system

Abstract

The system according to this embodiment aims to provide efficient educational support to new employees and new participants. [Solution] The system according to this embodiment comprises an information gathering unit, an education support unit, and an evaluation unit. The information gathering unit collects general knowledge from company documents and the web. The education support unit provides pair programming and education support to new employees and new participants based on the information collected by the information gathering unit. The evaluation unit evaluates the results of the education provided by the education support unit and determines whether it is successful or not.

Description

Technical Field

[0006] , , ,

[0005] , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, educational support for new employees and new participants has not been sufficiently provided, and there is room for improvement in efficient engineer training.

[0005] The system according to the embodiment aims to provide efficient educational support for new employees and new participants.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an information gathering unit, an education support unit, and an evaluation unit. The information gathering unit collects general knowledge from internal company documents and the web. The education support unit provides pair programming and education support to new employees and new participants based on the information collected by the information gathering unit. The evaluation unit evaluates the results of the education provided by the education support unit and determines whether it is successful or not. [Effects of the Invention]

[0007] The system according to this embodiment can provide efficient educational support to new employees and new participants. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An engineer training system according to an embodiment of the present invention is a system that trains engineers who can judge the success or failure of deliverables by having an AI agent provide pair programming and educational support to new recruits and new participants based on internal company documents and general knowledge found on the web. This engineer training system collects internal company documents and general knowledge found on the web and uses this to provide pair programming and educational support to new recruits and new participants. Next, the engineer training system evaluates the deliverables of the trainees and determines whether they are successful or unsuccessful. Furthermore, after the quality and reliability of the engineer training system's deliverables have improved, it becomes possible to request the engineer training system to perform tasks and operate it as an AI employee. This system targets individuals studying to acquire qualifications, as well as companies and schools that provide training for new recruits and mid-career hires, with the aim of reducing training costs and achieving efficient human resource development. For example, the engineer training system collects internal company documents and general knowledge found on the web. For example, the engineer training system provides pair programming and educational support to new recruits and new participants based on the collected information. For example, the engineer training system evaluates the deliverables of the trainees and determines whether they are successful or unsuccessful. For example, the engineer training system provides assistance in setting up a development environment for programs (e.g., Java®) and assists in using internal company technologies. For example, in an engineer training system, once the quality and reliability of the AI ​​agent's deliverables have improved, the AI ​​agent can be assigned tasks and put into operation as an AI employee. This allows the engineer training system to cultivate engineers who can use internal documents and general knowledge found on the web to provide pair programming and educational support to new recruits and new participants, and who can judge the success or failure of deliverables.

[0029] The engineer training system according to this embodiment comprises an information gathering unit, an education support unit, and an evaluation unit. The information gathering unit collects general knowledge from internal company documents and the web. For example, the information gathering unit can collect information from internal technical documents and manuals, as well as from technical blogs and forums on the web. The information gathering unit can also automate information collection using AI. For example, the information gathering unit can periodically collect information from the web using a web crawler and store it in a database. The education support unit provides pair programming and educational support to new employees and new participants based on the collected information. For example, the education support unit can set up pair programming sessions and code together with new employees and new participants. The education support unit can also provide online learning materials and tutorials to create an environment where trainees can learn independently. The education support unit can also customize the content of educational support using AI. For example, the education support unit can provide appropriate learning materials and assignments according to the trainee's skill level and learning progress. The evaluation unit evaluates the results of the education provided by the education support unit and determines its success or failure. The evaluation unit can, for example, assess the quality and completeness of programs created by trainees. It can also evaluate the trainees' learning progress and understanding. The evaluation unit can also automate evaluations using AI. For example, it can analyze program code to detect bugs and errors. Furthermore, it can analyze trainees' learning data to automatically calculate grades and evaluations. As a result, the engineer training system according to this embodiment can cultivate engineers who can provide pair programming and training support to new recruits and new participants, based on internal documents and general knowledge found on the web, and who can judge the success or failure of deliverables.

[0030] The Information Gathering Department collects internal documents and general knowledge from the web. For example, it can gather information from internal technical documents and manuals, as well as online technical blogs and forums. Specifically, internal technical documents and manuals contain detailed information on project progress, technical challenges, and solutions. By collecting this information, engineers can understand potential problems they may face and their solutions. Online technical blogs and forums share the latest technology trends, best practices, and experiences of other engineers. By collecting this information, engineers can learn about the latest technology trends and apply them in practice. The Information Gathering Department can also automate information gathering using AI. For example, it can use a web crawler to periodically collect information from the web and store it in a database. The web crawler visits specified websites and automatically collects new posts from technical blogs and forums. The collected information is classified and organized using natural language processing technology and stored in the database. This allows the Information Gathering Department to efficiently gather information from a wide range of sources and provide the knowledge necessary for engineer training. Furthermore, the Information Gathering Department can evaluate the quality of the collected information and select only reliable information. For example, the information gathering department prioritizes collecting highly reliable information, using criteria such as the source of the information, the accuracy of its content, and the frequency of updates. This allows the information gathering department to provide high-quality information necessary for engineer training, thereby improving the overall reliability and effectiveness of the system.

[0031] The Education Support Department provides pair programming and educational support to new recruits and new participants based on the information it collects. For example, the Education Support Department sets up pair programming sessions and codes together with new recruits and new participants. Pair programming is an effective way for experienced engineers and new recruits to acquire practical skills by working together. The Education Support Department provides an efficient learning environment by coordinating pair programming schedules and forming appropriate pairs. The Education Support Department can also provide online learning materials and tutorials to create an environment where trainees can learn independently. These online materials and tutorials include videos and interactive content, allowing trainees to learn at their own pace. The Education Support Department can also customize the content of educational support using AI. For example, the Education Support Department provides appropriate materials and assignments according to the trainee's skill level and learning progress. The AI ​​analyzes the trainee's learning history and performance data to create a customized learning plan tailored to individual needs. This allows the Education Support Department to efficiently support the skill improvement of trainees and maximize the effectiveness of engineer training. Furthermore, the Educational Support Department can collect feedback from trainees and use it to improve the curriculum. For example, it can analyze how trainees reacted to different teaching materials and assignments to improve the quality of the curriculum. This allows the Educational Support Department to provide effective educational support that constantly incorporates the latest information and technologies, thereby enhancing the quality of engineer training.

[0032] The Evaluation Department evaluates the outcomes of education conducted by the Educational Support Department and determines their success or failure. For example, the Evaluation Department evaluates the quality and completeness of programs created by trainees. Specifically, it objectively evaluates trainees' skills using criteria such as the accuracy, efficiency, and maintainability of the program code. The Evaluation Department can also evaluate trainees' learning progress and understanding. For example, it assesses how much knowledge trainees have acquired and what tasks they have completed to understand their learning progress. The Evaluation Department can also automate evaluation using AI. For example, the Evaluation Department analyzes program code and detects bugs and errors. AI automatically evaluates the quality of the code and points out problems using static and dynamic analysis tools. The Evaluation Department also analyzes trainees' learning data and automatically calculates grades and evaluations. Based on trainees' learning history and performance data, AI calculates individual grades and provides evaluation results. This allows the Evaluation Department to efficiently and accurately evaluate educational outcomes and support the skill improvement of trainees. Furthermore, the evaluation department can provide the evaluation results as feedback to the education support department, which can then be used to improve the educational content. For example, based on the evaluation results, the education support department can provide additional teaching materials and assignments to reinforce the weaknesses of the trainees. In this way, the evaluation department can work in cooperation with the education support department to continuously improve the quality of engineer training.

[0033] The information gathering department can collect internal company documents and general knowledge from the web. For example, the information gathering department can collect internal technical documents and manuals. For example, the information gathering department can also collect information from technical blogs and forums on the web. For example, the information gathering department can automate information collection using AI. This allows the information gathering department to provide the information necessary for educational support. Some or all of the above processes in the information gathering department may be performed using AI, for example, or not using AI. For example, the information gathering department may periodically collect information from the web using a web crawler and store it in a database.

[0034] The Education Support Department can provide pair programming and educational support to new employees and new participants based on the information it has collected. For example, the Education Support Department can set up pair programming sessions and code together with new employees and new participants. The Education Support Department can also provide online learning materials and tutorials to create an environment where trainees can learn independently. The Education Support Department can also customize the content of educational support using AI. This allows the Education Support Department to help new employees and new participants improve their skills. Some or all of the above processes performed by the Education Support Department may be done using AI or not. For example, the Education Support Department can provide appropriate learning materials and assignments according to the trainees' skill levels and learning progress.

[0035] The evaluation unit can assess the deliverables of the trainees and determine their success or failure. For example, the evaluation unit can evaluate the quality and completeness of the programs created by the trainees. The evaluation unit can also evaluate the trainees' learning progress and understanding. The evaluation unit can also automate the evaluation process using AI, for example. This allows the evaluation unit to confirm the effectiveness of the education. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can analyze the program code and detect bugs and errors.

[0036] The Education Support Department can provide assistance with setting up program development environments and assisting with the use of in-house technologies. For example, the Education Support Department can provide procedures for setting up program development environments. The Education Support Department can also provide tutorials explaining how to use in-house technologies. The Education Support Department can also customize the content of educational support using AI. This allows the Education Support Department to help improve the skills of those being trained. Some or all of the above processes performed by the Education Support Department may be performed using AI, for example, or not. For example, the Education Support Department can provide appropriate teaching materials and assignments according to the skill level and learning progress of those being trained.

[0037] After the quality and reliability of the AI ​​agent's deliverables have improved, the evaluation department can assign tasks to the AI ​​agent and have it operate as an AI employee. For example, the evaluation department can evaluate the quality and reliability of the AI ​​agent's deliverables. The evaluation department can also assign new tasks to the AI ​​agent and evaluate the deliverables. The evaluation department can also automate the evaluation process using AI. This allows the evaluation department to improve the efficiency of its operations. Some or all of the processes described above in the evaluation department may be performed using AI, or not. For example, the evaluation department can evaluate the quality and reliability of the AI ​​agent's deliverables.

[0038] The information gathering unit can analyze past information gathering history and select the optimal information gathering method. For example, the information gathering unit can identify the most effective information gathering method from past information gathering history and use it preferentially. The information gathering unit can also analyze past information gathering history and optimize the frequency and timing of information gathering. For example, the information gathering unit can prioritize information gathering from specific information sources based on past information gathering history. This allows the information gathering unit to select the optimal information gathering method. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input past information gathering history into a generating AI and have the generating AI select the optimal information gathering method.

[0039] The information gathering unit can filter information based on the user's current projects and areas of interest during the information gathering process. For example, the information gathering unit can prioritize collecting information related to the user's current projects. The information gathering unit can also filter and collect highly relevant information based on the user's areas of interest. The information gathering unit can also provide necessary information in a timely manner according to the progress of the user's projects. This allows the information gathering unit to provide highly relevant information. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input the user's project information into a generating AI and have the generating AI perform the filtering of highly relevant information.

[0040] The information collection unit can prioritize collecting highly relevant information based on the user's geographical location information during information collection. For example, the information collection unit can prioritize collecting information related to the user's current location. The information collection unit can also collect region-specific information based on the user's geographical location information. The information collection unit can also collect highly relevant information by considering the user's travel history. This allows the information collection unit to provide region-specific information. Some or all of the above processing in the information collection unit may be performed using AI, for example, or without AI. For example, the information collection unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant information.

[0041] The information gathering unit can analyze the user's social media activity and collect relevant information during the information gathering process. For example, the information gathering unit can analyze the content of the user's social media posts and collect relevant information. The information gathering unit can also collect relevant information based on the user's social media followers and the accounts they follow. For example, the information gathering unit can analyze the user's social media activity history and collect information based on their interests. This allows the information gathering unit to provide relevant information. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input the user's social media data into a generating AI and have the generating AI collect relevant information.

[0042] The Educational Support Department can adjust the level of detail of its support based on the level of understanding of the students being taught. For example, if the student's level of understanding is low, the Educational Support Department can provide a detailed explanation. If the student's level of understanding is high, the Educational Support Department can provide a concise explanation. The Educational Support Department can also adjust the pace of support according to the student's level of understanding. This enables the Educational Support Department to provide effective educational support. Some or all of the above processes in the Educational Support Department may be performed using AI, for example, or without AI. For example, the Educational Support Department can input student understanding data into a generating AI and have the generating AI adjust the level of detail of the support.

[0043] The Educational Support Department can apply different support algorithms to students according to their learning style. For example, for students with a visual learning style, the Educational Support Department can provide support that makes extensive use of diagrams and graphs. For students with an auditory learning style, the Educational Support Department can also provide support that makes extensive use of audio explanations. For students with an experiential learning style, the Educational Support Department can also provide support that makes extensive use of practical exercises. This enables the Educational Support Department to provide effective educational support. Some or all of the above processing in the Educational Support Department may be performed using AI, for example, or without AI. For example, the Educational Support Department can input the student's learning style data into a generating AI and have the generating AI execute the application of support algorithms.

[0044] The Educational Support Department can prioritize support based on the submission deadlines of the students receiving education. For example, the Educational Support Department can prioritize support for students whose submission deadlines are approaching. For example, the Educational Support Department can also postpone support for students whose submission deadlines are far away. For example, the Educational Support Department can adjust the support schedule according to the submission deadlines. This enables the Educational Support Department to provide effective educational support. Some or all of the above processes in the Educational Support Department may be performed using AI, for example, or not using AI. For example, the Educational Support Department can input student submission timing data into a generating AI and have the generating AI determine the support priorities.

[0045] The Educational Support Department can adjust the order of support based on the relevance of the educators during the provision of educational support. For example, the Educational Support Department may determine the order of support based on the importance of the educators' projects. The Educational Support Department may also adjust the order of support based on the skill level of the educators. The Educational Support Department may also determine the order of support based on the areas of interest of the educators. This enables the Educational Support Department to provide effective educational support. Some or all of the above processes in the Educational Support Department may be performed using AI, for example, or not using AI. For example, the Educational Support Department can input the relevance data of the educators into a generating AI and have the generating AI perform the adjustment of the order of support.

[0046] The evaluation unit can perform evaluations while considering the geographical distribution of deliverables. For example, the evaluation unit can analyze the geographical distribution of deliverables and perform evaluations for each region. The evaluation unit can also adjust the evaluation criteria while considering the geographical distribution of deliverables. For example, the evaluation unit can improve the accuracy of the evaluation based on the geographical distribution of deliverables. This enables the evaluation unit to perform region-specific evaluations. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input geographical distribution data of deliverables into a generating AI and have the generating AI perform the evaluation.

[0047] The evaluation unit can improve the accuracy of its evaluation by referring to relevant literature for the deliverables during the evaluation process. For example, the evaluation unit can refer to relevant literature for the deliverables and set evaluation criteria. The evaluation unit can also improve the accuracy of its evaluation based on relevant literature for the deliverables. The evaluation unit can also adjust the evaluation criteria by considering relevant literature for the deliverables. This improves the accuracy of the evaluation. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input relevant literature data for the deliverables into a generating AI and have the generating AI perform the evaluation.

[0048] The Education Support Department can adjust the level of detail of its assistance based on the skill level of the trainees when providing support for setting up program development environments or using internal technologies. For example, the Education Support Department can provide detailed procedures and explanations to beginner trainees. For example, it can provide only the necessary procedures to intermediate trainees, omitting detailed explanations. For example, it can provide only an outline of the procedures to advanced trainees, allowing them to build upon it freely. This enables the Education Support Department to provide effective educational support. Some or all of the above processes in the Education Support Department may be performed using AI, for example, or not. For example, the Education Support Department can input trainee skill level data into a generating AI and have the generating AI adjust the level of detail of the assistance.

[0049] The Education Support Department can provide optimal support when assisting with program development environment setup or the use of in-house technologies, taking into account the project status of the trainees. For example, the Education Support Department can provide necessary support according to the progress of the trainees' projects. The Education Support Department can also determine the priority of support based on the importance of the trainees' projects. The Education Support Department can also adjust the support schedule according to the deadlines of the trainees' projects. This enables the Education Support Department to provide effective educational support. Some or all of the above processes in the Education Support Department may be performed using AI, for example, or not. For example, the Education Support Department can input project status data of trainees into a generating AI and have the generating AI provide the optimal support.

[0050] After the quality and reliability of the AI ​​agent's deliverables have improved, the evaluation department can request the AI ​​agent to perform tasks and, when deploying it as an AI employee, select the optimal task by referring to its past task history. For example, the evaluation department can analyze the AI ​​agent's past task history and select the optimal task. For example, the evaluation department can also determine task priorities based on the AI ​​agent's past task history. For example, the evaluation department can adjust task schedules by referring to the AI ​​agent's past task history. This allows the evaluation department to select the optimal task. Some or all of the above processes in the evaluation department may be performed using AI, or not using AI. For example, the evaluation department can input the AI ​​agent's past task history data into a generating AI and have the generating AI select the optimal task.

[0051] After the quality and reliability of the AI ​​agent's deliverables have improved, the evaluation unit can assign tasks to the AI ​​agent and, when deploying it as an AI employee, determine the priority of its work based on the importance of the tasks. For example, the evaluation unit can determine the priority of the AI ​​agent's work based on the importance of the tasks. The evaluation unit can also adjust the AI ​​agent's work schedule based on the task deadline, for example. The evaluation unit can also adjust the level of detail of the AI ​​agent's work based on the complexity of the tasks, for example. This allows the evaluation unit to prioritize important tasks. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input task importance data into a generating AI and have the generating AI perform the task priority determination.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The Educational Support Department can apply different support algorithms depending on the learning style of the students being taught. For example, students with a visual learning style can receive support that makes extensive use of diagrams and graphs. Students with an auditory learning style can receive support that makes extensive use of audio explanations. Students with an experiential learning style can receive support that makes extensive use of practical exercises. This enables the Educational Support Department to provide effective educational support.

[0054] The information gathering department can analyze past information gathering history and select the most optimal information gathering method. For example, it can identify the most effective information gathering method from past history and prioritize its use. It can also analyze past information gathering history to optimize the frequency and timing of information gathering. It can also prioritize information gathering from specific information sources. In this way, the information gathering department can select the most optimal information gathering method.

[0055] The evaluation unit can perform evaluations while considering the geographical distribution of deliverables. For example, it can analyze the geographical distribution of deliverables and perform evaluations for each region. It can also adjust evaluation criteria while considering the geographical distribution of deliverables. It can also improve the accuracy of evaluations based on the geographical distribution of deliverables. This enables the evaluation unit to perform region-specific evaluations.

[0056] The Educational Support Department can prioritize support based on the submission deadlines of the students. For example, it can prioritize support for students whose submission deadlines are approaching, and postpone support for students whose deadlines are further away. It can also adjust the support schedule according to the submission deadlines. This allows the Educational Support Department to provide effective educational support.

[0057] The information gathering unit can filter information based on the user's current projects and areas of interest. For example, it can prioritize collecting information related to the user's current projects. It can also filter and collect highly relevant information based on the user's areas of interest. It can also provide necessary information in a timely manner according to the progress of the user's projects. This allows the information gathering unit to provide highly relevant information.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The information gathering department collects general knowledge from internal documents and the web. For example, information can be gathered from internal technical documents and manuals, as well as from technical blogs and forums on the web. The information gathering department can also automate information gathering using AI. For example, a web crawler can be used to periodically collect information from the web and store it in a database. Step 2: The Education Support Department provides pair programming and educational support to new employees and new participants based on the collected information. For example, they set up pair programming sessions and code together with new employees and new participants. They can also provide online learning materials and tutorials to create an environment where trainees can learn independently. The Education Support Department can also customize the content of educational support using AI. For example, they can provide appropriate learning materials and assignments according to the trainee's skill level and learning progress. Step 3: The evaluation department evaluates the educational outcomes provided by the education support department and determines their success or failure. For example, it evaluates the quality and completeness of the programs created by the trainees. It can also evaluate the trainees' learning progress and understanding. The evaluation department can also automate the evaluation using AI. For example, it can analyze the program code to detect bugs and errors. It can also analyze the trainees' learning data to automatically calculate grades and evaluations.

[0060] (Example of form 2) An engineer training system according to an embodiment of the present invention is a system that trains engineers who can judge the success or failure of deliverables by having an AI agent provide pair programming and educational support to new recruits and new participants based on internal company documents and general knowledge found on the web. This engineer training system collects internal company documents and general knowledge found on the web and uses this to provide pair programming and educational support to new recruits and new participants. Next, the engineer training system evaluates the deliverables of the trainees and determines whether they are successful or unsuccessful. Furthermore, after the quality and reliability of the engineer training system's deliverables have improved, it becomes possible to request the engineer training system to perform tasks and operate it as an AI employee. This system targets individuals studying to acquire qualifications, as well as companies and schools that provide training for new recruits and mid-career hires, with the aim of reducing training costs and achieving efficient human resource development. For example, the engineer training system collects internal company documents and general knowledge found on the web. For example, the engineer training system provides pair programming and educational support to new recruits and new participants based on the collected information. For example, the engineer training system evaluates the deliverables of the trainees and determines whether they are successful or unsuccessful. For example, the engineer training system provides assistance in setting up a program (e.g., Java) development environment and assistance in using internal company technologies. For example, in an engineer training system, once the quality and reliability of the AI ​​agent's deliverables have improved, the AI ​​agent can be assigned tasks and put into operation as an AI employee. This allows the engineer training system to cultivate engineers who can use internal documents and general knowledge found on the web to provide pair programming and educational support to new recruits and new participants, and who can judge the success or failure of deliverables.

[0061] The engineer training system according to this embodiment comprises an information gathering unit, an education support unit, and an evaluation unit. The information gathering unit collects general knowledge from internal company documents and the web. For example, the information gathering unit can collect information from internal technical documents and manuals, as well as from technical blogs and forums on the web. The information gathering unit can also automate information collection using AI. For example, the information gathering unit can periodically collect information from the web using a web crawler and store it in a database. The education support unit provides pair programming and educational support to new employees and new participants based on the collected information. For example, the education support unit can set up pair programming sessions and code together with new employees and new participants. The education support unit can also provide online learning materials and tutorials to create an environment where trainees can learn independently. The education support unit can also customize the content of educational support using AI. For example, the education support unit can provide appropriate learning materials and assignments according to the trainee's skill level and learning progress. The evaluation unit evaluates the results of the education provided by the education support unit and determines its success or failure. The evaluation unit can, for example, assess the quality and completeness of programs created by trainees. It can also evaluate the trainees' learning progress and understanding. The evaluation unit can also automate evaluations using AI. For example, it can analyze program code to detect bugs and errors. Furthermore, it can analyze trainees' learning data to automatically calculate grades and evaluations. As a result, the engineer training system according to this embodiment can cultivate engineers who can provide pair programming and training support to new recruits and new participants, based on internal documents and general knowledge found on the web, and who can judge the success or failure of deliverables.

[0062] The Information Gathering Department collects internal documents and general knowledge from the web. For example, it can gather information from internal technical documents and manuals, as well as online technical blogs and forums. Specifically, internal technical documents and manuals contain detailed information on project progress, technical challenges, and solutions. By collecting this information, engineers can understand potential problems they may face and their solutions. Online technical blogs and forums share the latest technology trends, best practices, and experiences of other engineers. By collecting this information, engineers can learn about the latest technology trends and apply them in practice. The Information Gathering Department can also automate information gathering using AI. For example, it can use a web crawler to periodically collect information from the web and store it in a database. The web crawler visits specified websites and automatically collects new posts from technical blogs and forums. The collected information is classified and organized using natural language processing technology and stored in the database. This allows the Information Gathering Department to efficiently gather information from a wide range of sources and provide the knowledge necessary for engineer training. Furthermore, the Information Gathering Department can evaluate the quality of the collected information and select only reliable information. For example, the information gathering department prioritizes collecting highly reliable information, using criteria such as the source of the information, the accuracy of its content, and the frequency of updates. This allows the information gathering department to provide high-quality information necessary for engineer training, thereby improving the overall reliability and effectiveness of the system.

[0063] The Education Support Department provides pair programming and educational support to new recruits and new participants based on the information it collects. For example, the Education Support Department sets up pair programming sessions and codes together with new recruits and new participants. Pair programming is an effective way for experienced engineers and new recruits to acquire practical skills by working together. The Education Support Department provides an efficient learning environment by coordinating pair programming schedules and forming appropriate pairs. The Education Support Department can also provide online learning materials and tutorials to create an environment where trainees can learn independently. These online materials and tutorials include videos and interactive content, allowing trainees to learn at their own pace. The Education Support Department can also customize the content of educational support using AI. For example, the Education Support Department provides appropriate materials and assignments according to the trainee's skill level and learning progress. The AI ​​analyzes the trainee's learning history and performance data to create a customized learning plan tailored to individual needs. This allows the Education Support Department to efficiently support the skill improvement of trainees and maximize the effectiveness of engineer training. Furthermore, the Educational Support Department can collect feedback from trainees and use it to improve the curriculum. For example, it can analyze how trainees reacted to different teaching materials and assignments to improve the quality of the curriculum. This allows the Educational Support Department to provide effective educational support that constantly incorporates the latest information and technologies, thereby enhancing the quality of engineer training.

[0064] The Evaluation Department evaluates the outcomes of education conducted by the Educational Support Department and determines their success or failure. For example, the Evaluation Department evaluates the quality and completeness of programs created by trainees. Specifically, it objectively evaluates trainees' skills using criteria such as the accuracy, efficiency, and maintainability of the program code. The Evaluation Department can also evaluate trainees' learning progress and understanding. For example, it assesses how much knowledge trainees have acquired and what tasks they have completed to understand their learning progress. The Evaluation Department can also automate evaluation using AI. For example, the Evaluation Department analyzes program code and detects bugs and errors. AI automatically evaluates the quality of the code and points out problems using static and dynamic analysis tools. The Evaluation Department also analyzes trainees' learning data and automatically calculates grades and evaluations. Based on trainees' learning history and performance data, AI calculates individual grades and provides evaluation results. This allows the Evaluation Department to efficiently and accurately evaluate educational outcomes and support the skill improvement of trainees. Furthermore, the evaluation department can provide the evaluation results as feedback to the education support department, which can then be used to improve the educational content. For example, based on the evaluation results, the education support department can provide additional teaching materials and assignments to reinforce the weaknesses of the trainees. In this way, the evaluation department can work in cooperation with the education support department to continuously improve the quality of engineer training.

[0065] The information gathering department can collect internal company documents and general knowledge from the web. For example, the information gathering department can collect internal technical documents and manuals. For example, the information gathering department can also collect information from technical blogs and forums on the web. For example, the information gathering department can automate information collection using AI. This allows the information gathering department to provide the information necessary for educational support. Some or all of the above processes in the information gathering department may be performed using AI, for example, or not using AI. For example, the information gathering department may periodically collect information from the web using a web crawler and store it in a database.

[0066] The Education Support Department can provide pair programming and educational support to new employees and new participants based on the information it has collected. For example, the Education Support Department can set up pair programming sessions and code together with new employees and new participants. The Education Support Department can also provide online learning materials and tutorials to create an environment where trainees can learn independently. The Education Support Department can also customize the content of educational support using AI. This allows the Education Support Department to help new employees and new participants improve their skills. Some or all of the above processes performed by the Education Support Department may be done using AI or not. For example, the Education Support Department can provide appropriate learning materials and assignments according to the trainees' skill levels and learning progress.

[0067] The evaluation unit can assess the deliverables of the trainees and determine their success or failure. For example, the evaluation unit can evaluate the quality and completeness of the programs created by the trainees. The evaluation unit can also evaluate the trainees' learning progress and understanding. The evaluation unit can also automate the evaluation process using AI, for example. This allows the evaluation unit to confirm the effectiveness of the education. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can analyze the program code and detect bugs and errors.

[0068] The Education Support Department can provide assistance with setting up program development environments and assisting with the use of in-house technologies. For example, the Education Support Department can provide procedures for setting up program development environments. The Education Support Department can also provide tutorials explaining how to use in-house technologies. The Education Support Department can also customize the content of educational support using AI. This allows the Education Support Department to help improve the skills of those being trained. Some or all of the above processes performed by the Education Support Department may be performed using AI, for example, or not. For example, the Education Support Department can provide appropriate teaching materials and assignments according to the skill level and learning progress of those being trained.

[0069] After the quality and reliability of the AI ​​agent's deliverables have improved, the evaluation department can assign tasks to the AI ​​agent and have it operate as an AI employee. For example, the evaluation department can evaluate the quality and reliability of the AI ​​agent's deliverables. The evaluation department can also assign new tasks to the AI ​​agent and evaluate the deliverables. The evaluation department can also automate the evaluation process using AI. This allows the evaluation department to improve the efficiency of its operations. Some or all of the processes described above in the evaluation department may be performed using AI, or not. For example, the evaluation department can evaluate the quality and reliability of the AI ​​agent's deliverables.

[0070] The information gathering unit can estimate the user's emotions and adjust the timing of information gathering based on the estimated emotions. For example, if the user is stressed, the information gathering unit can reduce the frequency of information gathering and collect information when the user is relaxed. For example, if the user is concentrating, the information gathering unit can adjust the timing of information gathering so as not to disturb the user's concentration. For example, if the user is tired, the information gathering unit can temporarily stop information gathering and resume it after the user has rested. This allows the information gathering unit to reduce the burden on the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information gathering unit may be performed using AI or not using AI. For example, the information gathering unit can analyze the user's emotion data in real time and immediately adjust the timing of information gathering.

[0071] The information gathering unit can analyze past information gathering history and select the optimal information gathering method. For example, the information gathering unit can identify the most effective information gathering method from past information gathering history and use it preferentially. The information gathering unit can also analyze past information gathering history and optimize the frequency and timing of information gathering. For example, the information gathering unit can prioritize information gathering from specific information sources based on past information gathering history. This allows the information gathering unit to select the optimal information gathering method. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input past information gathering history into a generating AI and have the generating AI select the optimal information gathering method.

[0072] The information gathering unit can filter information based on the user's current projects and areas of interest during the information gathering process. For example, the information gathering unit can prioritize collecting information related to the user's current projects. The information gathering unit can also filter and collect highly relevant information based on the user's areas of interest. The information gathering unit can also provide necessary information in a timely manner according to the progress of the user's projects. This allows the information gathering unit to provide highly relevant information. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input the user's project information into a generating AI and have the generating AI perform the filtering of highly relevant information.

[0073] The information gathering unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the information gathering unit will postpone collecting less important information and prioritize collecting more important information. For example, if the user is relaxed, the information gathering unit may prioritize collecting detailed information. For example, if the user is in a hurry, the information gathering unit can also quickly collect necessary information. This allows the information gathering unit to prioritize providing information that is important to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information gathering unit may be performed using AI or not using AI. For example, the information gathering unit can input user emotion data into a generative AI and have the generative AI perform the determination of information prioritization.

[0074] The information collection unit can prioritize collecting highly relevant information based on the user's geographical location information during information collection. For example, the information collection unit can prioritize collecting information related to the user's current location. The information collection unit can also collect region-specific information based on the user's geographical location information. The information collection unit can also collect highly relevant information by considering the user's travel history. This allows the information collection unit to provide region-specific information. Some or all of the above processing in the information collection unit may be performed using AI, for example, or without AI. For example, the information collection unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant information.

[0075] The information gathering unit can analyze the user's social media activity and collect relevant information during the information gathering process. For example, the information gathering unit can analyze the content of the user's social media posts and collect relevant information. The information gathering unit can also collect relevant information based on the user's social media followers and the accounts they follow. For example, the information gathering unit can analyze the user's social media activity history and collect information based on their interests. This allows the information gathering unit to provide relevant information. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input the user's social media data into a generating AI and have the generating AI collect relevant information.

[0076] The Educational Support Unit can estimate the user's emotions and adjust the way educational support is presented based on the estimated emotions. For example, if the user is tense, the Educational Support Unit can provide educational support in a calm tone. If the user is relaxed, the Educational Support Unit can also provide educational support in a casual tone. If the user is excited, the Educational Support Unit can also provide educational support in an energetic tone. This enables the Educational Support Unit to provide more effective educational support. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Educational Support Unit may be performed using AI or not. For example, the Educational Support Unit can input user emotion data into a generative AI and have the generative AI adjust the way educational support is presented.

[0077] The Educational Support Department can adjust the level of detail of its support based on the level of understanding of the students being taught. For example, if the student's level of understanding is low, the Educational Support Department can provide a detailed explanation. If the student's level of understanding is high, the Educational Support Department can provide a concise explanation. The Educational Support Department can also adjust the pace of support according to the student's level of understanding. This enables the Educational Support Department to provide effective educational support. Some or all of the above processes in the Educational Support Department may be performed using AI, for example, or without AI. For example, the Educational Support Department can input student understanding data into a generating AI and have the generating AI adjust the level of detail of the support.

[0078] The Educational Support Department can apply different support algorithms to students according to their learning style. For example, for students with a visual learning style, the Educational Support Department can provide support that makes extensive use of diagrams and graphs. For students with an auditory learning style, the Educational Support Department can also provide support that makes extensive use of audio explanations. For students with an experiential learning style, the Educational Support Department can also provide support that makes extensive use of practical exercises. This enables the Educational Support Department to provide effective educational support. Some or all of the above processing in the Educational Support Department may be performed using AI, for example, or without AI. For example, the Educational Support Department can input the student's learning style data into a generating AI and have the generating AI execute the application of support algorithms.

[0079] The Educational Support Unit can estimate the user's emotions and adjust the length of the educational support based on the estimated emotions. For example, if the user is tired, the Educational Support Unit can provide short educational support. For example, if the user is focused, the Educational Support Unit can provide long educational support. For example, if the user is in a hurry, the Educational Support Unit can provide short, concise educational support. This enables the Educational Support Unit to provide effective educational support. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Educational Support Unit may be performed using AI or not. For example, the Educational Support Unit can input user emotion data into the generative AI and have the generative AI adjust the length of the educational support.

[0080] The Educational Support Department can prioritize support based on the submission deadlines of the students receiving education. For example, the Educational Support Department can prioritize support for students whose submission deadlines are approaching. For example, the Educational Support Department can also postpone support for students whose submission deadlines are far away. For example, the Educational Support Department can adjust the support schedule according to the submission deadlines. This enables the Educational Support Department to provide effective educational support. Some or all of the above processes in the Educational Support Department may be performed using AI, for example, or not using AI. For example, the Educational Support Department can input student submission timing data into a generating AI and have the generating AI determine the support priorities.

[0081] The Educational Support Department can adjust the order of support based on the relevance of the educators during the provision of educational support. For example, the Educational Support Department may determine the order of support based on the importance of the educators' projects. The Educational Support Department may also adjust the order of support based on the skill level of the educators. The Educational Support Department may also determine the order of support based on the areas of interest of the educators. This enables the Educational Support Department to provide effective educational support. Some or all of the above processes in the Educational Support Department may be performed using AI, for example, or not using AI. For example, the Educational Support Department can input the relevance data of the educators into a generating AI and have the generating AI perform the adjustment of the order of support.

[0082] The evaluation unit can estimate the user's emotions and adjust the order in which the evaluation results are displayed based on the estimated emotions. For example, if the user is nervous, the evaluation unit may display important results later. For example, if the user is relaxed, the evaluation unit may also display important results earlier. For example, if the user is in a hurry, the evaluation unit may also display results quickly. This allows the evaluation unit to reduce the burden on the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not using AI. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI adjust the display order of the evaluation results.

[0083] The evaluation unit can perform evaluations while considering the geographical distribution of deliverables. For example, the evaluation unit can analyze the geographical distribution of deliverables and perform evaluations for each region. The evaluation unit can also adjust the evaluation criteria while considering the geographical distribution of deliverables. For example, the evaluation unit can improve the accuracy of the evaluation based on the geographical distribution of deliverables. This enables the evaluation unit to perform region-specific evaluations. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input geographical distribution data of deliverables into a generating AI and have the generating AI perform the evaluation.

[0084] The evaluation unit can improve the accuracy of its evaluation by referring to relevant literature for the deliverables during the evaluation process. For example, the evaluation unit can refer to relevant literature for the deliverables and set evaluation criteria. The evaluation unit can also improve the accuracy of its evaluation based on relevant literature for the deliverables. The evaluation unit can also adjust the evaluation criteria by considering relevant literature for the deliverables. This improves the accuracy of the evaluation. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input relevant literature data for the deliverables into a generating AI and have the generating AI perform the evaluation.

[0085] The Education Support Department can estimate the user's emotions when assisting with program development environment setup and the use of internal technologies, and adjust the development environment setup procedure based on the estimated user emotions. For example, if the user is nervous, the Education Support Department can provide concise instructions to reduce stress. For example, if the user is relaxed, the Education Support Department can provide detailed instructions to deepen understanding. For example, if the user is in a hurry, the Education Support Department can provide instructions that allow for quick setup. In this way, the Education Support Department can reduce the burden on the user. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Education Support Department may be performed using AI or not. For example, the Education Support Department can input user emotion data into a generative AI and have the generative AI adjust the development environment setup procedure.

[0086] The Education Support Department can adjust the level of detail of its assistance based on the skill level of the trainees when providing support for setting up program development environments or using internal technologies. For example, the Education Support Department can provide detailed procedures and explanations to beginner trainees. For example, it can provide only the necessary procedures to intermediate trainees, omitting detailed explanations. For example, it can provide only an outline of the procedures to advanced trainees, allowing them to build upon it freely. This enables the Education Support Department to provide effective educational support. Some or all of the above processes in the Education Support Department may be performed using AI, for example, or not. For example, the Education Support Department can input trainee skill level data into a generating AI and have the generating AI adjust the level of detail of the assistance.

[0087] The Education Support Department can estimate user emotions when assisting with program development environment setup and the use of internal technologies, and determine development environment setup priorities based on the estimated emotions. For example, if a user is stressed, the Education Support Department can postpone low-priority tasks and prioritize high-priority tasks. For example, if a user is relaxed, the Education Support Department can prioritize detailed tasks. For example, if a user is in a hurry, the Education Support Department can prioritize tasks that can be completed quickly. This allows the Education Support Department to reduce the burden on users. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Education Support Department may be performed using AI or not. For example, the Education Support Department can input user emotion data into a generative AI and have the generative AI determine the priorities for development environment setup.

[0088] The Education Support Department can provide optimal support when assisting with program development environment setup or the use of in-house technologies, taking into account the project status of the trainees. For example, the Education Support Department can provide necessary support according to the progress of the trainees' projects. The Education Support Department can also determine the priority of support based on the importance of the trainees' projects. The Education Support Department can also adjust the support schedule according to the deadlines of the trainees' projects. This enables the Education Support Department to provide effective educational support. Some or all of the above processes in the Education Support Department may be performed using AI, for example, or not. For example, the Education Support Department can input project status data of trainees into a generating AI and have the generating AI provide the optimal support.

[0089] After the quality and reliability of the AI ​​agent's deliverables have improved, the evaluation unit can assign tasks to the AI ​​agent and, when operating as an AI employee, estimate the user's emotions and adjust the AI ​​agent's tasks based on the estimated user emotions. For example, if the user is stressed, the evaluation unit may assign the AI ​​agent tasks of low importance. For example, if the user is relaxed, the evaluation unit may assign the AI ​​agent tasks of high importance. For example, if the user is in a hurry, the evaluation unit may assign the AI ​​agent tasks that can be completed quickly. This allows the evaluation unit to reduce the user's burden. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not using AI. For example, the evaluation unit may input user emotion data into a generative AI and have the generative AI adjust the AI ​​agent's tasks.

[0090] After the quality and reliability of the AI ​​agent's deliverables have improved, the evaluation department can request the AI ​​agent to perform tasks and, when deploying it as an AI employee, select the optimal task by referring to its past task history. For example, the evaluation department can analyze the AI ​​agent's past task history and select the optimal task. For example, the evaluation department can also determine task priorities based on the AI ​​agent's past task history. For example, the evaluation department can adjust task schedules by referring to the AI ​​agent's past task history. This allows the evaluation department to select the optimal task. Some or all of the above processes in the evaluation department may be performed using AI, or not using AI. For example, the evaluation department can input the AI ​​agent's past task history data into a generating AI and have the generating AI select the optimal task.

[0091] After the quality and reliability of the AI ​​agent's deliverables have improved, the evaluation unit can assign tasks to the AI ​​agent and, when deploying it as an AI employee, estimate the user's emotions and adjust the AI ​​agent's operational frequency based on the estimated user emotions. For example, if the user is stressed, the evaluation unit can reduce the AI ​​agent's operational frequency. For example, if the user is relaxed, the evaluation unit can increase the AI ​​agent's operational frequency. For example, if the user is in a hurry, the evaluation unit can adjust the AI ​​agent's operational frequency to complete tasks quickly. This allows the evaluation unit to reduce the user's burden. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI adjust the AI ​​agent's operational frequency.

[0092] After the quality and reliability of the AI ​​agent's deliverables have improved, the evaluation unit can assign tasks to the AI ​​agent and, when deploying it as an AI employee, determine the priority of its work based on the importance of the tasks. For example, the evaluation unit can determine the priority of the AI ​​agent's work based on the importance of the tasks. The evaluation unit can also adjust the AI ​​agent's work schedule based on the task deadline, for example. The evaluation unit can also adjust the level of detail of the AI ​​agent's work based on the complexity of the tasks, for example. This allows the evaluation unit to prioritize important tasks. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input task importance data into a generating AI and have the generating AI perform the task priority determination.

[0093] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0094] The information gathering unit can estimate the user's emotions and adjust the timing of information gathering based on those estimates. For example, if the user is stressed, it can reduce the frequency of information gathering and collect information when the user is relaxed. If the user is concentrating, it can also adjust the timing of information gathering to avoid interrupting the user's concentration. If the user is tired, it can temporarily stop information gathering and resume it after the user has rested. In this way, the information gathering unit can reduce the burden on the user.

[0095] The Educational Support Department can apply different support algorithms depending on the learning style of the students being taught. For example, students with a visual learning style can receive support that makes extensive use of diagrams and graphs. Students with an auditory learning style can receive support that makes extensive use of audio explanations. Students with an experiential learning style can receive support that makes extensive use of practical exercises. This enables the Educational Support Department to provide effective educational support.

[0096] The evaluation unit can estimate the user's emotions and adjust the order in which evaluation results are displayed based on the estimated emotions. For example, if the user is nervous, important results may be displayed later. If the user is relaxed, important results may be displayed first. If the user is in a hurry, results may be displayed quickly. This allows the evaluation unit to reduce the burden on the user.

[0097] The information gathering department can analyze past information gathering history and select the most optimal information gathering method. For example, it can identify the most effective information gathering method from past history and prioritize its use. It can also analyze past information gathering history to optimize the frequency and timing of information gathering. It can also prioritize information gathering from specific information sources. In this way, the information gathering department can select the most optimal information gathering method.

[0098] The Educational Support Department can estimate the user's emotions and adjust the way educational support is delivered based on those estimates. For example, if the user is nervous, educational support can be delivered in a calm tone. If the user is relaxed, educational support can be delivered in a casual tone. If the user is excited, educational support can be delivered in an energetic tone. This allows the Educational Support Department to provide more effective educational support.

[0099] The evaluation unit can perform evaluations while considering the geographical distribution of deliverables. For example, it can analyze the geographical distribution of deliverables and perform evaluations for each region. It can also adjust evaluation criteria while considering the geographical distribution of deliverables. It can also improve the accuracy of evaluations based on the geographical distribution of deliverables. This enables the evaluation unit to perform region-specific evaluations.

[0100] The Educational Support Department can prioritize support based on the submission deadlines of the students. For example, it can prioritize support for students whose submission deadlines are approaching, and postpone support for students whose deadlines are further away. It can also adjust the support schedule according to the submission deadlines. This allows the Educational Support Department to provide effective educational support.

[0101] After the quality and reliability of the AI ​​agent's deliverables have improved, the evaluation department can assign tasks to the AI ​​agent and, when deploying it as an AI employee, estimate the user's emotions and adjust the AI ​​agent's tasks based on those emotions. For example, if the user is stressed, the evaluation department can assign the AI ​​agent low-priority tasks. If the user is relaxed, the AI ​​agent can be assigned high-priority tasks. If the user is in a hurry, the AI ​​agent can be assigned tasks that can be completed quickly. This allows the evaluation department to reduce the burden on the user.

[0102] The information gathering unit can filter information based on the user's current projects and areas of interest. For example, it can prioritize collecting information related to the user's current projects. It can also filter and collect highly relevant information based on the user's areas of interest. It can also provide necessary information in a timely manner according to the progress of the user's projects. This allows the information gathering unit to provide highly relevant information.

[0103] The Education Support Department can estimate the user's emotions and adjust the length of the educational support based on those estimates. For example, if the user is tired, a short educational support session can be provided. If the user is focused, a longer session can be provided. If the user is in a hurry, a short, concise educational support session can be provided. This allows the Education Support Department to provide effective educational support.

[0104] The following briefly describes the processing flow for example form 2.

[0105] Step 1: The information gathering department collects general knowledge from internal documents and the web. For example, information can be gathered from internal technical documents and manuals, as well as from technical blogs and forums on the web. The information gathering department can also automate information gathering using AI. For example, a web crawler can be used to periodically collect information from the web and store it in a database. Step 2: The Education Support Department provides pair programming and educational support to new employees and new participants based on the collected information. For example, they set up pair programming sessions and code together with new employees and new participants. They can also provide online learning materials and tutorials to create an environment where trainees can learn independently. The Education Support Department can also customize the content of educational support using AI. For example, they can provide appropriate learning materials and assignments according to the trainee's skill level and learning progress. Step 3: The evaluation department evaluates the educational outcomes provided by the education support department and determines their success or failure. For example, it evaluates the quality and completeness of the programs created by the trainees. It can also evaluate the trainees' learning progress and understanding. The evaluation department can also automate the evaluation using AI. For example, it can analyze the program code to detect bugs and errors. It can also analyze the trainees' learning data to automatically calculate grades and evaluations.

[0106] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0107] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0108] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0109] Each of the multiple elements described above, including the information gathering unit, the education support unit, and the evaluation unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the information gathering unit is implemented by the computer 36 of the smart device 14 and the specific processing unit 290 of the data processing unit 12, and collects company documents and general knowledge from the web. The education support unit is implemented by, for example, the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing unit 12, and provides pair programming and educational support to new employees and new participants. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and evaluates the deliverables of the trainees and determines their success or failure. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0110] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0111] As shown in Figure 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.

[0112] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0113] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0114] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0116] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0117] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0118] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0119] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0120] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0122] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0124] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] Each of the multiple elements described above, including the information gathering unit, the education support unit, and the evaluation unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the information gathering unit is implemented by the computer 36 of the smart glasses 214 and the specific processing unit 290 of the data processing unit 12, and collects company documents and general knowledge from the web. The education support unit is implemented by, for example, the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing unit 12, and provides pair programming and educational support to new employees and new participants. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and evaluates the output of trainees and determines success or failure. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0126] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0127] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0129] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0130] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0132] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0133] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0134] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0135] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0136] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0140] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements described above, including the information gathering unit, the education support unit, and the evaluation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the information gathering unit is implemented by the computer 36 of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12, and collects company documents and general knowledge from the web. The education support unit is implemented by, for example, the control unit 46A of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12, and provides pair programming and educational support to new employees and new participants. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and evaluates the output of trainees and determines success or failure. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0142] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0143] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0150] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the information gathering unit, the education support unit, and the evaluation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the information gathering unit is implemented by the computer 36 of the robot 414 and the specific processing unit 290 of the data processing unit 12, and collects company documents and general knowledge from the web. The education support unit is implemented by, for example, the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing unit 12, and provides pair programming and educational support to new employees and new participants. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and evaluates the output of trainees and determines success or failure. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0159] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0161] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0162] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0163] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0166] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0167] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0168] 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.

[0169] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0170] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0171] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0172] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0174] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0175] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0176] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0177] (Note 1) The Information Gathering Department collects general knowledge from internal company documents and the web, Based on the information collected by the aforementioned information gathering department, the Education Support Department provides pair programming and educational support to new employees and new participants. The system includes an evaluation unit that evaluates the educational outcomes provided by the aforementioned education support unit and determines whether they are successful or not. A system characterized by the following features. (Note 2) The aforementioned information gathering unit, Collect general knowledge from internal company documents and the web. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned Educational Support Department, Based on the collected information, we provide pair programming and educational support to new employees and new participants. The system described in Appendix 1, characterized by the features described herein. (Note 4) The evaluation unit described above, Evaluate the output of the trainees and determine their success or failure. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned Educational Support Department, We provide assistance with setting up program development environments and supporting the use of in-house technologies. The system described in Appendix 1, characterized by the features described herein. (Note 6) The evaluation unit described above, After the quality and reliability of the AI ​​agent's output have improved, we will assign tasks to the AI ​​agent and have it operate as an AI employee. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned information gathering unit, It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned information gathering unit, Analyze past information gathering history and select the optimal information gathering method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned information gathering unit, When gathering information, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned information gathering unit, It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned information gathering unit, When gathering information, the system prioritizes collecting highly relevant information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned information gathering unit, When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned Educational Support Department, The system estimates the user's emotions and adjusts the presentation of educational support based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned Educational Support Department, When providing educational support, adjust the level of detail of the support based on the level of understanding of the recipient. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned Educational Support Department, When providing educational support, different support algorithms are applied according to the learning style of the person being educated. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned Educational Support Department, The system estimates the user's emotions and adjusts the length of educational support based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned Educational Support Department, When providing educational support, the priority of support is determined based on the submission timing of the recipients. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned Educational Support Department, When providing educational support, the order of support is adjusted based on the relevance of the individuals being educated. The system described in Appendix 1, characterized by the features described herein. (Note 19) The evaluation unit described above, It estimates the user's emotions and adjusts the order in which evaluation results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The evaluation unit described above, During the evaluation process, the geographical distribution of the deliverables will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit described above, During evaluation, refer to relevant literature related to the deliverables to improve the accuracy of the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned Educational Support Department, When assisting with program development environment setup or the use of internal technologies, the system estimates the user's emotions and adjusts the development environment setup procedure based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned Educational Support Department, When providing assistance with setting up program development environments or using internal technologies, the level of assistance will be adjusted based on the skill level of the person being trained. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned Educational Support Department, When assisting with program development environment setup or the use of internal technologies, the system estimates user sentiment and determines the priority of development environment setup based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned Educational Support Department, When providing assistance with setting up program development environments or using internal technologies, we provide the most appropriate support by considering the project status of the trainees. The system described in Appendix 1, characterized by the features described herein. (Note 26) The evaluation unit described above, After improving the quality and reliability of the AI ​​agent's deliverables, when you assign tasks to the AI ​​agent and have it operate as an AI employee, it will estimate the user's emotions and adjust the AI ​​agent's tasks based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The evaluation unit described above, After the quality and reliability of the AI ​​agent's deliverables have improved, when assigning tasks to the AI ​​agent and deploying it as an AI employee, the most suitable tasks will be selected by referring to its past task history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The evaluation unit described above, After improving the quality and reliability of the AI ​​agent's deliverables, when you assign tasks to the AI ​​agent and have it operate as an AI employee, it will estimate the user's emotions and adjust the AI ​​agent's operating frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The evaluation unit described above, After the quality and reliability of the AI ​​agent's deliverables have improved, you can assign tasks to the AI ​​agent and have it work as an AI employee, prioritizing its work based on the importance of the tasks. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The Information Gathering Department collects general knowledge from internal company documents and the web, Based on the information collected by the aforementioned information gathering department, the Education Support Department provides pair programming and educational support to new employees and new participants. The system includes an evaluation unit that evaluates the educational outcomes provided by the aforementioned education support unit and determines whether they are successful or not. A system characterized by the following features.

2. The aforementioned information gathering unit, Collect general knowledge from internal company documents and the web. The system according to feature 1.

3. The aforementioned Educational Support Department, Based on the collected information, we provide pair programming and educational support to new employees and new participants. The system according to feature 1.

4. The evaluation unit described above, Evaluate the output of the trainees and determine their success or failure. The system according to feature 1.

5. The aforementioned Educational Support Department, We provide assistance with setting up program development environments and supporting the use of in-house technologies. The system according to feature 1.

6. The evaluation unit described above, After the quality and reliability of the AI ​​agent's deliverables have improved, the AI ​​agent will be given tasks to perform and will operate as an AI employee. The system according to feature 1.

7. The aforementioned information gathering unit, It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned information gathering unit, Analyze past information gathering history and select the optimal information gathering method. The system according to feature 1.

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