and advice based on the user's personality and preferences. For example, the analysis department identifies the user's personality and preferences based on personality assessment tests and past behavioral history, and adjusts the content of feedback and advice accordingly. The service department provides feedback and advice based on the analysis results obtained by the analysis department. For example, the service department provides specific feedback and advice regarding the user's goals and challenges. For example, if the user's goal is to improve their project management skills, the service department will provide best practices and specific action plans for project management. Also, if the user's goal is to improve their presentation skills, the service department can provide feedback on specific areas for improvement in presentations. The data processing unit processes multiple types of data. For example, the data processing unit processes text data, image data, audio data, and video data. For example, the data processing unit analyzes text data entered by the user and generates feedback and advice.Furthermore, the data processing unit can analyze image and video data entered by the user and provide specific feedback on areas for improvement. It can also analyze audio data entered by the user and generate feedback and advice. The adjustment unit adjusts the communication style based on the user's personality and preferences. For example, if the user is introverted, the adjustment unit provides feedback in a calm tone. If the user is extroverted, the adjustment unit can provide feedback in an energetic tone. Furthermore, the adjustment unit can adjust the content of the feedback and advice based on the user's preferences. For example, if the user prefers visual information, the adjustment unit provides feedback using images and videos. As a result, the coaching system according to this embodiment can provide optimal feedback and advice for the user's goals and challenges, maximizing performance.

[0074] The reception desk receives goals and tasks entered by users. These goals and tasks include, but are not limited to, business goals, learning tasks, and personal goals. The reception desk accepts user input of goals and tasks in text format, for example. It can also accept multiple input formats, such as voice input and image input. For example, if a user enters goals or tasks by voice, the reception desk converts the voice data into text data for acceptance. Furthermore, if a user enters goals or tasks using images or videos, the reception desk can analyze and accept the image or video data. Specifically, in the case of voice input, speech recognition technology is used to convert the voice data into text data, accurately understanding the user's intent. In the case of image or video input, image recognition technology and video analysis technology are used to extract the goals or tasks indicated by the user and save them as text data. This allows the reception desk to accurately receive and pass on goals and tasks to the next processing stage, regardless of the format in which the user enters them. The reception desk also provides a function to review and correct the goals and tasks entered by the user through the user interface. For example, if a user makes a mistake in their input, the system will be made easy to correct, improving user convenience. Furthermore, the reception desk will save the user's input history, allowing them to refer to past goals and tasks. This will enable users to review past goals and tasks and check their progress.

[0075] The Analysis Department analyzes information received by the Reception Department. For example, the Analysis Department conducts analysis to provide optimal feedback and advice based on the user's goals and challenges. For instance, it analyzes the user's goal achievement level and past feedback results to provide optimal feedback and advice. The Analysis Department can also analyze the content of feedback and advice based on the user's personality and preferences. For example, it identifies the user's personality and preferences based on personality tests and past behavioral history, and adjusts the content of feedback and advice accordingly. Specifically, it uses AI to analyze user input data and natural language processing technology to understand the user's intentions and emotions. Furthermore, it uses machine learning algorithms to analyze the user's past behavioral data and feedback history, extracting patterns. This allows the Analysis Department to identify user behavioral tendencies and obstacles to goal achievement, generating foundational data for providing optimal advice. Additionally, the Analysis Department identifies the user's personality traits based on the results of personality tests and adjusts the style of feedback and advice accordingly. For example, advice can be provided in a calm tone to introverted users and in an energetic tone to extroverted users. This allows the analytics department to provide optimal feedback and advice tailored to each user's individual needs.

[0076] The service provider provides feedback and advice based on the analysis results obtained by the analysis provider. For example, the service provider provides specific feedback and advice regarding the user's goals and challenges. For instance, if the user's goal is to improve their project management skills, the service provider will provide best practices and specific action plans for project management. Similarly, if the user's goal is to improve their presentation skills, the service provider can provide feedback on specific areas for improvement in presentations. Specifically, the service provider generates customized feedback tailored to the user's goals and delivers it through the user interface. For example, feedback for improving project management skills might include specific suggestions on task prioritization, progress management methods, and improvements to team communication. Feedback for improving presentation skills might include specific suggestions on improving slide structure and visual elements, as well as speaking style and gesture usage. Furthermore, the service provider monitors the user's response to the feedback and provides additional advice and support as needed. For example, if the user has questions or concerns about the feedback provided, the service provider will respond in real time and provide additional information and explanations. This allows the service provider to help users develop concrete action plans to achieve their goals and provide continuous support.

[0077] The data processing unit processes multiple types of data. For example, it processes text data, image data, audio data, and video data. For instance, it analyzes text data entered by users to generate feedback and advice. It can also analyze image and video data entered by users to provide specific areas for improvement. Furthermore, it can analyze audio data entered by users to generate feedback and advice. Specifically, it uses natural language processing technology to analyze text data and understand the user's intentions and emotions. It uses image recognition technology to analyze image data and extract the user's goals and challenges. It uses speech recognition technology to convert audio data into text data and analyze it. It uses video analysis technology to analyze video data and analyze the user's actions and facial expressions. This allows the data processing unit to accurately analyze data regardless of the format in which it is entered by the user and generate feedback and advice. The data processing unit also centrally manages the analysis results and can share data with other departments. For example, it can save the analysis results to a cloud server, allowing the analysis and provision departments to access them. This allows the data processing unit to process data efficiently and effectively, improving the overall performance of the system.

[0078] The adjustment unit adjusts the communication style based on the user's personality and preferences. For example, if the user is introverted, the adjustment unit will provide feedback in a calm tone. If the user is extroverted, the adjustment unit can also provide feedback in an energetic tone. Furthermore, the adjustment unit can adjust the content of the feedback and advice based on the user's preferences. For example, if the user prefers visual information, the adjustment unit will provide feedback using images and videos. Specifically, the unit identifies the user's personality traits and preferences based on personality assessment tests and past behavioral history, and selects a communication style accordingly. For example, it provides feedback in a calm and composed tone to introverted users, and in a lively and energetic tone to extroverted users. Also, if the user prefers visual information, it will make extensive use of images and videos in the feedback, providing information in a visually easy-to-understand format. In this way, the adjustment unit can provide the optimal communication style tailored to the user's individual needs and maximize the effectiveness of the feedback and advice. Furthermore, the adjustment unit monitors the user's response to the feedback and adjusts the communication style as needed. For example, if a user responds positively to the feedback provided, the style is maintained; if they respond negatively, the style is changed. This allows the adjustment unit to optimize communication with the user and enhance the effectiveness of feedback and advice.

[0079] The data processing unit can process data such as text, images, audio, and video. For example, the data processing unit can analyze text data entered by a user and generate feedback and advice. For instance, it can analyze text data using natural language processing technology to generate specific feedback and advice regarding the user's goals and challenges. The data processing unit can also analyze image and video data entered by a user and provide feedback on specific areas for improvement. For example, it can analyze image data using image recognition technology and provide feedback on areas for improvement in the user's presentation. Furthermore, the data processing unit can analyze audio data entered by a user and generate feedback and advice. For example, it can convert audio data into text data using speech recognition technology and generate feedback and advice based on that text data. By processing multiple types of data in this way, the user's performance can be maximized. Some or all of the above-described processing in the data processing unit may be performed using AI, for example, or without AI. For example, the data processing unit can input text data entered by a user into a generating AI, which can then analyze the text data to generate feedback and advice.

[0080] The adjustment unit can adjust the communication style based on the user's personality and preferences. For example, if the user is introverted, the adjustment unit can provide feedback in a gentle tone. The adjustment unit can also provide feedback in an energetic tone if the user is extroverted. Furthermore, the adjustment unit can adjust the content of the feedback and advice based on the user's preferences. For example, if the user prefers visual information, the adjustment unit can provide feedback using images or videos. This enables effective feedback and advice by providing the user with the most suitable communication style. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the results of the user's personality test into a generating AI, which can analyze the user's personality and preferences to generate the optimal communication style.

[0081] The service provider can provide specific feedback and advice based on the user's goals and challenges. For example, if the user's goal is to improve their project management skills, the service provider can provide best practices and specific action plans for project management. For instance, the service provider can provide best practices for project management and specific action plans for the user to effectively manage projects. Furthermore, if the user's goal is to improve their presentation skills, the service provider can provide feedback on specific areas for improvement in their presentations. For example, the service provider can analyze a video of the user's presentation and provide feedback on specific areas for improvement. This allows the service provider to improve the user's performance by providing specific feedback and advice regarding their goals and challenges. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's goals and challenges into a generating AI, which can then generate optimal feedback and advice.

[0082] The analysis department can analyze users' goals and challenges and provide optimal feedback and advice. For example, the analysis department can perform analysis to provide optimal feedback and advice based on the user's goal achievement level and past feedback results. For example, the analysis department can evaluate the user's goal achievement level and provide specific feedback and advice based on the evaluation results. The analysis department can also perform analysis to adjust the content of feedback and advice based on the user's personality and preferences. For example, the analysis department can identify the user's personality and preferences based on the user's personality diagnostic test and past behavioral history, and adjust the content of feedback and advice based on that. In this way, by analyzing the user's goals and challenges, optimal feedback and advice can be provided. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input the user's goals and challenges into a generating AI and perform analysis so that the generating AI can generate optimal feedback and advice.

[0083] The reception unit can receive goals and tasks entered by users. For example, the reception unit can accept goals and tasks entered by users in text format. The reception unit can also accept multiple input formats, such as voice input and image input. For example, if a user enters goals or tasks by voice, the reception unit can convert that voice data into text data and accept it. Furthermore, if a user enters goals or tasks using images or videos, the reception unit can analyze and accept that image or video data. This allows the system to function properly by accepting the goals and tasks entered by users. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the goals and tasks entered by the user into a generating AI, which can then analyze and accept the goals and tasks.

[0084] The service provider is available 24 hours a day, 365 days a year. The service provider can operate 24 hours a day, 365 days a year, for example, by utilizing cloud services. Furthermore, the service provider can maintain 24 / 7 availability by implementing system redundancy. This allows users to receive feedback and advice at any time, as it is available 24 / 7. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can build an AI system that operates 24 / 7 using cloud services to provide feedback and advice to users.

[0085] The service provider is unbiased and objective. The service provider eliminates bias, for example, by ensuring the transparency of the algorithm. The service provider can also ensure objectivity by having a third party evaluate the service. This allows users to obtain reliable information by providing unbiased and objective feedback and advice. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can build an AI system that ensures the transparency of the algorithm and provides unbiased and objective feedback and advice.

[0086] The service provider can protect personal information. For example, the service provider can protect personal information using data encryption technology. The service provider can also protect personal information by implementing access restrictions. For example, the service provider can protect personal information by implementing access restrictions. Furthermore, the service provider can protect personal information by complying with a privacy policy. For example, the service provider can protect personal information by complying with a privacy policy. This allows users to use the system with peace of mind by protecting personal information. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can build an AI system that uses data encryption technology to protect personal information.

[0087] The reception desk can estimate the user's emotions and adjust the timing of goal and task input based on the estimated emotions. For example, if the user is feeling stressed, the reception desk can prompt the user to input goals and tasks during a time when they can relax. The reception desk can also prompt the user to input goals and tasks when they are concentrating. For example, if the reception desk is concentrating, the reception desk can prompt the user to input goals and tasks during a time when they can relax. Furthermore, if the user is tired, the reception desk can prompt the user to input goals and tasks after they have rested. For example, if the reception desk is tired, the reception desk can prompt the user to input goals and tasks after they have rested. By adjusting the input timing according to the user's emotions, more effective goal and task input becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception department could build an AI system to estimate the user's emotions and adjust the timing of goal and task input based on the user's feelings.

[0088] The reception desk can analyze the user's past goal and task input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also analyze patterns of goals and tasks that the user has entered in the past and suggest similar input methods. Furthermore, the reception desk can suggest the optimal input method for a specific time period based on the user's past input history. In this way, by analyzing past input history, the reception desk can provide the user with the most suitable input method. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's past goal and task input history into a generating AI, which can then select the optimal input method.

[0089] The reception desk can filter the input of goals and tasks based on the user's current business situation and areas of interest. For example, the reception desk can suggest relevant goals and tasks based on the user's current project status. The reception desk can also prioritize the display of relevant goals and tasks based on the user's areas of interest. Furthermore, the reception desk can filter and display appropriate goals and tasks according to the user's business situation. This allows for the provision of more relevant goals and tasks by filtering based on the user's business situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's current business situation and areas of interest into a generating AI, which can then filter and suggest the most suitable goals and tasks.

[0090] The reception desk can estimate the user's emotions and, based on the estimated emotions, determine the priority of goals and tasks to be entered. For example, if the user is stressed, the reception desk can prioritize easy goals and tasks. For example, if the user is relaxed, the reception desk can prioritize important goals and tasks. For example, if the user is relaxed, the reception desk can prioritize important goals and tasks. For example, if the user is focused, the reception desk can prioritize complex goals and tasks. For example, if the user is focused, the reception desk can prioritize complex goals and tasks. This allows for more effective input by determining the priority of goals and tasks according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception department could build an AI system to estimate the user's emotions and determine the priority of goals and tasks based on those emotions.

[0091] The reception desk can prioritize the input of highly relevant goals and tasks by considering the user's geographical location when users input goals and tasks. For example, if the user is in a specific region, the reception desk can prioritize the input of goals and tasks related to that region. For example, if the user is on a business trip, the reception desk can prioritize the input of goals and tasks related to the destination of the business trip. For example, if the user is on a business trip, the reception desk can prioritize the input of goals and tasks related to the destination of the business trip. Furthermore, if the user is at home, the reception desk can prioritize the input of goals and tasks that can be performed at home. For example, if the user is at home, the reception desk can prioritize the input of goals and tasks that can be performed at home. In this way, by considering the user's geographical location, highly relevant goals and tasks can be provided. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location information into a generating AI, which can then select the most appropriate goals and tasks.

[0092] The reception desk can analyze the user's social media activity when they input goals and challenges, and input relevant goals and challenges. For example, the reception desk can automatically input goals and challenges that the user has mentioned on social media. For example, the reception desk can automatically input goals and challenges that the user has mentioned on social media. The reception desk can also suggest goals and challenges related to the user's areas of interest based on their social media activity. For example, the reception desk can suggest goals and challenges related to the user's areas of interest based on their social media activity. Furthermore, the reception desk can input goals and challenges shared by the user's social media followers and friends as a reference. For example, the reception desk can input goals and challenges shared by the user's social media followers and friends as a reference. This allows the reception desk to provide relevant goals and challenges by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's social media activity into a generating AI, which can then select the most appropriate goals and challenges.

[0093] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide simple and easy-to-understand analysis results. For example, if the user is nervous, the analysis unit can provide simple and easy-to-understand analysis results. The analysis unit can also provide detailed analysis results if the user is relaxed. For example, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. In this way, by adjusting the presentation of the analysis according to the user's emotions, more effective analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis department can build an AI system that estimates user emotions and adjust the way the analysis is presented based on the user's emotions.

[0094] The analysis unit can adjust the level of detail of its analysis based on the importance of the goals and issues. For example, the analysis unit can perform a detailed analysis for important goals and issues. For example, the analysis unit can perform a detailed analysis for important goals and issues. The analysis unit can also perform a concise analysis for low-priority goals and issues. For example, the analysis unit can perform a concise analysis for low-priority goals and issues. Furthermore, the analysis unit can perform a rapid analysis for urgent goals and issues. For example, the analysis unit can perform a rapid analysis for urgent goals and issues. By adjusting the level of detail of the analysis based on the importance of the goals and issues, more effective analysis becomes possible. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the importance of goals and issues into a generating AI, which can then select the optimal level of detail for the analysis.

[0095] The analysis unit can apply different analysis algorithms depending on the category of goals and issues during analysis. For example, the analysis unit can apply a project management-specific analysis algorithm to goals and issues related to project management. For example, the analysis unit can apply a presentation-specific analysis algorithm to goals and issues related to presentations. For example, the analysis unit can apply a presentation-specific analysis algorithm to goals and issues related to presentations. Furthermore, the analysis unit can apply a communication-specific analysis algorithm to goals and issues related to communication skills. For example, the analysis unit can apply a communication-specific analysis algorithm to goals and issues related to communication skills. By applying different analysis algorithms depending on the category of goals and issues, more effective analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of goals and issues into a generating AI, which can then select the optimal analysis algorithm.

[0096] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, to-the-point analysis. For example, if the user is in a hurry, the analysis unit can provide a short, to-the-point analysis. For example, if the user is relaxed, the analysis unit can provide a detailed analysis. For example, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. For example, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. By adjusting the length of the analysis according to the user's emotions, more effective analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis department can build an AI system that estimates the user's emotions and adjust the length of the analysis based on the user's emotions.

[0097] The analysis department can prioritize analyses based on the submission deadlines for goals and tasks. For example, the analysis department can prioritize analyses for urgent goals and tasks. The analysis department can also quickly analyze goals and tasks with approaching deadlines. Furthermore, the analysis department can postpone analyses for goals and tasks with distant deadlines. This allows for more effective analyses by prioritizing analyses based on the submission deadlines for goals and tasks. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department can input the submission deadlines for goals and tasks into a generating AI, which can then determine the optimal analysis priority.

[0098] The analysis unit can adjust the order of analysis based on the relevance of goals and issues during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant goals and issues. The analysis unit can also postpone the analysis of less relevant goals and issues. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of goals and issues. This allows for more effective analysis by adjusting the order of analysis based on the relevance of goals and issues. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the relevance of goals and issues into a generating AI, which can then determine the optimal order of analysis.

[0099] The service provider can estimate the user's emotions and adjust the way feedback and advice are expressed based on the estimated emotions. For example, if the user is nervous, the service provider can provide feedback in a calm tone. For example, if the user is nervous, the service provider can provide feedback in a calm tone. The service provider can also provide detailed feedback if the user is relaxed. For example, if the user is relaxed, the service provider can provide detailed feedback. Furthermore, if the user is in a hurry, the service provider can provide concise feedback. For example, if the user is in a hurry, the service provider can provide concise feedback. This allows for more effective feedback and advice to be provided by adjusting the way feedback and advice are expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can build an AI system that estimates the user's emotions and adjust the way feedback and advice are expressed based on the user's emotions.

[0100] The service provider can adjust the level of detail when providing feedback and advice based on the importance of the goal or issue. For example, the service provider can provide detailed feedback for important goals and issues. For example, the service provider can provide detailed feedback for important goals and issues. The service provider can also provide concise feedback for low-priority goals and issues. Furthermore, the service provider can provide rapid feedback for urgent goals and issues. For example, the service provider can provide rapid feedback for urgent goals and issues. By adjusting the level of detail based on the importance of the goal or issue, more effective feedback and advice can be provided. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input the importance of the goal or issue into a generating AI, which can then select the optimal level of detail for the feedback and advice.

[0101] The service provider can apply different service algorithms depending on the category of the goal or issue when providing feedback or advice. For example, the service provider can apply a service algorithm specifically for project management to goals and issues related to project management. For example, the service provider can apply a service algorithm specifically for presentations to goals and issues related to presentations. For example, the service provider can apply a service algorithm specifically for presentations to goals and issues related to presentations. Furthermore, the service provider can apply a service algorithm specifically for communication to goals and issues related to communication skills. For example, the service provider can apply a service algorithm specifically for communication to goals and issues related to communication skills. By applying different service algorithms depending on the category of the goal or issue, more effective feedback and advice can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the category of the goal or issue into a generating AI, and the generating AI can select the optimal service algorithm.

[0102] The service provider can estimate the user's emotions and adjust the length of feedback and advice based on the estimated emotions. For example, if the user is in a hurry, the service provider can provide short, concise feedback. For example, if the user is in a hurry, the service provider can provide short, concise feedback. For example, if the user is relaxed, the service provider can provide detailed feedback. For example, if the user is excited, the service provider can provide feedback with visually stimulating effects. This allows for more effective feedback and advice by adjusting the length of feedback and advice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can build an AI system that estimates the user's emotions and adjust the length of feedback and advice based on the user's emotions.

[0103] The service provider can prioritize feedback and advice based on the submission deadlines for goals and tasks. For example, the service provider can prioritize feedback for urgent goals and tasks. The service provider can also provide prompt feedback for goals and tasks with approaching deadlines. Furthermore, the service provider can postpone providing feedback for goals and tasks with distant deadlines. This allows for more effective feedback and advice by prioritizing based on the submission deadlines of goals and tasks. Some or all of the above processes in the service provider may be performed using AI, or not. For example, the service provider can input the submission deadlines for goals and tasks into a generating AI, which can then determine the optimal priority for feedback and advice.

[0104] The service provider can adjust the order of feedback and advice based on the relevance of goals and issues. For example, the service provider can prioritize providing feedback on highly relevant goals and issues. The service provider can also postpone providing feedback on less relevant goals and issues. Furthermore, the service provider can dynamically adjust the order of feedback based on the relevance of goals and issues. This allows for more effective feedback and advice to be provided by adjusting the order based on the relevance of goals and issues. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the relevance of goals and issues into a generating AI, which can then determine the optimal order of feedback and advice.

[0105] The data processing unit can estimate the user's emotions and adjust the data processing method based on the estimated emotions. For example, if the user is relaxed, the data processing unit can perform detailed data processing. For example, if the user is relaxed, the data processing unit can perform detailed data processing. For example, if the user is in a hurry, the data processing unit can perform concise data processing. For example, if the user is excited, the data processing unit can perform data processing with visually stimulating effects. For example, if the user is excited, the data processing unit can perform data processing with visually stimulating effects. This allows for more effective data processing by adjusting the data processing method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data processing unit may be performed using AI, for example, or without AI. For example, the data processing unit can build an AI system that estimates the user's emotions and adjust the data processing method based on the user's emotions.

[0106] The data processing unit can optimize its processing algorithm based on the type of goal or issue during data processing. For example, the data processing unit can apply a processing algorithm specifically for project management to data related to project management. Similarly, the data processing unit can apply a processing algorithm specifically for presentations to data related to presentations. Furthermore, the data processing unit can apply a processing algorithm specifically for communication skills to data related to communication skills. This allows for more effective data processing by optimizing the processing algorithm based on the type of goal or issue. Some or all of the processing described above in the data processing unit may be performed using AI, or without AI. For example, the data processing unit can input the type of goal or issue into a generating AI, which can then select the optimal processing algorithm.

[0107] The data processing unit can select the optimal processing method by referring to the user's past data processing history during data processing. For example, the data processing unit can prioritize applying data processing methods previously used by the user. The data processing unit can also propose the optimal processing method based on the user's past data processing history. Furthermore, the data processing unit can analyze the user's past data processing patterns and select the optimal processing method. This allows the system to provide the optimal processing method by referring to the user's past data processing history. Some or all of the above processing in the data processing unit may be performed using AI, for example, or without AI. For example, the data processing unit can input the user's past data processing history into a generating AI, which can then select the optimal processing method.

[0108] The data processing unit can estimate the user's emotions and determine the priority of data processing based on the estimated emotions. For example, if the user is in a hurry, the data processing unit can prioritize urgent data processing. For example, if the user is relaxed, the data processing unit can prioritize detailed data processing. For example, if the user is relaxed, the data processing unit can prioritize detailed data processing. Furthermore, if the user is excited, the data processing unit can prioritize visually stimulating data processing. For example, if the user is excited, the data processing unit can prioritize visually stimulating data processing. This allows for more effective data processing by determining the priority of data processing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data processing unit may be performed using AI, for example, or without AI. For example, the data processing unit can build an AI system that estimates user emotions and determine data processing priorities based on those emotions.

[0109] The data processing unit can select the optimal processing method by considering the user's geographical location information during data processing. For example, if the user is in a specific region, the data processing unit can apply a data processing method relevant to that region. Furthermore, if the user is on a business trip, the data processing unit can apply a data processing method relevant to their destination. Additionally, if the user is at home, the data processing unit can apply a data processing method that can be executed at home. This allows the system to provide the optimal data processing method by considering the user's geographical location information. Some or all of the processing described above in the data processing unit may be performed using AI, or without AI. For example, the data processing unit can input the user's geographical location information into a generating AI, which can then select the optimal data processing method.

[0110] The data processing unit can analyze the user's social media activity and process relevant data during data processing. For example, the data processing unit can prioritize processing data mentioned by the user on social media. The data processing unit can also process data related to the user's areas of interest from the user's social media activity. Furthermore, the data processing unit can process data shared by the user's social media followers and friends as a reference. In this way, relevant data can be provided by analyzing the user's social media activity. Some or all of the processing described above in the data processing unit may be performed using AI, for example, or without AI. For example, the data processing unit can input the user's social media activity into a generating AI, which can then select the optimal data processing method.

[0111] The adjustment unit can estimate the user's emotions and adjust its communication style based on the estimated emotions. For example, if the user is nervous, the adjustment unit can communicate in a calm tone. For example, if the user is nervous, the adjustment unit can communicate in a calm tone. For example, if the user is relaxed, the adjustment unit can communicate in an energetic tone. For example, if the user is relaxed, the adjustment unit can communicate in an energetic tone. Furthermore, if the user is in a hurry, the adjustment unit can communicate quickly and concisely. For example, if the adjustment unit is in a hurry, the adjustment unit can communicate quickly and concisely. By adjusting the communication style according to the user's emotions, more effective communication becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can build an AI system that estimates the user's emotions and adjust the communication style based on the user's emotions.

[0112] The adjustment unit can select the optimal style by referring to the user's past feedback history when adjusting the communication style. For example, the adjustment unit can prioritize applying communication styles that the user has preferred in the past. The adjustment unit can also suggest the optimal communication style based on the user's past feedback history. Furthermore, the adjustment unit can analyze the user's past feedback patterns and select the optimal communication style. In this way, the optimal communication style can be provided by referring to the user's past feedback history. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's past feedback history into a generating AI, which can then select the optimal communication style.

[0113] The adjustment unit can customize the communication style based on the user's current business situation when adjusting the communication style. For example, if the user is working on a project, the adjustment unit can apply a communication style appropriate for the project. For example, if the user is preparing a presentation, the adjustment unit can apply a communication style appropriate for the presentation. For example, if the user is in a meeting, the adjustment unit can apply a communication style appropriate for the meeting. For example, if the user is in a meeting, the adjustment unit can apply a communication style appropriate for the meeting. By customizing the style based on the user's current business situation, more effective communication becomes possible. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input the user's current business situation into a generating AI, which can then select the optimal communication style.

[0114] The adjustment unit can estimate the user's emotions and determine the priority of communication styles based on the estimated emotions. For example, if the user is nervous, the adjustment unit may prioritize a calm communication style. For example, if the user is nervous, the adjustment unit may prioritize a calm communication style. For example, if the user is relaxed, the adjustment unit may prioritize an energetic communication style. For example, if the user is relaxed, the adjustment unit may prioritize an energetic communication style. Furthermore, if the user is in a hurry, the adjustment unit may prioritize a quick and concise communication style. For example, if the user is in a hurry, the adjustment unit may prioritize a quick and concise communication style. This allows for more effective communication by determining the priority of communication styles according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can build an AI system that estimates the user's emotions and determine the priority of communication styles based on the user's emotions.

[0115] The adjustment unit can select the optimal communication style by considering the user's geographical location information when adjusting the communication style. For example, if the user is in a specific region, the adjustment unit can apply a communication style appropriate for that region. For example, if the user is on a business trip, the adjustment unit can apply a communication style appropriate for the destination. For example, if the user is on a business trip, the adjustment unit can apply a communication style appropriate for the destination. For example, if the user is at home, the adjustment unit can apply a communication style appropriate for home. In this way, the optimal communication style can be provided by considering the user's geographical location information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's geographical location information into a generating AI, which can then select the optimal communication style.

[0116] The adjustment unit can analyze the user's social media activity and apply relevant styles when adjusting communication styles. For example, the adjustment unit can prioritize the application of communication styles mentioned by the user on social media. The adjustment unit can also apply styles of interest from the user's social media activity. Furthermore, the adjustment unit can refer to and apply styles shared by the user's social media followers and friends. In this way, by analyzing the user's social media activity, relevant communication styles can be provided. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's social media activity into a generating AI, which can then select the optimal communication style.

[0117] The adjustment unit can estimate the user's emotions and dynamically adjust the communication style based on the estimated emotions. For example, if the user's emotions change, the adjustment unit can adjust the communication style in real time. For example, if the user's emotions change, the adjustment unit can adjust the communication style in real time. The adjustment unit can also flexibly change the communication style in response to the user's emotions. For example, the adjustment unit can flexibly change the communication style in response to the user's emotions. Furthermore, the adjustment unit can provide the optimal communication style in response to changes in the user's emotions. For example, the adjustment unit can provide the optimal communication style in response to changes in the user's emotions. This enables more effective communication by dynamically adjusting the communication style in response to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can build an AI system that estimates the user's emotions and dynamically adjust the communication style based on the user's emotions.

[0118] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0119] The coaching system can also include a health management unit that monitors the user's health status. This unit can, for example, monitor the user's heart rate and sleep patterns, and evaluate their health based on this data. For instance, if a user has a high heart rate, they may be experiencing stress, and the system can provide advice on relaxation. Similarly, if a user's sleep patterns are disrupted, the system can provide advice on improving sleep quality. Furthermore, the health management unit can collect data on the user's diet and exercise, and provide advice on maintaining healthy lifestyle habits. This allows for comprehensive management of the user's health status and supports performance improvement.

[0120] The coaching system can also include a learning management unit to manage the user's learning progress. This unit can, for example, monitor the user's set learning goals and progress, and provide appropriate feedback. For instance, if a user creates a learning plan to acquire a new skill, the unit can regularly check their progress and provide advice to help them achieve their goals. The learning management unit can also suggest learning materials and resources tailored to the user's learning style. For example, if a user prefers visual learning, it can suggest video materials. Furthermore, the learning management unit can analyze the user's learning history and suggest effective learning methods. This maximizes the user's learning efficiency and supports them in achieving their goals.

[0121] The coaching system can further estimate the user's emotions and adjust the timing of feedback based on those emotions. For example, if the user is feeling stressed, the timing of providing feedback can be delayed so that they can receive it in a relaxed state. Conversely, if the user is focused, providing feedback at that time can lead to more effective advice. Furthermore, if the user is tired, feedback can be provided after they have rested. In this way, by adjusting the timing of feedback according to the user's emotions, more effective coaching can be achieved.

[0122] The coaching system can further leverage the user's social network to provide feedback. For example, it can collect feedback from the user's friends and colleagues and use that as a basis for providing advice. It can also gather information from online communities and forums the user participates in and provide relevant advice. Furthermore, it can analyze the user's social media activity and provide feedback related to their areas of interest. This allows for more personalized feedback by leveraging the user's social network.

[0123] The coaching system can further estimate the user's emotions and adjust the content of the feedback based on those emotions. For example, if the user is nervous, the system can provide feedback in a calm tone. If the user is relaxed, it can provide detailed feedback. Furthermore, if the user is in a hurry, it can provide concise feedback that gets straight to the point. By adjusting the content of the feedback according to the user's emotions, more effective coaching can be achieved.

[0124] The coaching system can further leverage the user's geographical location to provide feedback. For example, if the user is in a specific region, it can provide information and advice relevant to that region. Similarly, if the user is on a business trip, it can provide information and advice relevant to their destination. Furthermore, if the user is at home, it can provide advice that can be implemented at home. This allows for the provision of more relevant feedback by utilizing the user's geographical location.

[0125] The coaching system can further estimate the user's emotions and adjust the format of feedback based on those emotions. For example, if the user prefers visual information, feedback can be provided using images or videos. If the user prefers text-based information, detailed text feedback can be provided. Furthermore, if the user prefers audio feedback, audio feedback can be provided. By adjusting the format of feedback according to the user's emotions, more effective coaching can be achieved.

[0126] The coaching system can further analyze the user's past feedback history to provide optimal feedback. For example, it can analyze the content and effectiveness of feedback the user has received in the past and provide optimal feedback for similar situations. It can also identify effective feedback patterns from the user's past feedback history and provide feedback based on those patterns. Furthermore, it can adjust the timing and format of feedback based on the user's past feedback history. This allows for the provision of more effective feedback by leveraging the user's past feedback history.

[0127] The coaching system can further estimate the user's emotions and prioritize feedback based on those emotions. For example, if the user is stressed, it can prioritize providing feedback on stress reduction. If the user is relaxed, it can provide feedback on long-term goals. Furthermore, if the user is focused, it can provide feedback on complex tasks. By prioritizing feedback according to the user's emotions, more effective coaching can be achieved.

[0128] The coaching system can also provide feedback that takes into account the user's current business situation. For example, if the user is working on a project, it can provide feedback on project management. Similarly, if the user is preparing a presentation, it can provide feedback on presentation skills. Furthermore, if the user is in a meeting, it can provide feedback on meeting management. This allows for more relevant feedback that considers the user's current business situation.

[0129] The following briefly describes the processing flow for example form 2.

[0130] Step 1: The reception desk receives the goals and tasks entered by the user. These goals and tasks include business objectives, learning objectives, and personal goals. The reception desk can accept multiple input formats, such as text, voice input, and image input. For example, it can convert voice data into text data for acceptance, or analyze and accept image and video data. Step 2: The analysis department analyzes the information received by the reception department. Based on the user's goals and challenges, they conduct analysis to provide optimal feedback and advice. For example, they analyze the user's goal achievement level and past feedback results, and adjust the content of the feedback and advice based on the user's personality and preferences. Step 3: The service provider provides feedback and advice based on the analysis results obtained by the analysis provider. For example, they provide specific feedback and advice regarding the user's goals and challenges, and offer concrete action plans and improvement suggestions to help improve project management and presentation skills. Step 4: The data processing unit processes multiple types of data. For example, it processes text data, image data, audio data, video data, etc., and analyzes the data entered by the user to generate feedback and advice. Step 5: The adjustment unit adjusts the communication style based on the user's personality and preferences. For example, it provides feedback in a calm tone to introverted users and in an energetic tone to extroverted users. It also provides feedback using images or videos if the user prefers visual information, based on their preferences.

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0133] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, data processing unit, and adjustment unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives goals and tasks entered by the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs analysis to provide optimal feedback and advice based on the user's goals and tasks. The provision unit is implemented by the output device 40 of the smart device 14 and provides feedback and advice based on the analysis results. The data processing unit is implemented by the specific processing unit 290 of the data processing unit 12 and processes multiple types of data. The adjustment unit is implemented by the control unit 46A of the smart device 14 and adjusts the communication style based on the user's personality and preferences. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0135] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0136] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, data processing unit, and adjustment unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives goals and tasks entered by the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs analysis to provide optimal feedback and advice based on the user's goals and tasks. The provision unit is implemented by the speaker 240 of the smart glasses 214 and provides feedback and advice based on the analysis results. The data processing unit is implemented by the specific processing unit 290 of the data processing unit 12 and processes multiple types of data. The adjustment unit is implemented by the control unit 46A of the smart glasses 214 and adjusts the communication style based on the user's personality and preferences. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0151] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0152] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0159] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, data processing unit, and adjustment unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives goals and tasks entered by the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs analysis to provide optimal feedback and advice based on the user's goals and tasks. The provision unit is implemented by the speaker 240 of the headset terminal 314 and provides feedback and advice based on the analysis results. The data processing unit is implemented by the specific processing unit 290 of the data processing unit 12 and processes multiple types of data. The adjustment unit is implemented by the control unit 46A of the headset terminal 314 and adjusts the communication style based on the user's personality and preferences. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0167] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0168] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0169] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0170] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0171] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0172] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0173] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0174] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0175] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0176] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0177] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0178] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0179] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0180] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0181] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0182] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0183] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, data processing unit, and adjustment unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives goals and tasks entered by the user. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs analysis to provide optimal feedback and advice based on the user's goals and tasks. The provision unit is implemented by the speaker 240 of the robot 414 and provides feedback and advice based on the analysis results. The data processing unit is implemented by the specific processing unit 290 of the data processing unit 12 and processes multiple types of data. The adjustment unit is implemented by the control unit 46A of the robot 414 and adjusts the communication style based on the user's personality and preferences. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0184] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0185] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0186] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0187] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0188] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0189] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0190] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0191] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0192] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0193] 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.

[0194] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0195] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0196] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0197] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0198] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0199] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0200] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0201] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0202] (Note 1) A reception desk that accepts input of goals and tasks, An analysis unit analyzes the information received by the aforementioned reception unit, A provision unit provides feedback and advice based on the analysis results obtained by the aforementioned analysis unit, A data processing unit that processes multiple types of data, It includes an adjustment unit that adjusts the communication style based on the user's personality and preferences. A system characterized by the following features. (Note 2) The aforementioned data processing unit Processing data such as text, images, audio, and video. The system described in Appendix 1, characterized by the features described herein. (Note 3) The adjustment unit is, Adjust communication style based on user personality and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provide specific feedback and advice based on the user's goals and challenges. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is We analyze users' goals and challenges and provide optimal feedback and advice. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It accepts goals and tasks entered by the user. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, It is available 24 hours a day, 365 days a year. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned supply unit is, It is unbiased and objective. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned supply unit is, Protecting personal information The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of goal and task input based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is Analyze the user's past goal and task input history to select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users input goals and challenges, filtering is performed based on their current business situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is It estimates the user's emotions and determines the priority of input goals and tasks based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is When users input goals and tasks, the system prioritizes inputting highly relevant goals and tasks by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reception unit is When users enter their goals and challenges, the system analyzes their social media activity and inputs relevant goals and challenges. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During the analysis, adjust the level of detail based on the importance of the goals and issues. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During the analysis, different analytical algorithms are applied depending on the category of the goal or issue. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is When conducting the analysis, prioritize the analysis based on the deadlines for submitting goals and tasks. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is During the analysis, adjust the order of analysis based on the relevance of goals and issues. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way feedback and advice are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing feedback or advice, adjust the level of detail based on the importance of the goal or issue. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing feedback and advice, different delivery algorithms are applied depending on the category of the goal or issue. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of feedback and advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing feedback or advice, prioritize based on the deadlines for submitting goals and assignments. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing feedback or advice, adjust the order based on the relevance of goals and issues. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned data processing unit It estimates the user's emotions and adjusts the data processing method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned data processing unit When processing data, optimize the processing algorithm based on the type of goal or problem. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned data processing unit During data processing, the system selects the optimal processing method by referring to the user's past data processing history. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned data processing unit It estimates the user's emotions and determines the priority of data processing based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned data processing unit During data processing, the optimal processing method is selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned data processing unit During data processing, analyze users' social media activity and process relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 34) The adjustment unit is, It estimates the user's emotions and adjusts the communication style based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The adjustment unit is, When adjusting communication styles, the system selects the optimal style by referring to the user's past feedback history. The system described in Appendix 1, characterized by the features described herein. (Note 36) The adjustment unit is, When adjusting communication styles, customize the style based on the user's current business situation. The system described in Appendix 1, characterized by the features described herein. (Note 37) The adjustment unit is, It estimates the user's emotions and prioritizes communication styles based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The adjustment unit is, When adjusting communication styles, the optimal style is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 39) The adjustment unit is, When adjusting communication styles, we analyze the user's social media activity and apply relevant styles. The system described in Appendix 1, characterized by the features described herein. (Note 40) The adjustment unit is, It estimates the user's emotions and dynamically adjusts the communication style based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0203] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts input of goals and tasks, An analysis unit analyzes the information received by the aforementioned reception unit, A provision unit provides feedback and advice based on the analysis results obtained by the aforementioned analysis unit, A data processing unit that processes multiple types of data, It includes an adjustment unit that adjusts the communication style based on the user's personality and preferences. A system characterized by the following features.

2. The aforementioned data processing unit Processing data such as text, images, audio, and video. The system according to feature 1.

3. The adjustment unit is, Adjust communication style based on user personality and preferences. The system according to feature 1.

4. The aforementioned supply unit is, Provide specific feedback and advice based on the user's goals and challenges. The system according to feature 1.

5. The aforementioned analysis unit is We analyze users' goals and challenges and provide optimal feedback and advice. The system according to feature 1.

6. The aforementioned reception unit is It accepts goals and tasks entered by the user. The system according to feature 1.

7. The aforementioned supply unit is, It is available 24 hours a day, 365 days a year. The system according to feature 1.

8. The aforementioned supply unit is, It is unbiased and objective. The system according to feature 1.

9. The aforementioned supply unit is, Protecting personal information The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of goal and task input based on the estimated user emotions. The system according to feature 1.

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