System

Abstract

An object of the system according to the embodiment is to allow a user to analyze his / her job history and skill set and find a next necessary skill and qualification.SOLUTION: A system includes a carrier inventory part, a carrier up support part, and a position development support part. The carrier inventory analyzes the user's work experience and skill set. The carrier upgrading support section proposes next necessary skills and qualifications based on the job career and skill set analyzed by the carrier inventory section. The position exploitation support unit proposes suitable duties and positions based on the skills and qualifications proposed by the career-raising support unit.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 have had the problem of making it difficult for users to effectively analyze their own work history and skill sets and find the next skills and qualifications they need.

[0005] The system according to the embodiment aims to enable users to analyze their own work history and skill set and find the next skills and qualifications they need. [Means for solving the problem]

[0006] The system according to the embodiment includes a career inventory unit, a career advancement support unit, and a position development support unit. The career inventory unit analyzes the user's work history and skill set. The career advancement support unit proposes the next skills and qualifications required based on the work history and skill set analyzed by the career inventory unit. The position development support unit proposes suitable jobs and positions based on the skills and qualifications proposed by the career advancement support unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to analyze their own work history and skill set and find the next skills and qualifications they need. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The My Career Log system according to an embodiment of the present invention is a service that provides individuals with an opportunity to think about their careers and improve their career quality. This service supports the self-realization of each individual through career inventory, career advancement, and position development. As a result, the My Career Log system allows users to proactively think about their own careers and achieve high-quality career development.

[0029] The My Career Log system according to the embodiment includes a career inventory unit, a career advancement support unit, and a position development support unit. The career inventory unit analyzes a user's work history and skill set. For example, the career inventory unit uses a generation AI to analyze the work history and skill set entered by the user and organize them in a visually easy-to-understand format. When the user enters their past job descriptions and acquired qualifications, the career inventory unit uses the generation AI to analyze them and display them as graphs or charts. When analyzing the user's work history and skill set, the career inventory unit can also evaluate the success and failure rates of past projects and identify factors for success and failure. The career advancement support unit suggests the next necessary skills and qualifications based on the work history and skill set analyzed by the career inventory unit. For example, when a user enters "I want to become a project manager," the career advancement support unit uses the generation AI to suggest the necessary skills and qualifications, as well as specific learning resources. When analyzing the user's skill set and job description, the career advancement support unit can also suggest optimal learning methods by taking into account their past learning and training history. The position development support unit proposes suitable jobs and positions based on the skills and qualifications proposed by the career advancement support unit. For example, when a user inputs "I want to take on a new challenge," the position development support unit uses a generation AI to list suitable jobs and positions based on the user's skills and experience. Furthermore, when analyzing the user's skill set and work history, the position development support unit can evaluate the degree of match with the company's culture and values ​​and propose suitable companies. This allows the My Career Log system according to the embodiment to comprehensively support the user's career development. For example, the system makes it easier for the user to grasp the overall picture of their career and map out a specific path for career advancement. Furthermore, the system makes it easier for the user to find suitable jobs and positions, enabling them to create specific action plans for self-actualization.

[0030] When analyzing a user's work history and skill set, the career inventory unit can evaluate the success and failure rates of past projects and identify the factors that led to their success and failure. For example, when the career inventory unit inputs data on projects the user has been involved in in the past, the generation AI analyzes the success and failure rates of those projects and identifies the factors that led to their success and failure. For example, it evaluates the progress of projects and the quality of deliverables to extract commonalities between successful projects. The career inventory unit can also identify the factors that led to project failure and suggest improvements that can be applied to future projects. This allows the user to identify the factors that led to their success and failure in past projects, which can be used to help shape their future careers.

[0031] When analyzing a user's work history, the career inventory unit can collect feedback from colleagues and superiors and perform a 360-degree evaluation. For example, when a user inputs their past work history, the generation AI collects feedback from colleagues and superiors and performs a 360-degree evaluation. For example, it analyzes the content of the feedback and identifies the user's strengths and areas for improvement. The career inventory unit can also evaluate the user's work history from multiple angles based on the feedback, allowing for a more accurate career inventory. This enables a more accurate career inventory by evaluating the user's work history from multiple angles.

[0032] When analyzing a user's work history and skill set, the career inventory unit can evaluate the transferability to other industries or occupations and suggest new career paths. For example, when a user inputs their work history and skill set, the generation AI evaluates the transferability to other industries or occupations and suggests new career paths. For example, it suggests switching from a technical position to a management position. The career inventory unit can also suggest specific methods for transferring the user's skill set to other industries or occupations. This allows the user to find a new career path by transferring their skill set to other industries or occupations.

[0033] The career inventory unit can take into account the user's hobbies and interests when analyzing work history and skill sets, and make suggestions for balancing career and private life. For example, when a user inputs their work history and skill set, the career inventory unit's generation AI takes into account hobbies and interests and makes suggestions for balancing career and private life. For example, it can suggest jobs that make use of hobbies. The career inventory unit can also suggest specific ways to balance career and private life based on the user's interests. This allows for a balance between career and private life to be achieved by taking into account the user's hobbies and interests.

[0034] When analyzing a user's skill set and job content, the career advancement support unit can consider past learning and training history to suggest the optimal learning method. For example, when a user inputs their skill set and job content, the generation AI considers their past learning and training history to suggest the optimal learning method. For example, it can suggest online courses or workshops. The career advancement support unit can also suggest the next necessary skills and qualifications based on the user's learning history. This makes it possible to suggest the optimal learning method by considering the user's past learning and training history.

[0035] The career advancement support unit can reflect the latest industry trends and technological trends in real time when creating a user's career advancement plan. For example, when a user creates a career advancement plan, the generation AI collects the latest industry trends and technological trends in real time and reflects them in the plan. For example, it can suggest acquiring new technologies and skills. The career advancement support unit can also suggest specific methods for optimizing the user's career advancement plan based on the latest industry trends. This allows the user's career advancement plan to be optimized by reflecting the latest industry trends and technological trends in real time.

[0036] When analyzing a user's skill set and job description, the career advancement support unit can evaluate the possibility of career advancement in different industries and occupations and suggest crossover careers. For example, when a user inputs their skill set and job description, the career advancement support unit uses a generation AI to evaluate the possibility of career advancement in different industries and occupations and suggest crossover careers. For example, it can suggest a switch from a technical position to a managerial position. The career advancement support unit can also suggest specific methods for transferring the user's skill set to a different industry or occupation. This allows it to suggest new career paths to the user by evaluating the possibility of career advancement in different industries and occupations.

[0037] The career advancement support unit can propose flexible career plans by taking into account the user's life stage when making a career advancement plan. For example, when a user makes a career advancement plan, the generation AI can propose flexible career plans by taking into account the user's life stage. For example, the career advancement support unit can propose remote work to a user who is raising children. The career advancement support unit can also propose flexible working hours to a user who is caring for an elderly relative. This makes it possible to propose flexible career plans by taking into account the user's life stage.

[0038] When analyzing a user's skill set and work history, the position development support unit can evaluate past job change history and the success rate of job changes to suggest the most suitable position. For example, when a user inputs their skill set and work history, the position development support unit uses a generation AI to analyze past job change history and the success rate of job changes to suggest the most suitable position. For example, it can suggest a new job based on past success stories. The position development support unit can also identify and suggest positions with a high success rate based on the user's job change history. This makes it possible to suggest the most suitable position by evaluating the user's past job change history and the success rate of job changes.

[0039] When analyzing a user's skill set and work history, the position development support unit evaluates the degree of match with a company's culture and values ​​and can suggest suitable companies. For example, when a user inputs their skill set and work history, the generation AI evaluates the degree of match with the company's culture and values ​​and suggests suitable companies. For example, it compares the company's mission and vision with the user's values. The position development support unit can also identify and suggest companies that are best suited to the user based on the company's culture and values. This allows it to suggest suitable companies to the user by evaluating the degree of match with the company's culture and values.

[0040] When analyzing a user's skill set and work history, the position development support unit can evaluate the possibility of remote work or freelancing and suggest new work styles. For example, when a user inputs their skill set and work history, the position development support unit uses a generation AI to evaluate the possibility of remote work or freelancing and suggest new work styles. For example, it can suggest jobs suitable for remote work. The position development support unit can also suggest freelance work styles based on the user's skill set. This allows it to suggest new work styles to the user by evaluating the possibility of remote work or freelancing.

[0041] When providing support for position development, the position development support unit can analyze the user's networking activities and suggest ways to utilize the optimal network of contacts. For example, when the user inputs their networking activities, the position development support unit has the generation AI analyze the activities and suggest ways to utilize the optimal network of contacts. For example, it can suggest connections with experts in a specific industry. The position development support unit can also suggest effective ways to utilize network of contacts based on the user's networking activities. In this way, by analyzing the user's networking activities, it can suggest ways to utilize the optimal network of contacts.

[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] When analyzing a user's work history and skill set, the career inventory unit can take the user's hobbies and interests into consideration and make suggestions for balancing their career and private life. For example, when a user inputs their work history and skill set, the generation AI considers their hobbies and interests and makes suggestions for balancing their career and private life. It can also suggest jobs that utilize the user's hobbies, or suggest specific ways to balance their career and private life based on the user's interests. This allows the user to balance their career and private life by taking their hobbies and interests into consideration.

[0044] When analyzing a user's skill set and job description, the career advancement support unit can evaluate the possibility of career advancement in different industries and occupations and suggest crossover careers. For example, when a user inputs their skill set and job description, the generation AI evaluates the possibility of career advancement in different industries and occupations and suggests crossover careers. It can also suggest a transition from a technical position to a managerial position, or suggest specific methods for transferring the user's skill set to a different industry or occupation. This allows the system to suggest new career paths to the user by evaluating the possibility of career advancement in different industries and occupations.

[0045] When analyzing a user's skill set and work history, the Position Development Support Department can evaluate the possibility of remote work or freelancing and suggest new work styles. For example, when a user inputs their skill set and work history, the generation AI evaluates the possibility of remote work or freelancing and suggests new work styles. It can also suggest jobs suitable for remote work and freelance work based on the user's skill set. This allows it to suggest new work styles to users by evaluating the possibility of remote work or freelancing.

[0046] When analyzing a user's work history and skill set, the career inventory unit can evaluate the transferability to other industries or occupations and suggest new career paths. For example, when a user inputs their work history and skill set, the generation AI evaluates the transferability to other industries or occupations and suggests new career paths. It can also suggest a transition from a technical position to a management position, or suggest specific ways to transfer the user's skill set to other industries or occupations. This allows the user to find a new career path by transferring their skill set to other industries or occupations.

[0047] The career advancement support unit can propose flexible career plans by taking into account the user's life stage when making a career advancement plan. For example, when a user makes a career advancement plan, the generation AI can propose flexible career plans by taking into account the user's life stage. It can also propose remote work to users who are raising children, and flexible working hours to users who are caring for elderly relatives. This makes it possible to propose flexible career plans by taking into account the user's life stage.

[0048] When supporting position development, the position development support unit can analyze the user's networking activities and suggest ways to utilize the optimal network. For example, when a user inputs their networking activities, the generation AI analyzes those activities and suggests ways to utilize the optimal network. It can also suggest connections with experts in specific industries and suggest effective ways to utilize networks based on the user's networking activities. In this way, by analyzing the user's networking activities, it can suggest ways to utilize the optimal network.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The career inventory section analyzes the user's work history and skill set. For example, the generation AI analyzes the work history and skill set entered by the user and organizes them in a visually easy-to-understand format. In addition, when the user enters their past job descriptions and acquired qualifications, the generation AI can analyze them and display them as graphs and charts. It can also evaluate the success and failure rates of past projects and identify the factors that led to their success and failure. Step 2: The Career Advancement Support Department suggests the next necessary skills and qualifications based on the work history and skill set analyzed by the Career Inventory Department. For example, if a user inputs "I want to become a project manager," the generative AI will suggest the skills and qualifications needed to achieve that goal, as well as specific learning resources. It can also suggest the optimal learning method based on past learning and training history. Step 3: The Position Development Support Department suggests suitable jobs and positions based on the skills and qualifications proposed by the Career Advancement Support Department. For example, if a user inputs "I want to take on a new challenge," the Generative AI will list suitable jobs and positions based on the user's skills and experience. It can also evaluate the degree of match with the company's culture and values ​​and suggest suitable companies.

[0051] (Example 2) The My Career Log system according to an embodiment of the present invention is a service that provides individuals with an opportunity to think about their careers and improve their career quality. This service supports the self-realization of each individual through career inventory, career advancement, and position development. As a result, the My Career Log system allows users to proactively think about their own careers and achieve high-quality career development.

[0052] The My Career Log system according to the embodiment includes a career inventory unit, a career advancement support unit, and a position development support unit. The career inventory unit analyzes a user's work history and skill set. For example, the career inventory unit uses a generation AI to analyze the work history and skill set entered by the user and organize them in a visually easy-to-understand format. When the user enters their past job descriptions and acquired qualifications, the career inventory unit uses the generation AI to analyze them and display them as graphs or charts. When analyzing the user's work history and skill set, the career inventory unit can also evaluate the success and failure rates of past projects and identify factors for success and failure. The career advancement support unit suggests the next necessary skills and qualifications based on the work history and skill set analyzed by the career inventory unit. For example, when a user enters "I want to become a project manager," the career advancement support unit uses the generation AI to suggest the necessary skills and qualifications, as well as specific learning resources. When analyzing the user's skill set and job description, the career advancement support unit can also suggest optimal learning methods by taking into account their past learning and training history. The position development support unit proposes suitable jobs and positions based on the skills and qualifications proposed by the career advancement support unit. For example, when a user inputs "I want to take on a new challenge," the position development support unit uses a generation AI to list suitable jobs and positions based on the user's skills and experience. Furthermore, when analyzing the user's skill set and work history, the position development support unit can evaluate the degree of match with the company's culture and values ​​and propose suitable companies. This allows the My Career Log system according to the embodiment to comprehensively support the user's career development. For example, the system makes it easier for the user to grasp the overall picture of their career and map out a specific path for career advancement. Furthermore, the system makes it easier for the user to find suitable jobs and positions, enabling them to create specific action plans for self-actualization.

[0053] When analyzing a user's work history and skill set, the career inventory unit can evaluate the success and failure rates of past projects and identify the factors that led to their success and failure. For example, when the career inventory unit inputs data on projects the user has been involved in in the past, the generation AI analyzes the success and failure rates of those projects and identifies the factors that led to their success and failure. For example, it evaluates the progress of projects and the quality of deliverables to extract commonalities between successful projects. The career inventory unit can also identify the factors that led to project failure and suggest improvements that can be applied to future projects. This allows the user to identify the factors that led to their success and failure in past projects, which can be used to help shape their future careers.

[0054] When analyzing a user's work history, the career inventory unit can collect feedback from colleagues and superiors and perform a 360-degree evaluation. For example, when a user inputs their past work history, the generation AI collects feedback from colleagues and superiors and performs a 360-degree evaluation. For example, it analyzes the content of the feedback and identifies the user's strengths and areas for improvement. The career inventory unit can also evaluate the user's work history from multiple angles based on the feedback, allowing for a more accurate career inventory. This enables a more accurate career inventory by evaluating the user's work history from multiple angles.

[0055] The career inventory unit uses the emotion estimation function to analyze how the user felt about their past work experiences and can emphasize positive experiences. For example, when a user inputs their past work experiences, the generation AI uses the emotion estimation function to analyze the emotions they felt about those experiences. For example, it can emphasize experiences that they felt positive about and present them as the user's strengths. The career inventory unit can also analyze experiences that they felt negative about and suggest areas for improvement. This can increase self-esteem by emphasizing the user's positive work experiences.

[0056] When analyzing a user's work history and skill set, the career inventory unit can evaluate the transferability to other industries or occupations and suggest new career paths. For example, when a user inputs their work history and skill set, the generation AI evaluates the transferability to other industries or occupations and suggests new career paths. For example, it suggests switching from a technical position to a management position. The career inventory unit can also suggest specific methods for transferring the user's skill set to other industries or occupations. This allows the user to find a new career path by transferring their skill set to other industries or occupations.

[0057] The career inventory unit can take into account the user's hobbies and interests when analyzing work history and skill sets, and make suggestions for balancing career and private life. For example, when a user inputs their work history and skill set, the career inventory unit's generation AI takes into account hobbies and interests and makes suggestions for balancing career and private life. For example, it can suggest jobs that make use of hobbies. The career inventory unit can also suggest specific ways to balance career and private life based on the user's interests. This allows for a balance between career and private life to be achieved by taking into account the user's hobbies and interests.

[0058] The career inventory unit uses the emotion estimation function to monitor the user's emotions in real time when taking a career inventory and can make relaxation suggestions to reduce stress. For example, when a user takes a career inventory, the career inventory unit uses the emotion estimation function to monitor the user's emotions in real time. For example, if the user is feeling stressed, it can suggest relaxation methods. The career inventory unit can also analyze the user's emotions and suggest specific relaxation methods to reduce stress. This allows the user to take a smooth career inventory by making relaxation suggestions to reduce stress.

[0059] When analyzing a user's skill set and job content, the career advancement support unit can consider past learning and training history to suggest the optimal learning method. For example, when a user inputs their skill set and job content, the generation AI considers their past learning and training history to suggest the optimal learning method. For example, it can suggest online courses or workshops. The career advancement support unit can also suggest the next necessary skills and qualifications based on the user's learning history. This makes it possible to suggest the optimal learning method by considering the user's past learning and training history.

[0060] The career advancement support unit can reflect the latest industry trends and technological trends in real time when creating a user's career advancement plan. For example, when a user creates a career advancement plan, the generation AI collects the latest industry trends and technological trends in real time and reflects them in the plan. For example, it can suggest acquiring new technologies and skills. The career advancement support unit can also suggest specific methods for optimizing the user's career advancement plan based on the latest industry trends. This allows the user's career advancement plan to be optimized by reflecting the latest industry trends and technological trends in real time.

[0061] The career advancement support unit can use the emotion estimation function to analyze the motivation of the user when making a career advancement plan and suggest specific actions to maintain motivation. For example, when the user makes a career advancement plan, the career advancement support unit uses the emotion estimation function to analyze the motivation of the user through the generation AI. For example, if motivation is declining, an encouraging message is displayed. The career advancement support unit can also suggest specific actions to maintain the user's motivation. This makes it possible to support the execution of the career advancement plan by suggesting specific actions to maintain the user's motivation.

[0062] When analyzing a user's skill set and job description, the career advancement support unit can evaluate the possibility of career advancement in different industries and occupations and suggest crossover careers. For example, when a user inputs their skill set and job description, the career advancement support unit uses a generation AI to evaluate the possibility of career advancement in different industries and occupations and suggest crossover careers. For example, it can suggest a switch from a technical position to a managerial position. The career advancement support unit can also suggest specific methods for transferring the user's skill set to a different industry or occupation. This allows it to suggest new career paths to the user by evaluating the possibility of career advancement in different industries and occupations.

[0063] The career advancement support unit can propose flexible career plans by taking into account the user's life stage when making a career advancement plan. For example, when a user makes a career advancement plan, the generation AI can propose flexible career plans by taking into account the user's life stage. For example, the career advancement support unit can propose remote work to a user who is raising children. The career advancement support unit can also propose flexible working hours to a user who is caring for an elderly relative. This makes it possible to propose flexible career plans by taking into account the user's life stage.

[0064] The career advancement support unit can use the emotion estimation function to monitor the user's emotions in real time when making a career advancement plan and provide feedback to elicit positive emotions. For example, when a user makes a career advancement plan, the generation AI uses the emotion estimation function to monitor the user's emotions in real time. For example, an encouraging message to elicit positive emotions is displayed. The career advancement support unit can also analyze the user's emotions and provide specific feedback to elicit positive emotions. This can support the execution of the career advancement plan by providing feedback to elicit positive emotions from the user.

[0065] When analyzing a user's skill set and work history, the position development support unit can evaluate past job change history and the success rate of job changes to suggest the most suitable position. For example, when a user inputs their skill set and work history, the position development support unit uses a generation AI to analyze past job change history and the success rate of job changes to suggest the most suitable position. For example, it can suggest a new job based on past success stories. The position development support unit can also identify and suggest positions with a high success rate based on the user's job change history. This makes it possible to suggest the most suitable position by evaluating the user's past job change history and the success rate of job changes.

[0066] When analyzing a user's skill set and work history, the position development support unit evaluates the degree of match with a company's culture and values ​​and can suggest suitable companies. For example, when a user inputs their skill set and work history, the generation AI evaluates the degree of match with the company's culture and values ​​and suggests suitable companies. For example, it compares the company's mission and vision with the user's values. The position development support unit can also identify and suggest companies that are best suited to the user based on the company's culture and values. This allows it to suggest suitable companies to the user by evaluating the degree of match with the company's culture and values.

[0067] The position development support unit uses the emotion estimation function to analyze the emotions of users when searching for a new position and can suggest specific actions to elicit positive emotions. For example, when a user searches for a new position, the position development support unit uses the emotion estimation function to analyze the emotions of the generation AI. For example, it displays an encouraging message to elicit positive emotions. The position development support unit can also suggest specific actions to elicit positive emotions based on the user's emotions. This makes it possible to support the search for a new position by suggesting specific actions to elicit positive emotions from the user.

[0068] When analyzing a user's skill set and work history, the position development support unit can evaluate the possibility of remote work or freelancing and suggest new work styles. For example, when a user inputs their skill set and work history, the position development support unit uses a generation AI to evaluate the possibility of remote work or freelancing and suggest new work styles. For example, it can suggest jobs suitable for remote work. The position development support unit can also suggest freelance work styles based on the user's skill set. This allows it to suggest new work styles to the user by evaluating the possibility of remote work or freelancing.

[0069] When providing support for position development, the position development support unit can analyze the user's networking activities and suggest ways to utilize the optimal network of contacts. For example, when the user inputs their networking activities, the position development support unit has the generation AI analyze the activities and suggest ways to utilize the optimal network of contacts. For example, it can suggest connections with experts in a specific industry. The position development support unit can also suggest effective ways to utilize network of contacts based on the user's networking activities. In this way, by analyzing the user's networking activities, it can suggest ways to utilize the optimal network of contacts.

[0070] The position development support unit uses the emotion estimation function to monitor the user's emotions in real time when searching for a new position and can suggest relaxation methods to reduce stress. For example, when a user searches for a new position, the position development support unit uses the emotion estimation function to monitor the user's emotions in real time. For example, if the user is feeling stressed, it can suggest relaxation methods. The position development support unit can also analyze the user's emotions and suggest specific relaxation methods to reduce stress. This allows the user to smoothly search for a new position by suggesting relaxation methods to reduce stress.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] When analyzing a user's work history and skill set, the career inventory unit can take the user's hobbies and interests into consideration and make suggestions for balancing their career and private life. For example, when a user inputs their work history and skill set, the generation AI considers their hobbies and interests and makes suggestions for balancing their career and private life. It can also suggest jobs that utilize the user's hobbies, or suggest specific ways to balance their career and private life based on the user's interests. This allows the user to balance their career and private life by taking their hobbies and interests into consideration.

[0073] When analyzing a user's skill set and job description, the career advancement support unit can evaluate the possibility of career advancement in different industries and occupations and suggest crossover careers. For example, when a user inputs their skill set and job description, the generation AI evaluates the possibility of career advancement in different industries and occupations and suggests crossover careers. It can also suggest a transition from a technical position to a managerial position, or suggest specific methods for transferring the user's skill set to a different industry or occupation. This allows the system to suggest new career paths to the user by evaluating the possibility of career advancement in different industries and occupations.

[0074] When analyzing a user's skill set and work history, the Position Development Support Department can evaluate the possibility of remote work or freelancing and suggest new work styles. For example, when a user inputs their skill set and work history, the generation AI evaluates the possibility of remote work or freelancing and suggests new work styles. It can also suggest jobs suitable for remote work and freelance work based on the user's skill set. This allows it to suggest new work styles to users by evaluating the possibility of remote work or freelancing.

[0075] The career inventory unit uses the emotion estimation function to analyze how the user felt about their past work experiences and highlight positive experiences. For example, when a user inputs their past work experiences, the generation AI uses the emotion estimation function to analyze their feelings about those experiences. It can highlight experiences that they felt positive about and present them as strengths for the user, or it can analyze experiences that they felt negative about and suggest areas for improvement. This can increase self-esteem by highlighting the user's positive work experiences.

[0076] The career advancement support unit can use the emotion estimation function to analyze the user's motivation when making a career advancement plan and suggest specific actions to maintain motivation. For example, when a user makes a career advancement plan, the generation AI uses the emotion estimation function to analyze the user's motivation. If motivation is low, it can display an encouraging message or suggest specific actions to maintain the user's motivation. This makes it possible to support the execution of the career advancement plan by suggesting specific actions to maintain the user's motivation.

[0077] The position development support unit uses the emotion estimation function to analyze the emotions of users when searching for a new position and can suggest specific actions to elicit positive emotions. For example, when a user searches for a new position, the generation AI uses the emotion estimation function to analyze the user's emotions. It can also display encouraging messages to elicit positive emotions and suggest specific actions to elicit positive emotions based on the user's emotions. This allows it to support the user's search for a new position by suggesting specific actions to elicit positive emotions.

[0078] When analyzing a user's work history and skill set, the career inventory unit can evaluate the transferability to other industries or occupations and suggest new career paths. For example, when a user inputs their work history and skill set, the generation AI evaluates the transferability to other industries or occupations and suggests new career paths. It can also suggest a transition from a technical position to a management position, or suggest specific ways to transfer the user's skill set to other industries or occupations. This allows the user to find a new career path by transferring their skill set to other industries or occupations.

[0079] The career advancement support unit can propose flexible career plans by taking into account the user's life stage when making a career advancement plan. For example, when a user makes a career advancement plan, the generation AI can propose flexible career plans by taking into account the user's life stage. It can also propose remote work to users who are raising children, and flexible working hours to users who are caring for elderly relatives. This makes it possible to propose flexible career plans by taking into account the user's life stage.

[0080] The position development support unit uses the emotion estimation function to monitor the user's emotions in real time as they search for a new position and can suggest relaxation methods to reduce stress. For example, when a user searches for a new position, the generation AI uses the emotion estimation function to monitor their emotions in real time. It can also suggest relaxation methods if the user is feeling stressed, or analyze the user's emotions and suggest specific relaxation methods to reduce stress. This makes it easier for the user to search for a new position by suggesting relaxation methods to reduce stress.

[0081] When supporting position development, the position development support unit can analyze the user's networking activities and suggest ways to utilize the optimal network. For example, when a user inputs their networking activities, the generation AI analyzes those activities and suggests ways to utilize the optimal network. It can also suggest connections with experts in specific industries and suggest effective ways to utilize networks based on the user's networking activities. In this way, by analyzing the user's networking activities, it can suggest ways to utilize the optimal network.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The career inventory section analyzes the user's work history and skill set. For example, the generation AI analyzes the work history and skill set entered by the user and organizes them in a visually easy-to-understand format. In addition, when the user enters their past job descriptions and acquired qualifications, the generation AI can analyze them and display them as graphs and charts. It can also evaluate the success and failure rates of past projects and identify the factors that led to their success and failure. Step 2: The Career Advancement Support Department suggests the next necessary skills and qualifications based on the work history and skill set analyzed by the Career Inventory Department. For example, if a user inputs "I want to become a project manager," the generative AI will suggest the skills and qualifications needed to achieve that goal, as well as specific learning resources. It can also suggest the optimal learning method based on past learning and training history. Step 3: The Position Development Support Department suggests suitable jobs and positions based on the skills and qualifications proposed by the Career Advancement Support Department. For example, if a user inputs "I want to take on a new challenge," the Generative AI will list suitable jobs and positions based on the user's skills and experience. It can also evaluate the degree of match with the company's culture and values ​​and suggest suitable companies.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

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

[0120] The 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.

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

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A career inventory department that analyzes users' work history and skill sets, a career advancement support unit that proposes next necessary skills and qualifications based on the work history and the skill set analyzed by the career inventory unit; a position development support unit that proposes suitable jobs and positions based on the skills and qualifications proposed by the career advancement support unit. A system characterized by:

2. The carrier inventory unit When analyzing the user's work history and skill set, evaluate the success and failure rates of past projects and identify factors that contribute to success and failure.

2. The system of claim 1.

3. The carrier inventory unit When analyzing the user's work history and skill set, the system evaluates the transferability of the skills to other industries and occupations and suggests new career paths.

2. The system of claim 1.

4. The career advancement support department When analyzing the user's skill set and job content, the system takes into account past learning and training history and suggests optimal learning methods.

2. The system of claim 1.

5. The position development support unit When analyzing the user's skill set and work history, the system evaluates the user's job history and the success rate of job changes, and suggests the most suitable position.

2. The system of claim 1.

6. The carrier inventory unit Analyze how the user felt about their past work experiences and highlight positive experiences 2. The system of claim 1.

7. The career advancement support department Analyzing the motivation of the user when making a career advancement plan and proposing specific actions to maintain the motivation 2. The system of claim 1.

8. The position development support unit Analyze the user's emotions when searching for a new position and suggest specific actions to elicit positive emotions.

2. The system of claim 1.

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