system

Abstract

The system according to the embodiment aims to enable business people to obtain appropriate feedback and advice to achieve their goals and solve their problems. [Solution] A system according to an embodiment includes a receiving unit, an analysis unit, a generation unit, a provision unit, and an adjustment unit. The receiving unit receives input of a goal or task. The analysis unit analyzes the information received by the receiving unit. The generation unit generates feedback and advice based on the information analyzed by the analysis unit. The provision unit provides the feedback and advice generated by the generation unit to the user. The adjustment unit adjusts the feedback and advice generated by the generation unit to suit the user's personality and 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] Conventional technology makes it difficult for business people to receive appropriate feedback and advice to achieve their goals and solve problems, and there is room for improvement.

[0005] The system according to the embodiment aims to enable business people to obtain appropriate feedback and advice to achieve their goals and solve their problems. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analyzing unit, a generating unit, a providing unit, and an adjusting unit. The receiving unit receives input of a goal or task. The analyzing unit analyzes the information received by the receiving unit. The generating unit generates feedback and advice based on the information analyzed by the analyzing unit. The providing unit provides the feedback and advice generated by the generating unit to the user. The adjusting unit adjusts the feedback and advice generated by the generating unit to suit the user's personality and preferences. [Effects of the Invention]

[0007] The system according to the embodiment allows business people to receive appropriate feedback and advice to achieve their goals and solve their problems. [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) An AI coaching system according to an embodiment of the present invention is a system for improving the performance and careers of business people. In this AI coaching system, when a user inputs their goals and challenges, the AI ​​provides optimal feedback and advice. For example, if a user inputs a goal or challenge such as "I want to improve project progress management efficiency" or "I want to improve my presentation skills," the AI ​​analyzes the input and suggests specific techniques and practice methods. This system is less expensive than assigning a human expert, is unbiased, and objective. Personal information is also protected. Furthermore, the AI ​​adjusts its communication style to match the user's personality and preferences, acting as a personal coach. For example, if the user is introverted, the AI ​​provides gentle, encouraging feedback, while if the user is extroverted, it provides challenging advice. In this way, feedback and advice can be provided in the optimal communication style. This allows for unbiased, objective feedback and advice to be provided at a lower cost than assigning a human expert. Furthermore, personal information is protected, allowing users to use the service with peace of mind. For example, information about the user's goals and challenges is anonymized during analysis by the AI, eliminating the risk of personal information leaks. In this way, by using an AI coaching service, business people can obtain a specific action plan to improve their performance and career. For example, they can receive specific advice such as techniques for more efficiently managing project progress or practice methods to improve presentation skills. In addition, AI can adjust its communication style to match the person's personality and preferences, providing optimal feedback and advice to the user. This allows users to grow at their own pace and achieve their goals. In this way, the AI ​​coaching system can provide optimal feedback and advice for the user's goals and challenges, improving their performance and career.

[0029] An AI coaching system according to an embodiment includes a reception unit, an analysis unit, a generation unit, a provision unit, and an adjustment unit. The reception unit receives a user's input of goals and challenges. Examples of the goals and challenges include, but are not limited to, improving project progress management and presentation skills. The reception unit receives the user's input goals and challenges in digital format. The reception unit can also support multiple input methods, such as voice input and handwritten input. The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the user's goals and challenges using, for example, natural language processing technology and extracts information for generating optimal feedback and advice. The analysis unit searches a database for related information based on the user's goals and challenges and generates an analysis result. The generation unit generates feedback and advice based on the information analyzed by the analysis unit. The generation unit generates specific feedback and advice for the user's goals and challenges using, for example, a generation AI. The generation unit suggests, for example, methods for improving project progress management and practice methods for improving presentation skills. The providing unit provides the feedback and advice generated by the generating unit to the user. The providing unit provides the generated feedback and advice to the user in, for example, text, audio, or video format. The providing unit, for example, sends a notification to the user's device and displays the feedback and advice. The adjusting unit adjusts the feedback and advice generated by the generating unit to match the user's personality and preferences. For example, if the user is introverted, the adjusting unit provides gentle and encouraging feedback, and if the user is extroverted, the adjusting unit provides challenging advice. The adjusting unit selects an optimal communication style based on, for example, the user's past behavioral history and survey results. As a result, the AI ​​coaching system according to the embodiment can provide optimal feedback and advice for the user's goals and challenges, thereby improving performance and career.

[0030] The reception unit can analyze the user's past input history of goals and tasks and select the optimal input method. For example, the reception unit can prioritize and suggest 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. The reception unit can also predict and suggest the input method to be used in a specific time period based on the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history. 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 past input data into a generation AI and have the generation AI select the optimal input method.

[0031] When inputting goals and tasks, the reception unit can filter the data based on the user's current project or area of ​​interest. For example, the reception unit can prioritize inputting goals and tasks related to the user's ongoing project. The reception unit can also suggest related goals and tasks based on the user's area of ​​interest. The reception unit can also input optimal goals and tasks based on the user's current work content. This allows goals and tasks related to the user's current project or area of ​​interest to be prioritized. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's project data into a generation AI and have the generation AI filter out related goals and tasks.

[0032] When inputting goals and tasks, the reception unit can prioritize inputting highly relevant goals and tasks based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize inputting goals and tasks related to that area. Furthermore, if the user is on a business trip, the reception unit can prioritize inputting goals and tasks related to the business trip destination. Furthermore, if the user is at home, the reception unit can prioritize inputting goals and tasks that can be performed at home. This makes it possible to input optimal goals and tasks based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's location information data to the generation AI and cause the generation AI to select relevant goals and tasks.

[0033] When a goal or task is input, the reception unit can analyze the user's social media activity and input related goals or tasks. The reception unit can, for example, suggest related goals or tasks based on information shared by the user on social media. The reception unit can also suggest related goals or tasks based on topics the user follows on social media. The reception unit can also suggest related goals or tasks based on groups the user participates in on social media. This makes it possible to input 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 data into a generation AI and cause the generation AI to select related goals and tasks.

[0034] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the goals and tasks. For example, the analysis unit performs a detailed analysis on important goals and tasks. The analysis unit can also perform a simplified analysis on low-priority goals and tasks. The analysis unit can also perform an analysis with an appropriate level of detail on goals and tasks of medium importance. This allows analysis to be performed with an optimal level of detail depending on the importance of the goals and tasks. Some or all of the above-mentioned 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 importance data of goals and tasks to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0035] During analysis, the analysis unit can apply different analysis algorithms depending on the category of goals and challenges. For example, the analysis unit can apply an analysis algorithm dedicated to project management to goals and challenges related to project management. The analysis unit can also apply an analysis algorithm dedicated to communication skills to goals and challenges related to presentation skills. The analysis unit can also apply an analysis algorithm dedicated to team dynamics to goals and challenges related to team building. This makes it possible to apply the optimal analysis algorithm depending on the category of goals and challenges. Some or all of the above-mentioned 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 category data of goals and challenges into the generation AI and cause the generation AI to apply the analysis algorithm.

[0036] During analysis, the analysis unit can determine the priority of analysis based on the submission dates of goals and assignments. For example, the analysis unit can prioritize analysis of urgent goals and assignments. The analysis unit can also prioritize analysis of goals and assignments with upcoming submission deadlines. The analysis unit can also postpone analysis of goals and assignments with distant submission deadlines. This allows analysis to be performed with optimal priority depending on the submission dates of goals and assignments. Some or all of the above-mentioned 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 data on the submission dates of goals and assignments into the generation AI and have the generation AI determine the analysis priority.

[0037] During analysis, the analysis unit can adjust the order of analysis based on the relevance of goals and tasks. For example, the analysis unit prioritizes analysis of highly relevant goals and tasks. The analysis unit can also postpone analysis of less relevant goals and tasks. The analysis unit can also analyze goals and tasks with a moderate degree of relevance in an appropriate order. This allows analysis to be performed in an optimal order depending on the relevance of goals and tasks. Some or all of the above-mentioned 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 relevance data of goals and tasks to the generation AI and have the generation AI adjust the order of analysis.

[0038] When generating feedback and advice, the generation unit can adjust the level of detail of the feedback and advice based on the importance of the goal or task. For example, the generation unit provides detailed feedback and advice for important goals and tasks. The generation unit can also provide simplified feedback and advice for low-priority goals and tasks. The generation unit can also provide feedback and advice with an appropriate level of detail for goals and tasks of medium importance. This makes it possible to provide feedback and advice with an optimal level of detail depending on the importance of the goal or task. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input importance data of goals and tasks into the generation AI and cause the generation AI to adjust the level of detail of the feedback and advice.

[0039] The generation unit can apply different generation algorithms depending on the category of the goal or challenge when generating feedback or advice. For example, the generation unit can apply a generation algorithm dedicated to project management to goals and challenges related to project management. The generation unit can also apply a generation algorithm dedicated to communication skills to goals and challenges related to presentation skills. The generation unit can also apply a generation algorithm dedicated to team dynamics to goals and challenges related to team building. This makes it possible to apply the optimal generation algorithm depending on the category of the goal or challenge. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input category data of the goal or challenge into the generation AI and cause the generation AI to apply the generation algorithm.

[0040] When generating feedback and advice, the generation unit can determine the priority of generation based on the submission dates of goals and assignments. For example, the generation unit can prioritize providing feedback and advice for urgent goals and assignments. The generation unit can also prioritize providing feedback and advice for goals and assignments with upcoming submission deadlines. The generation unit can also postpone providing feedback and advice for goals and assignments with distant submission deadlines. This makes it possible to provide feedback and advice with optimal priority depending on the submission dates of goals and assignments. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input data on the submission dates of goals and assignments into the generation AI and have the generation AI determine the priority of feedback and advice.

[0041] When generating feedback and advice, the generation unit can adjust the order of generation based on the relevance of goals and tasks. For example, the generation unit can provide feedback and advice preferentially to goals and tasks that are highly relevant. The generation unit can also provide feedback and advice later to goals and tasks that are less relevant. The generation unit can also provide feedback and advice in an appropriate order to goals and tasks that are medium relevance. This makes it possible to provide feedback and advice in an optimal order depending on the relevance of goals and tasks. Some or all of the above-described processing in the generation unit may be performed using, or without, AI, for example. For example, the generation unit can input relevance data of goals and tasks to the generation AI and cause the generation AI to adjust the order of feedback and advice.

[0042] When providing feedback or advice, the providing unit can select the optimal method of providing feedback or advice by referring to the user's past feedback history. For example, the providing unit can prioritize providing feedback formats that the user has previously preferred. The providing unit can also provide feedback at the optimal timing based on the user's past feedback history. The providing unit can also analyze the user's past feedback history and select the most effective method of providing feedback or advice. This makes it possible to provide feedback or advice in the optimal manner based on the user's past feedback history. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into the generation AI and cause the generation AI to select the optimal method of providing feedback.

[0043] The providing unit can customize the means of providing feedback or advice based on the user's current situation when providing feedback or advice. For example, if the user is in a meeting, the providing unit provides feedback after the meeting ends. The providing unit can also provide voice feedback if the user is on the move. The providing unit can also provide text feedback if the user is doing desk work. This makes it possible to provide feedback or advice by the most appropriate means depending on the user's current situation. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's current situation data into the generating AI and cause the generating AI to customize the means of providing.

[0044] When providing feedback or advice, the providing unit can select the optimal delivery method by taking into account the user's geographical location information. For example, if the user is in a specific area, the providing unit can provide feedback related to that area. Furthermore, if the user is on a business trip, the providing unit can also provide feedback related to the business trip destination. Furthermore, if the user is at home, the providing unit can also provide feedback that can be performed at home. This makes it possible to provide feedback or advice in the optimal method based on the user's geographical location information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's location information data into the generation AI and cause the generation AI to select the optimal delivery method.

[0045] When providing feedback or advice, the providing unit can analyze the user's social media activity and suggest a means of providing the feedback or advice. The providing unit can provide relevant feedback based on, for example, information shared by the user on social media. The providing unit can also provide relevant feedback based on topics the user follows on social media. The providing unit can also provide relevant feedback based on groups the user participates in on social media. This makes it possible to provide feedback or advice in an optimal manner based on the user's social media activity. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's social media data into a generation AI and cause the generation AI to suggest an optimal means of providing the feedback or advice.

[0046] When adjusting feedback or advice, the adjustment unit can select the optimal adjustment method by referring to the user's past feedback history. For example, the adjustment unit can preferentially provide feedback formats that the user has previously preferred. The adjustment unit can also provide feedback at the optimal timing based on the user's past feedback history. The adjustment unit can also analyze the user's past feedback history and select the most effective provision method. This makes it possible to adjust feedback or advice in the optimal manner based on the user's past feedback history. Some or all of the above-mentioned processing in the adjustment unit may be performed using, or without, AI, for example. For example, the adjustment unit can input the user's past feedback data into the generation AI and cause the generation AI to select the optimal adjustment method.

[0047] When adjusting feedback or advice, the adjustment unit can customize the means of adjustment based on the user's current situation. For example, if the user is in a meeting, the adjustment unit provides feedback after the meeting ends. The adjustment unit can also provide voice feedback if the user is on the move. The adjustment unit can also provide text feedback if the user is doing desk work. This makes it possible to adjust feedback or advice using the optimal means depending on the user's current situation. Some or all of the above-mentioned 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 current situation data into the generation AI and cause the generation AI to customize the means of adjustment.

[0048] When adjusting feedback or advice, the adjustment unit can select the optimal adjustment method by taking into account the user's geographical location information. For example, if the user is in a specific area, the adjustment unit can provide feedback related to the area. Furthermore, if the user is on a business trip, the adjustment unit can also provide feedback related to the business trip destination. Furthermore, if the user is at home, the adjustment unit can also provide feedback that can be implemented at home. This makes it possible to adjust feedback and advice in an optimal manner based on the user's geographical location information. Some or all of the above-mentioned processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI. For example, the adjustment unit can input the user's location information data into the generation AI and cause the generation AI to select the optimal adjustment method.

[0049] When adjusting feedback or advice, the adjustment unit can analyze the user's social media activity and suggest adjustment measures. The adjustment unit can provide relevant feedback based on, for example, information shared by the user on social media. The adjustment unit can also provide relevant feedback based on topics the user follows on social media. The adjustment unit can also provide relevant feedback based on groups the user participates in on social media. This makes it possible to adjust feedback or advice in an optimal manner based on the user's social media activity. 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 the user's social media data into the generation AI and cause the generation AI to suggest optimal adjustment measures.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The reception unit can analyze the user's past feedback history and suggest the optimal input method. For example, if the user has previously preferred voice input, the reception unit can preferentially suggest voice input. Also, if the user has previously input data during a specific time period, the reception unit can encourage input during that time period. Furthermore, the reception unit can analyze patterns of goals and tasks that the user has previously input and suggest similar goals and tasks. This makes it possible to suggest the optimal input method based on the user's past behavioral history.

[0052] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the goals and tasks. For example, a detailed analysis can be performed for important goals and tasks. A simplified analysis can also be performed for goals and tasks with low priority. Furthermore, an analysis with an appropriate level of detail can be performed for goals and tasks with medium importance. This allows the analysis to be performed with the optimal level of detail depending on the importance of the goals and tasks.

[0053] When generating feedback and advice, the generator can adjust the level of detail based on the importance of the goal or task. For example, detailed feedback and advice can be provided for important goals and tasks. Simplified feedback and advice can also be provided for goals and tasks with low priority. Furthermore, feedback and advice with an appropriate level of detail can be provided for goals and tasks with medium importance. This makes it possible to provide feedback and advice with an optimal level of detail depending on the importance of the goal or task.

[0054] When providing feedback or advice, the providing unit can select the optimal method of providing the feedback or advice by referring to the user's past feedback history. For example, the providing unit can provide feedback in a format that the user has previously preferred preferentially. The providing unit can also provide feedback at the optimal timing based on the user's past feedback history. Furthermore, the providing unit can analyze the user's past feedback history and select the most effective method of providing the feedback or advice. This makes it possible to provide feedback or advice in the optimal manner based on the user's past feedback history.

[0055] When adjusting feedback or advice, the adjustment unit can select the optimal adjustment method by referring to the user's past feedback history. For example, the adjustment unit can provide feedback in a format that the user has previously preferred preferentially. The adjustment unit can also provide feedback at the optimal timing based on the user's past feedback history. Furthermore, the adjustment unit can analyze the user's past feedback history and select the most effective method of providing feedback or advice. This makes it possible to adjust feedback or advice in the optimal way based on the user's past feedback history.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The user inputs their goals and challenges into the reception unit. Examples of goals and challenges that users input include wanting to streamline project progress management or improve presentation skills. The reception unit accepts the goals and challenges entered by the user in digital form. It can also support multiple input methods, such as voice input and handwriting input. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit uses natural language processing technology to analyze the user's goals and challenges, and extracts information to generate optimal feedback and advice. Furthermore, based on the user's goals and challenges, the analysis unit searches a database for related information and generates analysis results. Step 3: The generation unit generates feedback and advice based on the information analyzed by the analysis unit. Using the generation AI, the generation unit generates specific feedback and advice for the user's goals and challenges. For example, it suggests methods for streamlining project progress management or practice methods for improving presentation skills. Step 4: The providing unit provides the feedback or advice generated by the generating unit to the user. The providing unit provides the generated feedback or advice to the user in a text format, an audio format, a video format, or the like. For example, the providing unit may send a notification to the user's device and display the feedback or advice. Step 5: The adjustment unit adjusts the feedback and advice generated by the generation unit to suit the user's personality and preferences. If the user is introverted, the adjustment unit provides gentle, encouraging feedback, and if the user is extroverted, the adjustment unit provides challenging advice. Furthermore, the adjustment unit selects the optimal communication style based on the user's past behavioral history and survey results.

[0058] (Example 2) An AI coaching system according to an embodiment of the present invention is a system for improving the performance and careers of business people. In this AI coaching system, when a user inputs their goals and challenges, the AI ​​provides optimal feedback and advice. For example, if a user inputs a goal or challenge such as "I want to improve project progress management efficiency" or "I want to improve my presentation skills," the AI ​​analyzes the input and suggests specific techniques and practice methods. This system is less expensive than assigning a human expert, is unbiased, and objective. Personal information is also protected. Furthermore, the AI ​​adjusts its communication style to match the user's personality and preferences, acting as a personal coach. For example, if the user is introverted, the AI ​​provides gentle, encouraging feedback, while if the user is extroverted, it provides challenging advice. In this way, feedback and advice can be provided in the optimal communication style. This allows for unbiased, objective feedback and advice to be provided at a lower cost than assigning a human expert. Furthermore, personal information is protected, allowing users to use the service with peace of mind. For example, information about the user's goals and challenges is anonymized during analysis by the AI, eliminating the risk of personal information leaks. In this way, by using an AI coaching service, business people can obtain a specific action plan to improve their performance and career. For example, they can receive specific advice such as techniques for more efficiently managing project progress or practice methods to improve presentation skills. In addition, AI can adjust its communication style to match the person's personality and preferences, providing optimal feedback and advice to the user. This allows users to grow at their own pace and achieve their goals. In this way, the AI ​​coaching system can provide optimal feedback and advice for the user's goals and challenges, improving their performance and career.

[0059] An AI coaching system according to an embodiment includes a reception unit, an analysis unit, a generation unit, a provision unit, and an adjustment unit. The reception unit receives a user's input of goals and challenges. Examples of the goals and challenges include, but are not limited to, improving project progress management and presentation skills. The reception unit receives the user's input goals and challenges in digital format. The reception unit can also support multiple input methods, such as voice input and handwritten input. The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the user's goals and challenges using, for example, natural language processing technology and extracts information for generating optimal feedback and advice. The analysis unit searches a database for related information based on the user's goals and challenges and generates an analysis result. The generation unit generates feedback and advice based on the information analyzed by the analysis unit. The generation unit generates specific feedback and advice for the user's goals and challenges using, for example, a generation AI. The generation unit suggests, for example, methods for improving project progress management and practice methods for improving presentation skills. The providing unit provides the feedback and advice generated by the generating unit to the user. The providing unit provides the generated feedback and advice to the user in, for example, text, audio, or video format. The providing unit, for example, sends a notification to the user's device and displays the feedback and advice. The adjusting unit adjusts the feedback and advice generated by the generating unit to match the user's personality and preferences. For example, if the user is introverted, the adjusting unit provides gentle and encouraging feedback, and if the user is extroverted, the adjusting unit provides challenging advice. The adjusting unit selects an optimal communication style based on, for example, the user's past behavioral history and survey results. As a result, the AI ​​coaching system according to the embodiment can provide optimal feedback and advice for the user's goals and challenges, thereby improving performance and career.

[0060] The reception unit can estimate the user's emotions and adjust the timing of inputting goals and tasks based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can 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 can also prompt the user to input goals and tasks at that time. Furthermore, if the user is tired, the reception unit can also prompt the user to input goals and tasks after a break. This allows the user to be prompted to input goals and tasks at the optimal time depending on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0061] The reception unit can analyze the user's past input history of goals and tasks and select the optimal input method. For example, the reception unit can prioritize and suggest 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. The reception unit can also predict and suggest the input method to be used in a specific time period based on the user's past input history. This makes it possible to suggest the optimal input method based on the user's past input history. 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 past input data into a generation AI and have the generation AI select the optimal input method.

[0062] When inputting goals and tasks, the reception unit can filter the data based on the user's current project or area of ​​interest. For example, the reception unit can prioritize inputting goals and tasks related to the user's ongoing project. The reception unit can also suggest related goals and tasks based on the user's area of ​​interest. The reception unit can also input optimal goals and tasks based on the user's current work content. This allows goals and tasks related to the user's current project or area of ​​interest to be prioritized. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's project data into a generation AI and have the generation AI filter out related goals and tasks.

[0063] The reception unit can estimate the user's emotions and determine the priority of the goals and tasks to be input based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit can prioritize inputting easy goals and tasks. Furthermore, when the user is concentrating, the reception unit can prioritize inputting important goals and tasks. Furthermore, when the user is relaxed, the reception unit can prioritize inputting long-term goals and tasks. This allows goals and tasks to be input in the optimal priority order 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. 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, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data to the generation AI and have the generation AI perform emotion estimation.

[0064] When inputting goals and tasks, the reception unit can prioritize inputting highly relevant goals and tasks based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize inputting goals and tasks related to that area. Furthermore, if the user is on a business trip, the reception unit can prioritize inputting goals and tasks related to the business trip destination. Furthermore, if the user is at home, the reception unit can prioritize inputting goals and tasks that can be performed at home. This makes it possible to input optimal goals and tasks based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's location information data to the generation AI and cause the generation AI to select relevant goals and tasks.

[0065] When a goal or task is input, the reception unit can analyze the user's social media activity and input related goals or tasks. The reception unit can, for example, suggest related goals or tasks based on information shared by the user on social media. The reception unit can also suggest related goals or tasks based on topics the user follows on social media. The reception unit can also suggest related goals or tasks based on groups the user participates in on social media. This makes it possible to input 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 data into a generation AI and cause the generation AI to select related goals and tasks.

[0066] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple and visual analysis result. Furthermore, if the user is concentrating, the analysis unit can provide a detailed analysis result. Furthermore, if the user is relaxed, the analysis unit can provide an interactive analysis result. This allows the analysis result to be presented in an optimal presentation method depending on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, 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 analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0067] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the goals and tasks. For example, the analysis unit performs a detailed analysis on important goals and tasks. The analysis unit can also perform a simplified analysis on low-priority goals and tasks. The analysis unit can also perform an analysis with an appropriate level of detail on goals and tasks of medium importance. This allows analysis to be performed with an optimal level of detail depending on the importance of the goals and tasks. Some or all of the above-mentioned 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 importance data of goals and tasks to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0068] During analysis, the analysis unit can apply different analysis algorithms depending on the category of goals and challenges. For example, the analysis unit can apply an analysis algorithm dedicated to project management to goals and challenges related to project management. The analysis unit can also apply an analysis algorithm dedicated to communication skills to goals and challenges related to presentation skills. The analysis unit can also apply an analysis algorithm dedicated to team dynamics to goals and challenges related to team building. This makes it possible to apply the optimal analysis algorithm depending on the category of goals and challenges. Some or all of the above-mentioned 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 category data of goals and challenges into the generation AI and cause the generation AI to apply the analysis algorithm.

[0069] 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, concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. The analysis unit can also provide an interactive analysis result if the user is concentrating. This allows the analysis result to be provided at an optimal length depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0070] During analysis, the analysis unit can determine the priority of analysis based on the submission dates of goals and assignments. For example, the analysis unit can prioritize analysis of urgent goals and assignments. The analysis unit can also prioritize analysis of goals and assignments with upcoming submission deadlines. The analysis unit can also postpone analysis of goals and assignments with distant submission deadlines. This allows analysis to be performed with optimal priority depending on the submission dates of goals and assignments. Some or all of the above-mentioned 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 data on the submission dates of goals and assignments into the generation AI and have the generation AI determine the analysis priority.

[0071] During analysis, the analysis unit can adjust the order of analysis based on the relevance of goals and tasks. For example, the analysis unit prioritizes analysis of highly relevant goals and tasks. The analysis unit can also postpone analysis of less relevant goals and tasks. The analysis unit can also analyze goals and tasks with a moderate degree of relevance in an appropriate order. This allows analysis to be performed in an optimal order depending on the relevance of goals and tasks. Some or all of the above-mentioned 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 relevance data of goals and tasks to the generation AI and have the generation AI adjust the order of analysis.

[0072] The generation unit can estimate the user's emotions and adjust the manner in which feedback and advice are expressed based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can provide gentle, encouraging feedback. Furthermore, if the user is concentrating, the generation unit can provide specific and detailed advice. Furthermore, if the user is relaxed, the generation unit can provide interactive feedback. This allows feedback and advice to be provided in an optimal manner 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. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0073] When generating feedback and advice, the generation unit can adjust the level of detail of the feedback and advice based on the importance of the goal or task. For example, the generation unit provides detailed feedback and advice for important goals and tasks. The generation unit can also provide simplified feedback and advice for low-priority goals and tasks. The generation unit can also provide feedback and advice with an appropriate level of detail for goals and tasks of medium importance. This makes it possible to provide feedback and advice with an optimal level of detail depending on the importance of the goal or task. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input importance data of goals and tasks into the generation AI and cause the generation AI to adjust the level of detail of the feedback and advice.

[0074] The generation unit can apply different generation algorithms depending on the category of the goal or challenge when generating feedback or advice. For example, the generation unit can apply a generation algorithm dedicated to project management to goals and challenges related to project management. The generation unit can also apply a generation algorithm dedicated to communication skills to goals and challenges related to presentation skills. The generation unit can also apply a generation algorithm dedicated to team dynamics to goals and challenges related to team building. This makes it possible to apply the optimal generation algorithm depending on the category of the goal or challenge. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input category data of the goal or challenge into the generation AI and cause the generation AI to apply the generation algorithm.

[0075] The generation unit can estimate the user's emotions and adjust the length of the feedback or advice based on the estimated user's emotions. For example, if the user is in a hurry, the generation unit can provide short and to-the-point feedback or advice. The generation unit can also provide detailed feedback or advice if the user is relaxed. The generation unit can also provide interactive feedback or advice if the user is concentrating. This allows feedback or advice to be provided at an optimal length depending on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0076] When generating feedback and advice, the generation unit can determine the priority of generation based on the submission dates of goals and assignments. For example, the generation unit can prioritize providing feedback and advice for urgent goals and assignments. The generation unit can also prioritize providing feedback and advice for goals and assignments with upcoming submission deadlines. The generation unit can also postpone providing feedback and advice for goals and assignments with distant submission deadlines. This makes it possible to provide feedback and advice with optimal priority depending on the submission dates of goals and assignments. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input data on the submission dates of goals and assignments into the generation AI and have the generation AI determine the priority of feedback and advice.

[0077] When generating feedback and advice, the generation unit can adjust the order of generation based on the relevance of goals and tasks. For example, the generation unit can provide feedback and advice preferentially to goals and tasks that are highly relevant. The generation unit can also provide feedback and advice later to goals and tasks that are less relevant. The generation unit can also provide feedback and advice in an appropriate order to goals and tasks that are medium relevance. This makes it possible to provide feedback and advice in an optimal order depending on the relevance of goals and tasks. Some or all of the above-described processing in the generation unit may be performed using, or without, AI, for example. For example, the generation unit can input relevance data of goals and tasks to the generation AI and cause the generation AI to adjust the order of feedback and advice.

[0078] The providing unit can estimate the user's emotions and adjust the method of providing feedback and advice based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide gentle, encouraging feedback. Furthermore, if the user is concentrating, the providing unit can also provide specific and detailed advice. Furthermore, if the user is relaxed, the providing unit can also provide interactive feedback. This makes it possible to provide feedback and advice in an optimal manner 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. 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, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0079] When providing feedback or advice, the providing unit can select the optimal method of providing feedback or advice by referring to the user's past feedback history. For example, the providing unit can prioritize providing feedback formats that the user has previously preferred. The providing unit can also provide feedback at the optimal timing based on the user's past feedback history. The providing unit can also analyze the user's past feedback history and select the most effective method of providing feedback or advice. This makes it possible to provide feedback or advice in the optimal manner based on the user's past feedback history. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into the generation AI and cause the generation AI to select the optimal method of providing feedback.

[0080] The providing unit can customize the means of providing feedback or advice based on the user's current situation when providing feedback or advice. For example, if the user is in a meeting, the providing unit provides feedback after the meeting ends. The providing unit can also provide voice feedback if the user is on the move. The providing unit can also provide text feedback if the user is doing desk work. This makes it possible to provide feedback or advice by the most appropriate means depending on the user's current situation. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's current situation data into the generating AI and cause the generating AI to customize the means of providing.

[0081] The providing unit can estimate the user's emotions and determine the priority of providing feedback and advice based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can prioritize providing feedback that will help the user relax. Furthermore, if the user is concentrating, the providing unit can prioritize providing important feedback. Furthermore, if the user is relaxed, the providing unit can prioritize providing long-term feedback. This makes it possible to provide feedback and advice with optimal priority 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. 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 providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0082] When providing feedback or advice, the providing unit can select the optimal delivery method by taking into account the user's geographical location information. For example, if the user is in a specific area, the providing unit can provide feedback related to that area. Furthermore, if the user is on a business trip, the providing unit can also provide feedback related to the business trip destination. Furthermore, if the user is at home, the providing unit can also provide feedback that can be performed at home. This makes it possible to provide feedback or advice in the optimal method based on the user's geographical location information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's location information data into the generation AI and cause the generation AI to select the optimal delivery method.

[0083] When providing feedback or advice, the providing unit can analyze the user's social media activity and suggest a means of providing the feedback or advice. The providing unit can provide relevant feedback based on, for example, information shared by the user on social media. The providing unit can also provide relevant feedback based on topics the user follows on social media. The providing unit can also provide relevant feedback based on groups the user participates in on social media. This makes it possible to provide feedback or advice in an optimal manner based on the user's social media activity. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's social media data into a generation AI and cause the generation AI to suggest an optimal means of providing the feedback or advice.

[0084] The adjustment unit can estimate the user's emotions and determine how to adjust feedback and advice based on the estimated user emotions. For example, if the user is feeling stressed, the adjustment unit can provide gentle, encouraging feedback. Furthermore, if the user is concentrating, the adjustment unit can also provide specific and detailed advice. Furthermore, if the user is relaxed, the adjustment unit can also provide interactive feedback. This makes it possible to adjust feedback and advice in an optimal manner 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. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the adjustment unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0085] When adjusting feedback or advice, the adjustment unit can select the optimal adjustment method by referring to the user's past feedback history. For example, the adjustment unit can preferentially provide feedback formats that the user has previously preferred. The adjustment unit can also provide feedback at the optimal timing based on the user's past feedback history. The adjustment unit can also analyze the user's past feedback history and select the most effective provision method. This makes it possible to adjust feedback or advice in the optimal manner based on the user's past feedback history. Some or all of the above-mentioned processing in the adjustment unit may be performed using, or without, AI, for example. For example, the adjustment unit can input the user's past feedback data into the generation AI and cause the generation AI to select the optimal adjustment method.

[0086] When adjusting feedback or advice, the adjustment unit can customize the means of adjustment based on the user's current situation. For example, if the user is in a meeting, the adjustment unit provides feedback after the meeting ends. The adjustment unit can also provide voice feedback if the user is on the move. The adjustment unit can also provide text feedback if the user is doing desk work. This makes it possible to adjust feedback or advice using the optimal means depending on the user's current situation. Some or all of the above-mentioned 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 current situation data into the generation AI and cause the generation AI to customize the means of adjustment.

[0087] The adjustment unit can estimate the user's emotions and determine the adjustment priority of feedback and advice based on the estimated user emotions. For example, if the user is feeling stressed, the adjustment unit can prioritize providing relaxing feedback. Furthermore, if the user is concentrating, the adjustment unit can prioritize providing important feedback. Furthermore, if the user is relaxed, the adjustment unit can prioritize providing long-term feedback. This makes it possible to adjust feedback and advice with optimal priorities according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the adjustment unit can be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0088] When adjusting feedback or advice, the adjustment unit can select the optimal adjustment method by taking into account the user's geographical location information. For example, if the user is in a specific area, the adjustment unit can provide feedback related to the area. Furthermore, if the user is on a business trip, the adjustment unit can also provide feedback related to the business trip destination. Furthermore, if the user is at home, the adjustment unit can also provide feedback that can be implemented at home. This makes it possible to adjust feedback and advice in an optimal manner based on the user's geographical location information. Some or all of the above-mentioned processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI. For example, the adjustment unit can input the user's location information data into the generation AI and cause the generation AI to select the optimal adjustment method.

[0089] When adjusting feedback or advice, the adjustment unit can analyze the user's social media activity and suggest adjustment measures. The adjustment unit can provide relevant feedback based on, for example, information shared by the user on social media. The adjustment unit can also provide relevant feedback based on topics the user follows on social media. The adjustment unit can also provide relevant feedback based on groups the user participates in on social media. This makes it possible to adjust feedback or advice in an optimal manner based on the user's social media activity. 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 the user's social media data into the generation AI and cause the generation AI to suggest optimal adjustment measures. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, provision unit, and adjustment unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14, and the user inputs their goals and challenges. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the user's goals and challenges. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates feedback and advice. The provision unit is realized by the output device 40 of the smart device 14, and provides the generated feedback and advice to the user. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12, and adjusts the feedback and advice to suit the user's personality and preferences. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, provision unit, and adjustment unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214, and the user inputs their goals and challenges. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the user's goals and challenges. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates feedback and advice. The provision unit is realized by the speaker 240 of the smart glasses 214, and provides the generated feedback and advice to the user. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12, and adjusts the feedback and advice to suit the user's personality and preferences. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, provision 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 microphone 238 of the headset-type terminal 314, and the user inputs their goals and challenges. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the user's goals and challenges. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates feedback and advice. The provision unit is realized by the speaker 240 of the headset-type terminal 314, and provides the generated feedback and advice to the user. The adjustment unit is realized by the specific processing unit 290 of the data processing device 12, and adjusts the feedback and advice to suit the user's personality and preferences. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, provision unit, and adjustment unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, and the user inputs their goals and challenges. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's goals and challenges. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates feedback and advice. The provision unit is realized, for example, by the speaker 240 of the robot 414, and provides the generated feedback and advice to the user. The adjustment unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and adjusts the feedback and advice to suit the user's personality and preferences.

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

[0091] The reception unit can analyze the user's past feedback history and suggest the optimal input method. For example, if the user has previously preferred voice input, the reception unit can preferentially suggest voice input. Also, if the user has previously input data during a specific time period, the reception unit can encourage input during that time period. Furthermore, the reception unit can analyze patterns of goals and tasks that the user has previously input and suggest similar goals and tasks. This makes it possible to suggest the optimal input method based on the user's past behavioral history.

[0092] The reception unit can estimate the user's emotions and determine the priority of the goals and tasks to be input based on the estimated user's emotions. For example, if the user is feeling stressed, it can prioritize inputting easy goals and tasks. Also, if the user is concentrating, it can prioritize inputting important goals and tasks. Furthermore, if the user is relaxed, it can prioritize inputting long-term goals and tasks. In this way, it is possible to input goals and tasks with optimal priority according to the user's emotions.

[0093] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is feeling stressed, a simple, visual analysis result can be provided. If the user is concentrating, a detailed analysis result can be provided. Furthermore, if the user is relaxed, an interactive analysis result can be provided. This makes it possible to provide analysis results in the most appropriate way depending on the user's emotions.

[0094] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the goals and tasks. For example, a detailed analysis can be performed for important goals and tasks. A simplified analysis can also be performed for goals and tasks with low priority. Furthermore, an analysis with an appropriate level of detail can be performed for goals and tasks with medium importance. This allows the analysis to be performed with the optimal level of detail depending on the importance of the goals and tasks.

[0095] The generation unit can estimate the user's emotions and adjust the way feedback and advice is expressed based on the estimated user's emotions. For example, if the user is feeling stressed, gentle and encouraging feedback can be provided. If the user is concentrating, specific and detailed advice can be provided. Furthermore, if the user is relaxed, interactive feedback can be provided. This makes it possible to provide feedback and advice in the most appropriate way depending on the user's emotions.

[0096] When generating feedback and advice, the generator can adjust the level of detail based on the importance of the goal or task. For example, detailed feedback and advice can be provided for important goals and tasks. Simplified feedback and advice can also be provided for goals and tasks with low priority. Furthermore, feedback and advice with an appropriate level of detail can be provided for goals and tasks with medium importance. This makes it possible to provide feedback and advice with an optimal level of detail depending on the importance of the goal or task.

[0097] The providing unit can estimate the user's emotions and adjust the method of providing feedback and advice based on the estimated user's emotions. For example, if the user is feeling stressed, it can provide gentle, encouraging feedback. If the user is concentrating, it can also provide specific, detailed advice. Furthermore, if the user is relaxed, it can also provide interactive feedback. This makes it possible to provide feedback and advice in an optimal manner according to the user's emotions.

[0098] When providing feedback or advice, the providing unit can select the optimal method of providing the feedback or advice by referring to the user's past feedback history. For example, the providing unit can provide feedback in a format that the user has previously preferred preferentially. The providing unit can also provide feedback at the optimal timing based on the user's past feedback history. Furthermore, the providing unit can analyze the user's past feedback history and select the most effective method of providing the feedback or advice. This makes it possible to provide feedback or advice in the optimal manner based on the user's past feedback history.

[0099] The adjustment unit can estimate the user's emotions and determine how to adjust feedback and advice based on the estimated user's emotions. For example, if the user is feeling stressed, it can provide gentle, encouraging feedback. If the user is concentrating, it can provide specific, detailed advice. Furthermore, if the user is relaxed, it can provide interactive feedback. This makes it possible to adjust feedback and advice in an optimal way according to the user's emotions.

[0100] When adjusting feedback or advice, the adjustment unit can select the optimal adjustment method by referring to the user's past feedback history. For example, the adjustment unit can provide feedback in a format that the user has previously preferred preferentially. The adjustment unit can also provide feedback at the optimal timing based on the user's past feedback history. Furthermore, the adjustment unit can analyze the user's past feedback history and select the most effective method of providing feedback or advice. This makes it possible to adjust feedback or advice in the optimal way based on the user's past feedback history.

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

[0102] Step 1: The user inputs their goals and challenges into the reception unit. Examples of goals and challenges that users input include wanting to streamline project progress management or improve presentation skills. The reception unit accepts the goals and challenges entered by the user in digital form. It can also support multiple input methods, such as voice input and handwriting input. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit uses natural language processing technology to analyze the user's goals and challenges, and extracts information to generate optimal feedback and advice. Furthermore, based on the user's goals and challenges, the analysis unit searches a database for related information and generates analysis results. Step 3: The generation unit generates feedback and advice based on the information analyzed by the analysis unit. Using the generation AI, the generation unit generates specific feedback and advice for the user's goals and challenges. For example, it suggests methods for streamlining project progress management or practice methods for improving presentation skills. Step 4: The providing unit provides the feedback or advice generated by the generating unit to the user. The providing unit provides the generated feedback or advice to the user in a text format, an audio format, a video format, or the like. For example, the providing unit may send a notification to the user's device and display the feedback or advice. Step 5: The adjustment unit adjusts the feedback and advice generated by the generation unit to suit the user's personality and preferences. If the user is introverted, the adjustment unit provides gentle, encouraging feedback, and if the user is extroverted, the adjustment unit provides challenging advice. Furthermore, the adjustment unit selects the optimal communication style based on the user's past behavioral history and survey results.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

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

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

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

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

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

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

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

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is 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.

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

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

[0166] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

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

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

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

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

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

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

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input of a goal or task; an analysis unit that analyzes the information received by the reception unit; a generation unit that generates feedback and advice based on the information analyzed by the analysis unit; a providing unit that provides the feedback and advice generated by the generating unit to a user; and an adjustment unit that adjusts the feedback and advice generated by the generation unit to suit the user's personality and preferences. A system characterized by:

2. The reception unit Estimate the user's emotions and adjust the timing of inputting goals and tasks based on the estimated user emotions. The system of claim 1 .

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

4. The reception unit Filter goals and challenges based on your current projects or areas of interest The system of claim 1 .

5. The reception unit Estimate the user's emotions and prioritize the goals and tasks to be entered based on the estimated user emotions. The system of claim 1 .

6. The reception unit When entering goals and tasks, prioritize the most relevant goals and tasks based on the user's geographic location. The system of claim 1 .

7. The reception unit When entering goals and challenges, analyze the user's social media activity and enter related goals and challenges. The system of claim 1 .

8. The analysis unit Inferring user emotions and adjusting the presentation of analysis based on the estimated user emotions The system of claim 1 .

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