System

Abstract

A system according to an embodiment aims to provide personalized carrier support tailored to the needs of each and every employee.SOLUTION: A system according to an embodiment includes a generation AI, a career support unit, and a subscription management unit. The generation AI analyzes the needs of the employees using the generation AI. The career support unit provides personalized career support based on the needs analyzed by the generating AI. The subscription management unit manages programs on a subscription basis.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] With conventional technology, it was difficult to provide career support tailored to the needs of each employee, posing challenges in reducing turnover rates and improving employee satisfaction.

[0005] The system according to the embodiment aims to provide personalized career support tailored to the needs of each employee. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, a career support unit, and a subscription management unit. The generation AI analyzes employee needs using the generation AI. The career support unit provides personalized career support based on the needs analyzed by the generation AI. The subscription management unit manages programs on a subscription basis. [Effects of the Invention]

[0007] The system according to the embodiment can provide personalized career support tailored to the needs of each employee. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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) A mentoring program according to an embodiment of the present invention is a system that uses generative AI to provide personalized career support tailored to the needs of each employee, thereby significantly reducing a company's turnover rate and improving employee satisfaction and productivity.

[0029] A mentoring program according to an embodiment includes a generation AI, a career support unit, and a subscription management unit. The generation AI analyzes employee needs. For example, the generation AI analyzes input information such as an employee's past performance data, skill set, and career goals to generate an individually customized mentoring plan. The generation AI can also analyze employee feedback data and adjust the mentoring plan as needed. The career support unit provides personalized career support based on the needs analyzed by the generation AI. For example, the career support unit provides training programs for skill development, career path suggestions, and action plans for goal achievement. The career support unit can also provide regular feedback and evaluations to improve employee satisfaction. The subscription management unit manages the program on a subscription basis. For example, the subscription management unit allows companies to use the program for only the required period, minimizing the costs of employee career support. The subscription management unit can also automatically manage and renew subscriptions. As a result, the mentoring program according to an embodiment provides career support tailored to the needs of each employee, reducing corporate turnover and improving employee satisfaction and productivity.

[0030] Generative AI can analyze an employee's internal network and automatically match them with the most suitable mentor. For example, generative AI selects a mentor based on the employee's project history and evaluation data. For example, generative AI automatically matches the most suitable mentor based on the employee's skills and experience. Generative AI can also analyze the structure and connections of the internal network to optimize the relationship between mentor and mentee. For example, generative AI selects a mentor based on the structure of the project team and connections between departments, and provides appropriate support to the mentee. This makes it possible to analyze an employee's internal network and automatically match them with the most suitable mentor, thereby increasing the effectiveness of career support.

[0031] Generative AI can analyze employees' past project data and provide specific advice based on success and failure cases. Generative AI can, for example, analyze employees' past project data and provide specific advice based on success cases. For example, generative AI can extract the factors behind successful projects and propose action plans for similar situations. Generative AI can also propose improvement measures based on failure cases. For example, generative AI can identify the causes of failed projects and provide specific advice to prevent recurrence. This makes it possible to improve the quality of career support by analyzing employees' past project data and providing specific advice.

[0032] Generative AI can analyze employees' hobbies and interests and reflect them in career support. For example, generative AI can analyze employees' hobbies and interests and provide opportunities to improve skills related to their hobbies. For example, generative AI can suggest online courses or workshops related to employees' hobbies. Generative AI can also suggest career paths based on employees' interests. For example, generative AI can analyze employees' technical and business interests and provide career support based on those interests. In this way, by analyzing employees' hobbies and interests and reflecting them in career support, employee satisfaction and productivity can be improved.

[0033] Generative AI can analyze employees' health data and provide career support according to their health condition. For example, generative AI can analyze employees' health data and suggest training that is focused on periods when their health is good. For example, generative AI can optimize training schedules based on health data. Generative AI can also provide career support according to employees' health condition. For example, generative AI can suggest a relaxation program for employees whose health condition is deteriorating based on their health data. In this way, by analyzing employees' health data and providing career support according to their health condition, employee satisfaction and productivity can be improved.

[0034] Generative AI can analyze employee feedback and propose specific action plans to improve satisfaction. Generative AI can, for example, analyze employee feedback and propose specific action plans to improve satisfaction. For example, generative AI can propose improvements to the work environment based on feedback data. Generative AI can also adjust the content of career support based on employee feedback. For example, generative AI can analyze employee feedback and adjust mentoring plans as needed. In this way, by analyzing employee feedback and proposing specific action plans, employee satisfaction and productivity can be improved.

[0035] Generative AI can regularly evaluate employees' progress toward achieving their career goals and strengthen support for them toward achieving those goals. Generative AI can, for example, regularly evaluate employees' progress toward achieving their career goals and strengthen support for them toward achieving those goals. For example, generative AI can adjust training programs based on the level of goal achievement. Generative AI can also suggest a review of an employee's career path based on the level of employee career goal achievement. For example, generative AI can evaluate an employee's progress toward achieving their career goals and adjust their career path as necessary. In this way, by regularly evaluating employees' progress toward achieving their career goals and strengthening support, employee satisfaction and productivity can be improved.

[0036] Generative AI can analyze an employee's family structure and lifestyle, and provide career support that takes work-life balance into consideration. Generative AI can, for example, analyze an employee's family structure and lifestyle, and provide career support that takes work-life balance into consideration. For example, generative AI can suggest flexible working hours based on family structure. Generative AI can also provide career support based on an employee's lifestyle. For example, generative AI can analyze an employee's lifestyle habits and hobbies and preferences, and provide career support based on that. In this way, by analyzing an employee's family structure and lifestyle, and providing career support that takes work-life balance into consideration, employee satisfaction and productivity can be improved.

[0037] Generative AI can analyze employees' internal communication data and make suggestions for improving communication. Generative AI can, for example, analyze employees' internal communication data and make suggestions for improving communication. For example, generative AI can evaluate the frequency and quality of communication and suggest improvement measures. Generative AI can also suggest team building activities based on employees' internal communication data. For example, generative AI can analyze employees' communication data and suggest specific action plans for team building. In this way, by analyzing employees' internal communication data and making suggestions for improving communication, it is possible to improve employee satisfaction and productivity.

[0038] Generative AI can analyze a company's usage status and automatically propose the optimal subscription plan. Generative AI can, for example, analyze a company's usage status and automatically propose the optimal subscription plan. For example, Generative AI can customize plans based on frequency of use and feature usage. Generative AI can also provide discounts for frequently used features based on a company's usage data. For example, Generative AI can analyze a company's usage status and propose the optimal pricing plan. This makes it possible to improve a company's cost efficiency by analyzing a company's usage status and automatically proposing the optimal subscription plan.

[0039] Generative AI can analyze subscription usage data and optimize pricing plans based on usage frequency and effectiveness. Generative AI can, for example, analyze subscription usage data and optimize pricing plans based on usage frequency and effectiveness. For example, Generative AI can provide discounts for frequently used features. Generative AI can also set pricing based on usage frequency based on usage data. For example, Generative AI can lower fees for infrequently used features. In this way, analyzing subscription usage data and optimizing pricing plans can improve a company's cost efficiency.

[0040] Generative AI can provide subscription plans according to a company's growth stage. For example, Generative AI can prepare plans for startups and plans for large corporations. Generative AI can also propose the optimal plan based on a company's growth stage. For example, Generative AI can customize a plan based on a company's sales growth rate and increase in number of employees. This allows for subscription plans according to a company's growth stage to be offered, making it possible to provide the optimal plan that meets the needs of the company.

[0041] Generative AI can analyze a company's industry characteristics and provide a subscription plan specialized for that industry. Generative AI can, for example, analyze a company's industry characteristics and provide a subscription plan specialized for that industry. For example, generative AI can prepare plans for the manufacturing industry and plans for the service industry. Generative AI can also customize plans based on industry trends and the actions of competitors. For example, generative AI can analyze industry characteristics and propose the optimal plan. By analyzing a company's industry characteristics and providing a subscription plan specialized for that industry, it is possible to provide the optimal plan that meets the company's needs.

[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] Generative AI can analyze employees' learning styles and suggest optimal learning methods. For example, if an employee prefers visual learning, generative AI can suggest a training program that makes heavy use of visual content. Alternatively, if an employee prefers practical learning, generative AI can suggest a hands-on workshop. Furthermore, if an employee prefers self-study, generative AI can provide online courses and e-learning resources. This maximizes learning effectiveness and improves employee satisfaction and productivity by providing career support tailored to each employee's learning style.

[0044] Generative AI can suggest collaboration with external experts based on an employee's career goals. For example, Generative AI can suggest mentoring sessions with industry experts to improve specific skills. Generative AI can also introduce external training programs when an employee is trying a new field. Generative AI can also suggest collaboration with external consultants when an employee is working on a specific project. This allows employees to utilize external resources that match their career goals to help them improve their skills and develop their careers.

[0045] Generative AI can suggest internal transfers or project participation based on an employee's career goals. For example, Generative AI can suggest a transfer to a different department so that the employee can acquire new skills. Generative AI can also suggest that the employee participate in a new project so that the employee can gain specific experience. Furthermore, Generative AI can suggest a team leader role so that the employee can improve their leadership skills. In this way, by suggesting internal transfers and project participation that correspond to the employee's career goals, it is possible to support career development and increase satisfaction and productivity.

[0046] Generative AI can match internal mentors and mentees based on employees' career goals. For example, Generative AI can select the most suitable mentor based on an employee's skill set and career goals. Generative AI can also optimize mentor-mentee relationships based on an employee's past project experience. Furthermore, Generative AI can adjust the mentor-mentee match based on employee feedback. This makes it possible to match mentors and mentees according to employees' career goals, thereby increasing the effectiveness of career support and improving satisfaction and productivity.

[0047] Generative AI can optimally allocate internal resources based on employees' career goals. For example, generative AI can assemble optimal project teams based on employees' skill sets and career goals. Generative AI can also optimize resource allocation based on employees' past performance data. Furthermore, generative AI can adjust resource allocation based on employee feedback. In this way, allocating resources according to employees' career goals can increase the success rate of projects and improve satisfaction and productivity.

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

[0049] Step 1: Generative AI analyzes the employee's needs. For example, Generative AI analyzes the employee's past performance data, skill set, career goals, and other input information to generate an individually customized mentoring plan. Generative AI can also analyze the employee's feedback data and adjust the mentoring plan as needed. Step 2: The career support department provides personalized career support based on the needs analyzed by the generative AI. For example, the career support department offers training programs for skill development, career path suggestions, and action plans for goal achievement. The career support department can also provide regular feedback and evaluations to improve employee satisfaction. Step 3: The subscription manager manages the program on a subscription basis. For example, the subscription manager allows companies to use the program for only as long as they need it, minimizing the costs of employee career support. The subscription manager can also automatically manage subscriptions and renewals.

[0050] (Example 2) A mentoring program according to an embodiment of the present invention is a system that uses generative AI to provide personalized career support tailored to the needs of each employee, thereby significantly reducing a company's turnover rate and improving employee satisfaction and productivity.

[0051] A mentoring program according to an embodiment includes a generation AI, a career support unit, and a subscription management unit. The generation AI analyzes employee needs. For example, the generation AI analyzes input information such as an employee's past performance data, skill set, and career goals to generate an individually customized mentoring plan. The generation AI can also analyze employee feedback data and adjust the mentoring plan as needed. The career support unit provides personalized career support based on the needs analyzed by the generation AI. For example, the career support unit provides training programs for skill development, career path suggestions, and action plans for goal achievement. The career support unit can also provide regular feedback and evaluations to improve employee satisfaction. The subscription management unit manages the program on a subscription basis. For example, the subscription management unit allows companies to use the program for only the required period, minimizing the costs of employee career support. The subscription management unit can also automatically manage and renew subscriptions. As a result, the mentoring program according to an embodiment provides career support tailored to the needs of each employee, reducing corporate turnover and improving employee satisfaction and productivity.

[0052] Generative AI can estimate employee emotions in real time and provide emotion-based career support. For example, generative AI analyzes employees' facial expressions and voice to evaluate their stress levels. For example, generative AI can calculate an emotion score based on changes in employees' facial expressions and suggest a relaxation program for employees who are feeling stressed. Generative AI can also analyze the tone and speed of employees' voices to calculate an emotion score. For example, generative AI can estimate emotions based on employees' voice data and provide emotion-based career support. This can improve employee satisfaction and productivity by providing career support based on employees' emotions.

[0053] Generative AI can analyze an employee's internal network and automatically match them with the most suitable mentor. For example, generative AI selects a mentor based on the employee's project history and evaluation data. For example, generative AI automatically matches the most suitable mentor based on the employee's skills and experience. Generative AI can also analyze the structure and connections of the internal network to optimize the relationship between mentor and mentee. For example, generative AI selects a mentor based on the structure of the project team and connections between departments, and provides appropriate support to the mentee. This makes it possible to analyze an employee's internal network and automatically match them with the most suitable mentor, thereby increasing the effectiveness of career support.

[0054] Generative AI can analyze employees' past project data and provide specific advice based on success and failure cases. Generative AI can, for example, analyze employees' past project data and provide specific advice based on success cases. For example, generative AI can extract the factors behind successful projects and propose action plans for similar situations. Generative AI can also propose improvement measures based on failure cases. For example, generative AI can identify the causes of failed projects and provide specific advice to prevent recurrence. This makes it possible to improve the quality of career support by analyzing employees' past project data and providing specific advice.

[0055] Generative AI can analyze employees' hobbies and interests and reflect them in career support. For example, generative AI can analyze employees' hobbies and interests and provide opportunities to improve skills related to their hobbies. For example, generative AI can suggest online courses or workshops related to employees' hobbies. Generative AI can also suggest career paths based on employees' interests. For example, generative AI can analyze employees' technical and business interests and provide career support based on those interests. In this way, by analyzing employees' hobbies and interests and reflecting them in career support, employee satisfaction and productivity can be improved.

[0056] Generative AI can analyze employees' health data and provide career support according to their health condition. For example, generative AI can analyze employees' health data and suggest training that is focused on periods when their health is good. For example, generative AI can optimize training schedules based on health data. Generative AI can also provide career support according to employees' health condition. For example, generative AI can suggest a relaxation program for employees whose health condition is deteriorating based on their health data. In this way, by analyzing employees' health data and providing career support according to their health condition, employee satisfaction and productivity can be improved.

[0057] Generative AI can estimate employee emotions and provide career support based on those emotions. For example, generative AI can use its emotion estimation function to provide career support based on employee emotions. For example, generative AI can send encouraging messages to employees who are experiencing low motivation. Generative AI can also suggest training programs based on employees' emotions, based on their emotional data. For example, generative AI can suggest a relaxation program to employees who are feeling stressed, based on their emotional score. In this way, by providing career support based on employees' emotions, it is possible to improve employee satisfaction and productivity.

[0058] Generative AI can analyze employee feedback and propose specific action plans to improve satisfaction. Generative AI can, for example, analyze employee feedback and propose specific action plans to improve satisfaction. For example, generative AI can propose improvements to the work environment based on feedback data. Generative AI can also adjust the content of career support based on employee feedback. For example, generative AI can analyze employee feedback and adjust mentoring plans as needed. In this way, by analyzing employee feedback and proposing specific action plans, employee satisfaction and productivity can be improved.

[0059] Generative AI can regularly evaluate employees' progress toward achieving their career goals and strengthen support for them toward achieving those goals. Generative AI can, for example, regularly evaluate employees' progress toward achieving their career goals and strengthen support for them toward achieving those goals. For example, generative AI can adjust training programs based on the level of goal achievement. Generative AI can also suggest a review of an employee's career path based on the level of employee career goal achievement. For example, generative AI can evaluate an employee's progress toward achieving their career goals and adjust their career path as necessary. In this way, by regularly evaluating employees' progress toward achieving their career goals and strengthening support, employee satisfaction and productivity can be improved.

[0060] Generative AI can analyze an employee's family structure and lifestyle, and provide career support that takes work-life balance into consideration. Generative AI can, for example, analyze an employee's family structure and lifestyle, and provide career support that takes work-life balance into consideration. For example, generative AI can suggest flexible working hours based on family structure. Generative AI can also provide career support based on an employee's lifestyle. For example, generative AI can analyze an employee's lifestyle habits and hobbies and preferences, and provide career support based on that. In this way, by analyzing an employee's family structure and lifestyle, and providing career support that takes work-life balance into consideration, employee satisfaction and productivity can be improved.

[0061] Generative AI can analyze employees' internal communication data and make suggestions for improving communication. Generative AI can, for example, analyze employees' internal communication data and make suggestions for improving communication. For example, generative AI can evaluate the frequency and quality of communication and suggest improvement measures. Generative AI can also suggest team building activities based on employees' internal communication data. For example, generative AI can analyze employees' communication data and suggest specific action plans for team building. In this way, by analyzing employees' internal communication data and making suggestions for improving communication, it is possible to improve employee satisfaction and productivity.

[0062] Generative AI can estimate employee emotions and predict and address the risk of employee turnover based on those emotions. For example, generative AI uses its emotion estimation function to predict and address the risk of employee turnover based on employee emotions. For example, generative AI can suggest counseling for employees experiencing increasing stress. Generative AI can also suggest career path revisions for employees at high risk of turning over based on employee emotion data. For example, generative AI can suggest specific measures for employees at high risk of turning over based on their emotion scores. In this way, by predicting and addressing the risk of employee turnover based on employee emotions, it is possible to reduce turnover rates and improve employee satisfaction and productivity.

[0063] Generative AI can analyze a company's usage status and automatically propose the optimal subscription plan. Generative AI can, for example, analyze a company's usage status and automatically propose the optimal subscription plan. For example, Generative AI can customize plans based on frequency of use and feature usage. Generative AI can also provide discounts for frequently used features based on a company's usage data. For example, Generative AI can analyze a company's usage status and propose the optimal pricing plan. This makes it possible to improve a company's cost efficiency by analyzing a company's usage status and automatically proposing the optimal subscription plan.

[0064] Generative AI can analyze subscription usage data and optimize pricing plans based on usage frequency and effectiveness. Generative AI can, for example, analyze subscription usage data and optimize pricing plans based on usage frequency and effectiveness. For example, Generative AI can provide discounts for frequently used features. Generative AI can also set pricing based on usage frequency based on usage data. For example, Generative AI can lower fees for infrequently used features. In this way, analyzing subscription usage data and optimizing pricing plans can improve a company's cost efficiency.

[0065] Generative AI can provide subscription plans according to a company's growth stage. For example, Generative AI can prepare plans for startups and plans for large corporations. Generative AI can also propose the optimal plan based on a company's growth stage. For example, Generative AI can customize a plan based on a company's sales growth rate and increase in number of employees. This allows for subscription plans according to a company's growth stage to be offered, making it possible to provide the optimal plan that meets the needs of the company.

[0066] Generative AI can analyze a company's industry characteristics and provide a subscription plan specialized for that industry. Generative AI can, for example, analyze a company's industry characteristics and provide a subscription plan specialized for that industry. For example, generative AI can prepare plans for the manufacturing industry and plans for the service industry. Generative AI can also customize plans based on industry trends and the actions of competitors. For example, generative AI can analyze industry characteristics and propose the optimal plan. By analyzing a company's industry characteristics and providing a subscription plan specialized for that industry, it is possible to provide the optimal plan that meets the company's needs.

[0067] The generation AI can estimate the emotions of company representatives and propose subscription plans based on those emotions. For example, the generation AI uses the emotion estimation function to propose subscription plans based on the emotions of company representatives. For example, if the representative is feeling anxious, the generation AI will propose a plan that gives the representative a sense of security. The generation AI can also propose plans that correspond to the representative's emotions based on the representative's emotion data. For example, the generation AI will propose the optimal subscription plan based on the representative's emotion score. In this way, by estimating the emotions of company representatives and proposing subscription plans based on their emotions, the optimal plan that meets the needs of the company can be provided.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] Generative AI can analyze employees' learning styles and suggest optimal learning methods. For example, if an employee prefers visual learning, generative AI can suggest a training program that makes heavy use of visual content. Alternatively, if an employee prefers practical learning, generative AI can suggest a hands-on workshop. Furthermore, if an employee prefers self-study, generative AI can provide online courses and e-learning resources. This maximizes learning effectiveness and improves employee satisfaction and productivity by providing career support tailored to each employee's learning style.

[0070] Generative AI can estimate an employee's emotions and provide feedback based on their emotions. For example, if an employee feels anxious, generative AI can send an encouraging message. If an employee feels a sense of accomplishment, generative AI can also send a message of praise. Furthermore, if an employee feels stressed, generative AI can suggest relaxation activities. In this way, providing feedback based on an employee's emotions can help maintain employee motivation and improve satisfaction and productivity.

[0071] Generative AI can suggest collaboration with external experts based on an employee's career goals. For example, Generative AI can suggest mentoring sessions with industry experts to improve specific skills. Generative AI can also introduce external training programs when an employee is trying a new field. Generative AI can also suggest collaboration with external consultants when an employee is working on a specific project. This allows employees to utilize external resources that match their career goals to help them improve their skills and develop their careers.

[0072] Generative AI can estimate employees' emotions and suggest team building activities based on their emotions. For example, if team members are feeling stressed, generative AI can suggest team building activities for relaxation. If team members need to be motivated, generative AI can also suggest competitive games or challenges. Furthermore, if team members need to strengthen cooperation, generative AI can suggest cooperative projects or workshops. In this way, by providing team building activities based on employees' emotions, it is possible to strengthen team cohesion and improve satisfaction and productivity.

[0073] Generative AI can suggest internal transfers or project participation based on an employee's career goals. For example, Generative AI can suggest a transfer to a different department so that the employee can acquire new skills. Generative AI can also suggest that the employee participate in a new project so that the employee can gain specific experience. Furthermore, Generative AI can suggest a team leader role so that the employee can improve their leadership skills. In this way, by suggesting internal transfers and project participation that correspond to the employee's career goals, it is possible to support career development and increase satisfaction and productivity.

[0074] Generative AI can estimate employee emotions and conduct emotion-based performance evaluations. For example, if an employee is highly motivated, generative AI can provide positive feedback. Also, if an employee is feeling stressed, generative AI can suggest support to reduce stress. Furthermore, if an employee is feeling anxious, generative AI can provide feedback to reassure them. In this way, performance evaluations based on employee emotions can maintain employee motivation and improve satisfaction and productivity.

[0075] Generative AI can match internal mentors and mentees based on employees' career goals. For example, Generative AI can select the most suitable mentor based on an employee's skill set and career goals. Generative AI can also optimize mentor-mentee relationships based on an employee's past project experience. Furthermore, Generative AI can adjust the mentor-mentee match based on employee feedback. This makes it possible to match mentors and mentees according to employees' career goals, thereby increasing the effectiveness of career support and improving satisfaction and productivity.

[0076] Generative AI can estimate employees' emotions and provide stress management programs based on their emotions. For example, if an employee feels high stress, generative AI can suggest yoga or meditation programs for relaxation. If an employee feels anxious, generative AI can also suggest counseling sessions. Furthermore, if an employee feels fatigued, generative AI can suggest activities for rest. In this way, providing stress management programs based on employees' emotions can improve employee health and satisfaction.

[0077] Generative AI can optimally allocate internal resources based on employees' career goals. For example, generative AI can assemble optimal project teams based on employees' skill sets and career goals. Generative AI can also optimize resource allocation based on employees' past performance data. Furthermore, generative AI can adjust resource allocation based on employee feedback. In this way, allocating resources according to employees' career goals can increase the success rate of projects and improve satisfaction and productivity.

[0078] Generative AI can estimate an employee's emotions and suggest a career path review based on their emotions. For example, if an employee is losing motivation, generative AI can suggest a new career path. Also, if an employee feels a sense of accomplishment, generative AI can suggest further challenges. Furthermore, if an employee is feeling stressed, generative AI can suggest a career path to reduce stress. In this way, by suggesting a career path review based on an employee's emotions, it is possible to improve employee satisfaction and productivity.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: Generative AI analyzes the employee's needs. For example, Generative AI analyzes the employee's past performance data, skill set, career goals, and other input information to generate an individually customized mentoring plan. Generative AI can also analyze the employee's feedback data and adjust the mentoring plan as needed. Step 2: The career support department provides personalized career support based on the needs analyzed by the generative AI. For example, the career support department offers training programs for skill development, career path suggestions, and action plans for goal achievement. The career support department can also provide regular feedback and evaluations to improve employee satisfaction. Step 3: The subscription manager manages the program on a subscription basis. For example, the subscription manager allows companies to use the program for only as long as they need it, minimizing the costs of employee career support. The subscription manager can also automatically manage subscriptions and renewals.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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]

[0148] 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. Generative AI that analyzes employee needs using generative AI, A career support unit that provides personalized career support based on the needs analyzed by the generation AI; A subscription management unit that manages programs on a subscription basis. A system characterized by:

2. The generated AI is Estimate employee emotions in real time and provide career support based on those emotions 2. The system of claim 1.

3. The generated AI is Analyzing employees' hobbies and interests and reflecting them in career support 2. The system of claim 1.

4. The generated AI is Analyzes company usage and automatically suggests the most suitable subscription plan 2. The system of claim 1.

5. The generated AI is Analyze employees' internal networks and automatically match them with the most suitable mentors 2. The system of claim 1.

6. The generated AI is Analyzing employee health data and providing career support according to health status 2. The system of claim 1.

7. The generated AI is Analyze subscription usage data and optimize pricing plans based on frequency of use and effectiveness 2. The system of claim 1.

8. The generated AI is Estimate the sentiment of company representatives and suggest subscription plans based on said sentiment 2. The system of claim 1.

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