System

Abstract

An object of the system according to the embodiment is to allow a retired person to find a new occupation by utilizing his / her own skill and experience.SOLUTION: A system includes a skill analysis part, an occupation proposal part, and a company matching part. A skill analysis part analyzes the skill or experience of the retired person. The occupation proposal unit proposes a new occupation based on the skill or experience analyzed by the skill analysis unit. The company matching unit searches for a company related to the occupation proposed by the occupation proposal unit, and performs matching between the retired person and the company.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has made it difficult for retirees to find new jobs that utilize their skills and experience.

[0005] The system according to the embodiment aims to help retirees find new jobs by utilizing their skills and experience. [Means for solving the problem]

[0006] The system according to the embodiment includes a skill analysis unit, a career proposal unit, and a company matching unit. The skill analysis unit analyzes the skills or experience of retirees. The career proposal unit proposes new careers based on the skills or experience analyzed by the skill analysis unit. The company matching unit searches for companies related to the careers proposed by the career proposal unit and matches retirees with companies. [Effects of the Invention]

[0007] The system according to the embodiment allows retired people to find new jobs by utilizing their skills and experience. [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 career suggestion and company matching system according to an embodiment of the present invention analyzes the skills and experience of retirees, and uses a generation AI to suggest optimal careers and match them with companies. As a result, the career suggestion and company matching system can help retirees make use of their skills and experience to smoothly transition to new careers.

[0029] A career suggestion and company matching system according to an embodiment includes a skill analysis unit, a career suggestion unit, and a company matching unit. The skill analysis unit analyzes the skills or experience of a retiree. For example, detailed data on projects the retiree has previously worked on is collected, and the generation AI analyzes the data. The skill analysis unit can also enable the generation AI to evaluate the retiree's contribution based on detailed records of the work content. The skill analysis unit can also specifically analyze the results of the work the retiree has previously worked on, and the generation AI can evaluate those results. For example, the generation AI quantifies and evaluates the project's scale, duration, and achieved results. The career suggestion unit proposes new careers based on the skills or experience analyzed by the skill analysis unit. For example, for a retiree with quality control experience, the generation AI can propose careers such as consultant, trainer, or quality control system developer. The career suggestion unit can also customize career suggestions based on the retiree's preferences and conditions. For example, if a retiree inputs preferences such as "I want to work only three days a week" or "I want to work within commuting distance of my home," the generation AI can suggest careers that match those preferences. The company matching unit searches for companies related to the occupations proposed by the career proposal unit and matches retirees with companies. For example, the generation AI analyzes companies' job information and selects companies that best suit the retiree's skills and experience. The company matching unit can also analyze corporate cultures and values ​​and prioritize matching with companies that match the retiree's values. In this way, the career proposal and company matching system according to the embodiment can support retirees in smoothly transitioning to a new occupation by utilizing their skills and experience. For example, it provides retirees with information on training and skill development related to the new occupation. Furthermore, when a retiree inputs a question or inquiry about a career, the generation AI provides an appropriate answer to the question or inquiry.

[0030] The skill analysis unit can perform a detailed analysis of the retiree's past projects or work content, and quantify and evaluate their achievements or contributions. For example, the skill analysis unit collects detailed data on projects in which the retiree was involved in the past, and the generation AI analyzes that data. For example, it quantifies and evaluates the project's scale, duration, and achieved results. The skill analysis unit also uses detailed records of the work content to have the generation AI evaluate the retiree's contributions. For example, it quantifies the extent to which the retiree contributed to improving work efficiency and reducing costs. The skill analysis unit also specifically analyzes the results of work that the retiree was responsible for in the past, and the generation AI evaluates those results. For example, it quantifies the extent to which the retiree contributed to improving quality control or developing new products. This makes it possible to quantify and evaluate the retiree's specific achievements and contributions.

[0031] When analyzing the skills or experience of a retiree, the skill analysis unit also incorporates recommendation letters or evaluation comments from colleagues or superiors, allowing for a more multifaceted analysis. For example, the skill analysis unit collects recommendation letters and evaluation comments from the retiree's colleagues and superiors, and the generation AI analyzes them. For example, it performs text analysis of the content of the recommendation letters and reflects this in the evaluation of skills and experience. The skill analysis unit also allows the generation AI to evaluate the retiree's skills and experience from multiple perspectives based on feedback from colleagues and superiors. For example, it analyzes the content of the feedback and identifies the strengths and weaknesses of the skills. The skill analysis unit also analyzes recommendation letters and evaluation comments, allowing the generation AI to more accurately evaluate the retiree's skills and experience. For example, it evaluates skills based on specific episodes and examples. This allows for a multifaceted evaluation of the retiree's skills and experience.

[0032] The skill analysis unit also analyzes non-occupational experience, such as the hobbies or volunteer activities of retired individuals, making it possible to extract a wide range of skill sets. For example, the skill analysis unit collects information about retired individuals' hobbies and volunteer activities, and the generation AI analyzes this information. For example, it extracts skills acquired through hobbies and volunteer activities. The skill analysis unit also analyzes non-occupational experience, and the generation AI evaluates the retired individual's wide range of skill sets. For example, it evaluates leadership and communication skills acquired through hobbies and volunteer activities. The skill analysis unit also analyzes information about retired individuals' hobbies and volunteer activities, and the generation AI extracts skill sets based on this information. For example, it evaluates the techniques and knowledge acquired through hobbies and volunteer activities. This makes it possible to include non-occupational experience in the analysis and extract a wide range of skill sets.

[0033] When analyzing the skills or experience of a retiree, the skill analysis unit can perform a comparative analysis with other retirees to identify common skills or experience. The skill analysis unit, for example, performs a comparative analysis with the skills and experience of other retirees to identify common skills and experience. For example, the comparison is made based on data of retirees who worked in the same industry or occupation. The skill analysis unit also compares the skills and experience of a retiree with other retirees to extract common skills and experience. For example, the comparison is made based on data related to the same job content or project. The skill analysis unit also performs a comparative analysis with other retirees to identify common skills and experience. For example, commonalities are found based on data of retirees who worked in the same industry or occupation. This makes it possible to perform a comparative analysis with other retirees to identify common skills and experience.

[0034] The career suggestion unit can present past success stories or career paths for the proposed career, allowing the retiree to envision a specific future. The career suggestion unit, for example, presents past success stories for the proposed career, allowing the retiree to envision a specific future. For example, it can introduce the career paths of people who have been successful in the same career. The career suggestion unit can also perform a detailed analysis of the skills and qualifications required for the proposed career, allowing the retiree to envision a specific future. For example, it can list the skills and qualifications required for the career and explain how to obtain them. The career suggestion unit can also present past success stories or career paths for the proposed career, allowing the retiree to envision a specific future. For example, it can introduce interviews and personal stories of successful people. This allows the retiree to envision a specific future.

[0035] The career proposal unit can analyze in detail the skills or qualifications required for the proposed career and provide a training plan to complement the skills that the retiree lacks. The career proposal unit, for example, analyzes in detail the skills and qualifications required for the proposed career and provides a training plan to complement the skills that the retiree lacks. For example, it introduces online courses and workshops. The career proposal unit also provides a training plan to enable the retiree to acquire the skills and qualifications required for the proposed career. For example, it proposes teaching materials and training programs for improving skills. The career proposal unit also analyzes in detail the skills and qualifications required for the proposed career and provides a training plan to complement the skills that the retiree lacks. For example, it proposes instruction and coaching by experts. In this way, it is possible to provide a training plan to complement the skills that the retiree lacks.

[0036] The career proposal unit can propose various working styles for the proposed occupation, including flexible working styles such as remote work or freelance. The career proposal unit, for example, proposes various working styles for the proposed occupation, including flexible working styles such as remote work or freelance. For example, it introduces occupations that allow working from home or occupations that allow working as a freelancer. The career proposal unit also proposes flexible working styles such as remote work or freelance, depending on the working style desired by the retired person. For example, it proposes occupations that allow working only a few days a week or occupations that allow working on a project basis. The career proposal unit also proposes various working styles for the proposed occupation, including flexible working styles such as remote work or freelance. For example, it explains the advantages and disadvantages of remote work and proposes suitable occupations. This makes it possible to propose flexible working styles such as remote work or freelance.

[0037] The career proposal unit can propose careers that allow retirees to take on new challenges, including careers in different industries or fields, compared to the careers it proposes. The career proposal unit, for example, proposes careers that allow retirees to take on new challenges, including careers in different industries or fields, compared to the careers it proposes. For example, it introduces careers in completely different fields or careers that require new skills. The career proposal unit also proposes careers in different industries or fields, so that retirees can take on new challenges. For example, it proposes careers that allow retirees to be active in a new field while making use of their previous experience. The career proposal unit also proposes careers that allow retirees to take on new challenges, including careers in different industries or fields, compared to the careers it proposes. For example, it introduces careers that support career changes in a different industry. In this way, it is possible to propose careers that allow retirees to take on new challenges.

[0038] The company matching department analyzes not only the recruitment information of companies but also the company culture or values, and is able to prioritize matching with companies that match the values ​​of retirees. The company matching department, for example, analyzes not only the recruitment information of companies but also the company culture and values, and prioritizes matching with companies that match the values ​​of retirees. For example, it analyzes the mission and vision of the company. The company matching department also analyzes the company culture and values ​​to select companies that match the values ​​of retirees. For example, it analyzes the internal culture and working style of the company to propose companies that suit the preferences of retirees. The company matching department also analyzes not only the recruitment information of companies but also the company culture and values, and prioritizes matching with companies that match the values ​​of retirees. For example, it analyzes the values ​​and philosophy of the company to select companies that match the values ​​of retirees. This makes it possible to prioritize matching with companies that match the values ​​of retirees.

[0039] When matching with companies, the company matching unit can refer to past cases where similar matching was successful and perform matching with a high success rate. For example, when matching with companies, the company matching unit can refer to past cases where similar matching was successful and perform matching with a high success rate. For example, it analyzes past successful cases and performs matching under similar conditions. Furthermore, the company matching unit uses a generation AI to perform matching with a high success rate based on past successful matching cases. For example, it analyzes data on successful cases and performs matching under similar conditions. Furthermore, when matching with companies, the company matching unit can refer to past cases where similar matching was successful and perform matching with a high success rate. For example, it performs matching under similar conditions based on data on successful cases. This allows for matching with a high success rate.

[0040] When matching with companies, the Company Matching Department can also propose matching with a trial period, such as an internship or short-term project. For example, when matching with companies, the Company Matching Department can also propose matching with a trial period, such as an internship or short-term project. For example, compatibility with the company can be confirmed through short-term work experience. In addition, when matching retirees with companies, the Company Matching Department can propose internships or short-term projects. For example, compatibility with the company can be confirmed through short-term work experience. In addition, when matching with companies, the Company Matching Department can also propose matching with a trial period, such as an internship or short-term project. For example, compatibility with the company can be confirmed through short-term work experience. This makes it possible to propose matching with a trial period.

[0041] The company matching department can provide a platform that allows retirees to be matched with multiple companies simultaneously when matching with companies. For example, the company matching department provides a platform that allows retirees to be matched with multiple companies simultaneously when matching with companies. For example, it can display job information from multiple companies all at once, increasing the number of options. The company matching department also provides a platform that allows retirees to be matched with multiple companies simultaneously. For example, it can build a system that allows interviews and communication with multiple companies simultaneously. The company matching department also provides a platform that allows retirees to be matched with multiple companies simultaneously when matching with companies. For example, it can display job information from multiple companies all at once, increasing the number of options. This makes it possible to provide a platform that allows retirees to be matched with multiple companies simultaneously.

[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 the skills and experience of retirees, the Skills Analysis Department can also incorporate evaluations of teamwork and leadership in past work. For example, it can analyze what role the retiree played as a leader in the past and what kind of cooperative relationships they had with their team members. The Skills Analysis Department can also evaluate leadership and teamwork skills based on successful examples of team projects in which the retiree participated in the past. Furthermore, the Skills Analysis Department can collect specific episodes in which the retiree demonstrated leadership in the past, and the generation AI can analyze and evaluate these. This makes it possible to evaluate the leadership and teamwork skills of retirees from multiple perspectives.

[0044] When analyzing the skills and experience of retirees, the skill analysis unit can also evaluate their stress tolerance and problem-solving ability in past work. For example, it analyzes how the retiree solved difficult situations and problems they faced in the past. The skill analysis unit can also evaluate how the retiree responded to stressful situations they experienced in the past. Furthermore, the skill analysis unit can specifically analyze the approaches the retiree took to solving problems in the past, and the generation AI can evaluate those approaches. This allows for a multifaceted evaluation of the retiree's stress tolerance and problem-solving ability.

[0045] When analyzing the skills and experience of retirees, the Skill Analysis Department can also incorporate evaluations of creativity and innovation in past work. For example, it can analyze what new ideas and projects the retiree has proposed in the past. The Skill Analysis Department can also evaluate creativity and innovation skills based on specific examples of innovations that the retiree has implemented in the past. Furthermore, the Skill Analysis Department can collect specific episodes in which the retiree has proposed creative solutions in the past, and the generative AI can analyze and evaluate these. This makes it possible to evaluate the creativity and innovation skills of retirees from multiple perspectives.

[0046] When analyzing the skills and experience of retirees, the Skills Analysis Department can also evaluate their international experience and intercultural ability in past work. For example, it can analyze the retiree's past experience participating in international projects and working with cross-cultural teams. The Skills Analysis Department can also evaluate the retiree's skills based on specific examples in which the retiree demonstrated intercultural ability in the past. Furthermore, the Skills Analysis Department can specifically analyze the results the retiree achieved in international work in the past, and the generation AI can evaluate those results. This allows for a multifaceted evaluation of the retiree's international experience and intercultural ability.

[0047] The career suggestion department can present past success stories and career paths for the proposed occupation, allowing retirees to visualize a specific future. For example, it can introduce the career paths of people who have been successful in the same occupation. The career suggestion department can also perform a detailed analysis of the skills and qualifications required for the proposed occupation, allowing retirees to visualize a specific future. For example, it can list the skills and qualifications required for the occupation and explain how to obtain them. The career suggestion department can also present past success stories and career paths for the proposed occupation, allowing retirees to visualize a specific future. For example, it can introduce interviews and personal stories of successful people. This allows retirees to visualize a specific future.

[0048] When matching with companies, the company matching department refers to past cases where similar matching was successful and can perform matching with a high success rate. For example, it analyzes past successful cases and performs matching under similar conditions. In addition, the company matching department uses a generation AI to perform matching with a high success rate based on past successful matching cases. For example, it analyzes data from successful cases and performs matching under similar conditions. In addition, when matching with companies, the company matching department refers to past cases where similar matching was successful and can perform matching with a high success rate. For example, it matches under similar conditions based on data from successful cases. This allows for matching with a high success rate.

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

[0050] Step 1: The skill analysis unit analyzes the skills or experience of the retiree. For example, detailed data on projects that the retiree has been involved in in the past is collected, and the generation AI analyzes that data. The skill analysis unit can also use the generation AI to evaluate the retiree's level of contribution based on detailed records of the work content. Furthermore, the skill analysis unit can specifically analyze the results of the work that the retiree has been responsible for in the past, and the generation AI can evaluate those results. For example, the generation AI can quantify and evaluate the scale, duration, and results achieved of the project. Step 2: The career suggestion unit suggests new occupations based on the skills or experience analyzed by the skill analysis unit. For example, for a retiree with experience in quality control, the generation AI might suggest occupations such as consultant, trainer, or quality control system developer. The career suggestion unit can also customize occupation suggestions based on the retiree's preferences and conditions. For example, if a person inputs preferences such as "I want to work only three days a week" or "I want to work within commuting distance from home," the generation AI will suggest occupations that match those preferences. Step 3: The company matching department searches for companies related to the occupations proposed by the career proposal department and matches the retiree with the company. For example, the generation AI analyzes companies' job information and selects the company that best suits the retiree's skills and experience. The company matching department can also analyze the company's culture and values ​​and prioritize matching companies that match the retiree's values.

[0051] (Example 2) The career suggestion and company matching system according to an embodiment of the present invention analyzes the skills and experience of retirees, and uses a generation AI to suggest optimal careers and match them with companies. As a result, the career suggestion and company matching system can help retirees make use of their skills and experience to smoothly transition to new careers.

[0052] A career suggestion and company matching system according to an embodiment includes a skill analysis unit, a career suggestion unit, and a company matching unit. The skill analysis unit analyzes the skills or experience of a retiree. For example, detailed data on projects the retiree has previously worked on is collected, and the generation AI analyzes the data. The skill analysis unit can also enable the generation AI to evaluate the retiree's contribution based on detailed records of the work content. The skill analysis unit can also specifically analyze the results of the work the retiree has previously worked on, and the generation AI can evaluate those results. For example, the generation AI quantifies and evaluates the project's scale, duration, and achieved results. The career suggestion unit proposes new careers based on the skills or experience analyzed by the skill analysis unit. For example, for a retiree with quality control experience, the generation AI can propose careers such as consultant, trainer, or quality control system developer. The career suggestion unit can also customize career suggestions based on the retiree's preferences and conditions. For example, if a retiree inputs preferences such as "I want to work only three days a week" or "I want to work within commuting distance of my home," the generation AI can suggest careers that match those preferences. The company matching unit searches for companies related to the occupations proposed by the career proposal unit and matches retirees with companies. For example, the generation AI analyzes companies' job information and selects companies that best suit the retiree's skills and experience. The company matching unit can also analyze corporate cultures and values ​​and prioritize matching with companies that match the retiree's values. In this way, the career proposal and company matching system according to the embodiment can support retirees in smoothly transitioning to a new occupation by utilizing their skills and experience. For example, it provides retirees with information on training and skill development related to the new occupation. Furthermore, when a retiree inputs a question or inquiry about a career, the generation AI provides an appropriate answer to the question or inquiry.

[0053] The skill analysis unit can perform a detailed analysis of the retiree's past projects or work content, and quantify and evaluate their achievements or contributions. For example, the skill analysis unit collects detailed data on projects in which the retiree was involved in the past, and the generation AI analyzes that data. For example, it quantifies and evaluates the project's scale, duration, and achieved results. The skill analysis unit also uses detailed records of the work content to have the generation AI evaluate the retiree's contributions. For example, it quantifies the extent to which the retiree contributed to improving work efficiency and reducing costs. The skill analysis unit also specifically analyzes the results of work that the retiree was responsible for in the past, and the generation AI evaluates those results. For example, it quantifies the extent to which the retiree contributed to improving quality control or developing new products. This makes it possible to quantify and evaluate the retiree's specific achievements and contributions.

[0054] When analyzing the skills or experience of a retiree, the skill analysis unit also incorporates recommendation letters or evaluation comments from colleagues or superiors, allowing for a more multifaceted analysis. For example, the skill analysis unit collects recommendation letters and evaluation comments from the retiree's colleagues and superiors, and the generation AI analyzes them. For example, it performs text analysis of the content of the recommendation letters and reflects this in the evaluation of skills and experience. The skill analysis unit also allows the generation AI to evaluate the retiree's skills and experience from multiple perspectives based on feedback from colleagues and superiors. For example, it analyzes the content of the feedback and identifies the strengths and weaknesses of the skills. The skill analysis unit also analyzes recommendation letters and evaluation comments, allowing the generation AI to more accurately evaluate the retiree's skills and experience. For example, it evaluates skills based on specific episodes and examples. This allows for a multifaceted evaluation of the retiree's skills and experience.

[0055] The skill analysis unit uses the emotion estimation function to analyze what emotions a retiree had toward their past work, and can preferentially extract skills related to work for which they have positive emotions. The skill analysis unit, for example, analyzes the emotions a retiree had toward their past work and extracts skills related to work for which they have positive emotions. For example, it analyzes their satisfaction with and sense of accomplishment toward their work. The skill analysis unit also uses the emotion estimation function to analyze what emotions a retiree had toward their past work, and preferentially evaluates skills related to work for which they have positive emotions. The skill analysis unit also analyzes the emotions a retiree had toward their past work, and extracts skills related to work for which they have positive emotions. For example, it evaluates skills based on an emotion score toward the work. This makes it possible to preferentially extract skills related to work for which they have positive emotions.

[0056] The skill analysis unit also analyzes non-occupational experience, such as the hobbies or volunteer activities of retired individuals, making it possible to extract a wide range of skill sets. For example, the skill analysis unit collects information about retired individuals' hobbies and volunteer activities, and the generation AI analyzes this information. For example, it extracts skills acquired through hobbies and volunteer activities. The skill analysis unit also analyzes non-occupational experience, and the generation AI evaluates the retired individual's wide range of skill sets. For example, it evaluates leadership and communication skills acquired through hobbies and volunteer activities. The skill analysis unit also analyzes information about retired individuals' hobbies and volunteer activities, and the generation AI extracts skill sets based on this information. For example, it evaluates the techniques and knowledge acquired through hobbies and volunteer activities. This makes it possible to include non-occupational experience in the analysis and extract a wide range of skill sets.

[0057] When analyzing the skills or experience of a retiree, the skill analysis unit can perform a comparative analysis with other retirees to identify common skills or experience. The skill analysis unit, for example, performs a comparative analysis with the skills and experience of other retirees to identify common skills and experience. For example, the comparison is made based on data of retirees who worked in the same industry or occupation. The skill analysis unit also compares the skills and experience of a retiree with other retirees to extract common skills and experience. For example, the comparison is made based on data related to the same job content or project. The skill analysis unit also performs a comparative analysis with other retirees to identify common skills and experience. For example, commonalities are found based on data of retirees who worked in the same industry or occupation. This makes it possible to perform a comparative analysis with other retirees to identify common skills and experience.

[0058] The skill analysis unit uses the emotion estimation function to analyze the emotions of the retired person when they enter their skills and experience in real time, and can provide input support that draws out positive emotions. The skill analysis unit uses the emotion estimation function to analyze the emotions of the retired person when they enter their skills and experience in real time. For example, it analyzes facial expressions and voices when they enter their skills and experience to draw out positive emotions. The skill analysis unit also uses the emotion estimation function to analyze the emotions of the retired person when they enter their skills and experience in real time, and provides input support that draws out positive emotions. For example, it provides encouraging messages and positive feedback. The skill analysis unit also uses the emotion estimation function to analyze the emotions of the retired person when they enter their skills and experience in real time, and provides input support that draws out positive emotions. For example, it provides appropriate advice and support according to the input content. This makes it possible to analyze the emotions of the retired person when they enter their skills and experience in real time, and provide input support that draws out positive emotions.

[0059] The career suggestion unit can present past success stories or career paths for the proposed career, allowing the retiree to envision a specific future. The career suggestion unit, for example, presents past success stories for the proposed career, allowing the retiree to envision a specific future. For example, it can introduce the career paths of people who have been successful in the same career. The career suggestion unit can also perform a detailed analysis of the skills and qualifications required for the proposed career, allowing the retiree to envision a specific future. For example, it can list the skills and qualifications required for the career and explain how to obtain them. The career suggestion unit can also present past success stories or career paths for the proposed career, allowing the retiree to envision a specific future. For example, it can introduce interviews and personal stories of successful people. This allows the retiree to envision a specific future.

[0060] The career proposal unit can analyze in detail the skills or qualifications required for the proposed career and provide a training plan to complement the skills that the retiree lacks. The career proposal unit, for example, analyzes in detail the skills and qualifications required for the proposed career and provides a training plan to complement the skills that the retiree lacks. For example, it introduces online courses and workshops. The career proposal unit also provides a training plan to enable the retiree to acquire the skills and qualifications required for the proposed career. For example, it proposes teaching materials and training programs for improving skills. The career proposal unit also analyzes in detail the skills and qualifications required for the proposed career and provides a training plan to complement the skills that the retiree lacks. For example, it proposes instruction and coaching by experts. In this way, it is possible to provide a training plan to complement the skills that the retiree lacks.

[0061] The career suggestion unit can use the emotion estimation function to analyze the emotional response of the retiree to the proposed career and preferentially suggest careers that show a positive response. The career suggestion unit, for example, uses the emotion estimation function to analyze the emotional response of the retiree to the proposed career and preferentially suggest careers that show a positive response. For example, it analyzes facial expressions and voice at the time of proposal. The career suggestion unit also analyzes the emotions the retiree has toward the proposed career and preferentially suggest careers that show a positive response. For example, it selects a career based on an emotion score. The career suggestion unit also uses the emotion estimation function to analyze the emotional response of the retiree to the proposed career and preferentially suggest careers that show a positive response. For example, it selects a career based on emotion data at the time of proposal. In this way, it is possible to analyze the emotional response of the retiree and preferentially suggest careers that show a positive response.

[0062] The career proposal unit can propose various working styles for the proposed occupation, including flexible working styles such as remote work or freelance. The career proposal unit, for example, proposes various working styles for the proposed occupation, including flexible working styles such as remote work or freelance. For example, it introduces occupations that allow working from home or occupations that allow working as a freelancer. The career proposal unit also proposes flexible working styles such as remote work or freelance, depending on the working style desired by the retired person. For example, it proposes occupations that allow working only a few days a week or occupations that allow working on a project basis. The career proposal unit also proposes various working styles for the proposed occupation, including flexible working styles such as remote work or freelance. For example, it explains the advantages and disadvantages of remote work and proposes suitable occupations. This makes it possible to propose flexible working styles such as remote work or freelance.

[0063] The career proposal unit can propose careers that allow retirees to take on new challenges, including careers in different industries or fields, compared to the careers it proposes. The career proposal unit, for example, proposes careers that allow retirees to take on new challenges, including careers in different industries or fields, compared to the careers it proposes. For example, it introduces careers in completely different fields or careers that require new skills. The career proposal unit also proposes careers in different industries or fields, so that retirees can take on new challenges. For example, it proposes careers that allow retirees to be active in a new field while making use of their previous experience. The career proposal unit also proposes careers that allow retirees to take on new challenges, including careers in different industries or fields, compared to the careers it proposes. For example, it introduces careers that support career changes in a different industry. In this way, it is possible to propose careers that allow retirees to take on new challenges.

[0064] The career suggestion unit also uses the emotion estimation function to collect the emotional reactions of family or friends to the proposed career, and can suggest a career that will allow the retiree to easily receive support from those around them. The career suggestion unit, for example, uses the emotion estimation function to collect the emotional reactions of family and friends to the proposed career, and suggests a career that will allow the retiree to easily receive support from those around them. For example, the career suggestion unit selects a career based on the opinions of family and friends. The career suggestion unit also collects the emotional reactions of family and friends to the proposed career of the retiree, and preferentially suggests careers that show positive reactions. For example, the career suggestion unit selects a career based on the emotion scores of family and friends. The career suggestion unit also uses the emotion estimation function to collect the emotional reactions of family and friends to the proposed career, and suggests a career that will allow the retiree to easily receive support from those around them. For example, the career suggestion unit selects a career based on the emotion data of family and friends. This makes it possible to suggest a career that will allow the retiree to easily receive support from those around them.

[0065] The company matching department analyzes not only the recruitment information of companies but also the company culture or values, and is able to prioritize matching with companies that match the values ​​of retirees. The company matching department, for example, analyzes not only the recruitment information of companies but also the company culture and values, and prioritizes matching with companies that match the values ​​of retirees. For example, it analyzes the mission and vision of the company. The company matching department also analyzes the company culture and values ​​to select companies that match the values ​​of retirees. For example, it analyzes the internal culture and working style of the company to propose companies that suit the preferences of retirees. The company matching department also analyzes not only the recruitment information of companies but also the company culture and values, and prioritizes matching with companies that match the values ​​of retirees. For example, it analyzes the values ​​and philosophy of the company to select companies that match the values ​​of retirees. This makes it possible to prioritize matching with companies that match the values ​​of retirees.

[0066] When matching with companies, the company matching unit can refer to past cases where similar matching was successful and perform matching with a high success rate. For example, when matching with companies, the company matching unit can refer to past cases where similar matching was successful and perform matching with a high success rate. For example, it analyzes past successful cases and performs matching under similar conditions. Furthermore, the company matching unit uses a generation AI to perform matching with a high success rate based on past successful matching cases. For example, it analyzes data on successful cases and performs matching under similar conditions. Furthermore, when matching with companies, the company matching unit can refer to past cases where similar matching was successful and perform matching with a high success rate. For example, it performs matching under similar conditions based on data on successful cases. This allows for matching with a high success rate.

[0067] The company matching unit can use the emotion estimation function to analyze the emotions felt by retired employees when interviewing or communicating with companies, and provide advice to draw out positive emotions. The company matching unit, for example, uses the emotion estimation function to analyze the emotions felt by retired employees when interviewing or communicating with companies, and provide advice to draw out positive emotions. For example, it analyzes facial expressions and voice during the interview. The company matching unit also analyzes the emotions felt by retired employees when interviewing or communicating with companies, and provides advice to draw out positive emotions. For example, it provides advice based on the emotion score during the interview. The company matching unit also uses the emotion estimation function to analyze the emotions felt by retired employees when interviewing or communicating with companies, and provides advice to draw out positive emotions. For example, it provides advice based on emotion data during the interview. This makes it possible to provide advice to draw out positive emotions from retired employees when interviewing or communicating with companies.

[0068] When matching with companies, the Company Matching Department can also propose matching with a trial period, such as an internship or short-term project. For example, when matching with companies, the Company Matching Department can also propose matching with a trial period, such as an internship or short-term project. For example, compatibility with the company can be confirmed through short-term work experience. In addition, when matching retirees with companies, the Company Matching Department can propose internships or short-term projects. For example, compatibility with the company can be confirmed through short-term work experience. In addition, when matching with companies, the Company Matching Department can also propose matching with a trial period, such as an internship or short-term project. For example, compatibility with the company can be confirmed through short-term work experience. This makes it possible to propose matching with a trial period.

[0069] The company matching department can provide a platform that allows retirees to be matched with multiple companies simultaneously when matching with companies. For example, the company matching department provides a platform that allows retirees to be matched with multiple companies simultaneously when matching with companies. For example, it can display job information from multiple companies all at once, increasing the number of options. The company matching department also provides a platform that allows retirees to be matched with multiple companies simultaneously. For example, it can build a system that allows interviews and communication with multiple companies simultaneously. The company matching department also provides a platform that allows retirees to be matched with multiple companies simultaneously when matching with companies. For example, it can display job information from multiple companies all at once, increasing the number of options. This makes it possible to provide a platform that allows retirees to be matched with multiple companies simultaneously.

[0070] The company matching unit uses the emotion estimation function to analyze the emotional reactions of the company's recruiters, thereby realizing a match that satisfies both the retiree and the company. The company matching unit, for example, uses the emotion estimation function to analyze the emotional reactions of the company's recruiters, thereby realizing a match that satisfies both the retiree and the company. For example, it analyzes the recruiters' facial expressions and voice. The company matching unit also analyzes the emotional reactions of the company's recruiters, thereby realizing a match that satisfies both the retiree and the company. For example, it performs matching based on the recruiters' emotional scores. The company matching unit also uses the emotion estimation function to analyze the emotional reactions of the company's recruiters, thereby realizing a match that satisfies both the retiree and the company. For example, it performs matching based on the recruiters' emotional data. This makes it possible to realize a match that satisfies both the retiree and the company.

[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 the skills and experience of retirees, the Skills Analysis Department can also incorporate evaluations of teamwork and leadership in past work. For example, it can analyze what role the retiree played as a leader in the past and what kind of cooperative relationships they had with their team members. The Skills Analysis Department can also evaluate leadership and teamwork skills based on successful examples of team projects in which the retiree participated in the past. Furthermore, the Skills Analysis Department can collect specific episodes in which the retiree demonstrated leadership in the past, and the generation AI can analyze and evaluate these. This makes it possible to evaluate the leadership and teamwork skills of retirees from multiple perspectives.

[0073] When analyzing the skills and experience of retirees, the skill analysis unit can also evaluate their stress tolerance and problem-solving ability in past work. For example, it analyzes how the retiree solved difficult situations and problems they faced in the past. The skill analysis unit can also evaluate how the retiree responded to stressful situations they experienced in the past. Furthermore, the skill analysis unit can specifically analyze the approaches the retiree took to solving problems in the past, and the generation AI can evaluate those approaches. This allows for a multifaceted evaluation of the retiree's stress tolerance and problem-solving ability.

[0074] When analyzing the skills and experience of retirees, the Skill Analysis Department can also incorporate evaluations of creativity and innovation in past work. For example, it can analyze what new ideas and projects the retiree has proposed in the past. The Skill Analysis Department can also evaluate creativity and innovation skills based on specific examples of innovations that the retiree has implemented in the past. Furthermore, the Skill Analysis Department can collect specific episodes in which the retiree has proposed creative solutions in the past, and the generative AI can analyze and evaluate these. This makes it possible to evaluate the creativity and innovation skills of retirees from multiple perspectives.

[0075] The skill analysis unit can use the emotion estimation function to analyze the emotions that a retiree had toward past work and provide a training plan to complement the skills related to the work for which they have negative emotions. For example, the skill analysis unit can analyze the emotions that a retiree had toward work that they felt were difficult in the past and propose a training plan to strengthen the skills related to that work. The skill analysis unit can also use the emotion estimation function to analyze the negative emotions that a retiree had toward past work and provide support to help them overcome those emotions. The skill analysis unit can also analyze the emotions that a retiree had toward work that they felt were difficult in the past and propose a training plan to strengthen the skills related to that work. This makes it possible to provide a training plan to complement the skills related to the work for which a retiree has negative emotions.

[0076] When analyzing the skills and experience of retirees, the Skills Analysis Department can also evaluate their international experience and intercultural ability in past work. For example, it can analyze the retiree's past experience participating in international projects and working with cross-cultural teams. The Skills Analysis Department can also evaluate the retiree's skills based on specific examples in which the retiree demonstrated intercultural ability in the past. Furthermore, the Skills Analysis Department can specifically analyze the results the retiree achieved in international work in the past, and the generation AI can evaluate those results. This allows for a multifaceted evaluation of the retiree's international experience and intercultural ability.

[0077] The skill analysis unit can use the emotion estimation function to analyze the emotions that a retiree had toward past work and prioritize extraction of skills related to work for which they have positive emotions. For example, it can analyze the emotions that a retiree had toward work for which they felt satisfaction or a sense of accomplishment in the past and prioritize evaluation of skills related to that work. The skill analysis unit can also use the emotion estimation function to analyze the positive emotions that a retiree had toward past work and evaluate skills based on those emotions. Furthermore, the skill analysis unit can analyze the emotions that a retiree had toward work for which they had positive emotions in the past and prioritize extraction of skills related to that work. This makes it possible to prioritize extraction of skills related to work for which a retiree has positive emotions.

[0078] The career suggestion department can present past success stories and career paths for the proposed occupation, allowing retirees to visualize a specific future. For example, it can introduce the career paths of people who have been successful in the same occupation. The career suggestion department can also perform a detailed analysis of the skills and qualifications required for the proposed occupation, allowing retirees to visualize a specific future. For example, it can list the skills and qualifications required for the occupation and explain how to obtain them. The career suggestion department can also present past success stories and career paths for the proposed occupation, allowing retirees to visualize a specific future. For example, it can introduce interviews and personal stories of successful people. This allows retirees to visualize a specific future.

[0079] The career suggestion unit uses the emotion estimation function to analyze the emotional response of the retiree to the proposed career, and can preferentially suggest careers that elicit a positive response. For example, it analyzes facial expressions and voice at the time of proposal. The career suggestion unit also analyzes the emotions the retiree has toward the proposed career, and preferentially suggests careers that elicit a positive response. For example, it selects a career based on an emotion score. The career suggestion unit also uses the emotion estimation function to analyze the emotional response of the retiree to the proposed career, and preferentially suggests careers that elicit a positive response. For example, it selects a career based on emotion data at the time of proposal. In this way, it is possible to analyze the emotional response of the retiree and preferentially suggest careers that elicit a positive response.

[0080] When matching with companies, the company matching department refers to past cases where similar matching was successful and can perform matching with a high success rate. For example, it analyzes past successful cases and performs matching under similar conditions. In addition, the company matching department uses a generation AI to perform matching with a high success rate based on past successful matching cases. For example, it analyzes data from successful cases and performs matching under similar conditions. In addition, when matching with companies, the company matching department refers to past cases where similar matching was successful and can perform matching with a high success rate. For example, it matches under similar conditions based on data from successful cases. This allows for matching with a high success rate.

[0081] The company matching unit uses the emotion estimation function to analyze the emotional responses of company recruiters, thereby realizing a match that satisfies both the retiree and the company. For example, it analyzes the facial expressions and voice of the recruiters. The company matching unit also analyzes the emotional responses of company recruiters, thereby realizing a match that satisfies both the retiree and the company. For example, it performs matching based on the recruiters' emotional scores. The company matching unit also uses the emotion estimation function to analyze the emotional responses of company recruiters, thereby realizing a match that satisfies both the retiree and the company. For example, it performs matching based on the recruiters' emotional data. This makes it possible to realize a match that satisfies both the retiree and the company.

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

[0083] Step 1: The skill analysis unit analyzes the skills or experience of the retiree. For example, detailed data on projects that the retiree has been involved in in the past is collected, and the generation AI analyzes that data. The skill analysis unit can also use the generation AI to evaluate the retiree's level of contribution based on detailed records of the work content. Furthermore, the skill analysis unit can specifically analyze the results of the work that the retiree has been responsible for in the past, and the generation AI can evaluate those results. For example, the generation AI can quantify and evaluate the scale, duration, and results achieved of the project. Step 2: The career suggestion unit suggests new occupations based on the skills or experience analyzed by the skill analysis unit. For example, for a retiree with experience in quality control, the generation AI might suggest occupations such as consultant, trainer, or quality control system developer. The career suggestion unit can also customize occupation suggestions based on the retiree's preferences and conditions. For example, if a person inputs preferences such as "I want to work only three days a week" or "I want to work within commuting distance from home," the generation AI will suggest occupations that match those preferences. Step 3: The company matching department searches for companies related to the occupations proposed by the career proposal department and matches the retiree with the company. For example, the generation AI analyzes companies' job information and selects the company that best suits the retiree's skills and experience. The company matching department can also analyze the company's culture and values ​​and prioritize matching companies that match the retiree's values.

[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 skill analysis unit that analyzes the skills or experience of retirees; an occupation suggestion unit that suggests a new occupation based on the skills or experiences analyzed by the skill analysis unit; a company matching unit that searches for companies related to the occupations proposed by the occupation proposal unit and matches the retiree with the companies. A system characterized by:

2. The skill analysis unit Analyzing how the retired person felt about their past work, and preferentially extracting the skills related to the work for which they have positive feelings.

2. The system of claim 1.

3. The career suggestion section Present past success stories or career paths for the proposed occupations, allowing the retiree to visualize a concrete future.

2. The system of claim 1.

4. The company matching department: Analyzing not only the job information of the company but also the company's culture or values, and giving priority to matching with companies that match the retiree's values 2. The system of claim 1.

5. The career suggestion section Analyzing the retiree's emotional response to the proposed occupations and preferentially suggesting the occupations that generate positive responses.

2. The system of claim 1.

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