System

Abstract

The system according to the embodiment aims to effectively match a company's problem with a university's research result to increase the number of doctor's degrees.SOLUTION: A system according to an embodiment includes a collection unit, a matching unit, an advancement unit, and a support unit. The collection unit collects problems in each department or each business of the company. The matching unit matches the problem collected by the collecting unit with research results of each university and each laboratory. The advancement unit allows the employee to advance to the doctor's degree course of the laboratory matched by the matching unit. The support unit supports the employee who has entered the graduate school and the student in the doctoral program of the corresponding laboratory.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to effectively link corporate challenges with university research results, resulting in the issue of a decline in the number of people obtaining doctorates.

[0005] The system according to the embodiment aims to effectively match corporate challenges with university research results and increase the number of people obtaining doctorates. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a matching unit, an advancement department, and a support unit. The collection unit collects issues for each department or business of a company. The matching unit matches the issues collected by the collection unit with research results from each university or laboratory. The advancement department advances employees to doctoral programs in laboratories matched by the matching unit. The support unit provides support to employees who advance to doctoral programs through the advancement department and to doctoral students in the corresponding laboratories. [Effects of the Invention]

[0007] The system according to the embodiment can effectively match corporate challenges with university research results, thereby increasing the number of people obtaining doctorates. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A system according to an embodiment of the present invention aims to resolve the declining trend in the number of doctoral degree recipients and to become a science-based nation. This system uses generative AI to match challenges in each department and business of a company with research results from universities and laboratories, promote employees to doctoral programs in the corresponding laboratories, and provide support to doctoral students in those laboratories. As a result, within a few years, the system will produce many degree-holder employees and new employees within the company, realizing a science-based society rather than a science-based nation. For example, the system collects challenges in each department and business of a company, and the generative AI matches those challenges with research results from universities and laboratories. Next, employees are promoted to doctoral programs in the matched laboratories, and further support is provided to doctoral students in those laboratories. This promotes concrete research aimed at solving the company's challenges, ensures research continuity, and maximizes research results. This system revitalizes research and development within the company, realizing a science-based society rather than a science-based nation. It also provides students with research continuity and maximizes research results. Furthermore, we can provide our employees with a path to becoming cutting-edge global leaders.

[0029] The system according to the embodiment includes a collection unit, a matching unit, a placement department, and a support unit. The collection unit collects issues in each department or business of a company. For example, the collection unit can identify detailed issues based on feedback from staff in each department or business. The collection unit can also use AI to automatically collect issues in each department or business of a company. For example, the collection unit can collect issues through questionnaire surveys or interviews. The matching unit matches the issues collected by the collection unit with research results from each university or laboratory. The matching unit uses a generation AI to analyze the research themes and results of each laboratory and identify the laboratory best suited to the company's challenges. For example, the matching unit can use the generation AI to analyze the research themes and results and identify the laboratory best suited to the company's challenges. The placement department allows employees to advance to doctoral programs in laboratories matched by the matching unit. For example, the placement department can support employees in the application process for admission to higher education. The placement department can also use AI to support employees in their application process for admission to higher education. For example, the placement department can assist employees in selecting a university and completing the application process for admission to higher education. The support department provides support to employees who advance to higher education through the advanced education program and to doctoral students in the relevant laboratories. For example, the support department can provide research funding and opportunities for collaborative research with companies. The support department can also use AI to ensure the continuity of students' research and maximize research results. For example, the support department can provide research funding, technical support, mentoring, and more. This allows the system according to the embodiment to effectively match corporate challenges with university research results, enabling employees to advance to higher education and support students. This allows the system to produce a large number of employees and new employees with degrees within the company in a few years, realizing a science-based company rather than a science-based nation.

[0030] The collection unit can identify detailed issues based on feedback from personnel in each department and each task. Examples of feedback include, but are not limited to, questionnaire surveys, interviews, and periodic reports. The collection unit can collect feedback from personnel in each department and each task through, for example, questionnaire surveys. The collection unit can also identify detailed issues through interviews. For example, the collection unit can collect feedback from personnel in each department and each task through periodic reports. This enables more accurate issue collection by identifying detailed issues based on feedback from personnel. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the results of a questionnaire survey into AI, which can then identify detailed issues.

[0031] The matching unit can analyze the research themes and results of each laboratory and identify a laboratory that is best suited to the company's challenges. Research themes and results include, but are not limited to, the research field, research progress, and results evaluation methods. The matching unit can, for example, use a generation AI to analyze the research themes and results of each laboratory and identify the laboratory that is best suited to the company's challenges. The matching unit can also identify the laboratory that is best suited to the company's challenges based on the analysis results of the research themes and results. For example, the matching unit can use a generation AI to analyze the research themes and results and identify the laboratory that is best suited to the company's challenges. This identifies the laboratory that is best suited to the company's challenges, thereby improving the accuracy of problem solving. Some or all of the above-described processing in the matching unit can be performed, for example, using a generation AI, or can be performed without a generation AI. For example, the matching unit can input data on research themes and results into a generation AI, which can then identify the laboratory that is best suited to the company's challenges.

[0032] The education department can support employees in their pursuit of higher education. Examples of support include, but are not limited to, assistance with the admission procedures, tuition assistance, and learning support. For example, the education department can provide assistance with admission procedures. The education department can also provide tuition assistance. For example, the education department can provide learning support. This supports employees' education, thereby stimulating research and development within the company. Some or all of the above-mentioned processing in the education department can be performed using, or without, AI. For example, the education department can input assistance with admission procedures into AI, which can then support the admission procedures.

[0033] The support department can provide research funding or opportunities for collaborative research with companies. The provision of research funding includes, but is not limited to, the amount provided, the conditions for provision, and the period of provision. For example, the support department can provide research funding. The support department can also provide opportunities for collaborative research with companies. For example, the support department can provide research funding. By providing research funding or opportunities for collaborative research, this ensures the continuity of students' research and maximizes research results. Some or all of the above-described processing in the support department can be performed, for example, using AI, or can be performed without using AI. For example, the support department can input the provision of research funding into AI, which then provides the research funding.

[0034] The support department can ensure the continuity of students' research and maximize research results. Research continuity includes, but is not limited to, securing research funding, preparing a research environment, and formulating a research plan. For example, the support department can secure research funding. The support department can also prepare a research environment. For example, the support department can formulate a research plan. This ensures the continuity of students' research and maximizes research results, thereby improving the quality of research. Some or all of the above-mentioned processing in the support department can be performed using, for example, AI, or can be performed without using AI. For example, the support department can input the securing of research funding into AI, and the AI ​​can secure the research funding.

[0035] The collection unit can analyze the past assignment submission history of each department or each task and select an appropriate collection method. The past assignment submission history includes, for example, the type of assignment submitted, the frequency of submission, and the attributes of the submitter, but is not limited to these examples. For example, the collection unit can identify patterns of frequently submitted assignments from the past submission history and select an efficient collection method. The collection unit can also predict which assignments will be submitted frequently during a specific period based on the submission history and select a collection method appropriate for that period. For example, the collection unit can analyze the submission history to identify trends in assignments submitted by a specific person and select a collection method appropriate for that person. This enables efficient assignment collection by analyzing the past submission history. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the past assignment submission history into AI, which then selects the optimal collection method.

[0036] When collecting issues, the collection unit can filter the issues based on the current projects and areas of interest of each department or each business. Examples of current projects and areas of interest include, but are not limited to, the progress of projects and methods for identifying areas of interest. For example, the collection unit can prioritize collection of issues related to ongoing projects. The collection unit can also filter and collect highly relevant issues based on the areas of interest of each department. For example, the collection unit can collect issues at an appropriate time, taking into account the current status of each business. By filtering based on the current projects and areas of interest, highly relevant issues can be efficiently collected. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data on current projects and areas of interest into AI, which then performs filtering.

[0037] When collecting tasks, the collection unit can select an appropriate collection means depending on the input method of the person in charge. Input methods include, but are not limited to, voice input, text input, and image input. For example, the collection unit can provide a task collection means using voice recognition technology to a person in charge who prefers voice input. The collection unit can also provide an easy-to-enter text form to a person in charge who prefers text input. For example, the collection unit can provide a task collection means using image recognition technology to a person in charge who prefers image input. This enables efficient task collection by selecting the optimal collection means depending on the person in charge's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data on the person in charge's input method into AI, which can select the optimal collection means.

[0038] When collecting tasks, the collection unit can prioritize collecting related tasks by taking into account the geographical location information of each department or each task. Examples of geographical location information include, but are not limited to, GPS data, address information, and location information analysis methods. For example, the collection unit can prioritize collecting tasks from geographically close departments or tasks. The collection unit can also filter and collect highly related tasks based on the geographical location information. For example, the collection unit can postpone collection of tasks from geographically distant departments or tasks. This allows highly related tasks to be collected efficiently by taking geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographical location information data into AI, which can select the optimal collection method.

[0039] When collecting issues, the collection unit can analyze social media activity of each department or each business and collect related issues. Social media activity includes, but is not limited to, for example, post content, number of followers, and engagement rate. The collection unit can, for example, analyze social media post content and collect related issues. The collection unit can also prioritize collection of highly relevant issues based on the frequency of social media activity. For example, the collection unit can analyze social media trends and collect related issues. In this way, highly relevant issues can be efficiently collected by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI, or can be performed without using AI. For example, the collection unit can input social media activity data into AI, which can collect related issues.

[0040] When collecting issues, the collection unit can customize the collection method by reflecting past feedback from each department or each task. Past feedback includes, for example, the type of feedback and feedback evaluation criteria, but is not limited to these examples. For example, the collection unit can improve the collection method based on the past feedback and perform efficient issue collection. The collection unit can also customize the collection method by reflecting feedback from each department or each task. For example, the collection unit can optimize the collection means based on the feedback and improve the accuracy of issue collection. This enables efficient issue collection by reflecting past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past feedback data into AI, which can customize the collection method.

[0041] The matching unit can improve the accuracy of matching by taking into account the interrelationship between a company's issues and research results during matching. Examples of interrelationships include, but are not limited to, the relevance between the issues and research results and methods for evaluating the interactions. For example, the matching unit can analyze the relevance between a company's issues and research results to perform optimal matching. The matching unit can also improve the accuracy of matching by taking into account the interrelationship between the issues and research results. For example, the matching unit can identify the optimal laboratory based on the interrelationship between a company's issues and research results. This improves the accuracy of matching by taking into account the interrelationship between a company's issues and research results. Some or all of the above-described processing in the matching unit can be performed using, or without, a generation AI. For example, the matching unit can input data on a company's issues and research results into a generation AI, which can analyze the interrelationship to improve the accuracy of matching.

[0042] When matching, the matching unit can perform matching by taking into account attribute information of the company's assignment submitter. Attribute information includes, for example, age, gender, job position, etc., but is not limited to these examples. The matching unit can, for example, match the most appropriate laboratory by taking into account the assignment submitter's field of expertise. The matching unit can also match the appropriate laboratory by taking into account the assignment submitter's years of experience. For example, the matching unit can match the most appropriate laboratory based on the assignment submitter's past submission history. This enables optimal matching by taking into account the assignment submitter's attribute information. Some or all of the above-mentioned processing in the matching unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the matching unit can input the assignment submitter's attribute information into a generation AI, which can then match the most appropriate laboratory.

[0043] During matching, the matching unit can set a matching weight based on the frequency of assignment submissions from companies. Submission frequency includes, but is not limited to, the number of submissions and the interval between submissions. For example, the matching unit can weight frequently submitted assignments and match them to the most suitable laboratories. The matching unit can also prioritize matching for frequently submitted assignments. For example, the matching unit can improve matching accuracy by weighting assignments based on submission frequency. This enables efficient matching by weighting assignments based on assignment submission frequency. Some or all of the above-described processing in the matching unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the matching unit can input submission frequency data into the generation AI, which can then weight the assignments to improve matching accuracy.

[0044] When matching, the matching unit can perform matching based on the geographic distribution of the company's tasks. Geographic distribution includes, but is not limited to, for example, the distribution of tasks by region and geographic influences. For example, the matching unit can prioritize matching with laboratories that are geographically close. The matching unit can also consider the geographic distribution of tasks to match the optimal laboratory. For example, the matching unit can improve the accuracy of matching by considering geographic factors. This enables efficient matching by considering the geographic distribution. Some or all of the above-mentioned processing in the matching unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the matching unit can input geographic distribution data into the generation AI, which can then match the optimal laboratory.

[0045] During matching, the matching unit can improve the accuracy of matching by referring to literature related to the company's issues. Examples of related literature include, but are not limited to, academic papers, patent documents, and technical reports. For example, the matching unit can analyze literature related to the issue to match the most appropriate laboratory. The matching unit can also improve the accuracy of matching based on the content of the literature. For example, the matching unit can refer to the latest research results related to the issue to match the most appropriate laboratory. By referring to the related literature, the accuracy of matching is improved. Some or all of the above-described processing in the matching unit may be performed using, or without, a generation AI. For example, the matching unit can input data on related literature into the generation AI, which then analyzes the literature to improve the accuracy of matching.

[0046] During matching, the matching unit can perform matching based on the market value of the company's project. Market value includes, but is not limited to, market size, competitive situation, and demand forecast. For example, the matching unit can match the most suitable laboratory to a project with high market value. The matching unit can also improve the accuracy of matching by taking into account the market value of the project. For example, the matching unit can prioritize matching to projects with high market value. This enables efficient matching by taking market value into consideration. Some or all of the above-mentioned processing in the matching unit may be performed using, or without, a generation AI. For example, the matching unit can input market value data into the generation AI, which can then match the most suitable laboratory.

[0047] When an employee advances to higher education, the education department can analyze the employee's past educational background and work history to select an appropriate path for further education. Educational background and work history include, but are not limited to, degrees, work experience, and specialized fields. The education department can select the optimal path for further education, for example, based on the employee's past educational background. The education department can also select an appropriate path for further education by taking into account the employee's work history. For example, the education department can suggest the optimal path for further education based on the employee's past experience. This allows the optimal path for further education to be selected by analyzing the employee's past educational background and work history. Some or all of the above-described processing in the education department can be performed using, for example, AI, or without AI. For example, the education department can input data on the employee's educational background and work history into AI, which can then select the optimal path for further education.

[0048] The advancement department can adjust the advancement method based on the employee's current work situation when advancing to higher education. Work situation includes, but is not limited to, the progress of work, the type of work, and the priority of work. For example, the advancement department can customize the advancement method taking into account the current work situation. The advancement department can also adjust the timing of advancement to avoid busy periods. For example, the advancement department can optimize the advancement method according to the work content. Customizing the advancement method based on the current work situation enables efficient advancement. Some or all of the above-described processing in the advancement department may be performed using, or without, AI. For example, the advancement department can input data on the employee's work situation into AI, which can then select the optimal advancement method.

[0049] The admissions department can improve its support methods for university admission by reflecting employee feedback at the time of admission. Examples of feedback include, but are not limited to, the type of feedback and feedback evaluation criteria. For example, the admissions department can improve its support methods for university admission based on employee feedback. The admissions department can also optimize the means of university admission by reflecting the feedback. For example, the admissions department can incorporate employee opinions and customize the support methods for university admission. By reflecting the feedback, the support methods for university admission can be improved. Some or all of the above-described processing in the admissions department may be performed using, for example, AI, or may be performed without using AI. For example, the admissions department can input employee feedback data into AI, which can then improve the support methods for university admission.

[0050] When an employee advances to higher education, the department can select an appropriate path for further education by taking into account the employee's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, address information, and location information analysis methods. For example, the department can prioritize selecting a university or research lab that is geographically close. The department can also suggest the optimal path for further education based on the geographical location information. For example, the department can optimize the means of further education by taking geographical factors into account. This allows the optimal path for further education to be selected by taking geographical location information into consideration. Some or all of the above-described processing in the department can be performed using, or without, AI. For example, the department can input geographical location information data into AI, which can then select the optimal path for further education.

[0051] The admissions department can analyze employees' social media activity to suggest ways to advance to higher education when they apply for higher education. Social media activity includes, but is not limited to, the content of posts, the number of followers, and engagement rates. For example, the admissions department can suggest optimal ways to advance to higher education based on the content of social media activity. The admissions department can also analyze social media posts to customize the admissions department. For example, the admissions department can suggest ways to advance to higher education based on the educational status of friends on social media. By analyzing social media activity, the department can suggest optimal ways to advance to higher education. Some or all of the above-described processing in the admissions department may be performed using, for example, AI, or may be performed without AI. For example, the admissions department can input social media activity data into AI, which can then suggest optimal ways to advance to higher education.

[0052] When an employee advances to higher education, the advancement department can adjust the support method for advancement by reflecting past feedback from the employee. Examples of feedback include, but are not limited to, the type of feedback and feedback evaluation criteria. For example, the advancement department can customize the support method for advancement based on past feedback. The advancement department can also reflect the feedback and optimize the means for advancement. For example, the advancement department can improve the support method for advancement by referring to past feedback. This allows the support method for advancement to be customized by reflecting past feedback. Some or all of the above-described processing in the advancement department may be performed using, for example, AI, or may be performed without using AI. For example, the advancement department can input past feedback data into AI, which can then adjust the support method for advancement.

[0053] When providing support, the support department can analyze the student's past research history and select an appropriate support method. Research history includes, but is not limited to, for example, research topic, research results, and research progress. The support department can select the optimal support method, for example, based on the student's past research history. The support department can also select an appropriate support method by taking the research history into consideration. For example, the support department can propose the optimal support method based on the student's past research results. This allows the optimal support method to be selected by analyzing the past research history. Some or all of the above-mentioned processing in the support department can be performed, for example, using AI, or can be performed without using AI. For example, the support department can input data on the student's research history into AI, which can select the optimal support method.

[0054] When providing support, the support unit can adjust the support measures based on the student's current research status. Examples of research status include, but are not limited to, the research progress, the type of research, and the research priority. For example, the support unit can customize the support measures taking into account the current research status. The support unit can also optimize the support measures according to the research progress. For example, the support unit can customize the support measures according to the research content. This enables efficient support by customizing the support measures based on the current research status. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input data on the student's research status into AI, which can then select the optimal support measures.

[0055] The support unit can improve the support method by reflecting the student's feedback during support. The feedback can include, but is not limited to, for example, the type of feedback and feedback evaluation criteria. The support unit can improve the support method, for example, based on the student's feedback. The support unit can also reflect the feedback and optimize the support means. For example, the support unit can incorporate the student's opinion and customize the support method. In this way, the support method can be improved by reflecting the feedback. Some or all of the above-mentioned processing in the support unit can be performed, for example, using AI or without using AI. For example, the support unit can input student feedback data into AI, which can then improve the support method.

[0056] When providing support, the support unit can select an appropriate support method by taking into account the student's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, address information, and location information analysis methods. For example, the support unit can prioritize selecting a geographically close support method. The support unit can also propose an optimal support method based on the geographical location information. For example, the support unit can optimize support methods by taking geographical factors into account. This allows the optimal support method to be selected by taking the geographical location information into account. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input geographical location information data into AI, which can then select the optimal support method.

[0057] When providing support, the support department can analyze the student's social media activity and suggest support measures. Social media activity includes, but is not limited to, for example, the content of posts, the number of followers, and the engagement rate. The support department can suggest optimal support measures, for example, based on the content of social media activity. The support department can also analyze the content of social media posts and customize support measures. For example, the support department can suggest support measures based on the activity of friends on social media. In this way, the analysis of social media activity can suggest optimal support measures. Some or all of the above-described processing in the support department can be performed, for example, using AI, or can be performed without using AI. For example, the support department can input social media activity data into AI, which can then suggest optimal support measures.

[0058] When providing support, the support unit can adjust the support method by reflecting the student's past feedback. The feedback includes, for example, the type of feedback and feedback evaluation criteria, but is not limited to these examples. The support unit can, for example, customize the support method based on the past feedback. The support unit can also reflect the feedback and optimize the support means. For example, the support unit can improve the support method by referring to the past feedback. In this way, the support method can be customized by reflecting the past feedback. Some or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input data of past feedback into AI, which can adjust the support method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] When collecting issues for each department and business operation in a company, the data collection department can evaluate the automation level of each department's business process and select an appropriate collection method. For example, the data collection department can use automated data collection tools for departments with highly automated business processes. The data collection department can also collect issues through interviews and questionnaire surveys for departments where business processes are performed manually. Furthermore, the data collection department can adjust the collection frequency and collection method depending on the automation level of the business process. This enables efficient issue collection based on the automation level of the business process.

[0061] When matching a company's challenges with the research results of each laboratory, the Matching Department can take into account the company's long-term strategic goals. For example, the Matching Department can prioritize matching research themes that will be important in the future based on the company's five-year growth strategy. The Matching Department can also prioritize matching environmentally friendly research themes based on the company's sustainability goals. Furthermore, the Matching Department can prioritize matching research themes related to digital technology based on the company's digital transformation strategy. This allows the optimal laboratory to be identified based on the company's long-term strategic goals.

[0062] When supporting employees' further education, the Preparatory School can select a university that takes into consideration the employee's career path. For example, if an employee is aiming for a leadership position in the future, the Preparatory School can select a university that offers a program to strengthen leadership skills. Also, if an employee wants to deepen their specialized skills, the Preparatory School can select a research lab that specializes in that field. Furthermore, if an employee is aiming for an international career, the Preparatory School can select a university with an international network. This allows the school to select the optimal university based on the employee's career path.

[0063] When providing research funding or opportunities for collaborative research with companies, the Support Department can evaluate the social impact of research and determine support priorities. For example, the Support Department can evaluate the impact of research on the local community and prioritize support for research that contributes to the local community. The Support Department can also evaluate the impact of research on the environment and prioritize support for environmentally friendly research. Furthermore, the Support Department can evaluate the impact of research on the economy and prioritize support for research that contributes to economic growth. This allows the optimal support to be provided, taking into account the social impact of research.

[0064] The support department can strengthen students' research networks to ensure continuity of their research and maximize their research results. For example, the support department can provide opportunities for students to conduct collaborative research with other researchers. The support department can also support students in participating in international research conferences. Furthermore, the support department can provide support for students to exchange information with other researchers through online platforms. This can strengthen students' research networks and maximize their research continuity and results.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection department collects issues in each department or business of the company. The collection department can identify detailed issues based on feedback from the people in charge of each department or business. It is also possible to use AI to automatically collect issues in each department or business of the company. For example, issues can be collected through questionnaire surveys or interviews. Step 2: The matching department matches the issues collected by the collection department with the research results of each university and laboratory. The matching department uses generation AI to analyze the research themes and results of each laboratory and identify the laboratory that is best suited to the company's issues. For example, generation AI can analyze research themes and results and identify the laboratory that is best suited to the company's issues. Step 3: The Department of Advancement allows employees to advance to doctoral programs in laboratories matched by the Matching Department. The Department of Advancement can support employees in the procedures for continuing their education. It is also possible to use AI to support employees in their further education. For example, it can assist with selecting a school to continue their education and the procedures for entering the school. Step 4: The Support Department provides support to employees who have advanced to higher education and to doctoral students in the relevant laboratories. The Support Department can provide research funding and opportunities for joint research with companies. It can also use AI to ensure the continuity of students' research and maximize research results. For example, it can provide research funding, technical support, mentoring, etc.

[0067] (Example 2) A system according to an embodiment of the present invention aims to resolve the declining trend in the number of doctoral degree recipients and to become a science-based nation. This system uses generative AI to match challenges in each department and business of a company with research results from universities and laboratories, promote employees to doctoral programs in the corresponding laboratories, and provide support to doctoral students in those laboratories. As a result, within a few years, the system will produce many degree-holder employees and new employees within the company, realizing a science-based society rather than a science-based nation. For example, the system collects challenges in each department and business of a company, and the generative AI matches those challenges with research results from universities and laboratories. Next, employees are promoted to doctoral programs in the matched laboratories, and further support is provided to doctoral students in those laboratories. This promotes concrete research aimed at solving the company's challenges, ensures research continuity, and maximizes research results. This system revitalizes research and development within the company, realizing a science-based society rather than a science-based nation. It also provides students with research continuity and maximizes research results. Furthermore, we can provide our employees with a path to becoming cutting-edge global leaders.

[0068] The system according to the embodiment includes a collection unit, a matching unit, a placement department, and a support unit. The collection unit collects issues in each department or business of a company. For example, the collection unit can identify detailed issues based on feedback from staff in each department or business. The collection unit can also use AI to automatically collect issues in each department or business of a company. For example, the collection unit can collect issues through questionnaire surveys or interviews. The matching unit matches the issues collected by the collection unit with research results from each university or laboratory. The matching unit uses a generation AI to analyze the research themes and results of each laboratory and identify the laboratory best suited to the company's challenges. For example, the matching unit can use the generation AI to analyze the research themes and results and identify the laboratory best suited to the company's challenges. The placement department allows employees to advance to doctoral programs in laboratories matched by the matching unit. For example, the placement department can support employees in the application process for admission to higher education. The placement department can also use AI to support employees in their application process for admission to higher education. For example, the placement department can assist employees in selecting a university and completing the application process for admission to higher education. The support department provides support to employees who advance to higher education through the advanced education program and to doctoral students in the relevant laboratories. For example, the support department can provide research funding and opportunities for collaborative research with companies. The support department can also use AI to ensure the continuity of students' research and maximize research results. For example, the support department can provide research funding, technical support, mentoring, and more. This allows the system according to the embodiment to effectively match corporate challenges with university research results, enabling employees to advance to higher education and support students. This allows the system to produce a large number of employees and new employees with degrees within the company in a few years, realizing a science-based company rather than a science-based nation.

[0069] The collection unit can identify detailed issues based on feedback from personnel in each department and each task. Examples of feedback include, but are not limited to, questionnaire surveys, interviews, and periodic reports. The collection unit can collect feedback from personnel in each department and each task through, for example, questionnaire surveys. The collection unit can also identify detailed issues through interviews. For example, the collection unit can collect feedback from personnel in each department and each task through periodic reports. This enables more accurate issue collection by identifying detailed issues based on feedback from personnel. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the results of a questionnaire survey into AI, which can then identify detailed issues.

[0070] The matching unit can analyze the research themes and results of each laboratory and identify a laboratory that is best suited to the company's challenges. Research themes and results include, but are not limited to, the research field, research progress, and results evaluation methods. The matching unit can, for example, use a generation AI to analyze the research themes and results of each laboratory and identify the laboratory that is best suited to the company's challenges. The matching unit can also identify the laboratory that is best suited to the company's challenges based on the analysis results of the research themes and results. For example, the matching unit can use a generation AI to analyze the research themes and results and identify the laboratory that is best suited to the company's challenges. This identifies the laboratory that is best suited to the company's challenges, thereby improving the accuracy of problem solving. Some or all of the above-described processing in the matching unit can be performed, for example, using a generation AI, or can be performed without a generation AI. For example, the matching unit can input data on research themes and results into a generation AI, which can then identify the laboratory that is best suited to the company's challenges.

[0071] The education department can support employees in their pursuit of higher education. Examples of support include, but are not limited to, assistance with the admission procedures, tuition assistance, and learning support. For example, the education department can provide assistance with admission procedures. The education department can also provide tuition assistance. For example, the education department can provide learning support. This supports employees' education, thereby stimulating research and development within the company. Some or all of the above-mentioned processing in the education department can be performed using, or without, AI. For example, the education department can input assistance with admission procedures into AI, which can then support the admission procedures.

[0072] The support department can provide research funding or opportunities for collaborative research with companies. The provision of research funding includes, but is not limited to, the amount provided, the conditions for provision, and the period of provision. For example, the support department can provide research funding. The support department can also provide opportunities for collaborative research with companies. For example, the support department can provide research funding. By providing research funding or opportunities for collaborative research, this ensures the continuity of students' research and maximizes research results. Some or all of the above-described processing in the support department can be performed, for example, using AI, or can be performed without using AI. For example, the support department can input the provision of research funding into AI, which then provides the research funding.

[0073] The support department can ensure the continuity of students' research and maximize research results. Research continuity includes, but is not limited to, securing research funding, preparing a research environment, and formulating a research plan. For example, the support department can secure research funding. The support department can also prepare a research environment. For example, the support department can formulate a research plan. This ensures the continuity of students' research and maximizes research results, thereby improving the quality of research. Some or all of the above-mentioned processing in the support department can be performed using, for example, AI, or can be performed without using AI. For example, the support department can input the securing of research funding into AI, and the AI ​​can secure the research funding.

[0074] The collection unit can estimate the user's emotions and adjust the timing of task collection based on the estimated user emotions. Emotion estimation includes, but is not limited to, emotion recognition technology, emotion evaluation criteria, and the like. For example, if the user is feeling stressed, the collection unit can collect tasks during a time when the user is able to relax. Furthermore, if the user is concentrating, the collection unit can also collect tasks at that time. For example, if the user is tired, the collection unit can collect tasks after a break. This allows for efficient task collection by adjusting the timing of task collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI, which can then adjust the timing of task collection.

[0075] The collection unit can analyze the past assignment submission history of each department or each task and select an appropriate collection method. The past assignment submission history includes, for example, the type of assignment submitted, the frequency of submission, and the attributes of the submitter, but is not limited to these examples. For example, the collection unit can identify patterns of frequently submitted assignments from the past submission history and select an efficient collection method. The collection unit can also predict which assignments will be submitted frequently during a specific period based on the submission history and select a collection method appropriate for that period. For example, the collection unit can analyze the submission history to identify trends in assignments submitted by a specific person and select a collection method appropriate for that person. This enables efficient assignment collection by analyzing the past submission history. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the past assignment submission history into AI, which then selects the optimal collection method.

[0076] When collecting issues, the collection unit can filter the issues based on the current projects and areas of interest of each department or each business. Examples of current projects and areas of interest include, but are not limited to, the progress of projects and methods for identifying areas of interest. For example, the collection unit can prioritize collection of issues related to ongoing projects. The collection unit can also filter and collect highly relevant issues based on the areas of interest of each department. For example, the collection unit can collect issues at an appropriate time, taking into account the current status of each business. By filtering based on the current projects and areas of interest, highly relevant issues can be efficiently collected. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data on current projects and areas of interest into AI, which then performs filtering.

[0077] When collecting tasks, the collection unit can select an appropriate collection means depending on the input method of the person in charge. Input methods include, but are not limited to, voice input, text input, and image input. For example, the collection unit can provide a task collection means using voice recognition technology to a person in charge who prefers voice input. The collection unit can also provide an easy-to-enter text form to a person in charge who prefers text input. For example, the collection unit can provide a task collection means using image recognition technology to a person in charge who prefers image input. This enables efficient task collection by selecting the optimal collection means depending on the person in charge's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data on the person in charge's input method into AI, which can select the optimal collection means.

[0078] The collection unit can estimate the user's emotions and determine the priority of tasks to be collected based on the estimated user emotions. Emotion estimation can include, but is not limited to, emotion recognition technology, emotion evaluation criteria, and the like. For example, when the user is stressed, the collection unit can prioritize collecting tasks with low importance. Furthermore, when the user is relaxed, the collection unit can prioritize collecting tasks with high importance. For example, when the user is concentrating, the collection unit can prioritize collecting tasks with high urgency. This enables efficient task collection by determining the priority of tasks based on the user's emotions. Emotion estimation can be achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into a generation AI, which can then prioritize the tasks.

[0079] When collecting tasks, the collection unit can prioritize collecting related tasks by taking into account the geographical location information of each department or each task. Examples of geographical location information include, but are not limited to, GPS data, address information, and location information analysis methods. For example, the collection unit can prioritize collecting tasks from geographically close departments or tasks. The collection unit can also filter and collect highly related tasks based on the geographical location information. For example, the collection unit can postpone collection of tasks from geographically distant departments or tasks. This allows highly related tasks to be collected efficiently by taking geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographical location information data into AI, which can select the optimal collection method.

[0080] When collecting issues, the collection unit can analyze social media activity of each department or each business and collect related issues. Social media activity includes, but is not limited to, for example, post content, number of followers, and engagement rate. The collection unit can, for example, analyze social media post content and collect related issues. The collection unit can also prioritize collection of highly relevant issues based on the frequency of social media activity. For example, the collection unit can analyze social media trends and collect related issues. In this way, highly relevant issues can be efficiently collected by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI, or can be performed without using AI. For example, the collection unit can input social media activity data into AI, which can collect related issues.

[0081] When collecting issues, the collection unit can customize the collection method by reflecting past feedback from each department or each task. Past feedback includes, for example, the type of feedback and feedback evaluation criteria, but is not limited to these examples. For example, the collection unit can improve the collection method based on the past feedback and perform efficient issue collection. The collection unit can also customize the collection method by reflecting feedback from each department or each task. For example, the collection unit can optimize the collection means based on the feedback and improve the accuracy of issue collection. This enables efficient issue collection by reflecting past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past feedback data into AI, which can customize the collection method.

[0082] The matching unit can estimate the user's emotion and adjust matching criteria based on the estimated user's emotion. Emotion estimation can include, but is not limited to, emotion recognition technology, emotion evaluation criteria, and the like. For example, the matching unit can apply simple matching criteria when the user is stressed. The matching unit can also apply detailed matching criteria when the user is relaxed. For example, the matching unit can apply strict matching criteria when the user is focused. This enables efficient matching by adjusting the matching criteria based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the matching unit can be performed using, for example, the generation AI. For example, the matching unit can input the user's emotion data into the generation AI, which can then adjust the matching criteria.

[0083] The matching unit can improve the accuracy of matching by taking into account the interrelationship between a company's issues and research results during matching. Examples of interrelationships include, but are not limited to, the relevance between the issues and research results and methods for evaluating the interactions. For example, the matching unit can analyze the relevance between a company's issues and research results to perform optimal matching. The matching unit can also improve the accuracy of matching by taking into account the interrelationship between the issues and research results. For example, the matching unit can identify the optimal laboratory based on the interrelationship between a company's issues and research results. This improves the accuracy of matching by taking into account the interrelationship between a company's issues and research results. Some or all of the above-described processing in the matching unit can be performed using, or without, a generation AI. For example, the matching unit can input data on a company's issues and research results into a generation AI, which can analyze the interrelationship to improve the accuracy of matching.

[0084] When matching, the matching unit can perform matching by taking into account attribute information of the company's assignment submitter. Attribute information includes, for example, age, gender, job position, etc., but is not limited to these examples. The matching unit can, for example, match the most appropriate laboratory by taking into account the assignment submitter's field of expertise. The matching unit can also match the appropriate laboratory by taking into account the assignment submitter's years of experience. For example, the matching unit can match the most appropriate laboratory based on the assignment submitter's past submission history. This enables optimal matching by taking into account the assignment submitter's attribute information. Some or all of the above-mentioned processing in the matching unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the matching unit can input the assignment submitter's attribute information into a generation AI, which can then match the most appropriate laboratory.

[0085] During matching, the matching unit can set a matching weight based on the frequency of assignment submissions from companies. Submission frequency includes, but is not limited to, the number of submissions and the interval between submissions. For example, the matching unit can weight frequently submitted assignments and match them to the most suitable laboratories. The matching unit can also prioritize matching for frequently submitted assignments. For example, the matching unit can improve matching accuracy by weighting assignments based on submission frequency. This enables efficient matching by weighting assignments based on assignment submission frequency. Some or all of the above-described processing in the matching unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the matching unit can input submission frequency data into the generation AI, which can then weight the assignments to improve matching accuracy.

[0086] The matching unit can estimate the user's emotions and adjust the display order of matching results based on the estimated user emotions. Emotion estimation includes, but is not limited to, emotion recognition technology, emotion evaluation criteria, and the like. For example, if the user is stressed, the matching unit can display results in a simple order. Furthermore, if the user is relaxed, the matching unit can display results in a detailed order. For example, if the user is concentrating, the matching unit can display results in order of importance. This enables efficient matching by adjusting the display order of results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the matching unit may be performed using, for example, the generation AI. For example, the matching unit can input the user's emotion data into the generation AI, which can then adjust the display order of the results.

[0087] When matching, the matching unit can perform matching based on the geographic distribution of the company's tasks. Geographic distribution includes, but is not limited to, for example, the distribution of tasks by region and geographic influences. For example, the matching unit can prioritize matching with laboratories that are geographically close. The matching unit can also consider the geographic distribution of tasks to match the optimal laboratory. For example, the matching unit can improve the accuracy of matching by considering geographic factors. This enables efficient matching by considering the geographic distribution. Some or all of the above-mentioned processing in the matching unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the matching unit can input geographic distribution data into the generation AI, which can then match the optimal laboratory.

[0088] During matching, the matching unit can improve the accuracy of matching by referring to literature related to the company's issues. Examples of related literature include, but are not limited to, academic papers, patent documents, and technical reports. For example, the matching unit can analyze literature related to the issue to match the most appropriate laboratory. The matching unit can also improve the accuracy of matching based on the content of the literature. For example, the matching unit can refer to the latest research results related to the issue to match the most appropriate laboratory. By referring to the related literature, the accuracy of matching is improved. Some or all of the above-described processing in the matching unit may be performed using, or without, a generation AI. For example, the matching unit can input data on related literature into the generation AI, which then analyzes the literature to improve the accuracy of matching.

[0089] During matching, the matching unit can perform matching based on the market value of the company's project. Market value includes, but is not limited to, market size, competitive situation, and demand forecast. For example, the matching unit can match the most suitable laboratory to a project with high market value. The matching unit can also improve the accuracy of matching by taking into account the market value of the project. For example, the matching unit can prioritize matching to projects with high market value. This enables efficient matching by taking market value into consideration. Some or all of the above-mentioned processing in the matching unit may be performed using, or without, a generation AI. For example, the matching unit can input market value data into the generation AI, which can then match the most suitable laboratory.

[0090] The advancement department can estimate the user's emotions and adjust the support method for continuing education based on the estimated user emotions. Emotion estimation includes, but is not limited to, emotion recognition technology, emotion evaluation criteria, and the like. For example, if the user is stressed, the advancement department can provide a simple support method for continuing education. Furthermore, if the user is relaxed, the advancement department can provide a detailed support method for continuing education. For example, if the user is focused, the advancement department can provide a strict support method for continuing education. This enables efficient support for continuing education by adjusting the support method for continuing education based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the advancement department can be performed using AI, for example, or without AI. For example, the advancement department can input the user's emotion data into a generation AI, which can then adjust the support method for continuing education.

[0091] When an employee advances to higher education, the education department can analyze the employee's past educational background and work history to select an appropriate path for further education. Educational background and work history include, but are not limited to, degrees, work experience, and specialized fields. The education department can select the optimal path for further education, for example, based on the employee's past educational background. The education department can also select an appropriate path for further education by taking into account the employee's work history. For example, the education department can suggest the optimal path for further education based on the employee's past experience. This allows the optimal path for further education to be selected by analyzing the employee's past educational background and work history. Some or all of the above-described processing in the education department can be performed using, for example, AI, or without AI. For example, the education department can input data on the employee's educational background and work history into AI, which can then select the optimal path for further education.

[0092] The advancement department can adjust the advancement method based on the employee's current work situation when advancing to higher education. Work situation includes, but is not limited to, the progress of work, the type of work, and the priority of work. For example, the advancement department can customize the advancement method taking into account the current work situation. The advancement department can also adjust the timing of advancement to avoid busy periods. For example, the advancement department can optimize the advancement method according to the work content. Customizing the advancement method based on the current work situation enables efficient advancement. Some or all of the above-described processing in the advancement department may be performed using, or without, AI. For example, the advancement department can input data on the employee's work situation into AI, which can then select the optimal advancement method.

[0093] The admissions department can improve its support methods for university admission by reflecting employee feedback at the time of admission. Examples of feedback include, but are not limited to, the type of feedback and feedback evaluation criteria. For example, the admissions department can improve its support methods for university admission based on employee feedback. The admissions department can also optimize the means of university admission by reflecting the feedback. For example, the admissions department can incorporate employee opinions and customize the support methods for university admission. By reflecting the feedback, the support methods for university admission can be improved. Some or all of the above-described processing in the admissions department may be performed using, for example, AI, or may be performed without using AI. For example, the admissions department can input employee feedback data into AI, which can then improve the support methods for university admission.

[0094] The advancement department can estimate the user's emotions and determine the advancement priority based on the estimated user emotions. Emotion estimation includes, but is not limited to, emotion recognition technology, emotion evaluation criteria, and the like. For example, if the user is feeling stressed, the advancement department can set the advancement priority to low. Furthermore, if the user is relaxed, the advancement department can set the advancement priority to high. For example, if the user is concentrating, the advancement department can set the advancement priority to highest. This enables efficient advancement by determining the advancement priority based on the user's emotions. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the advancement department can be performed using AI, for example, or without AI. For example, the advancement department can input the user's emotion data into a generation AI, which can then determine the advancement priority.

[0095] When an employee advances to higher education, the department can select an appropriate path for further education by taking into account the employee's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, address information, and location information analysis methods. For example, the department can prioritize selecting a university or research lab that is geographically close. The department can also suggest the optimal path for further education based on the geographical location information. For example, the department can optimize the means of further education by taking geographical factors into account. This allows the optimal path for further education to be selected by taking geographical location information into consideration. Some or all of the above-described processing in the department can be performed using, or without, AI. For example, the department can input geographical location information data into AI, which can then select the optimal path for further education.

[0096] The admissions department can analyze employees' social media activity to suggest ways to advance to higher education when they apply for higher education. Social media activity includes, but is not limited to, the content of posts, the number of followers, and engagement rates. For example, the admissions department can suggest optimal ways to advance to higher education based on the content of social media activity. The admissions department can also analyze social media posts to customize the admissions department. For example, the admissions department can suggest ways to advance to higher education based on the educational status of friends on social media. By analyzing social media activity, the department can suggest optimal ways to advance to higher education. Some or all of the above-described processing in the admissions department may be performed using, for example, AI, or may be performed without AI. For example, the admissions department can input social media activity data into AI, which can then suggest optimal ways to advance to higher education.

[0097] When an employee advances to higher education, the advancement department can adjust the support method for advancement by reflecting past feedback from the employee. Examples of feedback include, but are not limited to, the type of feedback and feedback evaluation criteria. For example, the advancement department can customize the support method for advancement based on past feedback. The advancement department can also reflect the feedback and optimize the means for advancement. For example, the advancement department can improve the support method for advancement by referring to past feedback. This allows the support method for advancement to be customized by reflecting past feedback. Some or all of the above-described processing in the advancement department may be performed using, for example, AI, or may be performed without using AI. For example, the advancement department can input past feedback data into AI, which can then adjust the support method for advancement.

[0098] The support unit can estimate the user's emotions and adjust the support method based on the estimated user's emotions. Emotion estimation can include, but is not limited to, emotion recognition technology, emotion evaluation criteria, and the like. For example, if the user is feeling stressed, the support unit can provide a simple support method. Furthermore, if the user is relaxed, the support unit can provide a detailed support method. For example, if the user is concentrating, the support unit can provide a strict support method. This enables efficient support by adjusting the support method based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the support unit can be performed using, for example, AI, or without AI. For example, the support unit can input the user's emotion data into the generation AI, which can then adjust the support method.

[0099] When providing support, the support department can analyze the student's past research history and select an appropriate support method. Research history includes, but is not limited to, for example, research topic, research results, and research progress. The support department can select the optimal support method, for example, based on the student's past research history. The support department can also select an appropriate support method by taking the research history into consideration. For example, the support department can propose the optimal support method based on the student's past research results. This allows the optimal support method to be selected by analyzing the past research history. Some or all of the above-mentioned processing in the support department can be performed, for example, using AI, or can be performed without using AI. For example, the support department can input data on the student's research history into AI, which can select the optimal support method.

[0100] When providing support, the support unit can adjust the support measures based on the student's current research status. Examples of research status include, but are not limited to, the research progress, the type of research, and the research priority. For example, the support unit can customize the support measures taking into account the current research status. The support unit can also optimize the support measures according to the research progress. For example, the support unit can customize the support measures according to the research content. This enables efficient support by customizing the support measures based on the current research status. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input data on the student's research status into AI, which can then select the optimal support measures.

[0101] The support unit can improve the support method by reflecting the student's feedback during support. The feedback can include, but is not limited to, for example, the type of feedback and feedback evaluation criteria. The support unit can improve the support method, for example, based on the student's feedback. The support unit can also reflect the feedback and optimize the support means. For example, the support unit can incorporate the student's opinion and customize the support method. In this way, the support method can be improved by reflecting the feedback. Some or all of the above-mentioned processing in the support unit can be performed, for example, using AI or without using AI. For example, the support unit can input student feedback data into AI, which can then improve the support method.

[0102] The support unit can estimate the user's emotions and determine the priority of support based on the estimated user emotions. Emotion estimation can include, but is not limited to, emotion recognition technology, emotion evaluation criteria, and the like. For example, if the user is feeling stressed, the support unit can set the priority of support to a low level. Furthermore, if the user is relaxed, the support unit can set the priority of support to a high level. For example, if the user is concentrating, the support unit can set the priority of support to the highest level. This enables efficient support by determining the priority of support based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the support unit can be performed using, for example, an AI, or without an AI. For example, the support unit can input the user's emotion data into the generation AI, which can then determine the priority of support.

[0103] When providing support, the support unit can select an appropriate support method by taking into account the student's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, address information, and location information analysis methods. For example, the support unit can prioritize selecting a geographically close support method. The support unit can also propose an optimal support method based on the geographical location information. For example, the support unit can optimize support methods by taking geographical factors into account. This allows the optimal support method to be selected by taking the geographical location information into account. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input geographical location information data into AI, which can then select the optimal support method.

[0104] When providing support, the support department can analyze the student's social media activity and suggest support measures. Social media activity includes, but is not limited to, for example, the content of posts, the number of followers, and the engagement rate. The support department can suggest optimal support measures, for example, based on the content of social media activity. The support department can also analyze the content of social media posts and customize support measures. For example, the support department can suggest support measures based on the activity of friends on social media. In this way, the analysis of social media activity can suggest optimal support measures. Some or all of the above-described processing in the support department can be performed, for example, using AI, or can be performed without using AI. For example, the support department can input social media activity data into AI, which can then suggest optimal support measures.

[0105] When providing support, the support unit can adjust the support method by reflecting the student's past feedback. The feedback includes, for example, the type of feedback and feedback evaluation criteria, but is not limited to these examples. The support unit can, for example, customize the support method based on the past feedback. The support unit can also reflect the feedback and optimize the support means. For example, the support unit can improve the support method by referring to the past feedback. In this way, the support method can be customized by reflecting the past feedback. Some or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input data of past feedback into AI, which can adjust the support method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, matching unit, advancement unit, and support unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 and collects assignments for each department and each business of the company. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and matches the collected assignments with research results from each university and each laboratory. The advancement unit is realized, for example, by the control unit 46A of the smart device 14 and allows employees to advance to doctoral programs at matched laboratories. The support unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides support to employees who have advanced to doctoral programs and to doctoral students at the corresponding laboratories. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, matching unit, advancement unit, and support unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 and collects assignments for each department and each business of a company. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and matches the collected assignments with research results from each university and each laboratory. The advancement unit is realized, for example, by the control unit 46A of the smart glasses 214 and advances employees to doctoral programs at matched laboratories. The support unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides support to employees who have advanced to doctoral programs and to doctoral students at the corresponding laboratories. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, matching unit, advancement unit, and support unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset terminal 314 and collects assignments for each department and each business of the company. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and matches the collected assignments with research results from each university and each laboratory. The advancement unit is realized, for example, by the control unit 46A of the headset terminal 314 and allows employees to advance to the doctoral program of the matched laboratory. The support unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides support to employees who have advanced to higher education and to doctoral students in the corresponding laboratory. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, matching unit, advancement unit, and support unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 and collects assignments for each department and each business of the company. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and matches the collected assignments with research results from each university and each laboratory. The advancement unit is realized, for example, by the control unit 46A of the robot 414 and advances employees to doctoral programs at matched laboratories. The support unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides support to employees who have advanced to doctoral programs and to doctoral students at the corresponding laboratories.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] When collecting issues for each department and business operation in a company, the data collection department can evaluate the automation level of each department's business process and select an appropriate collection method. For example, the data collection department can use automated data collection tools for departments with highly automated business processes. The data collection department can also collect issues through interviews and questionnaire surveys for departments where business processes are performed manually. Furthermore, the data collection department can adjust the collection frequency and collection method depending on the automation level of the business process. This enables efficient issue collection based on the automation level of the business process.

[0108] When matching a company's challenges with the research results of each laboratory, the Matching Department can take into account the company's long-term strategic goals. For example, the Matching Department can prioritize matching research themes that will be important in the future based on the company's five-year growth strategy. The Matching Department can also prioritize matching environmentally friendly research themes based on the company's sustainability goals. Furthermore, the Matching Department can prioritize matching research themes related to digital technology based on the company's digital transformation strategy. This allows the optimal laboratory to be identified based on the company's long-term strategic goals.

[0109] When supporting employees' further education, the Preparatory School can select a university that takes into consideration the employee's career path. For example, if an employee is aiming for a leadership position in the future, the Preparatory School can select a university that offers a program to strengthen leadership skills. Also, if an employee wants to deepen their specialized skills, the Preparatory School can select a research lab that specializes in that field. Furthermore, if an employee is aiming for an international career, the Preparatory School can select a university with an international network. This allows the school to select the optimal university based on the employee's career path.

[0110] When providing research funding or opportunities for collaborative research with companies, the Support Department can evaluate the social impact of research and determine support priorities. For example, the Support Department can evaluate the impact of research on the local community and prioritize support for research that contributes to the local community. The Support Department can also evaluate the impact of research on the environment and prioritize support for environmentally friendly research. Furthermore, the Support Department can evaluate the impact of research on the economy and prioritize support for research that contributes to economic growth. This allows the optimal support to be provided, taking into account the social impact of research.

[0111] The support department can strengthen students' research networks to ensure continuity of their research and maximize their research results. For example, the support department can provide opportunities for students to conduct collaborative research with other researchers. The support department can also support students in participating in international research conferences. Furthermore, the support department can provide support for students to exchange information with other researchers through online platforms. This can strengthen students' research networks and maximize their research continuity and results.

[0112] The collection unit can estimate the user's emotions and customize the task collection method based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can collect tasks in the form of simple questions. If the user is relaxed, the collection unit can collect tasks through a detailed questionnaire. Furthermore, if the user is concentrating, the collection unit can collect tasks in the form of an interview. This allows for efficient task collection by customizing the task collection method according to the user's emotions.

[0113] The matching unit can estimate the user's emotions and adjust the format in which the matching results are displayed based on the estimated user's emotions. For example, if the user is feeling stressed, the matching unit can display the results in a simple list format. If the user is feeling relaxed, the matching unit can display the results using detailed graphs and charts. Furthermore, if the user is concentrating, the matching unit can display the results in an interactive dashboard format. This allows for efficient information provision by adjusting the display format of the results based on the user's emotions.

[0114] The college entrance department can estimate the user's emotions and adjust the support content for further education based on the estimated user's emotions. For example, if the user is feeling stressed, the college entrance department can provide a simple college entrance guide. If the user is relaxed, the college entrance department can also provide a detailed college entrance plan. Furthermore, if the user is concentrating, the college entrance department can provide individual college entrance counseling. This allows for efficient support for further education by adjusting the support content based on the user's emotions.

[0115] The support unit can estimate the user's emotions and adjust the timing of support based on the estimated user's emotions. For example, if the user is feeling stressed, the support unit can provide support during a time period when the user is able to relax. Also, if the user is relaxed, the support unit can provide support at that time. Furthermore, if the user is concentrating, the support unit can prioritize providing support with high urgency. This allows for efficient support by adjusting the timing of support according to the user's emotions.

[0116] The support unit can estimate the user's emotions and customize the content of support based on the estimated user's emotions. For example, if the user is feeling stressed, the support unit can provide support content that helps the user relax. Furthermore, if the user is relaxed, the support unit can provide detailed support content. Furthermore, if the user is concentrating, the support unit can provide specialized support content. This enables efficient support by customizing the content of support based on the user's emotions.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The collection department collects issues in each department or business of the company. The collection department can identify detailed issues based on feedback from the people in charge of each department or business. It is also possible to use AI to automatically collect issues in each department or business of the company. For example, issues can be collected through questionnaire surveys or interviews. Step 2: The matching department matches the issues collected by the collection department with the research results of each university and laboratory. The matching department uses generation AI to analyze the research themes and results of each laboratory and identify the laboratory that is best suited to the company's issues. For example, generation AI can analyze research themes and results and identify the laboratory that is best suited to the company's issues. Step 3: The Department of Advancement allows employees to advance to doctoral programs in laboratories matched by the Matching Department. The Department of Advancement can support employees in the procedures for continuing their education. It is also possible to use AI to support employees in their further education. For example, it can assist with selecting a school to continue their education and the procedures for entering the school. Step 4: The Support Department provides support to employees who have advanced to higher education and to doctoral students in the relevant laboratories. The Support Department can provide research funding and opportunities for joint research with companies. It can also use AI to ensure the continuity of students' research and maximize research results. For example, it can provide research funding, technical support, mentoring, etc.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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 collection department that collects issues from each department or business of the company; a matching unit that matches the issues collected by the collection unit with the research results of each university and each laboratory; a department for advancing employees to doctoral courses in laboratories matched by the matching department; and a support department that provides support to employees who have advanced to higher education through the advanced education department and to doctoral students in the relevant laboratory. A system characterized by:

2. The collecting unit Identify detailed issues based on feedback from each department and each task manager 2. The system of claim 1.

3. The matching unit Analyze the research themes and results of each laboratory and identify laboratories that are best suited to the company's challenges 2. The system of claim 1.

4. The above-mentioned college of further education is Supporting employees' further education 2. The system of claim 1.

5. The support unit Providing research funding or opportunities for collaborative research with companies 2. The system of claim 1.

6. The support unit Ensuring continuity of student research and maximizing research results 2. The system of claim 1.

7. The collecting unit Estimate user emotions and adjust the timing of task collection based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Analyze the past assignment submission history of each department and each task and select the appropriate collection method 2. The system of claim 1.

Images