system

Abstract

The system according to the embodiment aims to enable business people to easily receive coaching that is suited to them. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a provision unit, a data processing unit, and an adjustment unit. The reception unit receives input of a user's goals or challenges. The analysis unit analyzes the goals or challenges received by the reception unit. The provision unit provides feedback or advice based on the analysis results obtained by the analysis unit. The data processing unit processes at least one of text, image, audio, and video data. The adjustment unit adjusts the communication style to match the user's personality or preferences.

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, business people had to pay high fees to receive coaching tailored to their needs, and it was difficult to find an appropriate coach.

[0005] The system according to the embodiment aims to enable business people to easily receive coaching that is suited to them. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, a data processing unit, and an adjustment unit. The reception unit receives input of a user's goals or challenges. The analysis unit analyzes the goals or challenges received by the reception unit. The provision unit provides feedback or advice based on the analysis results obtained by the analysis unit. The data processing unit processes at least one of text, image, audio, and video data. The adjustment unit adjusts the communication style to match the user's personality or preferences. [Effects of the Invention]

[0007] The system according to the embodiment allows business people to easily receive coaching that is suited to them. [Brief explanation of the drawings]

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

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A coaching system according to an embodiment of the present invention is designed to improve the performance and careers of businesspeople. This system utilizes multimodal AI, capable of processing multiple types of data, including not only text but also images, audio, and video, to maximize user performance. For example, if a user inputs "I want to improve my presentation skills," the AI ​​analyzes the user's past presentation videos and provides specific feedback on areas for improvement. Similarly, if a user inputs "I want to learn how to manage stress," the AI ​​analyzes the user's voice data, assesses their stress level, and provides appropriate advice. A major advantage of this system is that it is less expensive than hiring a human coach with specialized knowledge. It is also available 24 hours a day, 365 days a year, and can tailor its communication style to suit the user's personality and preferences. Furthermore, AI provides unbiased, objective feedback and protects personal information. For example, if a user inputs "I want advice on career advancement," the AI ​​analyzes the user's resume and work history to suggest specific career paths and ways to improve skills. Additionally, if a user inputs, "I want to improve my leadership skills," the AI ​​will analyze the user's past leadership experience and provide specific feedback on areas for improvement. In this way, the coaching system becomes a powerful support tool for business people to achieve their goals and advance their careers. This allows the coaching system to provide optimal feedback and advice for the user's goals and challenges, helping them improve their performance and careers.

[0029] A coaching system according to an embodiment includes a reception unit, an analysis unit, a provision unit, a data processing unit, and an adjustment unit. The reception unit receives input of a user's goals or challenges. Examples of the user's goals and challenges include, but are not limited to, business goals, learning challenges, and personal goals. The reception unit receives, for example, the user's input of the goals and challenges in text format. The reception unit can also receive multiple input formats, such as audio input, image input, and video input. The analysis unit analyzes the goals and challenges received by the reception unit. The analysis unit analyzes text data using, for example, natural language processing technology. The analysis unit can also analyze image data using image recognition technology. The analysis unit can also analyze audio data using audio analysis technology. For example, the analysis unit analyzes the content of the user's goals and challenges and extracts related information. The provision unit provides feedback and advice based on the analysis results obtained by the analysis unit. The provision unit provides, for example, the feedback and advice in text format. The provision unit can also provide the feedback and advice in audio format or video format. For example, the providing unit proposes a specific action plan for the user's goals and challenges. The data processing unit processes at least one of data types selected from text, image, audio, and video. For example, the data processing unit processes text data using natural language processing technology. The data processing unit can also process image data using image recognition technology. The data processing unit can also process audio data using audio analysis technology. For example, the data processing unit analyzes the user's past presentation videos and provides feedback on specific areas for improvement. The adjustment unit adjusts the communication style to match the user's personality and preferences. The adjustment unit selects an optimal communication style based on the user's psychological test results and past behavioral history, for example. The adjustment unit can also adjust the communication style based on the user's feedback. As a result, the coaching system according to the embodiment can provide optimal feedback and advice for the user's goals and challenges, and support the user's performance and career improvement.

[0030] The data processing unit can analyze a user's past presentation videos and provide feedback on specific areas for improvement. For example, the data processing unit can analyze a user's past presentation videos and provide feedback on specific areas for improvement. For example, the data processing unit can analyze the presentation structure and identify areas for improvement. The data processing unit can also analyze the speaking style and use of visual materials and provide feedback on specific areas for improvement. For example, the data processing unit can analyze the presentation structure and suggest ways to ensure a logical flow and emphasize important points. The data processing unit can also analyze the speaking speed and tone and suggest effective speaking methods. Furthermore, the data processing unit can analyze the use of visual materials and suggest effective slide designs and the use of graphs. This can help improve the user's presentation skills. Some or all of the above-described processing in the data processing unit can be performed using, for example, AI, or without AI. For example, the data processing unit can input a user's presentation video into a generation AI and cause the generation AI to analyze the video and extract areas for improvement.

[0031] The data processing unit can analyze the user's voice data, determine the user's stress level, and provide appropriate advice. The data processing unit, for example, analyzes the user's voice data and determines the user's stress level. For example, the data processing unit can analyze the user's voice tone and speaking rate to determine the user's stress level. The data processing unit can also analyze the user's choice of words to determine the user's stress level. For example, the data processing unit can determine that the user's stress level is high if the user's voice tone is high and the user speaks quickly. The data processing unit can also determine that the user's stress level is high if the user's choice of words is negative. The data processing unit can also provide appropriate advice based on the user's stress level. For example, the data processing unit can suggest relaxation methods if the user's stress level is high. The data processing unit can also suggest stress management methods if the user's stress level is low. For example, the data processing unit can suggest relaxation methods such as deep breathing and meditation. The data processing unit can also suggest time management and task management methods for stress management. This can assist the user in managing their stress. Some or all of the above-described processing in the data processing unit can be performed using, for example, AI, or without AI. For example, the data processing unit can input the user's voice data into the generation AI and have the generation AI determine the stress level and provide advice.

[0032] The providing unit can analyze the user's resume or work history and suggest specific career paths and methods for improving skills. For example, the providing unit can analyze the user's resume or work history and suggest specific career paths and methods for improving skills. For example, the providing unit can analyze the user's work history and suggest a career path. The providing unit can also analyze the user's skill set and suggest methods for improving skills. For example, the providing unit can analyze the user's work history and suggest the next job or industry the user should aim for. The providing unit can also analyze the user's skill set and suggest the necessary skills and qualifications. Furthermore, the providing unit can suggest specific training programs and learning resources. For example, the providing unit can identify the user's skill gaps and suggest training programs to fill them. The providing unit can also suggest learning resources according to the user's career goals. This can support the user's career advancement. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or without using AI. For example, the providing unit can input the user's resume and job history into the generating AI and have the generating AI make suggestions about career paths and ways to improve skills.

[0033] The providing unit can analyze the user's past leadership experience and provide feedback on specific areas for improvement. For example, the providing unit can analyze the user's past leadership experience and provide feedback on specific areas for improvement. For example, the providing unit can analyze the user's leadership style and identify areas for improvement. The providing unit can also analyze team management and communication skills and provide feedback on specific areas for improvement. For example, the providing unit can analyze the user's leadership style and suggest effective leadership methods. The providing unit can also analyze team management skills and suggest methods for increasing team motivation. The providing unit can also analyze communication skills and suggest effective communication methods. For example, the providing unit can analyze the user's leadership style and identify areas for leadership improvement. The providing unit can also analyze the user's team management skills and suggest methods for improving team performance. This can help improve the user's leadership skills. Some or all of the above-described processing by the providing unit can be performed using, or without, AI. For example, the providing unit can input the user's leadership experience into a generation AI and cause the generation AI to provide feedback on areas for improvement.

[0034] The adjustment unit can adjust the communication style to match the user's personality and preferences. The adjustment unit adjusts the communication style to match the user's personality and preferences, for example. For example, the adjustment unit selects an optimal communication style based on the user's psychological test results and past behavioral history. The adjustment unit can also adjust the communication style based on user feedback. For example, the adjustment unit can apply a conservative communication style if the user is introverted. The adjustment unit can also apply an aggressive communication style if the user is extroverted. The adjustment unit also provides a customized communication style according to the user's preferences. For example, the adjustment unit selects the user's preferred tone and style of communication and communicates based on that tone and style. This makes it possible to provide the user with an optimal communication style. Some or all of the above-described processing in the adjustment unit may be performed using, or without, AI. For example, the adjustment unit can input data regarding the user's personality and preferences into the generation AI and cause the generation AI to select an optimal communication style.

[0035] The reception unit can analyze the user's past input history of goals and tasks and select the optimal input method. The reception unit, for example, analyzes the user's past input history of goals and tasks and selects the optimal input method. For example, the reception unit prioritizes and suggests input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also analyze patterns of goals and tasks that the user has previously input and suggest the optimal input method. Furthermore, the reception unit can suggest the optimal input method for a specific time period based on the user's past input history. For example, if the user has frequently used voice input in the past, the reception unit can prioritize and suggest voice input. Also, if the user has frequently used text input in the past, the reception unit can prioritize and suggest text input. This makes it possible to provide the user with the optimal input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data to the generation AI and cause the generation AI to select the optimal input method.

[0036] The reception unit may filter the input of goals and tasks based on the user's current business situation and areas of interest. For example, the reception unit may prioritize input of goals and tasks related to the user's current project. The reception unit may also suggest related goals and tasks based on the user's areas of interest. Furthermore, the reception unit may filter and input appropriate goals and tasks based on the user's business situation. For example, the reception unit prioritizes input of goals and tasks related to the user's ongoing project. The reception unit may also suggest related goals and tasks based on the user's areas of interest. This allows for providing goals and tasks that are appropriate for the user's business situation and areas of interest. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit may input data related to the user's business situation and areas of interest to a generation AI and have the generation AI perform filtering.

[0037] When inputting goals or tasks, the reception unit can prioritize inputting highly relevant goals or tasks taking into account the user's geographical location information. For example, when inputting goals or tasks, the reception unit prioritizes inputting highly relevant goals or tasks taking into account the user's geographical location information. For example, when the user is on a business trip, the reception unit prioritizes goals and tasks related to the business trip destination. Furthermore, when the user is at home, the reception unit can prioritize goals and tasks that can be completed at home. Furthermore, when the user is in the office, the reception unit can prioritize goals and tasks related to office work. For example, the reception unit can acquire the user's geographical location information using GPS data or an IP address and suggest goals and tasks based on the acquired information. Furthermore, the reception unit can acquire the user's geographical location information using a location information service and suggest goals and tasks based on the acquired information. This makes it possible to provide optimal goals and tasks according to the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information into the generation AI and have the generation AI select highly relevant goals and tasks.

[0038] The reception unit can analyze the user's social media activity when inputting a goal or task and input related goals or tasks. For example, when inputting a goal or task, the reception unit can analyze the user's social media activity and input related goals or tasks. For example, the reception unit can suggest goals or tasks related to topics in which the user has shown interest on social media. The reception unit can also analyze the user's social media activity history and input related goals or tasks. Furthermore, the reception unit can suggest goals or tasks based on the opinions of experts the user follows on social media. For example, the reception unit can analyze the content of the user's social media posts and the number of likes, and suggest goals or tasks based on the results. The reception unit can also analyze the attributes of the user's followers and suggest goals or tasks based on the results. This makes it possible to provide optimal goals and tasks based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's social media activity data into a generation AI and cause the generation AI to select related goals and tasks.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the goals and tasks during the analysis. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the goals and tasks during the analysis. For example, the analysis unit can perform a detailed analysis for goals and tasks with high importance. The analysis unit can also perform a concise analysis for goals and tasks with low importance. Furthermore, the analysis unit can adjust the depth and scope of the analysis based on the importance. For example, the analysis unit can evaluate the importance of the user's goals and tasks and adjust the level of detail of the analysis based on the evaluation. The analysis unit can also adjust the level of detail of the analysis based on the user's business impact and urgency. This makes it possible to provide optimal analysis results based on the importance of the goals and tasks. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input importance data of the user's goals and tasks into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the category of the goal or task during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of the goal or task during analysis. For example, the analysis unit can apply a voice analysis algorithm to the goal of improving presentation skills. The analysis unit can also apply a voice data analysis algorithm to the goal of stress management. Furthermore, the analysis unit can apply a resume or curriculum vitae analysis algorithm to the goal of career advancement. For example, the analysis unit can analyze voice data using a voice analysis algorithm to extract specific areas for improvement for the goal of improving presentation skills. The analysis unit can also determine stress levels using a voice data analysis algorithm to provide appropriate advice for the goal of stress management. This makes it possible to provide optimal analysis results depending on the category of the goal or task. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without AI. For example, the analysis unit can input category data of the user's goals and tasks into the generation AI and cause the generation AI to apply different analysis algorithms.

[0041] The analysis unit can determine the analysis priorities based on the submission dates of goals and assignments during analysis. For example, the analysis unit can determine the analysis priorities based on the submission dates of goals and assignments during analysis. For example, the analysis unit prioritizes the analysis of goals and assignments with upcoming deadlines. The analysis unit can also postpone goals and assignments with distant submission dates. Furthermore, the analysis unit can adjust the analysis priorities based on the submission dates. For example, the analysis unit can evaluate the submission dates and deadlines of the user's goals and assignments and determine the analysis priorities based on the evaluation. The analysis unit can also adjust the analysis priorities based on the progress of the project. This makes it possible to provide optimal analysis results based on the submission dates of goals and assignments. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input data on the submission dates of the user's goals and assignments into the generation AI and have the generation AI determine the analysis priorities.

[0042] The analysis unit can adjust the order of analysis based on the relevance of goals and tasks during analysis. For example, the analysis unit can prioritize analysis of highly relevant goals and tasks. The analysis unit can also postpone less relevant goals and tasks. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of goals and tasks. For example, the analysis unit can evaluate the commonality and dependency of the themes of the user's goals and tasks and determine the order of analysis based on that. The analysis unit can also adjust the order of analysis based on the collaboration status of the project. This makes it possible to provide optimal analysis results based on the relevance of goals and tasks. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input relevance data of the user's goals and tasks into the generation AI and have the generation AI adjust the order of analysis.

[0043] The providing unit can adjust the level of detail based on the importance of the goal or task when providing feedback or advice. For example, the providing unit can adjust the level of detail based on the importance of the goal or task when providing feedback or advice. For example, the providing unit can provide detailed feedback for goals or tasks with high importance. The providing unit can also provide concise feedback for goals or tasks with low importance. Furthermore, the providing unit can adjust the depth and scope of the feedback based on the importance. For example, the providing unit can evaluate the importance of the user's goal or task and adjust the level of detail of the feedback based on the evaluation. The providing unit can also adjust the level of detail of the feedback based on the user's business impact and urgency. This makes it possible to provide optimal feedback and advice based on the importance of the goal or task. Some or all of the above-described processing by the providing unit can be performed using, or without, AI. For example, the providing unit can input importance data of the user's goal or task into the generating AI and cause the generating AI to adjust the level of detail of the feedback.

[0044] The providing unit can apply different provision algorithms depending on the category of the goal or challenge when providing feedback or advice. For example, the providing unit can apply different provision algorithms depending on the category of the goal or challenge when providing feedback or advice. For example, the providing unit can apply a voice analysis algorithm to the goal of improving presentation skills. The providing unit can also apply a voice data analysis algorithm to the goal of stress management. Furthermore, the providing unit can apply a resume or CV analysis algorithm to the goal of career advancement. For example, the providing unit can analyze voice data using a voice analysis algorithm to extract specific areas for improvement for the goal of improving presentation skills. The providing unit can also determine the stress level using a voice data analysis algorithm to provide appropriate advice for the goal of stress management. This makes it possible to provide optimal feedback and advice depending on the category of the goal or challenge. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input category data of the user's goals and challenges into the generation AI and cause the generation AI to apply different provision algorithms.

[0045] The providing unit can determine priorities based on the submission dates of goals and assignments when providing feedback or advice. For example, the providing unit determines priorities based on the submission dates of goals and assignments when providing feedback or advice. For example, the providing unit prioritizes feedback for goals and assignments with upcoming deadlines. The providing unit can also postpone goals and assignments with distant submission dates. Furthermore, the providing unit can adjust the priority of feedback based on the submission dates. For example, the providing unit evaluates the submission dates and deadlines of the user's goals and assignments and determines the priority of feedback based on the evaluation. The providing unit can also adjust the priority of feedback based on the progress of the project. This makes it possible to provide optimal feedback and advice based on the submission dates of goals and assignments. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input data on the submission dates of the user's goals and assignments into a generating AI and cause the generating AI to determine the priority of feedback.

[0046] The providing unit can adjust the order of feedback and advice based on the relevance of goals and tasks when providing feedback or advice. For example, the providing unit adjusts the order based on the relevance of goals and tasks when providing feedback or advice. For example, the providing unit prioritizes providing feedback for highly relevant goals and tasks. The providing unit can also postpone less relevant goals and tasks. Furthermore, the providing unit can adjust the order of feedback based on the relevance of goals and tasks. For example, the providing unit evaluates the commonality and dependency of the themes of the user's goals and tasks and determines the order of feedback based on this. The providing unit can also adjust the order of feedback based on the collaboration status of the project. This makes it possible to provide optimal feedback and advice based on the relevance of goals and tasks. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input relevance data of the user's goals and tasks to a generation AI and cause the generation AI to adjust the order of feedback.

[0047] The data processing unit can select an optimal processing method by referring to past data during data processing. For example, the data processing unit can select an optimal processing method by referring to past data during data processing. For example, the data processing unit can select an optimal data processing method by referring to a user's past presentation videos. The data processing unit can also select an optimal data processing method by referring to the user's past voice data. Furthermore, the data processing unit can select an optimal data processing method by referring to the user's past resume or work history. For example, the data processing unit can analyze the user's past presentation videos and extract specific areas for improvement. The data processing unit can also analyze the user's past voice data, determine the user's stress level, and provide appropriate advice. This makes it possible to provide an optimal data processing method based on past data. Some or all of the above-described processing in the data processing unit can be performed using, or without, AI. For example, the data processing unit can input the user's past data into a generation AI and have the generation AI select an optimal processing method.

[0048] The data processing unit can apply different processing algorithms depending on the type of data during data processing. For example, the data processing unit can apply different processing algorithms depending on the type of data during data processing. For example, the data processing unit can apply a natural language processing algorithm to text data. The data processing unit can also apply an image recognition algorithm to image data. The data processing unit can also apply a voice analysis algorithm to voice data. For example, the data processing unit can analyze text data using a natural language processing algorithm to extract specific information. The data processing unit can also analyze image data using an image recognition algorithm to extract visual information. This makes it possible to provide optimal data processing results depending on the type of data. Some or all of the above-mentioned processing in the data processing unit can be performed using, for example, AI, or can be performed without using AI. For example, the data processing unit can input the type of user's data to the generation AI and cause the generation AI to apply different processing algorithms.

[0049] The data processing unit can determine the processing priority based on the data submission time during data processing. The data processing unit, for example, determines the processing priority based on the data submission time during data processing. For example, the data processing unit prioritizes processing of data with an upcoming deadline. The data processing unit can also postpone data with a distant submission time. Furthermore, the data processing unit can adjust the data processing priority based on the submission time. For example, the data processing unit evaluates the user's data submission date and deadline and determines the processing priority based on the evaluation. The data processing unit can also adjust the processing priority based on the progress of the project. This makes it possible to provide optimal data processing results according to the data submission time. Some or all of the above-mentioned processing in the data processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the data processing unit can input the user's data submission time data to the generation AI and have the generation AI determine the processing priority.

[0050] The data processing unit can adjust the processing order based on the relevance of the data during data processing. The data processing unit, for example, adjusts the processing order based on the relevance of the data during data processing. For example, the data processing unit prioritizes processing of highly relevant data. The data processing unit can also postpone processing of less relevant data. Furthermore, the data processing unit can adjust the processing order according to the relevance of the data. For example, the data processing unit evaluates the commonality and dependency of the themes of the user's data and determines the processing order based on this. The data processing unit can also adjust the processing order based on the collaboration status of the project. This makes it possible to provide optimal data processing results according to the relevance of the data. Some or all of the above-mentioned processing in the data processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the data processing unit can input relevance data of the user's data to a generation AI and cause the generation AI to adjust the processing order.

[0051] When adjusting a communication style, the adjustment unit can select an optimal style by referring to the user's past communication history. For example, when adjusting a communication style, the adjustment unit selects an optimal style by referring to the user's past communication history. For example, the adjustment unit preferentially applies a communication style that the user previously preferred. The adjustment unit can also analyze the user's past communication history and select an optimal style. Furthermore, the adjustment unit can also select an optimal communication style by referring to the user's past feedback. For example, the adjustment unit analyzes the user's email history or chat history and selects an optimal communication style based thereon. The adjustment unit can also analyze the user's conversation log and select an optimal communication style based thereon. This makes it possible to provide an optimal communication style based on the user's past communication history. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's past communication history data into a generation AI and cause the generation AI to select an optimal style.

[0052] The adjustment unit can apply different styles depending on the user's personality and preferences when adjusting the communication style. For example, the adjustment unit can apply different styles depending on the user's personality and preferences when adjusting the communication style. For example, if the user is introverted, the adjustment unit can apply a reserved communication style. Also, if the user is extroverted, the adjustment unit can apply a proactive communication style. Furthermore, the adjustment unit can provide a customized communication style depending on the user's preferences. For example, the adjustment unit can select an optimal communication style based on the user's psychological test results and past behavioral history. Also, the adjustment unit can adjust the communication style based on user feedback. This makes it possible to provide an optimal communication style depending on the user's personality and preferences. Some or all of the above-described processing in the adjustment unit can be performed using, for example, AI, or can be performed without using AI. For example, the adjustment unit can input data regarding the user's personality and preferences into the generation AI and cause the generation AI to select an optimal communication style.

[0053] When adjusting a communication style, the adjustment unit can select an optimal style by taking into account the user's geographical location information. For example, when adjusting a communication style, the adjustment unit selects an optimal style by taking into account the user's geographical location information. For example, when the user is on a business trip, the adjustment unit applies a communication style that is suited to the culture of the business trip destination. Furthermore, when the user is at home, the adjustment unit can apply a relaxed communication style. Furthermore, when the user is in an office, the adjustment unit can apply a communication style that is suitable for business. For example, the adjustment unit acquires the user's geographical location information using GPS data or an IP address and selects an optimal communication style based on the acquired information. Furthermore, the adjustment unit can acquire the user's geographical location information using a location information service and select an optimal communication style based on the acquired information. This makes it possible to provide an optimal communication style according to the user's geographical location information. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit may input the user's geographical location information to a generation AI and cause the generation AI to select an optimal style.

[0054] When adjusting a communication style, the adjustment unit can analyze the user's social media activity and suggest an optimal style. For example, when adjusting a communication style, the adjustment unit can analyze the user's social media activity and suggest an optimal style. For example, the adjustment unit can suggest a communication style related to topics in which the user has shown interest on social media. The adjustment unit can also analyze the user's social media activity history and suggest an optimal communication style. Furthermore, the adjustment unit can suggest a communication style based on the opinions of experts the user follows on social media. For example, the adjustment unit can analyze the content of the user's social media posts and the number of likes, and suggest an optimal communication style based on the content. The adjustment unit can also analyze the attributes of the user's followers and suggest an optimal communication style based on the content. This makes it possible to provide an optimal communication style based on the user's social media activity. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without AI. For example, the adjustment unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest an optimal style.

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

[0056] The analysis unit can analyze the user's past goal achievement history and adjust the content of the feedback based on the level of achievement. For example, a more challenging goal can be suggested to a user who has demonstrated a high level of achievement in the past. Also, an easier goal can be suggested to a user who has demonstrated a low level of achievement. Furthermore, the level of detail of the feedback and the specificity of the advice can be adjusted according to the level of achievement. This makes it possible to provide optimal feedback according to the user's past level of achievement. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past goal achievement history data into the generation AI and have the generation AI adjust the content of the feedback.

[0057] The adjustment unit can analyze the user's past feedback history and select the optimal feedback format. For example, if a user has previously preferred text-format feedback, the feedback can be provided in text format. Also, if a user has previously preferred audio-format feedback, the feedback can be provided in audio format. Furthermore, if a user has previously preferred video-format feedback, the feedback can be provided in video format. This makes it possible to provide the optimal feedback format based on the user's past feedback history. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's past feedback history data into the generation AI and cause the generation AI to select the optimal feedback format.

[0058] The data processing unit can analyze the user's past data processing history and select the optimal data processing method. For example, if data processing using a natural language processing algorithm was effective in the past, a similar algorithm can be applied. Also, if an image recognition algorithm was effective, a similar algorithm can be applied. Furthermore, if a voice analysis algorithm was effective, a similar algorithm can be applied. This makes it possible to provide an optimal data processing method based on the user's past data processing history. Some or all of the above-mentioned processing in the data processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the data processing unit can input the user's past data processing history data into a generation AI and have the generation AI select the optimal data processing method.

[0059] The adjustment unit can analyze the user's past communication history and select the optimal communication timing. For example, if effective communication was previously conducted in the morning, the communication can be conducted at a similar time. Also, if effective communication was previously conducted in the evening, the communication can be conducted at a similar time. Furthermore, the optimal communication timing can be selected by referring to the user's past feedback. This makes it possible to provide optimal communication timing based on the user's past communication history. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's past communication history data into the generation AI and cause the generation AI to select the optimal communication timing.

[0060] The providing unit can analyze the user's past feedback history and select the optimal feedback timing. For example, if a user has previously preferred to receive feedback immediately, the providing unit can provide feedback immediately. Also, if a user has previously preferred to receive feedback after a certain period of time, the providing unit can provide feedback at an appropriate timing. Furthermore, the optimal feedback timing can be selected based on the user's past feedback history. This makes it possible to provide the optimal feedback timing based on the user's past feedback history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback history data into the generating AI and cause the generating AI to select the optimal feedback timing.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit receives input of the user's goals or challenges. The user's goals and challenges may include business goals, learning challenges, personal goals, etc. The reception unit can receive input in multiple formats, such as text, voice input, image input, and video input. Step 2: The analysis unit analyzes the goals and tasks received by the reception unit. The analysis unit analyzes the text data using natural language processing technology, the image data using image recognition technology, and the audio data using audio analysis technology. This allows the analysis of the content of the user's goals and tasks and extracts related information. Step 3: The provision unit provides feedback and advice based on the analysis results obtained by the analysis unit. The provision unit can provide feedback and advice in text, audio, or video format. For example, it can propose a specific action plan for the user's goals or challenges. Step 4: The data processing unit processes at least one of text, image, audio, and video data. The data processing unit processes text data using natural language processing technology, image data using image recognition technology, and audio data using audio analysis technology. For example, the data processing unit analyzes videos of the user's past presentations and provides feedback on specific areas for improvement. Step 5: The adjustment unit adjusts the communication style to suit the user's personality and preferences. The adjustment unit selects the optimal communication style based on the user's psychological test results and past behavioral history, and adjusts the communication style based on the user's feedback.

[0063] (Example 2) A coaching system according to an embodiment of the present invention is designed to improve the performance and careers of businesspeople. This system utilizes multimodal AI, capable of processing multiple types of data, including not only text but also images, audio, and video, to maximize user performance. For example, if a user inputs "I want to improve my presentation skills," the AI ​​analyzes the user's past presentation videos and provides specific feedback on areas for improvement. Similarly, if a user inputs "I want to learn how to manage stress," the AI ​​analyzes the user's voice data, assesses their stress level, and provides appropriate advice. A major advantage of this system is that it is less expensive than hiring a human coach with specialized knowledge. It is also available 24 hours a day, 365 days a year, and can tailor its communication style to suit the user's personality and preferences. Furthermore, AI provides unbiased, objective feedback and protects personal information. For example, if a user inputs "I want advice on career advancement," the AI ​​analyzes the user's resume and work history to suggest specific career paths and ways to improve skills. Additionally, if a user inputs, "I want to improve my leadership skills," the AI ​​will analyze the user's past leadership experience and provide specific feedback on areas for improvement. In this way, the coaching system becomes a powerful support tool for business people to achieve their goals and advance their careers. This allows the coaching system to provide optimal feedback and advice for the user's goals and challenges, helping them improve their performance and careers.

[0064] A coaching system according to an embodiment includes a reception unit, an analysis unit, a provision unit, a data processing unit, and an adjustment unit. The reception unit receives input of a user's goals or challenges. Examples of the user's goals and challenges include, but are not limited to, business goals, learning challenges, and personal goals. The reception unit receives, for example, the user's input of the goals and challenges in text format. The reception unit can also receive multiple input formats, such as audio input, image input, and video input. The analysis unit analyzes the goals and challenges received by the reception unit. The analysis unit analyzes text data using, for example, natural language processing technology. The analysis unit can also analyze image data using image recognition technology. The analysis unit can also analyze audio data using audio analysis technology. For example, the analysis unit analyzes the content of the user's goals and challenges and extracts related information. The provision unit provides feedback and advice based on the analysis results obtained by the analysis unit. The provision unit provides, for example, the feedback and advice in text format. The provision unit can also provide the feedback and advice in audio format or video format. For example, the providing unit proposes a specific action plan for the user's goals and challenges. The data processing unit processes at least one of data types selected from text, image, audio, and video. For example, the data processing unit processes text data using natural language processing technology. The data processing unit can also process image data using image recognition technology. The data processing unit can also process audio data using audio analysis technology. For example, the data processing unit analyzes the user's past presentation videos and provides feedback on specific areas for improvement. The adjustment unit adjusts the communication style to match the user's personality and preferences. The adjustment unit selects an optimal communication style based on the user's psychological test results and past behavioral history, for example. The adjustment unit can also adjust the communication style based on the user's feedback. As a result, the coaching system according to the embodiment can provide optimal feedback and advice for the user's goals and challenges, and support the user's performance and career improvement.

[0065] The data processing unit can analyze a user's past presentation videos and provide feedback on specific areas for improvement. For example, the data processing unit can analyze a user's past presentation videos and provide feedback on specific areas for improvement. For example, the data processing unit can analyze the presentation structure and identify areas for improvement. The data processing unit can also analyze the speaking style and use of visual materials and provide feedback on specific areas for improvement. For example, the data processing unit can analyze the presentation structure and suggest ways to ensure a logical flow and emphasize important points. The data processing unit can also analyze the speaking speed and tone and suggest effective speaking methods. Furthermore, the data processing unit can analyze the use of visual materials and suggest effective slide designs and the use of graphs. This can help improve the user's presentation skills. Some or all of the above-described processing in the data processing unit can be performed using, for example, AI, or without AI. For example, the data processing unit can input a user's presentation video into a generation AI and cause the generation AI to analyze the video and extract areas for improvement.

[0066] The data processing unit can analyze the user's voice data, determine the user's stress level, and provide appropriate advice. The data processing unit, for example, analyzes the user's voice data and determines the user's stress level. For example, the data processing unit can analyze the user's voice tone and speaking rate to determine the user's stress level. The data processing unit can also analyze the user's choice of words to determine the user's stress level. For example, the data processing unit can determine that the user's stress level is high if the user's voice tone is high and the user speaks quickly. The data processing unit can also determine that the user's stress level is high if the user's choice of words is negative. The data processing unit can also provide appropriate advice based on the user's stress level. For example, the data processing unit can suggest relaxation methods if the user's stress level is high. The data processing unit can also suggest stress management methods if the user's stress level is low. For example, the data processing unit can suggest relaxation methods such as deep breathing and meditation. The data processing unit can also suggest time management and task management methods for stress management. This can assist the user in managing their stress. Some or all of the above-described processing in the data processing unit can be performed using, for example, AI, or without AI. For example, the data processing unit can input the user's voice data into the generation AI and have the generation AI determine the stress level and provide advice.

[0067] The providing unit can analyze the user's resume or work history and suggest specific career paths and methods for improving skills. For example, the providing unit can analyze the user's resume or work history and suggest specific career paths and methods for improving skills. For example, the providing unit can analyze the user's work history and suggest a career path. The providing unit can also analyze the user's skill set and suggest methods for improving skills. For example, the providing unit can analyze the user's work history and suggest the next job or industry the user should aim for. The providing unit can also analyze the user's skill set and suggest the necessary skills and qualifications. Furthermore, the providing unit can suggest specific training programs and learning resources. For example, the providing unit can identify the user's skill gaps and suggest training programs to fill them. The providing unit can also suggest learning resources according to the user's career goals. This can support the user's career advancement. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or without using AI. For example, the providing unit can input the user's resume and job history into the generating AI and have the generating AI make suggestions about career paths and ways to improve skills.

[0068] The providing unit can analyze the user's past leadership experience and provide feedback on specific areas for improvement. For example, the providing unit can analyze the user's past leadership experience and provide feedback on specific areas for improvement. For example, the providing unit can analyze the user's leadership style and identify areas for improvement. The providing unit can also analyze team management and communication skills and provide feedback on specific areas for improvement. For example, the providing unit can analyze the user's leadership style and suggest effective leadership methods. The providing unit can also analyze team management skills and suggest methods for increasing team motivation. The providing unit can also analyze communication skills and suggest effective communication methods. For example, the providing unit can analyze the user's leadership style and identify areas for leadership improvement. The providing unit can also analyze the user's team management skills and suggest methods for improving team performance. This can help improve the user's leadership skills. Some or all of the above-described processing by the providing unit can be performed using, or without, AI. For example, the providing unit can input the user's leadership experience into a generation AI and cause the generation AI to provide feedback on areas for improvement.

[0069] The adjustment unit can adjust the communication style to match the user's personality and preferences. The adjustment unit adjusts the communication style to match the user's personality and preferences, for example. For example, the adjustment unit selects an optimal communication style based on the user's psychological test results and past behavioral history. The adjustment unit can also adjust the communication style based on user feedback. For example, the adjustment unit can apply a conservative communication style if the user is introverted. The adjustment unit can also apply an aggressive communication style if the user is extroverted. The adjustment unit also provides a customized communication style according to the user's preferences. For example, the adjustment unit selects the user's preferred tone and style of communication and communicates based on that tone and style. This makes it possible to provide the user with an optimal communication style. Some or all of the above-described processing in the adjustment unit may be performed using, or without, AI. For example, the adjustment unit can input data regarding the user's personality and preferences into the generation AI and cause the generation AI to select an optimal communication style.

[0070] The reception unit can estimate the user's emotions and adjust the timing of goal and task input based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and adjusts the timing of goal and task input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit may prompt the user to input goals and tasks at a time when the user can relax. Furthermore, if the user is concentrating, the reception unit may prompt the user to input goals and tasks at that time. Furthermore, if the user is tired, the reception unit may prompt the user to input goals and tasks after resting. For example, the reception unit may capture the user's facial expression with a camera and estimate the user's emotions using an emotion estimation algorithm. The reception unit may also record the user's voice and estimate the user's emotions using voice analysis technology. Furthermore, the reception unit may collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the user's emotions using an emotion estimation algorithm. This allows the user to be prompted to input goals and tasks at the optimal timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI. For example, the reception unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion and adjust the input timing.

[0071] The reception unit can analyze the user's past input history of goals and tasks and select the optimal input method. The reception unit, for example, analyzes the user's past input history of goals and tasks and selects the optimal input method. For example, the reception unit prioritizes and suggests input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also analyze patterns of goals and tasks that the user has previously input and suggest the optimal input method. Furthermore, the reception unit can suggest the optimal input method for a specific time period based on the user's past input history. For example, if the user has frequently used voice input in the past, the reception unit can prioritize and suggest voice input. Also, if the user has frequently used text input in the past, the reception unit can prioritize and suggest text input. This makes it possible to provide the user with the optimal input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data to the generation AI and cause the generation AI to select the optimal input method.

[0072] The reception unit may filter the input of goals and tasks based on the user's current business situation and areas of interest. For example, the reception unit may prioritize input of goals and tasks related to the user's current project. The reception unit may also suggest related goals and tasks based on the user's areas of interest. Furthermore, the reception unit may filter and input appropriate goals and tasks based on the user's business situation. For example, the reception unit prioritizes input of goals and tasks related to the user's ongoing project. The reception unit may also suggest related goals and tasks based on the user's areas of interest. This allows for providing goals and tasks that are appropriate for the user's business situation and areas of interest. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit may input data related to the user's business situation and areas of interest to a generation AI and have the generation AI perform filtering.

[0073] The reception unit can estimate the user's emotions and prioritize the goals and tasks to be input based on the estimated user emotions. For example, the reception unit can estimate the user's emotions and prioritize the goals and tasks to be input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prioritize goals and tasks that help relieve stress. Furthermore, if the user is highly motivated, the reception unit can prioritize challenging goals and tasks. Furthermore, if the user is tired, the reception unit can prioritize easy goals and tasks. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the reception unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This makes it possible to provide optimal priority rankings for goals and tasks based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit may input user emotion data into the generation AI and cause the generation AI to estimate emotions and determine priorities.

[0074] When inputting goals or tasks, the reception unit can prioritize inputting highly relevant goals or tasks taking into account the user's geographical location information. For example, when inputting goals or tasks, the reception unit prioritizes inputting highly relevant goals or tasks taking into account the user's geographical location information. For example, when the user is on a business trip, the reception unit prioritizes goals and tasks related to the business trip destination. Furthermore, when the user is at home, the reception unit can prioritize goals and tasks that can be completed at home. Furthermore, when the user is in the office, the reception unit can prioritize goals and tasks related to office work. For example, the reception unit can acquire the user's geographical location information using GPS data or an IP address and suggest goals and tasks based on the acquired information. Furthermore, the reception unit can acquire the user's geographical location information using a location information service and suggest goals and tasks based on the acquired information. This makes it possible to provide optimal goals and tasks according to the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information into the generation AI and have the generation AI select highly relevant goals and tasks.

[0075] The reception unit can analyze the user's social media activity when inputting a goal or task and input related goals or tasks. For example, when inputting a goal or task, the reception unit can analyze the user's social media activity and input related goals or tasks. For example, the reception unit can suggest goals or tasks related to topics in which the user has shown interest on social media. The reception unit can also analyze the user's social media activity history and input related goals or tasks. Furthermore, the reception unit can suggest goals or tasks based on the opinions of experts the user follows on social media. For example, the reception unit can analyze the content of the user's social media posts and the number of likes, and suggest goals or tasks based on the results. The reception unit can also analyze the attributes of the user's followers and suggest goals or tasks based on the results. This makes it possible to provide optimal goals and tasks based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed using, or without, AI. For example, the reception unit can input the user's social media activity data into a generation AI and cause the generation AI to select related goals and tasks.

[0076] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that focus on the main points. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the system to provide optimal analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into the generation AI and cause the generation AI to estimate the emotion and adjust the method of expression of the analysis.

[0077] The analysis unit can adjust the level of detail of the analysis based on the importance of the goals and tasks during the analysis. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the goals and tasks during the analysis. For example, the analysis unit can perform a detailed analysis for goals and tasks with high importance. The analysis unit can also perform a concise analysis for goals and tasks with low importance. Furthermore, the analysis unit can adjust the depth and scope of the analysis based on the importance. For example, the analysis unit can evaluate the importance of the user's goals and tasks and adjust the level of detail of the analysis based on the evaluation. The analysis unit can also adjust the level of detail of the analysis based on the user's business impact and urgency. This makes it possible to provide optimal analysis results based on the importance of the goals and tasks. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input importance data of the user's goals and tasks into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0078] The analysis unit can apply different analysis algorithms depending on the category of the goal or task during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of the goal or task during analysis. For example, the analysis unit can apply a voice analysis algorithm to the goal of improving presentation skills. The analysis unit can also apply a voice data analysis algorithm to the goal of stress management. Furthermore, the analysis unit can apply a resume or curriculum vitae analysis algorithm to the goal of career advancement. For example, the analysis unit can analyze voice data using a voice analysis algorithm to extract specific areas for improvement for the goal of improving presentation skills. The analysis unit can also determine stress levels using a voice data analysis algorithm to provide appropriate advice for the goal of stress management. This makes it possible to provide optimal analysis results depending on the category of the goal or task. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without AI. For example, the analysis unit can input category data of the user's goals and tasks into the generation AI and cause the generation AI to apply different analysis algorithms.

[0079] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis. Also, if the user is relaxed, the analysis unit can provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows for providing optimal analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, 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 analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data to the generation AI and have the generation AI estimate the emotion and adjust the length of the analysis.

[0080] The analysis unit can determine the analysis priorities based on the submission dates of goals and assignments during analysis. For example, the analysis unit can determine the analysis priorities based on the submission dates of goals and assignments during analysis. For example, the analysis unit prioritizes the analysis of goals and assignments with upcoming deadlines. The analysis unit can also postpone goals and assignments with distant submission dates. Furthermore, the analysis unit can adjust the analysis priorities based on the submission dates. For example, the analysis unit can evaluate the submission dates and deadlines of the user's goals and assignments and determine the analysis priorities based on the evaluation. The analysis unit can also adjust the analysis priorities based on the progress of the project. This makes it possible to provide optimal analysis results based on the submission dates of goals and assignments. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input data on the submission dates of the user's goals and assignments into the generation AI and have the generation AI determine the analysis priorities.

[0081] The analysis unit can adjust the order of analysis based on the relevance of goals and tasks during analysis. For example, the analysis unit can prioritize analysis of highly relevant goals and tasks. The analysis unit can also postpone less relevant goals and tasks. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of goals and tasks. For example, the analysis unit can evaluate the commonality and dependency of the themes of the user's goals and tasks and determine the order of analysis based on that. The analysis unit can also adjust the order of analysis based on the collaboration status of the project. This makes it possible to provide optimal analysis results based on the relevance of goals and tasks. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input relevance data of the user's goals and tasks into the generation AI and have the generation AI adjust the order of analysis.

[0082] The providing unit can estimate the user's emotions and adjust the manner in which feedback and advice are presented based on the estimated user emotions. For example, the providing unit can estimate the user's emotions and adjust the manner in which feedback and advice are presented based on the estimated user emotions. For example, if the user is nervous, the providing unit can provide simple, highly visible feedback. Furthermore, if the user is relaxed, the providing unit can provide detailed feedback. Furthermore, if the user is in a hurry, the providing unit can provide feedback that focuses on the key points. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This makes it possible to provide optimal feedback and advice according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input user emotion data into the generation AI and cause the generation AI to estimate the emotion and adjust the way feedback and advice are expressed.

[0083] The providing unit can adjust the level of detail based on the importance of the goal or task when providing feedback or advice. For example, the providing unit can adjust the level of detail based on the importance of the goal or task when providing feedback or advice. For example, the providing unit can provide detailed feedback for goals or tasks with high importance. The providing unit can also provide concise feedback for goals or tasks with low importance. Furthermore, the providing unit can adjust the depth and scope of the feedback based on the importance. For example, the providing unit can evaluate the importance of the user's goal or task and adjust the level of detail of the feedback based on the evaluation. The providing unit can also adjust the level of detail of the feedback based on the user's business impact and urgency. This makes it possible to provide optimal feedback and advice based on the importance of the goal or task. Some or all of the above-described processing by the providing unit can be performed using, or without, AI. For example, the providing unit can input importance data of the user's goal or task into the generating AI and cause the generating AI to adjust the level of detail of the feedback.

[0084] The providing unit can apply different provision algorithms depending on the category of the goal or challenge when providing feedback or advice. For example, the providing unit can apply different provision algorithms depending on the category of the goal or challenge when providing feedback or advice. For example, the providing unit can apply a voice analysis algorithm to the goal of improving presentation skills. The providing unit can also apply a voice data analysis algorithm to the goal of stress management. Furthermore, the providing unit can apply a resume or CV analysis algorithm to the goal of career advancement. For example, the providing unit can analyze voice data using a voice analysis algorithm to extract specific areas for improvement for the goal of improving presentation skills. The providing unit can also determine the stress level using a voice data analysis algorithm to provide appropriate advice for the goal of stress management. This makes it possible to provide optimal feedback and advice depending on the category of the goal or challenge. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input category data of the user's goals and challenges into the generation AI and cause the generation AI to apply different provision algorithms.

[0085] The providing unit can estimate the user's emotions and adjust the length of feedback or advice based on the estimated user emotions. For example, the providing unit can estimate the user's emotions and adjust the length of feedback or advice based on the estimated user emotions. For example, if the user is in a hurry, the providing unit can provide short, to-the-point feedback. Furthermore, if the user is relaxed, the providing unit can provide detailed feedback. Furthermore, if the user is excited, the providing unit can provide feedback with visually stimulating effects. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This makes it possible to provide optimal feedback or advice based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input user emotion data into the generation AI and cause the generation AI to estimate the emotion and adjust the length of feedback and advice.

[0086] The providing unit can determine priorities based on the submission dates of goals and assignments when providing feedback or advice. For example, the providing unit determines priorities based on the submission dates of goals and assignments when providing feedback or advice. For example, the providing unit prioritizes feedback for goals and assignments with upcoming deadlines. The providing unit can also postpone goals and assignments with distant submission dates. Furthermore, the providing unit can adjust the priority of feedback based on the submission dates. For example, the providing unit evaluates the submission dates and deadlines of the user's goals and assignments and determines the priority of feedback based on the evaluation. The providing unit can also adjust the priority of feedback based on the progress of the project. This makes it possible to provide optimal feedback and advice based on the submission dates of goals and assignments. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input data on the submission dates of the user's goals and assignments into a generating AI and cause the generating AI to determine the priority of feedback.

[0087] The providing unit can adjust the order of feedback and advice based on the relevance of goals and tasks when providing feedback or advice. For example, the providing unit adjusts the order based on the relevance of goals and tasks when providing feedback or advice. For example, the providing unit prioritizes providing feedback for highly relevant goals and tasks. The providing unit can also postpone less relevant goals and tasks. Furthermore, the providing unit can adjust the order of feedback based on the relevance of goals and tasks. For example, the providing unit evaluates the commonality and dependency of the themes of the user's goals and tasks and determines the order of feedback based on this. The providing unit can also adjust the order of feedback based on the collaboration status of the project. This makes it possible to provide optimal feedback and advice based on the relevance of goals and tasks. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input relevance data of the user's goals and tasks to a generation AI and cause the generation AI to adjust the order of feedback.

[0088] The data processing unit can estimate the user's emotions and adjust the data processing method based on the estimated user emotions. For example, the data processing unit can estimate the user's emotions and adjust the data processing method based on the estimated user emotions. For example, if the user is nervous, the data processing unit can provide a simple, highly visible data processing result. Furthermore, if the user is relaxed, the data processing unit can provide a detailed data processing result. Furthermore, if the user is in a hurry, the data processing unit can provide a data processing result that focuses on the main points. For example, the data processing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The data processing unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the data processing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This makes it possible to provide an optimal data processing result according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data processing unit may be performed using AI, or may be performed without using AI. For example, the data processing unit may input user emotion data into the generation AI and cause the generation AI to estimate the emotion and adjust the data processing method.

[0089] The data processing unit can select an optimal processing method by referring to past data during data processing. For example, the data processing unit can select an optimal processing method by referring to past data during data processing. For example, the data processing unit can select an optimal data processing method by referring to a user's past presentation videos. The data processing unit can also select an optimal data processing method by referring to the user's past voice data. Furthermore, the data processing unit can select an optimal data processing method by referring to the user's past resume or work history. For example, the data processing unit can analyze the user's past presentation videos and extract specific areas for improvement. The data processing unit can also analyze the user's past voice data, determine the user's stress level, and provide appropriate advice. This makes it possible to provide an optimal data processing method based on past data. Some or all of the above-described processing in the data processing unit can be performed using, or without, AI. For example, the data processing unit can input the user's past data into a generation AI and have the generation AI select an optimal processing method.

[0090] The data processing unit can apply different processing algorithms depending on the type of data during data processing. For example, the data processing unit can apply different processing algorithms depending on the type of data during data processing. For example, the data processing unit can apply a natural language processing algorithm to text data. The data processing unit can also apply an image recognition algorithm to image data. The data processing unit can also apply a voice analysis algorithm to voice data. For example, the data processing unit can analyze text data using a natural language processing algorithm to extract specific information. The data processing unit can also analyze image data using an image recognition algorithm to extract visual information. This makes it possible to provide optimal data processing results depending on the type of data. Some or all of the above-mentioned processing in the data processing unit can be performed using, for example, AI, or can be performed without using AI. For example, the data processing unit can input the type of user's data to the generation AI and cause the generation AI to apply different processing algorithms.

[0091] The data processing unit can estimate the user's emotions and determine the priority of data processing based on the estimated user emotions. For example, the data processing unit can estimate the user's emotions and determine the priority of data processing based on the estimated user emotions. For example, if the user is feeling stressed, the data processing unit can prioritize processing data related to stress reduction. Furthermore, if the user is highly motivated, the data processing unit can prioritize processing challenging data. Furthermore, if the user is tired, the data processing unit can prioritize processing easy data. For example, the data processing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The data processing unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the data processing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This makes it possible to provide optimal data processing results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data processing unit may be performed using AI, or may be performed without using AI. For example, the data processing unit may input user emotion data into the generation AI and cause the generation AI to estimate the emotion and determine the priority of data processing.

[0092] The data processing unit can determine the processing priority based on the data submission time during data processing. The data processing unit, for example, determines the processing priority based on the data submission time during data processing. For example, the data processing unit prioritizes processing of data with an upcoming deadline. The data processing unit can also postpone data with a distant submission time. Furthermore, the data processing unit can adjust the data processing priority based on the submission time. For example, the data processing unit evaluates the user's data submission date and deadline and determines the processing priority based on the evaluation. The data processing unit can also adjust the processing priority based on the progress of the project. This makes it possible to provide optimal data processing results according to the data submission time. Some or all of the above-mentioned processing in the data processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the data processing unit can input the user's data submission time data to the generation AI and have the generation AI determine the processing priority.

[0093] The data processing unit can adjust the processing order based on the relevance of the data during data processing. The data processing unit, for example, adjusts the processing order based on the relevance of the data during data processing. For example, the data processing unit prioritizes processing of highly relevant data. The data processing unit can also postpone processing of less relevant data. Furthermore, the data processing unit can adjust the processing order according to the relevance of the data. For example, the data processing unit evaluates the commonality and dependency of the themes of the user's data and determines the processing order based on this. The data processing unit can also adjust the processing order based on the collaboration status of the project. This makes it possible to provide optimal data processing results according to the relevance of the data. Some or all of the above-mentioned processing in the data processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the data processing unit can input relevance data of the user's data to a generation AI and cause the generation AI to adjust the processing order.

[0094] The adjustment unit can estimate the user's emotions and adjust the communication style based on the estimated user emotions. For example, the adjustment unit estimates the user's emotions and adjusts the communication style based on the estimated user emotions. For example, if the user is nervous, the adjustment unit communicates in a calm tone. Furthermore, if the user is relaxed, the adjustment unit can communicate in a friendly tone. Furthermore, if the user is in a hurry, the adjustment unit can communicate quickly and concisely. For example, the adjustment unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The adjustment unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the adjustment unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This makes it possible to provide an optimal communication style according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit may input user emotion data to the generation AI and cause the generation AI to estimate emotions and adjust communication styles.

[0095] When adjusting a communication style, the adjustment unit can select an optimal style by referring to the user's past communication history. For example, when adjusting a communication style, the adjustment unit selects an optimal style by referring to the user's past communication history. For example, the adjustment unit preferentially applies a communication style that the user previously preferred. The adjustment unit can also analyze the user's past communication history and select an optimal style. Furthermore, the adjustment unit can also select an optimal communication style by referring to the user's past feedback. For example, the adjustment unit analyzes the user's email history or chat history and selects an optimal communication style based thereon. The adjustment unit can also analyze the user's conversation log and select an optimal communication style based thereon. This makes it possible to provide an optimal communication style based on the user's past communication history. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's past communication history data into a generation AI and cause the generation AI to select an optimal style.

[0096] The adjustment unit can apply different styles depending on the user's personality and preferences when adjusting the communication style. For example, the adjustment unit can apply different styles depending on the user's personality and preferences when adjusting the communication style. For example, if the user is introverted, the adjustment unit can apply a reserved communication style. Also, if the user is extroverted, the adjustment unit can apply a proactive communication style. Furthermore, the adjustment unit can provide a customized communication style depending on the user's preferences. For example, the adjustment unit can select an optimal communication style based on the user's psychological test results and past behavioral history. Also, the adjustment unit can adjust the communication style based on user feedback. This makes it possible to provide an optimal communication style depending on the user's personality and preferences. Some or all of the above-described processing in the adjustment unit can be performed using, for example, AI, or can be performed without using AI. For example, the adjustment unit can input data regarding the user's personality and preferences into the generation AI and cause the generation AI to select an optimal communication style.

[0097] The adjustment unit can estimate the user's emotions and prioritize communication styles based on the estimated user emotions. For example, the adjustment unit can estimate the user's emotions and prioritize communication styles based on the estimated user emotions. For example, if the user is feeling stressed, the adjustment unit can prioritize a communication style that helps reduce stress. Furthermore, if the user is highly motivated, the adjustment unit can prioritize a challenging communication style. Furthermore, if the user is tired, the adjustment unit can prioritize a communication style that helps relax. For example, the adjustment unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The adjustment unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the adjustment unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This makes it possible to provide an optimal communication style according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the adjustment unit may be performed using AI, or may be performed without using AI. For example, the adjustment unit may input user emotion data into the generation AI and cause the generation AI to estimate emotions and determine the priority of communication styles.

[0098] When adjusting a communication style, the adjustment unit can select an optimal style by taking into account the user's geographical location information. For example, when adjusting a communication style, the adjustment unit selects an optimal style by taking into account the user's geographical location information. For example, when the user is on a business trip, the adjustment unit applies a communication style that is suited to the culture of the business trip destination. Furthermore, when the user is at home, the adjustment unit can apply a relaxed communication style. Furthermore, when the user is in an office, the adjustment unit can apply a communication style that is suitable for business. For example, the adjustment unit acquires the user's geographical location information using GPS data or an IP address and selects an optimal communication style based on the acquired information. Furthermore, the adjustment unit can acquire the user's geographical location information using a location information service and select an optimal communication style based on the acquired information. This makes it possible to provide an optimal communication style according to the user's geographical location information. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit may input the user's geographical location information to a generation AI and cause the generation AI to select an optimal style.

[0099] When adjusting a communication style, the adjustment unit can analyze the user's social media activity and suggest an optimal style. For example, when adjusting a communication style, the adjustment unit can analyze the user's social media activity and suggest an optimal style. For example, the adjustment unit can suggest a communication style related to topics in which the user has shown interest on social media. The adjustment unit can also analyze the user's social media activity history and suggest an optimal communication style. Furthermore, the adjustment unit can suggest a communication style based on the opinions of experts the user follows on social media. For example, the adjustment unit can analyze the content of the user's social media posts and the number of likes, and suggest an optimal communication style based on the content. The adjustment unit can also analyze the attributes of the user's followers and suggest an optimal communication style based on the content. This makes it possible to provide an optimal communication style based on the user's social media activity. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without AI. For example, the adjustment unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest an optimal style. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, analysis unit, provision unit, data processing unit, and adjustment unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives input of the user's goals and tasks. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's goals and tasks. The provision unit is implemented, for example, by the control unit 46A of the smart device 14 and provides feedback and advice. The data processing unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and processes text, image, audio, and video data. The adjustment unit is implemented, for example, by the control unit 46A of the smart device 14 and adjusts the communication style to match the user's personality and preferences. The reception unit, for example, can estimate the user's emotions and adjust the timing of input of the goals and tasks based on the estimated emotions. === Hard Collateral 1-2 === Each of the above-described elements, including the reception unit, analysis unit, provision unit, data processing unit, and adjustment unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives input of the user's goals and tasks. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's goals and tasks. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides feedback and advice. The data processing unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and processes text, image, audio, and video data. The adjustment unit is implemented, for example, by the control unit 46A of the smart glasses 214 and adjusts the communication style to match the user's personality and preferences. The reception unit can, for example, estimate the user's emotions and adjust the timing of input of the goals and tasks based on the estimated emotions. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, provision unit, data processing unit, and adjustment unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and receives input of the user's goals and tasks. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's goals and tasks. The provision unit is realized by the control unit 46A of the headset-type terminal 314 and provides feedback and advice. The data processing unit is realized by the specific processing unit 290 of the data processing device 12 and processes text, image, audio, and video data. The adjustment unit is realized by the control unit 46A of the headset-type terminal 314 and adjusts the communication style to match the user's personality and preferences. The reception unit can, for example, estimate the user's emotions and adjust the timing of input of the goals and tasks based on the estimated emotions. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, provision unit, data processing unit, and adjustment unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives input of the user's goals and tasks. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's goals and tasks. The provision unit is realized by the control unit 46A of the robot 414 and provides feedback and advice. The data processing unit is realized by the specific processing unit 290 of the data processing device 12 and processes text, image, audio, and video data. The adjustment unit is realized by the control unit 46A of the robot 414 and adjusts the communication style to match the user's personality and preferences. The reception unit can, for example, estimate the user's emotions and adjust the timing of input of the goals and tasks based on the estimated emotions.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The reception unit can adjust the input method taking into account the user's current health condition when inputting the user's goals and tasks. For example, if the user is tired, the reception unit can preferentially suggest voice input. Also, if the user is concentrating, the reception unit can encourage text input. Furthermore, if the user is feeling stressed, the reception unit can suggest input in a relaxing environment. This makes it possible to provide the optimal input method according to the user's health condition. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's health condition data into the generation AI and have the generation AI select the optimal input method.

[0102] The analysis unit can analyze the user's past goal achievement history and adjust the content of the feedback based on the level of achievement. For example, a more challenging goal can be suggested to a user who has demonstrated a high level of achievement in the past. Also, an easier goal can be suggested to a user who has demonstrated a low level of achievement. Furthermore, the level of detail of the feedback and the specificity of the advice can be adjusted according to the level of achievement. This makes it possible to provide optimal feedback according to the user's past level of achievement. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past goal achievement history data into the generation AI and have the generation AI adjust the content of the feedback.

[0103] The providing unit can estimate the user's emotions and adjust the timing of feedback based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide feedback immediately. Also, if the user is feeling stressed, the providing unit can provide feedback at an appropriate timing. Furthermore, if the user is concentrating, the providing unit can provide feedback at that timing. This allows feedback to be provided at the optimal timing according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion and adjust the timing of the feedback.

[0104] The adjustment unit can analyze the user's past feedback history and select the optimal feedback format. For example, if a user has previously preferred text-format feedback, the feedback can be provided in text format. Also, if a user has previously preferred audio-format feedback, the feedback can be provided in audio format. Furthermore, if a user has previously preferred video-format feedback, the feedback can be provided in video format. This makes it possible to provide the optimal feedback format based on the user's past feedback history. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's past feedback history data into the generation AI and cause the generation AI to select the optimal feedback format.

[0105] The providing unit can estimate the user's emotions and adjust the content of the feedback based on the estimated user's emotions. For example, if the user is nervous, simple, highly visible feedback can be provided. If the user is relaxed, detailed feedback can be provided. Furthermore, if the user is in a hurry, feedback that focuses on the main points can be provided. This makes it possible to provide optimal feedback according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to estimate emotions and adjust the content of the feedback.

[0106] The data processing unit can analyze the user's past data processing history and select the optimal data processing method. For example, if data processing using a natural language processing algorithm was effective in the past, a similar algorithm can be applied. Also, if an image recognition algorithm was effective, a similar algorithm can be applied. Furthermore, if a voice analysis algorithm was effective, a similar algorithm can be applied. This makes it possible to provide an optimal data processing method based on the user's past data processing history. Some or all of the above-mentioned processing in the data processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the data processing unit can input the user's past data processing history data into a generation AI and have the generation AI select the optimal data processing method.

[0107] The providing unit can estimate the user's emotions and adjust the form of feedback based on the estimated user's emotions. For example, if the user is nervous, simple, highly visible text feedback can be provided. If the user is relaxed, detailed audio feedback can be provided. Furthermore, if the user is in a hurry, video feedback that focuses on the main points can be provided. This makes it possible to provide the optimal feedback form according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to estimate emotions and adjust the feedback format.

[0108] The adjustment unit can analyze the user's past communication history and select the optimal communication timing. For example, if effective communication was previously conducted in the morning, the communication can be conducted at a similar time. Also, if effective communication was previously conducted in the evening, the communication can be conducted at a similar time. Furthermore, the optimal communication timing can be selected by referring to the user's past feedback. This makes it possible to provide optimal communication timing based on the user's past communication history. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the user's past communication history data into the generation AI and cause the generation AI to select the optimal communication timing.

[0109] The data processing unit can estimate the user's emotions and determine the priority of data processing based on the estimated user emotions. For example, if the user is feeling stressed, data related to stress reduction can be processed preferentially. Furthermore, if the user is highly motivated, challenging data can be processed preferentially. Furthermore, if the user is tired, easy data can be processed preferentially. This makes it possible to provide optimal data processing results according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. Some or all of the above-described processing in the data processing unit may be performed using, for example, AI, or may be performed without AI. For example, the data processing unit can input the user's emotion data into a generation AI and have the generation AI perform emotion estimation and data processing priority determination.

[0110] The providing unit can analyze the user's past feedback history and select the optimal feedback timing. For example, if a user has previously preferred to receive feedback immediately, the providing unit can provide feedback immediately. Also, if a user has previously preferred to receive feedback after a certain period of time, the providing unit can provide feedback at an appropriate timing. Furthermore, the optimal feedback timing can be selected based on the user's past feedback history. This makes it possible to provide the optimal feedback timing based on the user's past feedback history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback history data into the generating AI and cause the generating AI to select the optimal feedback timing.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The reception unit receives input of the user's goals or challenges. The user's goals and challenges may include business goals, learning challenges, personal goals, etc. The reception unit can receive input in multiple formats, such as text, voice input, image input, and video input. Step 2: The analysis unit analyzes the goals and tasks received by the reception unit. The analysis unit analyzes the text data using natural language processing technology, the image data using image recognition technology, and the audio data using audio analysis technology. This allows the analysis of the content of the user's goals and tasks and extracts related information. Step 3: The provision unit provides feedback and advice based on the analysis results obtained by the analysis unit. The provision unit can provide feedback and advice in text, audio, or video format. For example, it can propose a specific action plan for the user's goals or challenges. Step 4: The data processing unit processes at least one of text, image, audio, and video data. The data processing unit processes text data using natural language processing technology, image data using image recognition technology, and audio data using audio analysis technology. For example, the data processing unit analyzes videos of the user's past presentations and provides feedback on specific areas for improvement. Step 5: The adjustment unit adjusts the communication style to suit the user's personality and preferences. The adjustment unit selects the optimal communication style based on the user's psychological test results and past behavioral history, and adjusts the communication style based on the user's feedback.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

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

[0156] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0157] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0158] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0160] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0161] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0168] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0169] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0170] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0174] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0175] 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.

[0176] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0177] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0178] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0179] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0181] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0182] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0183] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0184] [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system comprising: a reception unit that receives input of a user's goals or tasks; an analysis unit that analyzes the goals or tasks received by the reception unit; a provision unit that provides feedback or advice based on the analysis results obtained by the analysis unit; a data processing unit that processes at least one of text, image, audio, and video data; and an adjustment unit that adjusts the communication style to match the user's personality or preferences.

2. The data processing unit Analyze users' past presentation videos and provide feedback on specific areas for improvement 2. The system of claim 1.

3. The data processing unit Analyzes the user's voice data, determines their stress level, and provides appropriate advice 2. The system of claim 1.

4. The providing unit Analyzes users' resumes and work histories and suggests specific career paths and ways to improve skills 2. The system of claim 1.

5. The providing unit Analyze users' past leadership experiences and provide specific feedback on areas for improvement 2. The system of claim 1.

6. The adjustment unit Adapt your communication style to suit your personality and preferences 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the timing of inputting goals and tasks based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past goal and task input history and select the optimal input method 2. The system of claim 1.

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