System

Abstract

An object of a system according to an embodiment is to provide efficient license acquisition support based on the aptitude of an individual.SOLUTION: A system includes a aptitude test execution part, a learning plan creation part, and a learning state monitoring part. The aptitude test performing unit performs aptitude test. The learning plan creating section creates a learning plan based on the aptitude evaluated by the aptitude test executing section. The learning status monitoring unit monitors a learning status based on the learning plan created by the learning plan creating unit and provides feedback.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not provide sufficient support for qualification acquisition based on individual aptitude, and there was a problem in that it was difficult to create and monitor efficient learning plans.

[0005] The system according to the embodiment aims to provide efficient support for qualification acquisition based on individual aptitude. [Means for solving the problem]

[0006] The system according to the embodiment includes an aptitude test administration unit, a study plan creation unit, and a study status monitoring unit. The aptitude test administration unit administers an aptitude test. The study plan creation unit creates a study plan based on the aptitude assessed by the aptitude test administration unit. The study status monitoring unit monitors the study status based on the study plan created by the study plan creation unit and provides feedback. [Effects of the Invention]

[0007] The system according to the embodiment can provide efficient support for qualification acquisition based on individual aptitude. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A qualification acquisition support system according to an embodiment of the present invention utilizes generative AI to streamline support for qualification acquisition. This qualification acquisition support system conducts aptitude tests, identifies qualifications that match an individual's aptitude and qualifications that can be obtained for work-related reasons, creates a study plan with the period, method, and schedule for acquisition based on an individual's schedule, and provides the user with a learning plan. This allows the qualification acquisition support system to optimize each individual's aptitude and streamline qualification acquisition.

[0029] A qualification acquisition support system according to an embodiment includes an aptitude test implementation unit, a study plan creation unit, and a study status monitoring unit. The aptitude test implementation unit conducts an aptitude test. For example, a generation AI administers an aptitude test to a user and analyzes the results. The aptitude test implementation unit can also evaluate the user's interests, strengths, and weaknesses based on the questions answered by the user. Furthermore, the aptitude test implementation unit selects qualifications that are most suitable for the user or that can improve work efficiency based on the aptitude test results. For example, the generation AI receives a prompt such as "Please answer this question" and evaluates the user's aptitude. The study plan creation unit creates a study plan based on the aptitude evaluated by the aptitude test implementation unit. For example, the generation AI creates a period, method, and schedule for qualification acquisition based on the user's individual schedule and lifestyle. The study plan creation unit can also provide a study plan that includes specific study content and progress management methods. For example, the generation AI provides an optimal study plan based on information such as "how much time the user can devote to studying on weekdays." The learning status monitoring unit monitors the learning status based on the learning plan created by the learning plan creation unit and provides feedback. For example, the generation AI monitors the user's learning status in real time and provides feedback as needed. The learning status monitoring unit can also analyze whether the user is progressing with their studies as planned and where they are struggling, and provide appropriate advice or additional learning resources. For example, the generation AI provides feedback such as "It seems like you're struggling with this part." This allows the qualification acquisition support system according to the embodiment to support efficient qualification acquisition based on the user's aptitude.

[0030] The aptitude test implementation unit can analyze a user's past learning history and work history and dynamically adjust the content of the aptitude test questions based on that. In the aptitude test implementation unit, for example, the generation AI analyzes a user's past learning history to evaluate the depth of knowledge and interest in a specific field. For example, the content of the aptitude test questions is customized based on courses taken in the past and qualifications obtained. The aptitude test implementation unit also analyzes the user's work history and adjusts the aptitude test questions based on the user's current job content and past work experience. For example, related questions can be added for users with specific work skills and experience. In addition, the aptitude test implementation unit can dynamically change the content of the aptitude test questions based on the user's learning history and work history to perform more accurate aptitude assessments. For example, the difficulty and content of the questions can be adjusted to evaluate the depth of knowledge and interest in a specific field. This allows the aptitude test to be customized based on the user's past experience.

[0031] The aptitude test implementation unit can predict the user's future career path based on the results of the aptitude test and suggest the most suitable qualifications for that. In the aptitude test implementation unit, for example, a generation AI analyzes the results of the aptitude test and predicts the user's future career path based on the user's interests and strengths. For example, the aptitude test implementation unit evaluates skills and knowledge in a specific field and suggests the most suitable qualifications based on that. In addition, the aptitude test implementation unit can simulate the user's career path based on the results of the aptitude test and identify the skills and qualifications that will be required in the future. For example, the aptitude test implementation unit can predict a career path in a specific occupation or industry and suggest the most suitable qualifications for that. In addition, the aptitude test implementation unit can build a system in which a generation AI predicts the user's future career path based on the results of the aptitude test and suggests the most suitable qualifications for that. For example, the generation AI evaluates the user's interests and strengths and identifies the skills and qualifications that will be required in the future. This makes it possible to suggest the most suitable qualifications based on the user's future career path.

[0032] The study plan creation unit can analyze the user's lifestyle rhythm and health condition and suggest optimal study times based on that. In the study plan creation unit, for example, a generation AI analyzes the user's lifestyle rhythm and suggests optimal study times. For example, an efficient study time is set based on the user's sleep pattern and daily activity schedule. The study plan creation unit also analyzes the user's health condition and suggests optimal study times based on that. For example, the study schedule can be adjusted taking into account the user's energy level and peak concentration times. In addition, the study plan creation unit builds a system in which a generation AI analyzes the user's lifestyle rhythm and health condition and suggests optimal study times based on that. For example, an efficient study time is set based on the user's biorhythm and health data. This makes it possible to suggest optimal study times based on the user's lifestyle rhythm and health condition.

[0033] The learning plan creation unit can automatically generate learning materials that match the user's learning style. In the learning plan creation unit, for example, a generation AI analyzes the user's learning style and automatically generates learning materials that match it. For example, visual learning materials are provided to a user who is good at visual learning, and audio learning materials are provided to a user who is good at auditory learning. In addition, the learning plan creation unit automatically generates customized learning materials based on the user's learning style. For example, interactive learning materials can be provided to a user who is good at tactile learning. In addition, the learning plan creation unit builds a system in which a generation AI analyzes the user's learning style and automatically generates learning materials that match it. For example, learning materials corresponding to each style of visual, auditory, and tactile are provided. In this way, learning materials that match the user's learning style can be automatically generated.

[0034] The learning status monitoring unit can analyze the user's learning data and provide customized feedback to maximize the effectiveness of learning. For example, the learning status monitoring unit allows the generation AI to analyze the user's learning data and provide customized feedback to maximize the effectiveness of learning. For example, the generation AI may evaluate the level of understanding in a specific field and provide additional learning resources. The learning status monitoring unit also builds a system in which the generation AI provides customized feedback based on the user's learning data. For example, the generation AI may present specific advice and areas for improvement depending on the user's learning progress and level of understanding. The learning status monitoring unit also allows the generation AI to analyze the user's learning data and provide customized feedback to maximize the effectiveness of learning. For example, the generation AI may analyze the user's learning patterns and suggest efficient learning methods. This makes it possible to provide customized feedback based on the user's learning data.

[0035] The learning status monitoring unit predicts the user's learning performance and can detect problems early based on the prediction results. In the learning status monitoring unit, for example, the generation AI analyzes the user's learning data and predicts learning performance. For example, future learning progress and level of understanding are predicted based on past learning data. The learning status monitoring unit also builds a system in which the generation AI detects problems early based on the learning performance prediction results. For example, if learning progress is lagging behind, an alert can be issued early. The learning status monitoring unit also predicts the user's learning performance and detects problems early based on the prediction results. For example, if understanding in a particular area is low, additional learning resources are provided. This makes it possible to predict the user's learning performance and detect problems early.

[0036] The learning status monitoring unit can provide a function to compare the learning status monitoring results with other users and use them as a benchmark. The learning status monitoring unit, for example, provides a function to compare the learning status monitoring results with other users and use them as a benchmark. For example, comparing with the progress of other users aiming for the same qualification. The learning status monitoring unit also builds a system in which the generation AI compares the learning status monitoring results with other users and uses them as a benchmark. For example, it can compare learning progress and level of understanding with other users. The learning status monitoring unit also provides a function to compare the learning status monitoring results with other users and use them as a benchmark. For example, comparing with the progress of other users who share the same learning plan. This allows the user's learning status to be compared with other users and used as a benchmark.

[0037] The learning status monitoring unit can set future learning goals based on the user's learning data and provide step-by-step guidance for achieving those goals. In the learning status monitoring unit, for example, the generation AI analyzes the user's learning data and sets future learning goals. For example, it sets a specific goal for obtaining a specific qualification. The learning status monitoring unit also builds a system that provides step-by-step guidance for achieving the learning goals. For example, it can present specific learning content and progress management methods for each step. In addition, the learning status monitoring unit can set future learning goals based on the user's learning data and provide step-by-step guidance for achieving those goals. For example, it can suggest the next step depending on the user's learning progress. This makes it possible to set the user's learning goals and provide guidance for achieving them.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The qualification acquisition support system can further include an environmental adjustment unit to optimize the user's learning environment. The environmental adjustment unit, for example, adjusts the lighting and sound of the user's learning environment. Specifically, the generative AI monitors the user's learning environment and sets the optimal lighting brightness and color temperature. The environmental adjustment unit can also provide background music or white noise to enhance the user's concentration. Furthermore, the environmental adjustment unit can also adjust the temperature and humidity of the user's learning environment to provide a comfortable learning environment. This optimizes the user's learning environment and improves learning efficiency.

[0040] The qualification acquisition support system may further include a progress display unit for visually displaying the user's learning progress. For example, the generation AI displays the user's learning progress in graphs or charts in the progress display unit. Specifically, by visually showing the learning progress and achievement level, the user can grasp their learning situation at a glance. The progress display unit can also update the user's achievement level toward their learning goals in real time and provide feedback to maintain motivation. Furthermore, the progress display unit can compare the user's progress toward the learning goals set by the user and evaluate the achievement level. This visually displays the user's learning progress, enhancing motivation to learn.

[0041] The qualification acquisition support system can further include an outcome evaluation unit for evaluating the user's learning outcomes. In the outcome evaluation unit, for example, a generative AI evaluates the user's learning outcomes and provides feedback. Specifically, the outcome evaluation unit administers tests and quizzes on the content the user has learned and evaluates the results. The outcome evaluation unit can also suggest the user's next learning steps based on the user's learning outcomes. Furthermore, the outcome evaluation unit can compare the user's learning outcomes with those of other users and use them as a benchmark. This makes it possible to evaluate the user's learning outcomes and suggest the user's next learning steps.

[0042] The qualification acquisition support system can further include an incentive provision unit to increase the user's motivation to learn. In the incentive provision unit, for example, the generation AI provides incentives according to the user's learning progress. Specifically, badges and points are awarded according to the learning progress and achievement level. The incentive provision unit can also provide benefits and rewards when the user achieves the learning goals set by the user. Furthermore, the incentive provision unit can also provide incentives periodically to maintain the user's motivation to learn. This can increase the user's motivation to learn and support continued learning.

[0043] The qualification acquisition support system can further include a content provider for providing learning content tailored to the user's learning style. For example, the content provider uses a generation AI to analyze the user's learning style and provides learning content tailored to that style. Specifically, the content provider provides visual learning materials to users who are good at visual learning and audio learning materials to users who are good at auditory learning. The content provider can also provide customized learning content based on the user's learning style. Furthermore, the content provider can dynamically change appropriate learning content according to the user's learning progress. This allows the content provider to provide learning content tailored to the user's learning style and maximize learning effectiveness.

[0044] The qualification acquisition support system can further include a community function to increase users' motivation to learn. The community function, for example, provides a platform for the generative AI to promote interaction between users. Specifically, users aiming for the same qualification can exchange information and encourage each other. The community function also allows users to share their learning progress and results and provide each other with feedback. Furthermore, the community function can also provide a forum for users to ask questions or seek advice about their studies. This can increase motivation to learn through interaction between users and support continuation of learning.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The aptitude test implementation unit conducts an aptitude test. For example, the generation AI administers an aptitude test to the user and analyzes the results. The aptitude test implementation unit can also evaluate the user's interests, strengths, and weaknesses based on the questions answered by the user. Furthermore, based on the results of the aptitude test, the aptitude test implementation unit identifies qualifications that are most suitable for the user and qualifications that would improve work efficiency if obtained. For example, the generation AI receives a prompt such as "Please answer this question" and evaluates the user's aptitude. Step 2: The study plan creation unit creates a study plan based on the aptitude assessed by the aptitude test implementation unit. For example, the generation AI creates the period, method, and schedule for obtaining a qualification based on the user's individual schedule and lifestyle. The study plan creation unit can also provide a study plan that includes specific study content and progress management methods. For example, the generation AI provides an optimal study plan based on information such as "how much time can be allocated to studying on weekdays." Step 3: The learning status monitoring unit monitors the learning status based on the learning plan created by the learning plan creation unit and provides feedback. For example, the generation AI monitors the user's learning status in real time and provides feedback as needed. The learning status monitoring unit can also analyze whether the user is progressing with their learning as planned or where they are struggling, and provide appropriate advice or additional learning resources. For example, the generation AI provides feedback such as, "It seems like you're struggling with this part."

[0047] (Example 2) A qualification acquisition support system according to an embodiment of the present invention utilizes generative AI to streamline support for qualification acquisition. This qualification acquisition support system conducts aptitude tests, identifies qualifications that match an individual's aptitude and qualifications that can be obtained for work-related reasons, creates a study plan with the period, method, and schedule for acquisition based on an individual's schedule, and provides the user with a learning plan. This allows the qualification acquisition support system to optimize each individual's aptitude and streamline qualification acquisition.

[0048] A qualification acquisition support system according to an embodiment includes an aptitude test implementation unit, a study plan creation unit, and a study status monitoring unit. The aptitude test implementation unit conducts an aptitude test. For example, a generation AI administers an aptitude test to a user and analyzes the results. The aptitude test implementation unit can also evaluate the user's interests, strengths, and weaknesses based on the questions answered by the user. Furthermore, the aptitude test implementation unit selects qualifications that are most suitable for the user or that can improve work efficiency based on the aptitude test results. For example, the generation AI receives a prompt such as "Please answer this question" and evaluates the user's aptitude. The study plan creation unit creates a study plan based on the aptitude evaluated by the aptitude test implementation unit. For example, the generation AI creates a period, method, and schedule for qualification acquisition based on the user's individual schedule and lifestyle. The study plan creation unit can also provide a study plan that includes specific study content and progress management methods. For example, the generation AI provides an optimal study plan based on information such as "how much time the user can devote to studying on weekdays." The learning status monitoring unit monitors the learning status based on the learning plan created by the learning plan creation unit and provides feedback. For example, the generation AI monitors the user's learning status in real time and provides feedback as needed. The learning status monitoring unit can also analyze whether the user is progressing with their studies as planned and where they are struggling, and provide appropriate advice or additional learning resources. For example, the generation AI provides feedback such as "It seems like you're struggling with this part." This allows the qualification acquisition support system according to the embodiment to support efficient qualification acquisition based on the user's aptitude.

[0049] The aptitude test implementation unit can analyze a user's past learning history and work history and dynamically adjust the content of the aptitude test questions based on that. In the aptitude test implementation unit, for example, the generation AI analyzes a user's past learning history to evaluate the depth of knowledge and interest in a specific field. For example, the content of the aptitude test questions is customized based on courses taken in the past and qualifications obtained. The aptitude test implementation unit also analyzes the user's work history and adjusts the aptitude test questions based on the user's current job content and past work experience. For example, related questions can be added for users with specific work skills and experience. In addition, the aptitude test implementation unit can dynamically change the content of the aptitude test questions based on the user's learning history and work history to perform more accurate aptitude assessments. For example, the difficulty and content of the questions can be adjusted to evaluate the depth of knowledge and interest in a specific field. This allows the aptitude test to be customized based on the user's past experience.

[0050] The aptitude test implementation unit can predict the user's future career path based on the results of the aptitude test and suggest the most suitable qualifications for that. In the aptitude test implementation unit, for example, a generation AI analyzes the results of the aptitude test and predicts the user's future career path based on the user's interests and strengths. For example, the aptitude test implementation unit evaluates skills and knowledge in a specific field and suggests the most suitable qualifications based on that. In addition, the aptitude test implementation unit can simulate the user's career path based on the results of the aptitude test and identify the skills and qualifications that will be required in the future. For example, the aptitude test implementation unit can predict a career path in a specific occupation or industry and suggest the most suitable qualifications for that. In addition, the aptitude test implementation unit can build a system in which a generation AI predicts the user's future career path based on the results of the aptitude test and suggests the most suitable qualifications for that. For example, the generation AI evaluates the user's interests and strengths and identifies the skills and qualifications that will be required in the future. This makes it possible to suggest the most suitable qualifications based on the user's future career path.

[0051] The aptitude test implementation unit can use the emotion estimation function to analyze the user's emotions during the aptitude test in real time and insert questions to reduce stress and anxiety. The aptitude test implementation unit, for example, uses the emotion estimation function to analyze the user's facial expressions and voice during the aptitude test to detect stress and anxiety. For example, if the user is nervous, it inserts questions to help the user relax. The aptitude test implementation unit also analyzes the user's emotions during the aptitude test in real time and dynamically changes the questions to reduce stress and anxiety. For example, if the user is feeling anxious, it can provide simple questions or positive feedback. The aptitude test implementation unit also uses the emotion estimation function to build a system that analyzes the user's emotions during the aptitude test in real time and inserts questions to reduce stress and anxiety. For example, it provides questions and feedback to help the user relax depending on the user's emotional state. This allows the aptitude test to be adjusted according to the user's emotions, reducing stress and anxiety.

[0052] The study plan creation unit can analyze the user's lifestyle rhythm and health condition and suggest optimal study times based on that. In the study plan creation unit, for example, a generation AI analyzes the user's lifestyle rhythm and suggests optimal study times. For example, an efficient study time is set based on the user's sleep pattern and daily activity schedule. The study plan creation unit also analyzes the user's health condition and suggests optimal study times based on that. For example, the study schedule can be adjusted taking into account the user's energy level and peak concentration times. In addition, the study plan creation unit builds a system in which a generation AI analyzes the user's lifestyle rhythm and health condition and suggests optimal study times based on that. For example, an efficient study time is set based on the user's biorhythm and health data. This makes it possible to suggest optimal study times based on the user's lifestyle rhythm and health condition.

[0053] The learning plan creation unit can automatically generate learning materials that match the user's learning style. In the learning plan creation unit, for example, a generation AI analyzes the user's learning style and automatically generates learning materials that match it. For example, visual learning materials are provided to a user who is good at visual learning, and audio learning materials are provided to a user who is good at auditory learning. In addition, the learning plan creation unit automatically generates customized learning materials based on the user's learning style. For example, interactive learning materials can be provided to a user who is good at tactile learning. In addition, the learning plan creation unit builds a system in which a generation AI analyzes the user's learning style and automatically generates learning materials that match it. For example, learning materials corresponding to each style of visual, auditory, and tactile are provided. In this way, learning materials that match the user's learning style can be automatically generated.

[0054] The study plan creation unit can use the emotion estimation function to provide interactive content to restore motivation when a user's motivation to study drops. The study plan creation unit, for example, uses the emotion estimation function to provide interactive content to restore motivation when a user's motivation to study drops. For example, it presents encouraging messages or success stories. The study plan creation unit also builds a system that uses the emotion estimation function to provide interactive content to restore motivation when a user's motivation to study drops. For example, it can provide study content that incorporates game elements. The study plan creation unit also uses the emotion estimation function to provide interactive content to restore motivation when a user's motivation to study drops. For example, it dynamically changes appropriate content depending on the user's emotional state. This makes it possible to provide interactive content to restore motivation when a user's motivation to study drops.

[0055] The learning status monitoring unit can analyze the user's learning data and provide customized feedback to maximize the effectiveness of learning. For example, the learning status monitoring unit allows the generation AI to analyze the user's learning data and provide customized feedback to maximize the effectiveness of learning. For example, the generation AI may evaluate the level of understanding in a specific field and provide additional learning resources. The learning status monitoring unit also builds a system in which the generation AI provides customized feedback based on the user's learning data. For example, the generation AI may present specific advice and areas for improvement depending on the user's learning progress and level of understanding. The learning status monitoring unit also allows the generation AI to analyze the user's learning data and provide customized feedback to maximize the effectiveness of learning. For example, the generation AI may analyze the user's learning patterns and suggest efficient learning methods. This makes it possible to provide customized feedback based on the user's learning data.

[0056] The learning status monitoring unit predicts the user's learning performance and can detect problems early based on the prediction results. In the learning status monitoring unit, for example, the generation AI analyzes the user's learning data and predicts learning performance. For example, future learning progress and level of understanding are predicted based on past learning data. The learning status monitoring unit also builds a system in which the generation AI detects problems early based on the learning performance prediction results. For example, if learning progress is lagging behind, an alert can be issued early. The learning status monitoring unit also predicts the user's learning performance and detects problems early based on the prediction results. For example, if understanding in a particular area is low, additional learning resources are provided. This makes it possible to predict the user's learning performance and detect problems early.

[0057] The learning situation monitoring unit can use the emotion estimation function to analyze the user's emotions while studying in real time and suggest relaxation methods when negative emotions arise. The learning situation monitoring unit, for example, uses the emotion estimation function to analyze the user's emotions while studying in real time and suggest relaxation methods when negative emotions arise. For example, deep breathing or a short break is recommended. The learning situation monitoring unit can also build a system that analyzes the user's emotions while studying in real time and suggests relaxation methods when negative emotions arise. For example, an appropriate relaxation method can be provided depending on the emotional state. The learning situation monitoring unit can also use the emotion estimation function to analyze the user's emotions while studying in real time and suggest relaxation methods when negative emotions arise. For example, the timing and content of the relaxation methods can be dynamically changed depending on the user's emotional state. This makes it possible to detect the user's negative emotions and suggest relaxation methods.

[0058] The learning status monitoring unit can provide a function to compare the learning status monitoring results with other users and use them as a benchmark. The learning status monitoring unit, for example, provides a function to compare the learning status monitoring results with other users and use them as a benchmark. For example, comparing with the progress of other users aiming for the same qualification. The learning status monitoring unit also builds a system in which the generation AI compares the learning status monitoring results with other users and uses them as a benchmark. For example, it can compare learning progress and level of understanding with other users. The learning status monitoring unit also provides a function to compare the learning status monitoring results with other users and use them as a benchmark. For example, comparing with the progress of other users who share the same learning plan. This allows the user's learning status to be compared with other users and used as a benchmark.

[0059] The learning status monitoring unit can set future learning goals based on the user's learning data and provide step-by-step guidance for achieving those goals. In the learning status monitoring unit, for example, the generation AI analyzes the user's learning data and sets future learning goals. For example, it sets a specific goal for obtaining a specific qualification. The learning status monitoring unit also builds a system that provides step-by-step guidance for achieving the learning goals. For example, it can present specific learning content and progress management methods for each step. In addition, the learning status monitoring unit can set future learning goals based on the user's learning data and provide step-by-step guidance for achieving those goals. For example, it can suggest the next step depending on the user's learning progress. This makes it possible to set the user's learning goals and provide guidance for achieving them.

[0060] The learning status monitoring unit can use the emotion estimation function to analyze the user's emotions during learning and provide incentives to elicit positive emotions. The learning status monitoring unit, for example, uses the emotion estimation function to analyze the user's emotions during learning and provide incentives to elicit positive emotions. For example, badges or points are awarded according to learning progress. The learning status monitoring unit also builds a system that analyzes the user's emotions during learning in real time and provides incentives to elicit positive emotions. For example, it can provide appropriate incentives according to the emotional state. The learning status monitoring unit also uses the emotion estimation function to analyze the user's emotions during learning and provide incentives to elicit positive emotions. For example, the timing and content of the incentives are dynamically changed according to the user's emotional state. This makes it possible to provide incentives to elicit positive emotions from the user.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The qualification acquisition support system can further include an environmental adjustment unit to optimize the user's learning environment. The environmental adjustment unit, for example, adjusts the lighting and sound of the user's learning environment. Specifically, the generative AI monitors the user's learning environment and sets the optimal lighting brightness and color temperature. The environmental adjustment unit can also provide background music or white noise to enhance the user's concentration. Furthermore, the environmental adjustment unit can also adjust the temperature and humidity of the user's learning environment to provide a comfortable learning environment. This optimizes the user's learning environment and improves learning efficiency.

[0063] The qualification acquisition support system may further include a progress display unit for visually displaying the user's learning progress. For example, the generation AI displays the user's learning progress in graphs or charts in the progress display unit. Specifically, by visually showing the learning progress and achievement level, the user can grasp their learning situation at a glance. The progress display unit can also update the user's achievement level toward their learning goals in real time and provide feedback to maintain motivation. Furthermore, the progress display unit can compare the user's progress toward the learning goals set by the user and evaluate the achievement level. This visually displays the user's learning progress, enhancing motivation to learn.

[0064] The qualification acquisition support system can further include an outcome evaluation unit for evaluating the user's learning outcomes. In the outcome evaluation unit, for example, a generative AI evaluates the user's learning outcomes and provides feedback. Specifically, the outcome evaluation unit administers tests and quizzes on the content the user has learned and evaluates the results. The outcome evaluation unit can also suggest the user's next learning steps based on the user's learning outcomes. Furthermore, the outcome evaluation unit can compare the user's learning outcomes with those of other users and use them as a benchmark. This makes it possible to evaluate the user's learning outcomes and suggest the user's next learning steps.

[0065] The qualification acquisition support system can further include an incentive provision unit to increase the user's motivation to learn. In the incentive provision unit, for example, the generation AI provides incentives according to the user's learning progress. Specifically, badges and points are awarded according to the learning progress and achievement level. The incentive provision unit can also provide benefits and rewards when the user achieves the learning goals set by the user. Furthermore, the incentive provision unit can also provide incentives periodically to maintain the user's motivation to learn. This can increase the user's motivation to learn and support continued learning.

[0066] The qualification acquisition support system can further include a content provider for providing learning content tailored to the user's learning style. For example, the content provider uses a generation AI to analyze the user's learning style and provides learning content tailored to that style. Specifically, the content provider provides visual learning materials to users who are good at visual learning and audio learning materials to users who are good at auditory learning. The content provider can also provide customized learning content based on the user's learning style. Furthermore, the content provider can dynamically change appropriate learning content according to the user's learning progress. This allows the content provider to provide learning content tailored to the user's learning style and maximize learning effectiveness.

[0067] The qualification acquisition support system may further include an emotion adjustment unit that estimates the user's emotions and adjusts the study plan based on the estimated emotions. The emotion adjustment unit, for example, uses an emotion estimation function to analyze the user's emotions during study in real time and dynamically adjust the study plan. Specifically, if the user is feeling stressed, the emotion adjustment unit makes adjustments to reduce the study load. The emotion adjustment unit can also provide relaxation methods and content to increase motivation according to the user's emotional state. Furthermore, the emotion adjustment unit can monitor the user's emotional state and adjust the long-term study plan. This allows the study plan to be adjusted based on the user's emotions, maximizing the learning effect.

[0068] The qualification acquisition support system may further include an emotional environment adjustment unit that estimates the user's emotions and adjusts the learning environment based on the estimated emotions. The emotional environment adjustment unit, for example, uses an emotion estimation function to analyze the user's emotions during learning in real time and dynamically adjusts the learning environment. Specifically, if the user is relaxed, the emotional environment adjustment unit adjusts the environment to enhance concentration. The emotional environment adjustment unit can also adjust lighting and sound according to the user's emotional state. Furthermore, the emotional environment adjustment unit can monitor the user's emotional state and optimize the learning environment over the long term. This allows the learning environment to be adjusted based on the user's emotions, maximizing learning effectiveness.

[0069] The qualification acquisition support system may further include an emotional content adjustment unit that estimates the user's emotions and adjusts the learning content based on the estimated emotions. The emotional content adjustment unit, for example, uses an emotion estimation function to analyze the user's emotions during learning in real time and dynamically adjust the learning content. Specifically, if the user is interested, it provides content with more in-depth content. The emotional content adjustment unit may also adjust the difficulty and format of the learning content according to the user's emotional state. Furthermore, the emotional content adjustment unit may monitor the user's emotional state and optimize the learning content over the long term. This allows the learning content to be adjusted based on the user's emotions, maximizing learning effectiveness.

[0070] The qualification acquisition support system may further include an emotion feedback unit that estimates the user's emotions and provides learning feedback based on the estimated emotions. The emotion feedback unit, for example, uses an emotion estimation function to analyze the user's emotions during learning in real time and dynamically adjust the feedback. Specifically, if the user has positive emotions, it provides feedback that encourages further challenges. The emotion feedback unit may also adjust the content and format of the feedback according to the user's emotional state. Furthermore, the emotion feedback unit may monitor the user's emotional state and optimize the feedback over the long term. This allows learning feedback to be provided based on the user's emotions, maximizing the learning effect.

[0071] The qualification acquisition support system can further include a community function to increase users' motivation to learn. The community function, for example, provides a platform for the generative AI to promote interaction between users. Specifically, users aiming for the same qualification can exchange information and encourage each other. The community function also allows users to share their learning progress and results and provide each other with feedback. Furthermore, the community function can also provide a forum for users to ask questions or seek advice about their studies. This can increase motivation to learn through interaction between users and support continuation of learning.

[0072] The processing flow of the second embodiment will be briefly explained below.

[0073] Step 1: The aptitude test implementation unit conducts an aptitude test. For example, the generation AI administers an aptitude test to the user and analyzes the results. The aptitude test implementation unit can also evaluate the user's interests, strengths, and weaknesses based on the questions answered by the user. Furthermore, based on the results of the aptitude test, the aptitude test implementation unit identifies qualifications that are most suitable for the user and qualifications that would improve work efficiency if obtained. For example, the generation AI receives a prompt such as "Please answer this question" and evaluates the user's aptitude. Step 2: The study plan creation unit creates a study plan based on the aptitude assessed by the aptitude test implementation unit. For example, the generation AI creates the period, method, and schedule for obtaining a qualification based on the user's individual schedule and lifestyle. The study plan creation unit can also provide a study plan that includes specific study content and progress management methods. For example, the generation AI provides an optimal study plan based on information such as "how much time can be allocated to studying on weekdays." Step 3: The learning status monitoring unit monitors the learning status based on the learning plan created by the learning plan creation unit and provides feedback. For example, the generation AI monitors the user's learning status in real time and provides feedback as needed. The learning status monitoring unit can also analyze whether the user is progressing with their learning as planned or where they are struggling, and provide appropriate advice or additional learning resources. For example, the generation AI provides feedback such as, "It seems like you're struggling with this part."

[0074] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0075] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0076] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0077] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0078] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0079] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0080] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0081] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0082] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0083] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0084] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0085] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0086] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0087] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0088] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0089] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0090] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0091] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0092] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0093] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0094] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0095] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0096] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0097] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0098] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0099] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0100] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0101] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0102] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0103] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0104] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0107] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0108] 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.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0115] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0124] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0125] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0126] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0127] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0128] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0129] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0130] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0131] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0132] 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.

[0133] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0134] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0135] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0136] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0137] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0138] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0139] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0140] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0141] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an aptitude test implementation department that implements aptitude tests; a study plan creation unit that creates a study plan based on the aptitude evaluated by the aptitude test implementation unit; a learning status monitoring unit that monitors the learning status based on the learning plan created by the learning plan creation unit and provides feedback. A system characterized by:

2. The aptitude test implementation unit Dynamically adjust aptitude test questions based on an analysis of a user's learning history and work experience.

2. The system of claim 1.

3. The aptitude test implementation unit Based on the results of the aptitude test, the system predicts the user's future career path and suggests the most suitable qualifications for that path.

2. The system of claim 1.

4. The aptitude test implementation unit Analyzing the user's emotions in real time during the aptitude test and inserting questions to reduce stress and anxiety 2. The system of claim 1.

5. The learning plan creation unit Analyzes the user's lifestyle and health status and suggests optimal study times based on that 2. The system of claim 1.

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