System

Abstract

An object of the system according to the embodiment is to fill a generation gap between a recent graduate employee and a middle-ranking employee and promote mutual understanding and cooperation.SOLUTION: In one embodiment, a system includes a values analyzer, a training provider, a mentor system component, an event organizer, and a project implementation component. The values analysis unit analyzes differences in values, communication styles, and working methods between generations. The training provider provides interactive training and workshops based on the analysis results obtained by the values analyzer. The mentor system unit operates a mentor system. The event holding unit holds an event of team building. The project implementation unit implements the shared project.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 technologies lack effective means to bridge the generation gap between new graduates and mid-career employees, and there is room for improvement in promoting mutual understanding and cooperation within the workplace.

[0005] The system according to the embodiment aims to bridge the generation gap between new graduates and mid-career employees and promote mutual understanding and cooperation. [Means for solving the problem]

[0006] The system according to the embodiment includes a values ​​analysis unit, a training provision unit, a mentoring system unit, an event hosting unit, and a project implementation unit. The values ​​analysis unit analyzes differences in values, communication styles, and work styles between generations. The training provision unit provides interactive training and workshops based on the analysis results obtained by the values ​​analysis unit. The mentoring system unit operates a mentoring system. The event hosting unit hosts team building events. The project implementation unit implements shared projects. [Effects of the Invention]

[0007] The system according to the embodiment can bridge the generation gap between new graduates and mid-career employees and promote mutual understanding and cooperation. [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 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 BridgeGap Harmony system, an embodiment of the present invention, is a communication platform designed to bridge the generation gap between new graduates and mid-career employees and promote mutual understanding and cooperation. The system uses AI technology to analyze differences in values, communication styles, and work styles between generations, and provides interactive training and workshops to help both parties empathize with each other. It also breaks down generational barriers and strengthens cohesion within the workplace through mentoring programs, team-building events, and shared projects. In this way, the BridgeGap Harmony system can bridge the generation gap between new graduates and mid-career employees and promote mutual understanding and cooperation.

[0029] The BridgeGap Harmony system according to an embodiment includes a values ​​analysis unit, a training provision unit, a mentoring system unit, an event organization unit, and a project implementation unit. The values ​​analysis unit analyzes differences in employee values, communication styles, and work styles. For example, the values ​​analysis unit analyzes employee survey results and work data to clarify generational differences. The training provision unit provides interactive training and workshops that foster mutual empathy between both parties based on the analysis results of the values ​​analysis unit. For example, the training provision unit designs workshops to help new graduates and mid-career employees achieve common goals and training to help understand intergenerational communication styles. The mentoring system unit introduces a mentoring system between new graduates and mid-career employees. For example, mid-career employees mentor new graduates, providing work support and career advice. The event organization unit organizes team-building events to break down generational barriers. For example, the event organization unit provides opportunities for employees to cooperate with each other through sporting events and collaborative activities. The Project Implementation Department implements projects that new graduates and mid-career employees work on together. For example, the Project Implementation Department sets up projects that allow both parties to work together to achieve goals, such as new product development projects or business improvement projects. In this way, the BridgeGap Harmony system can bridge the generation gap between new graduates and mid-career employees and promote mutual understanding and cooperation. For example, through interactive training and workshops, employees can understand each other's values ​​and work styles and deepen empathy. In addition, mentoring programs and team-building events deepen trust between employees and strengthen unity within the workplace. Furthermore, through shared projects, employees can cooperate with each other to achieve goals.

[0030] The Values ​​Analysis Unit can analyze social media activities or online behavior to identify differences in values ​​and communication styles between generations. For example, the Values ​​Analysis Unit uses generative AI to analyze employees' social media posts and online behavior to identify differences in values ​​and communication styles between generations. For example, it can extract the SNS usage patterns preferred by young employees and the characteristics of online communication that mid-career employees value. This makes it possible to identify detailed differences in values ​​and communication styles between generations.

[0031] The Values ​​Analysis Department can analyze project history or work results and present specific examples of differences in work styles between generations. For example, the Values ​​Analysis Department uses a generative AI to analyze employees' past project history and present specific examples of differences in work styles between generations. For example, it can identify the project progress methods preferred by young employees and the project management methods that mid-career employees value. This makes it possible to present specific examples of differences in work styles between generations.

[0032] The training provision unit can provide individually customized training programs based on learning history or feedback. For example, the generative AI analyzes the learning history of employees and provides individually customized training programs. For example, it suggests optimal training content based on past training content and results. This makes it possible to provide individually customized training programs.

[0033] The training delivery department can analyze employee reactions in real time and instantly adjust the training content. For example, the training delivery department uses a generation AI to analyze employee reactions during training in real time and instantly adjust the content. For example, the training content can be changed according to the employee's level of understanding and interest. This makes it possible to instantly adjust the content according to the employee's reactions during training.

[0034] The Mentoring Program Department uses generative AI to optimize matching between mentors and mentees, and can automatically suggest pairs that are a good match. For example, the generative AI analyzes the skills and interests of mentors and mentees, and automatically suggests pairs that are a good match. For example, it matches mentors and mentees who share common interests and goals. This makes it possible to automatically suggest pairs of mentors and mentees that are a good match.

[0035] The mentoring system department can analyze the communication history between mentors and mentees and provide effective advice and support. For example, the generative AI can analyze the communication history between mentors and mentees and provide effective advice and support. For example, it can identify the mentee's issues from past interactions and provide appropriate advice. This makes it possible to provide effective advice and support based on the communication history between mentors and mentees.

[0036] The event organizing department can analyze the interests or hobbies of each employee and propose the best team building event for each employee. For example, the event organizing department uses a generative AI to analyze the interests and hobbies of each employee and propose the best team building event for each employee. For example, it can propose a sports event or an art workshop. This makes it possible to propose the best team building event based on the interests and hobbies of each employee.

[0037] The event organization department can analyze past feedback and optimize the content of the next event. For example, the generation AI analyzes feedback from past events and optimizes the content of the next event. For example, the event content can be adjusted based on employee satisfaction and participation rates. This makes it possible to optimize the content of the next event based on past feedback.

[0038] The project implementation department can analyze skill sets or interests and assemble the optimal project team. For example, the project implementation department can use generative AI to analyze employees' skill sets and interests and assemble the optimal project team. For example, a project team can be formed by gathering employees with specific skills. This makes it possible to assemble the optimal project team based on employees' skill sets and interests.

[0039] The project implementation department can monitor the progress of the project in real time and provide advice and support as needed. For example, generative AI can monitor the progress of the project in real time and provide advice and support as needed. For example, it can detect project delays and propose appropriate measures. This allows the project implementation department to monitor the progress of the project in real time and provide advice and support as needed.

[0040] The project implementation department can analyze successful projects from different industries or cultural spheres and design the optimal project. For example, the generative AI can analyze successful projects from different industries and use them as a basis to design the optimal project. For example, it can incorporate successful projects from the IT and manufacturing industries. This allows the optimal project to be designed based on successful projects from different industries and cultural spheres.

[0041] The project implementation department can analyze the results of the project and optimize the content of the next project. For example, the generative AI can analyze the results of the project and optimize the content of the next project. For example, the factors that made the project successful can be identified and reflected in the next project. This makes it possible to optimize the content of the next project based on the results of the project.

[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 BridgeGap Harmony system can also be equipped with a virtual reality (VR) section. This section provides simulations that allow employees to experience and deepen understanding of generational differences in a virtual space. For example, a simulation can be provided in which junior employees experience work from the perspective of mid-level employees, or mid-level employees learn new technologies from the perspective of junior employees. This allows employees to experience perspectives that would not be possible in a real work environment and deepen mutual understanding. The VR section can also provide scenarios for collaborative work and problem-solving in a virtual space as part of team building, increasing opportunities for employees to cooperate and build trust.

[0044] The BridgeGap Harmony system can also be equipped with a health management department. The health management department monitors employees' health status and provides advice on maintaining their health. For example, it can analyze employees' stress levels and sleep patterns and suggest appropriate relaxation methods and exercise programs. This helps maintain employees' health and improves work efficiency. The health management department can also hold regular health checks and wellness events to raise employees' health awareness. This promotes a health culture throughout the workplace and improves employee satisfaction.

[0045] The BridgeGap Harmony system can also include a Career Development Department, which supports employees' career paths and provides growth opportunities. For example, it can analyze employees' skill sets and interests and suggest the most suitable career path. This allows employees to effectively grow toward their career goals. The Career Development Department can also link with mentoring systems and training programs to provide comprehensive support for employees' career development. This can increase employee motivation and promote long-term career development within the workplace.

[0046] The BridgeGap Harmony system can also include a Cultural Exchange Department, which promotes interaction between employees with different cultural backgrounds and creates a work environment that respects diversity. For example, the department can hold workshops and international events to promote intercultural understanding. This allows employees to understand and respect different cultures and values. The Cultural Exchange Department can also provide tools and resources to facilitate intercultural communication. This increases the number of employees with a global perspective and improves diversity throughout the workplace.

[0047] The BridgeGap Harmony system can also be equipped with a feedback collection module. This module collects feedback from employees and helps improve the system. For example, it can collect employee opinions and requests through regular surveys and feedback sessions. This allows for continuous improvement of the system's functions and services and increases employee satisfaction. The feedback collection module can also analyze the collected data and propose specific improvement measures. This allows for flexible responses to employee needs and contributes to an improved work environment.

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

[0049] Step 1: The Values ​​Analysis Department analyzes differences in employee values, communication styles, and work styles. For example, the Values ​​Analysis Department analyzes employee survey results and work data to clarify generational differences. Step 2: Based on the analysis results of the Values ​​Analysis Department, the Training Department provides interactive training and workshops that resonate with both parties. For example, the Training Department designs a "workshop to help new graduates and mid-career employees share common goals" or "training to understand intergenerational communication styles." Step 3: The Mentoring Department will introduce a mentoring system between new graduates and mid-career employees. For example, the Mentoring Department will have mid-career employees act as mentors for new graduates, providing support for their work and career advice. Step 4: The Events Department organizes team-building events to break down generational barriers. For example, the Events Department can provide opportunities for employees to collaborate through sporting events or collaborative activities. Step 5: The Project Implementation Department implements projects that new graduates and mid-level employees work on together. For example, the Project Implementation Department sets up projects that allow both parties to work together to achieve goals, such as a new product development project or a business improvement project.

[0050] (Example 2) The BridgeGap Harmony system, an embodiment of the present invention, is a communication platform designed to bridge the generation gap between new graduates and mid-career employees and promote mutual understanding and cooperation. The system uses AI technology to analyze differences in values, communication styles, and work styles between generations, and provides interactive training and workshops to help both parties empathize with each other. It also breaks down generational barriers and strengthens cohesion within the workplace through mentoring programs, team-building events, and shared projects. In this way, the BridgeGap Harmony system can bridge the generation gap between new graduates and mid-career employees and promote mutual understanding and cooperation.

[0051] The BridgeGap Harmony system according to an embodiment includes a values ​​analysis unit, a training provision unit, a mentoring system unit, an event organization unit, and a project implementation unit. The values ​​analysis unit analyzes differences in employee values, communication styles, and work styles. For example, the values ​​analysis unit analyzes employee survey results and work data to clarify generational differences. The training provision unit provides interactive training and workshops that foster mutual empathy between both parties based on the analysis results of the values ​​analysis unit. For example, the training provision unit designs workshops to help new graduates and mid-career employees achieve common goals and training to help understand intergenerational communication styles. The mentoring system unit introduces a mentoring system between new graduates and mid-career employees. For example, mid-career employees mentor new graduates, providing work support and career advice. The event organization unit organizes team-building events to break down generational barriers. For example, the event organization unit provides opportunities for employees to cooperate with each other through sporting events and collaborative activities. The Project Implementation Department implements projects that new graduates and mid-career employees work on together. For example, the Project Implementation Department sets up projects that allow both parties to work together to achieve goals, such as new product development projects or business improvement projects. In this way, the BridgeGap Harmony system can bridge the generation gap between new graduates and mid-career employees and promote mutual understanding and cooperation. For example, through interactive training and workshops, employees can understand each other's values ​​and work styles and deepen empathy. In addition, mentoring programs and team-building events deepen trust between employees and strengthen unity within the workplace. Furthermore, through shared projects, employees can cooperate with each other to achieve goals.

[0052] The Values ​​Analysis Unit can analyze social media activities or online behavior to identify differences in values ​​and communication styles between generations. For example, the Values ​​Analysis Unit uses generative AI to analyze employees' social media posts and online behavior to identify differences in values ​​and communication styles between generations. For example, it can extract the SNS usage patterns preferred by young employees and the characteristics of online communication that mid-career employees value. This makes it possible to identify detailed differences in values ​​and communication styles between generations.

[0053] The Values ​​Analysis Department can analyze project history or work results and present specific examples of differences in work styles between generations. For example, the Values ​​Analysis Department uses a generative AI to analyze employees' past project history and present specific examples of differences in work styles between generations. For example, it can identify the project progress methods preferred by young employees and the project management methods that mid-career employees value. This makes it possible to present specific examples of differences in work styles between generations.

[0054] The value analysis unit can use the emotion estimation function to monitor emotional states in real time and analyze differences in emotional responses between generations. The value analysis unit, for example, uses the emotion estimation function to monitor employees' emotional states in real time and analyze differences in emotional responses between generations. For example, it identifies situations in which young employees are likely to feel stressed and situations in which mid-career employees feel secure. This makes it possible to analyze differences in emotional responses between generations in real time.

[0055] The training provision unit can provide individually customized training programs based on learning history or feedback. For example, the generative AI analyzes the learning history of employees and provides individually customized training programs. For example, it suggests optimal training content based on past training content and results. This makes it possible to provide individually customized training programs.

[0056] The training delivery department can analyze employee reactions in real time and instantly adjust the training content. For example, the training delivery department uses a generation AI to analyze employee reactions during training in real time and instantly adjust the content. For example, the training content can be changed according to the employee's level of understanding and interest. This makes it possible to instantly adjust the content according to the employee's reactions during training.

[0057] The training providing unit can use the emotion estimation function to monitor the emotional state and provide feedback according to the emotion. For example, the training providing unit uses the emotion estimation function to monitor the emotional state of the employee during training and provide feedback according to the emotion. For example, if the employee is feeling stressed, the training providing unit can provide advice on how to relax. This makes it possible to provide feedback according to the emotional state of the employee during training.

[0058] The Mentoring Program Department uses generative AI to optimize matching between mentors and mentees, and can automatically suggest pairs that are a good match. For example, the generative AI analyzes the skills and interests of mentors and mentees, and automatically suggests pairs that are a good match. For example, it matches mentors and mentees who share common interests and goals. This makes it possible to automatically suggest pairs of mentors and mentees that are a good match.

[0059] The mentoring system department can analyze the communication history between mentors and mentees and provide effective advice and support. For example, the generative AI can analyze the communication history between mentors and mentees and provide effective advice and support. For example, it can identify the mentee's issues from past interactions and provide appropriate advice. This makes it possible to provide effective advice and support based on the communication history between mentors and mentees.

[0060] The mentoring system unit can use the emotion estimation function to monitor the emotional states of the mentor and mentee and strengthen emotional support. The mentoring system unit, for example, uses the emotion estimation function to monitor the emotional states of the mentor and mentee and strengthen emotional support. For example, if the mentee is feeling stressed, the mentor provides appropriate support. This makes it possible to strengthen emotional support based on the emotional states of the mentor and mentee.

[0061] The event organizing department can analyze the interests or hobbies of each employee and propose the best team building event for each employee. For example, the event organizing department uses a generative AI to analyze the interests and hobbies of each employee and propose the best team building event for each employee. For example, it can propose a sports event or an art workshop. This makes it possible to propose the best team building event based on the interests and hobbies of each employee.

[0062] The event organization department can analyze past feedback and optimize the content of the next event. For example, the generation AI analyzes feedback from past events and optimizes the content of the next event. For example, the event content can be adjusted based on employee satisfaction and participation rates. This makes it possible to optimize the content of the next event based on past feedback.

[0063] The event hosting unit can use the emotion estimation function to monitor the emotional state of employees during the event and adjust the event content in real time. For example, the event hosting unit can use the emotion estimation function to monitor the emotional state of employees during the event and adjust the event content in real time. For example, if employees are enjoying themselves, the activity can be extended. This allows the event content to be adjusted in real time according to the emotional state of employees during the event.

[0064] The project implementation department can analyze skill sets or interests and assemble the optimal project team. For example, the project implementation department can use generative AI to analyze employees' skill sets and interests and assemble the optimal project team. For example, a project team can be formed by gathering employees with specific skills. This makes it possible to assemble the optimal project team based on employees' skill sets and interests.

[0065] The project implementation department can monitor the progress of the project in real time and provide advice and support as needed. For example, generative AI can monitor the progress of the project in real time and provide advice and support as needed. For example, it can detect project delays and propose appropriate measures. This allows the project implementation department to monitor the progress of the project in real time and provide advice and support as needed.

[0066] The project implementation department can use the emotion estimation function to monitor the emotional state of employees during the project and strengthen emotional support. For example, the project implementation department can use the emotion estimation function to monitor the emotional state of employees during the project and strengthen emotional support. For example, if an employee is feeling stressed, appropriate support can be provided. This makes it possible to strengthen emotional support based on the emotional state of employees during the project.

[0067] The project implementation department can analyze successful projects from different industries or cultural spheres and design the optimal project. For example, the generative AI can analyze successful projects from different industries and use them as a basis to design the optimal project. For example, it can incorporate successful projects from the IT and manufacturing industries. This allows the optimal project to be designed based on successful projects from different industries and cultural spheres.

[0068] The project implementation department can analyze the results of the project and optimize the content of the next project. For example, the generative AI can analyze the results of the project and optimize the content of the next project. For example, the factors that made the project successful can be identified and reflected in the next project. This makes it possible to optimize the content of the next project based on the results of the project.

[0069] The project implementation department can use the emotion estimation function to collect emotional reactions after the project and improve the content of the next project. For example, the project implementation department can use the emotion estimation function to collect emotional reactions of employees after the project and improve the content of the next project based on that data. For example, the project content that employees had positive feelings about can be strengthened. This makes it possible to improve the content of the next project based on the emotional reactions after the project.

[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 BridgeGap Harmony system can also be equipped with a virtual reality (VR) section. This section provides simulations that allow employees to experience and deepen understanding of generational differences in a virtual space. For example, a simulation can be provided in which junior employees experience work from the perspective of mid-level employees, or mid-level employees learn new technologies from the perspective of junior employees. This allows employees to experience perspectives that would not be possible in a real work environment and deepen mutual understanding. The VR section can also provide scenarios for collaborative work and problem-solving in a virtual space as part of team building, increasing opportunities for employees to cooperate and build trust.

[0072] The BridgeGap Harmony system can also be equipped with a health management department. The health management department monitors employees' health status and provides advice on maintaining their health. For example, it can analyze employees' stress levels and sleep patterns and suggest appropriate relaxation methods and exercise programs. This helps maintain employees' health and improves work efficiency. The health management department can also hold regular health checks and wellness events to raise employees' health awareness. This promotes a health culture throughout the workplace and improves employee satisfaction.

[0073] The BridgeGap Harmony system can also include a Career Development Department, which supports employees' career paths and provides growth opportunities. For example, it can analyze employees' skill sets and interests and suggest the most suitable career path. This allows employees to effectively grow toward their career goals. The Career Development Department can also link with mentoring systems and training programs to provide comprehensive support for employees' career development. This can increase employee motivation and promote long-term career development within the workplace.

[0074] The BridgeGap Harmony system can also include a Cultural Exchange Department, which promotes interaction between employees with different cultural backgrounds and creates a work environment that respects diversity. For example, the department can hold workshops and international events to promote intercultural understanding. This allows employees to understand and respect different cultures and values. The Cultural Exchange Department can also provide tools and resources to facilitate intercultural communication. This increases the number of employees with a global perspective and improves diversity throughout the workplace.

[0075] The BridgeGap Harmony system can also be equipped with a feedback collection module. This module collects feedback from employees and helps improve the system. For example, it can collect employee opinions and requests through regular surveys and feedback sessions. This allows for continuous improvement of the system's functions and services and increases employee satisfaction. The feedback collection module can also analyze the collected data and propose specific improvement measures. This allows for flexible responses to employee needs and contributes to an improved work environment.

[0076] The BridgeGap Harmony system also uses emotion estimation to monitor employees' emotional states in real time and provide training content based on their emotions. For example, if an employee is feeling stressed, it can provide relaxation and stress management training, while if an employee is motivated, it can provide challenging tasks. This allows for optimal training content tailored to each employee's emotional state and promotes effective learning. The emotion estimation function can also be used to monitor employees' emotional changes during training and adjust the training content as needed. This enables flexible training tailored to employees' emotional states, maximizing learning effectiveness.

[0077] The BridgeGap Harmony system also uses emotion estimation to monitor the emotional states of mentors and mentees in real time and provide emotionally-based support. For example, if a mentee feels anxious, the mentor can provide appropriate encouragement and advice, while if the mentee feels confident, the mentor can encourage them to take on new challenges. This allows for optimal support tailored to the emotional states of both mentors and mentees, resulting in effective mentoring. The emotion estimation function can also be used to monitor emotional changes during mentoring sessions and adjust support content as needed. This allows for flexible support tailored to the emotional states of both mentors and mentees, maximizing the effectiveness of mentoring.

[0078] The BridgeGap Harmony system also uses emotion estimation to monitor employees' emotional states in real time during an event and provide event content based on their emotions. For example, if employees are enjoying themselves, the system can extend the activity, and if they are bored, it can switch to a different activity. This allows the system to provide optimal event content based on employees' emotional state and maximize the effectiveness of the event. The emotion estimation function can also be used to monitor emotional changes during an event and adjust the event content as needed. This allows for flexible event management based on employees' emotional states, improving participant satisfaction.

[0079] The BridgeGap Harmony system also uses its emotion estimation feature to monitor employees' emotional states during projects in real time and provide support based on their emotions. For example, if an employee is feeling stressed, it can suggest appropriate relaxation methods, and if they are motivated, it can encourage them to take on more challenges. This allows for optimal support based on employees' emotional states and promotes project success. The emotion estimation feature can also be used to monitor emotional changes during projects and adjust support content as needed. This allows for flexible support based on employees' emotional states, maximizing the effectiveness of projects.

[0080] The BridgeGap Harmony system also uses its emotion estimation function to collect employees' emotional reactions after a project and improve the content of the next project. For example, it can strengthen project content that employees felt positive about and improve project content that employees felt negative about. This allows the system to optimize the content of the next project based on the employees' emotional reactions after the project and improve employee satisfaction. The emotion estimation function can also be used to monitor changes in emotions after a project and provide feedback as needed. This allows for flexible feedback based on employees' emotional state, maximizing the effectiveness of the project.

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

[0082] Step 1: The Values ​​Analysis Department analyzes differences in employee values, communication styles, and work styles. For example, the Values ​​Analysis Department analyzes employee survey results and work data to clarify generational differences. Step 2: Based on the analysis results of the Values ​​Analysis Department, the Training Department provides interactive training and workshops that resonate with both parties. For example, the Training Department designs a "workshop to help new graduates and mid-career employees share common goals" or "training to understand intergenerational communication styles." Step 3: The Mentoring Department will introduce a mentoring system between new graduates and mid-career employees. For example, the Mentoring Department will have mid-career employees act as mentors for new graduates, providing support for their work and career advice. Step 4: The Events Department organizes team-building events to break down generational barriers. For example, the Events Department can provide opportunities for employees to collaborate through sporting events or collaborative activities. Step 5: The Project Implementation Department implements projects that new graduates and mid-level employees work on together. For example, the Project Implementation Department sets up projects that allow both parties to work together to achieve goals, such as a new product development project or a business improvement project.

[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 a 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, 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.

[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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, 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 processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and 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. Value Analysis Department and Training Department and Mentoring Department and The event organizing department and A project implementation department is provided. A system characterized by:

2. The value analysis unit Analyze social media activity or online behavior to identify differences in values ​​and communication styles between generations 2. The system of claim 1.

3. The training providing unit Providing individually tailored training programs based on learning history or feedback 2. The system of claim 1.

4. The Mentor System Department: Using generative AI to optimize mentor-mentee matching and automatically suggest compatible pairs 2. The system of claim 1.

5. The event organizing department: Analyze interests or hobbies to suggest the best team building events for each employee 2. The system of claim 1.

6. The project implementation department: Analyze skill sets or interests to assemble the best project team 2. The system of claim 1.

7. The value analysis unit Emotion estimation capabilities are used to monitor emotional states in real time and analyze differences in emotional responses between generations.

2. The system of claim 1.

8. The training providing unit Emotion estimation function monitors emotional state and provides emotional feedback 2. The system of claim 1.

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