System

Abstract

An object of the system according to the embodiment is to individually address the mental health problems of employees and reduce the burden on managers and personnel.SOLUTION: A system according to an embodiment includes a mentoring providing unit, an information learning unit, and a character adjusting unit. The mentoring provider provides employee-specific mentoring. The information learning unit learns in-house information. The character adjusting unit adjusts the character or the tone according to the user's preference.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including 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 has made it difficult to respond individually to employee mental health issues, placing a heavy burden on managers and human resources.

[0005] The system according to the embodiment aims to address the mental health issues of employees individually and reduce the burden on managers and human resources. [Means for solving the problem]

[0006] The system according to the embodiment includes a mentoring provider, an information learning unit, and a character adjustment unit. The mentoring provider provides mentoring tailored to the employee. The information learning unit learns corporate information. The character adjustment unit adjusts the character or tone to suit the user's preferences. [Effects of the Invention]

[0007] The system according to the embodiment can address employees' mental health issues individually and reduce the burden on managers and human resources. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The AI ​​mentoring system according to an embodiment of the present invention uses generative AI to provide individualized mentoring for employees' mental health issues, learning corporate information and offering training, consultations, and problem-solving solutions. This allows the AI ​​mentoring system to efficiently and effectively address employee mental health issues, reducing the burden on managers and human resources and helping employees adapt quickly and improve their performance.

[0029] An AI mentoring system according to an embodiment includes a mentoring provider, an information learning unit, and a character adjustment unit. The mentoring provider provides mentoring tailored to each employee. For example, the generation AI provides mentoring tailored to the employee's situation and needs. For example, if an employee is feeling stressed, the generation AI identifies the cause and proposes appropriate measures. The generation AI can also provide specific advice in response to the employee's questions. The information learning unit learns internal company information. For example, the generation AI learns information about company policies and business processes and provides appropriate advice to employees based on that information. The generation AI can also present training, consultations, and problem-solving options based on internal company information. The character adjustment unit adjusts the character and tone to suit the user's preferences. For example, if an employee wants to talk in a relaxed atmosphere, the generation AI responds in a friendly tone. If an employee wants a serious consultation, the generation AI can also respond in a formal tone. This allows the AI ​​mentor system to individually address employees' mental health issues, utilize internal company information, and provide mentoring tailored to the user's preferences.

[0030] The mentoring provision unit can analyze an employee's past mental health history and propose a long-term mentoring plan. In the mentoring provision unit, for example, the generation AI collects an employee's past mental health history and identifies the causes and patterns of stress. For example, it analyzes past counseling records and self-reported data to create a long-term mentoring plan. The generation AI can also propose a mentoring plan tailored to individual needs based on the employee's mental health history. This makes it possible to provide a long-term mentoring plan based on the employee's past mental health history.

[0031] The mentoring provision unit monitors employees' work performance data in real time, can detect signs of performance decline at an early stage, and propose countermeasures. In the mentoring provision unit, for example, the generation AI collects employees' work performance data in real time and detects signs of performance decline. For example, it analyzes fluctuations in work speed and error rate and proposes countermeasures at an early stage. The generation AI can also propose individual countermeasures based on employees' work performance data. This makes it possible to monitor employees' work performance data in real time and propose countermeasures at an early stage.

[0032] The mentoring provision unit can suggest ways to refresh based on the employee's hobbies and interests, thereby improving their mental health. In the mentoring provision unit, for example, the generation AI suggests ways to refresh based on the employee's hobbies and interests. For example, it can provide ways to refresh that are tailored to individual hobbies, such as listening to music or playing sports. The generation AI can also suggest individual ways to refresh based on the employee's hobbies and interests. This makes it possible to suggest ways to refresh based on the employee's hobbies and interests, thereby improving their mental health.

[0033] The mentoring provision unit supports employees' communication with family and friends, strengthening their social support network. In the mentoring provision unit, for example, the generative AI supports employees' communication with family and friends. For example, it provides reminders to encourage regular contact and communication tips. The generative AI can also provide individualized support based on the employee's communication with family and friends. This supports employees' communication with family and friends, strengthening their social support network.

[0034] The information learning unit can analyze data from past projects within a company and provide advice based on success and failure cases. For example, the information learning unit allows the generation AI to collect data from past projects within a company and analyze success and failure cases. For example, it analyzes the progress and results of projects and identifies factors that led to success and failure. The generation AI can also provide specific advice based on data from past projects within a company. This allows it to analyze data from past projects within a company and provide advice based on success and failure cases.

[0035] The information learning unit can learn communication patterns within a company and propose effective communication methods. For example, the information learning unit's generation AI collects communication patterns within a company and proposes effective communication methods. For example, it analyzes email and chat exchanges and identifies areas for improvement. The generation AI can also propose specific communication methods based on communication patterns within a company. This allows it to learn communication patterns within a company and propose effective communication methods.

[0036] The information learning department can promote information sharing between different departments within a company and strengthen collaboration between them. For example, the information learning department provides a common platform for the generative AI to promote information sharing between different departments within a company. For example, it integrates project management tools and communication tools. The generative AI can also suggest specific collaboration methods based on information sharing between different departments within a company. This can promote information sharing between different departments within a company and strengthen collaboration between them.

[0037] The information learning unit can learn from feedback from a company's external partners and customers and propose improvements to internal processes. For example, the information learning unit uses a generative AI to collect feedback from a company's external partners and customers and propose improvements to internal processes. For example, it analyzes customer satisfaction surveys and opinions from partners to identify areas for improvement. The generative AI can also propose specific improvement measures based on feedback from a company's external partners and customers. This allows it to learn from feedback from a company's external partners and customers and propose improvements to internal processes.

[0038] The character adjustment unit can analyze an employee's past dialogue history and automatically select the most appropriate character and tone. For example, the generation AI collects an employee's past dialogue history and selects the most appropriate character and tone. For example, it analyzes the content and reactions of past dialogues and suggests the character that is most suitable for the employee. The generation AI can also select individual characters and tones based on the employee's dialogue history. This makes it possible to analyze an employee's past dialogue history and automatically select the most appropriate character and tone.

[0039] The character adjustment unit can provide characters and tones that take into account the cultural background and personal values ​​of employees. For example, the generation AI can provide optimal characters and tones by taking into account the cultural background of employees. For example, it can suggest a dialogue style that respects cultural differences. The generation AI can also provide individual characters and tones based on the personal values ​​of employees. This makes it possible to provide characters and tones that take into account the cultural background and personal values ​​of employees.

[0040] The character adjustment unit generates a visual avatar according to the employee's preferences, thereby improving the familiarity of the conversation. For example, the generation AI generates a visual avatar according to the employee's preferences, thereby improving the familiarity of the conversation. For example, the generation AI creates an avatar based on a character selected by the employee. The generation AI can also generate an individual visual avatar based on the employee's preferences. This allows the generation of a visual avatar according to the employee's preferences, thereby improving the familiarity of the conversation.

[0041] The character adjustment unit can continuously improve characters and tone based on employee feedback. In the character adjustment unit, for example, the generation AI collects employee feedback and continuously improves characters and tone. For example, feedback is obtained through a post-interaction survey. The generation AI can also continuously improve individual characters and tone based on employee feedback. This allows for continuous improvement of characters and tone based on employee feedback.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The AI ​​Mentor System also has a health management section. The health management section collects employees' physical health data and can analyze the correlation between mental health and physical health. For example, it can monitor employees' sleep patterns and exercise habits and provide physical health advice that helps improve mental health. The health management section can also propose individual health plans based on employees' physical health data. This allows the system to support employees' physical and mental health.

[0044] The AI ​​Mentor System also has a Career Development Department, which can analyze employees' career goals and skill sets and propose long-term career plans. For example, it can propose the skills and training necessary for career advancement based on an employee's past work history and skill assessment. The Career Development Department can also provide mentoring tailored to each employee's career goals. This supports employees' career development and improves their motivation.

[0045] The AI ​​Mentor System also includes a remote work support unit. The remote work support unit monitors employees' remote work environments and can suggest efficient ways of working. For example, it analyzes productivity data and communication patterns during remote work and suggests efficient remote work methods. The remote work support unit can also provide mentoring tailored to each employee's remote work environment. This can support the productivity and mental health of employees working remotely.

[0046] The AI ​​mentor system also includes a feedback collection unit. The feedback collection unit collects feedback from employees and can use it to improve the system. For example, it collects feedback from regular surveys and after conversations to improve the system's functions and mentoring content. The feedback collection unit can also adjust individual mentoring content based on employee feedback. This makes it possible to provide flexible mentoring that meets employees' needs.

[0047] The AI ​​mentor system also has a leadership support section. The leadership support section can support the improvement of the skills of managers and leaders. For example, it can provide leadership training and coaching programs to improve the skills of managers. The leadership support section can also provide mentoring to support the mental health of managers. This allows support to be provided in terms of both improving managers' skills and their mental health.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The mentoring provider provides mentoring tailored to the employee. For example, the generation AI provides mentoring tailored to the employee's situation and needs. If an employee is feeling stressed, the generation AI identifies the cause and suggests appropriate measures. The generation AI can also provide specific advice in response to employee questions. Step 2: The information learning unit learns information about the company. For example, the generation AI can learn information about the company's policies and business processes and provide appropriate advice to employees based on that information. The generation AI can also provide training, consultations, and problem-solving solutions based on the company's internal information. Step 3: The character adjustment unit adjusts the character and tone to suit the user's preferences. For example, if an employee wants to talk in a relaxed atmosphere, the AI ​​can respond in a friendly tone. Alternatively, if the employee wants to have a serious discussion, the AI ​​can respond in a more formal tone.

[0050] (Example 2) The AI ​​mentoring system according to an embodiment of the present invention uses generative AI to provide individualized mentoring for employees' mental health issues, learning corporate information and offering training, consultations, and problem-solving solutions. This allows the AI ​​mentoring system to efficiently and effectively address employee mental health issues, reducing the burden on managers and human resources and helping employees adapt quickly and improve their performance.

[0051] An AI mentoring system according to an embodiment includes a mentoring provider, an information learning unit, and a character adjustment unit. The mentoring provider provides mentoring tailored to each employee. For example, the generation AI provides mentoring tailored to the employee's situation and needs. For example, if an employee is feeling stressed, the generation AI identifies the cause and proposes appropriate measures. The generation AI can also provide specific advice in response to the employee's questions. The information learning unit learns internal company information. For example, the generation AI learns information about company policies and business processes and provides appropriate advice to employees based on that information. The generation AI can also present training, consultations, and problem-solving options based on internal company information. The character adjustment unit adjusts the character and tone to suit the user's preferences. For example, if an employee wants to talk in a relaxed atmosphere, the generation AI responds in a friendly tone. If an employee wants a serious consultation, the generation AI can also respond in a formal tone. This allows the AI ​​mentor system to individually address employees' mental health issues, utilize internal company information, and provide mentoring tailored to the user's preferences.

[0052] The mentoring provision unit can analyze an employee's past mental health history and propose a long-term mentoring plan. In the mentoring provision unit, for example, the generation AI collects an employee's past mental health history and identifies the causes and patterns of stress. For example, it analyzes past counseling records and self-reported data to create a long-term mentoring plan. The generation AI can also propose a mentoring plan tailored to individual needs based on the employee's mental health history. This makes it possible to provide a long-term mentoring plan based on the employee's past mental health history.

[0053] The mentoring provision unit monitors employees' work performance data in real time, can detect signs of performance decline at an early stage, and propose countermeasures. In the mentoring provision unit, for example, the generation AI collects employees' work performance data in real time and detects signs of performance decline. For example, it analyzes fluctuations in work speed and error rate and proposes countermeasures at an early stage. The generation AI can also propose individual countermeasures based on employees' work performance data. This makes it possible to monitor employees' work performance data in real time and propose countermeasures at an early stage.

[0054] The mentoring provision unit can use the emotion estimation function to analyze the emotional state of an employee in real time and provide mentoring content that corresponds to the emotion. In the mentoring provision unit, for example, the generation AI analyzes the emotional state of an employee in real time and provides mentoring content that corresponds to the emotion. For example, it analyzes facial expressions and voice tone and provides advice to reduce stress. The generation AI can also suggest individual mentoring content based on the emotional state of the employee. This makes it possible to analyze the emotional state of an employee in real time and provide mentoring content that corresponds to the emotion.

[0055] The mentoring provision unit can suggest ways to refresh based on the employee's hobbies and interests, thereby improving their mental health. In the mentoring provision unit, for example, the generation AI suggests ways to refresh based on the employee's hobbies and interests. For example, it can provide ways to refresh that are tailored to individual hobbies, such as listening to music or playing sports. The generation AI can also suggest individual ways to refresh based on the employee's hobbies and interests. This makes it possible to suggest ways to refresh based on the employee's hobbies and interests, thereby improving their mental health.

[0056] The mentoring provision unit supports employees' communication with family and friends, strengthening their social support network. In the mentoring provision unit, for example, the generative AI supports employees' communication with family and friends. For example, it provides reminders to encourage regular contact and communication tips. The generative AI can also provide individualized support based on the employee's communication with family and friends. This supports employees' communication with family and friends, strengthening their social support network.

[0057] The mentoring providing unit can use the emotion estimation function to provide relaxation music or meditation guides according to the employee's emotional state. For example, the generation AI analyzes the employee's emotional state in real time and provides relaxation music. For example, if stress is high, music with a relaxing effect is recommended. The generation AI can also provide meditation guides based on the employee's emotional state. This makes it possible to provide relaxation music or meditation guides according to the employee's emotional state.

[0058] The information learning unit can analyze data from past projects within a company and provide advice based on success and failure cases. For example, the information learning unit allows the generation AI to collect data from past projects within a company and analyze success and failure cases. For example, it analyzes the progress and results of projects and identifies factors that led to success and failure. The generation AI can also provide specific advice based on data from past projects within a company. This allows it to analyze data from past projects within a company and provide advice based on success and failure cases.

[0059] The information learning unit can learn communication patterns within a company and propose effective communication methods. For example, the information learning unit's generation AI collects communication patterns within a company and proposes effective communication methods. For example, it analyzes email and chat exchanges and identifies areas for improvement. The generation AI can also propose specific communication methods based on communication patterns within a company. This allows it to learn communication patterns within a company and propose effective communication methods.

[0060] The information learning unit uses the emotion estimation function to analyze the emotional states of employees within a company and visualize the mental health status of the entire organization. For example, the information learning unit uses a generation AI to analyze the emotional states of employees within a company in real time and visualize the mental health status of the entire organization. For example, it aggregates emotion scores and displays the mental health status of each department. The generation AI can also propose specific improvement measures based on the emotional states of employees within a company. This makes it possible to analyze the emotional states of employees within a company and visualize the mental health status of the entire organization.

[0061] The information learning department can promote information sharing between different departments within a company and strengthen collaboration between them. For example, the information learning department provides a common platform for the generative AI to promote information sharing between different departments within a company. For example, it integrates project management tools and communication tools. The generative AI can also suggest specific collaboration methods based on information sharing between different departments within a company. This can promote information sharing between different departments within a company and strengthen collaboration between them.

[0062] The information learning unit can learn from feedback from a company's external partners and customers and propose improvements to internal processes. For example, the information learning unit uses a generative AI to collect feedback from a company's external partners and customers and propose improvements to internal processes. For example, it analyzes customer satisfaction surveys and opinions from partners to identify areas for improvement. The generative AI can also propose specific improvement measures based on feedback from a company's external partners and customers. This allows it to learn from feedback from a company's external partners and customers and propose improvements to internal processes.

[0063] The information learning unit can use the emotion estimation function to suggest team building activities based on the emotional state of employees within a company. For example, the information learning unit uses the generation AI to analyze the emotional state of employees in real time and suggest team building activities based on their emotions. For example, the generation AI can suggest a refreshment event for a team with a low emotion score. The generation AI can also suggest specific team building activities based on the emotional state of employees. This makes it possible to suggest team building activities based on the emotional state of employees within a company.

[0064] The character adjustment unit can analyze an employee's past dialogue history and automatically select the most appropriate character and tone. For example, the generation AI collects an employee's past dialogue history and selects the most appropriate character and tone. For example, it analyzes the content and reactions of past dialogues and suggests the character that is most suitable for the employee. The generation AI can also select individual characters and tones based on the employee's dialogue history. This makes it possible to analyze an employee's past dialogue history and automatically select the most appropriate character and tone.

[0065] The character adjustment unit can provide characters and tones that take into account the cultural background and personal values ​​of employees. For example, the generation AI can provide optimal characters and tones by taking into account the cultural background of employees. For example, it can suggest a dialogue style that respects cultural differences. The generation AI can also provide individual characters and tones based on the personal values ​​of employees. This makes it possible to provide characters and tones that take into account the cultural background and personal values ​​of employees.

[0066] The character adjustment unit can use the emotion estimation function to adjust the character and tone in real time according to the employee's emotional state. For example, the generation AI analyzes the employee's emotional state in real time and adjusts the character and tone. For example, if the employee is emotionally charged, the generation AI can respond in a calm tone. The generation AI can also adjust individual characters and tones in real time based on the employee's emotional state. This makes it possible to adjust the character and tone in real time according to the employee's emotional state.

[0067] The character adjustment unit generates a visual avatar according to the employee's preferences, thereby improving the familiarity of the conversation. For example, the generation AI generates a visual avatar according to the employee's preferences, thereby improving the familiarity of the conversation. For example, the generation AI creates an avatar based on a character selected by the employee. The generation AI can also generate an individual visual avatar based on the employee's preferences. This allows the generation of a visual avatar according to the employee's preferences, thereby improving the familiarity of the conversation.

[0068] The character adjustment unit can continuously improve characters and tone based on employee feedback. In the character adjustment unit, for example, the generation AI collects employee feedback and continuously improves characters and tone. For example, feedback is obtained through a post-interaction survey. The generation AI can also continuously improve individual characters and tone based on employee feedback. This allows for continuous improvement of characters and tone based on employee feedback.

[0069] The character adjustment unit uses the emotion estimation function to automatically generate a dialogue scenario according to the employee's emotional state, thereby improving the quality of the dialogue. For example, the character adjustment unit uses a generation AI to analyze the employee's emotional state in real time and automatically generate a dialogue scenario according to the emotion. For example, if the employee is emotionally charged, it provides a scenario to calm them down. The generation AI can also automatically generate individual dialogue scenarios based on the employee's emotional state. This makes it possible to automatically generate a dialogue scenario according to the employee's emotional state and improve the quality of the dialogue.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The AI ​​Mentor System also has a health management section. The health management section collects employees' physical health data and can analyze the correlation between mental health and physical health. For example, it can monitor employees' sleep patterns and exercise habits and provide physical health advice that helps improve mental health. The health management section can also propose individual health plans based on employees' physical health data. This allows the system to support employees' physical and mental health.

[0072] The AI ​​Mentor System also has a Career Development Department, which can analyze employees' career goals and skill sets and propose long-term career plans. For example, it can propose the skills and training necessary for career advancement based on an employee's past work history and skill assessment. The Career Development Department can also provide mentoring tailored to each employee's career goals. This supports employees' career development and improves their motivation.

[0073] The AI ​​Mentor System also includes a remote work support unit. The remote work support unit monitors employees' remote work environments and can suggest efficient ways of working. For example, it analyzes productivity data and communication patterns during remote work and suggests efficient remote work methods. The remote work support unit can also provide mentoring tailored to each employee's remote work environment. This can support the productivity and mental health of employees working remotely.

[0074] The AI ​​mentor system also includes a feedback collection unit. The feedback collection unit collects feedback from employees and can use it to improve the system. For example, it collects feedback from regular surveys and after conversations to improve the system's functions and mentoring content. The feedback collection unit can also adjust individual mentoring content based on employee feedback. This makes it possible to provide flexible mentoring that meets employees' needs.

[0075] The AI ​​mentor system also has a leadership support section. The leadership support section can support the improvement of the skills of managers and leaders. For example, it can provide leadership training and coaching programs to improve the skills of managers. The leadership support section can also provide mentoring to support the mental health of managers. This allows support to be provided in terms of both improving managers' skills and their mental health.

[0076] The mentoring provision unit can use the emotion estimation function to provide a stress management program based on the employee's emotional state. For example, the mentoring provision unit can analyze the employee's emotional state in real time and suggest specific activities to reduce stress. The emotion estimation function can also be used to provide relaxation techniques and mindfulness exercises according to the employee's emotional state. This makes it possible to provide a stress management program based on the employee's emotional state and improve their mental health.

[0077] The mentoring providing unit can use the emotion estimation function to provide feedback based on the employee's emotional state. For example, the mentoring providing unit can analyze the employee's emotional state in real time and provide feedback according to the emotion. The emotion estimation function can also be used to provide positive feedback or constructive advice based on the employee's emotional state. This makes it possible to provide feedback based on the employee's emotional state and improve their motivation.

[0078] The mentoring provision unit can use the emotion estimation function to support the improvement of communication skills based on the emotional state of employees. For example, it can analyze the emotional state of employees in real time and provide communication skill training according to the emotions. It can also use the emotion estimation function to suggest specific communication techniques based on the emotional state of employees. This can support the improvement of communication skills based on the emotional state of employees and facilitate smooth communication in the workplace.

[0079] The mentoring providing unit can use the emotion estimation function to propose performance improvement measures based on the employee's emotional state. For example, it can analyze the employee's emotional state in real time and propose performance improvement measures according to the emotion. The emotion estimation function can also be used to provide specific work improvement measures based on the employee's emotional state. This makes it possible to propose performance improvement measures based on the employee's emotional state and improve work efficiency.

[0080] The mentoring providing unit can use the emotion estimation function to suggest team building activities based on the emotional state of employees. For example, the mentoring providing unit can analyze the emotional state of employees in real time and suggest team building activities according to the emotions. The emotion estimation function can also be used to provide specific team building events based on the emotional state of employees. This makes it possible to suggest team building activities based on the emotional state of employees and strengthen team cohesion.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The mentoring provider provides mentoring tailored to the employee. For example, the generation AI provides mentoring tailored to the employee's situation and needs. If an employee is feeling stressed, the generation AI identifies the cause and suggests appropriate measures. The generation AI can also provide specific advice in response to employee questions. Step 2: The information learning unit learns information about the company. For example, the generation AI can learn information about the company's policies and business processes and provide appropriate advice to employees based on that information. The generation AI can also provide training, consultations, and problem-solving solutions based on the company's internal information. Step 3: The character adjustment unit adjusts the character and tone to suit the user's preferences. For example, if an employee wants to talk in a relaxed atmosphere, the AI ​​can respond in a friendly tone. Alternatively, if the employee wants to have a serious discussion, the AI ​​can respond in a more formal tone.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] 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.

[0098] 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.

[0099] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

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

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0129] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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. [Explanation of symbols]

[0150] 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 mentoring department that provides mentoring tailored to employees, an information learning department that learns corporate information; A character adjustment unit that adjusts the character or tone to suit the user's preferences. A system characterized by:

2. The mentoring providing unit Real-time monitoring of employee performance data, early detection of signs of performance decline, and proposal of countermeasures.

2. The system of claim 1.

3. The information learning unit Analyzing past project data within the company and providing advice based on success and failure cases 2. The system of claim 1.

4. The character adjustment unit Analyzing the employee's past conversation history and automatically selecting the most appropriate character or tone 2. The system of claim 1.

5. The mentoring providing unit Using emotion estimation function, analyze the employee's emotional state in real time and provide mentoring content according to their emotions.

2. The system of claim 1.

6. The information learning unit Using emotion estimation capabilities, analyze the emotional state of employees within a company and visualize the mental health status of the entire organization.

2. The system of claim 1.

7. The character adjustment unit Using emotion estimation capabilities to adjust character or tone in real time according to the employee's emotional state.

2. The system of claim 1.

8. The character adjustment unit Using an emotion estimation function, a dialogue scenario is automatically generated according to the employee's emotional state, thereby improving the quality of the dialogue.

2. The system of claim 1.

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