system

Abstract

The system according to this embodiment aims to alleviate future economic anxieties and enable users to plan for the future they desire. [Solution] The system according to the embodiment comprises a measurement unit, a questioning unit, a generation unit, a planning unit, and a support unit. The measurement unit performs aptitude tests and measures the user's cognitive abilities. The questioning unit asks questions based on the data obtained by the measurement unit. The generation unit generates future image videos based on the answers obtained by the questioning unit. The planning unit calculates learning plans and expenditures based on the videos generated by the generation unit. The support unit supports income increase based on the plans calculated by the planning unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that simulation tools for eliminating future economic uncertainties are not well utilized, making it difficult to make future plans.

[0005] The system according to the embodiment aims to enable a user to eliminate future economic uncertainties and make plans for a desired future.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a measurement unit, a questioning unit, a generation unit, a planning unit, and a support unit. The measurement unit performs aptitude tests and measures the user's cognitive abilities. The questioning unit asks questions based on the data obtained by the measurement unit. The generation unit generates future image videos based on the answers obtained by the questioning unit. The planning unit calculates learning plans and expenditures based on the videos generated by the generation unit. The support unit supports income increase based on the plans calculated by the planning unit. [Effects of the Invention]

[0007] The system according to this embodiment allows users to alleviate future financial anxieties and make plans for the future they desire. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The simulation tool according to an embodiment of the present invention is a simulation tool for alleviating future anxieties. This simulation tool addresses the difficulty many people face in accurately calculating the money they will need in the future, which requires them to clearly define the kind of life they want to live. The simulation tool visualizes the future, such as one year or three years from now, in video form. This helps users understand what will happen if they continue as they are and supports them in taking action toward their desired future. For example, the simulation tool allows users to complete "aptitude tests" and "cognitive ability measurements" and "skill registration." Then, by simply answering questions from the AI, an image of the future is generated. Users can not only enjoy the visualized future self but also use it as material to consider how to live their lives in the future and to draw a path toward that future. There are two elements: rails and train. The rails calculate necessary learning plans and expenses in conjunction with the services of various companies. Based on that plan, it makes suggestions to supplement any shortfalls by utilizing current assets. If income is insufficient compared to the plan, it supports income increase through job change support and suggestions for side jobs in conjunction with the services of various companies. The target audience is newly married couples around the age of 30 who face the challenge of not knowing how much money they need to save for the future. Using AI, we visualize the future several years from now, allowing users to consider their own lifestyle based on this visualization. Once a lifestyle is decided, we offer various suggestions to help users achieve it. We provide suggestions for asset management and increasing income, supporting users in living the life they desire. The generating AI uses the results of aptitude tests and cognitive ability measurements, along with answers to pre-set questions, to build the foundational data for generating future scenarios. Based on the analyzed data, the AI ​​simulates multiple possibilities for future life and career, and creates a video based on that data. In this way, the simulation tool can alleviate users' anxieties about the future and provide concrete countermeasures for achieving their desired future.

[0029] The simulation tool according to this embodiment comprises a measurement unit, a questioning unit, a generation unit, a planning unit, and a support unit. The measurement unit performs aptitude tests and cognitive ability measurements of the user. For example, the measurement unit can conduct personality assessments and vocational aptitude tests. The measurement unit can also conduct IQ tests and cognitive ability tests. Furthermore, the measurement unit can measure the user's stress level and concentration. For example, as a personality assessment, the measurement unit asks questions to evaluate the user's personality traits. A vocational aptitude test is conducted to evaluate the user's vocational aptitude. An IQ test is conducted to evaluate the user's cognitive ability. A cognitive ability test is conducted to evaluate the user's cognitive function. The questioning unit asks questions based on the data obtained by the measurement unit. For example, the questioning unit can ask questions to the user in the form of a questionnaire. The questioning unit can also ask questions to the user in the form of an interview. Furthermore, the questioning unit can ask additional questions based on the user's answers. For example, the questioning unit asks questions about the user's living situation and areas of interest in the form of a questionnaire. In the form of an interview, it asks questions to collect detailed information about the user. Additional questions are asked to gather more specific information based on the user's answers. The generation unit generates a future image based on the answers obtained by the questioning unit. The generation unit can, for example, generate a simulation image. It can also generate a future image using animation. Furthermore, the generation unit can generate a customized future image based on the user's answers. For example, the generation unit visualizes scenarios of the user's future life and career as a simulation image. Animated images visually represent the user's image of the future. Customized images visualize individual scenarios based on the user's answers. The planning unit calculates learning plans and expenses based on the images generated by the generation unit. The planning unit can, for example, create a learning schedule. It can also select learning materials. Furthermore, the planning unit can calculate expenses for education and living expenses. For example, the planning unit creates a learning plan for the user as a learning schedule.The selection of learning materials involves choosing materials suitable for the user's learning content. The calculation of educational expenses involves calculating the costs associated with the user's education. The calculation of living expenses involves calculating the costs necessary for the user's living expenses. The support department supports income increase based on the plan calculated by the planning department. The support department can, for example, suggest side jobs. The support department can also provide support for skill development. Furthermore, the support department can also provide job change support. For example, as a suggestion for a side job, the support department provides the user with a suitable side job opportunity. Skill development support involves providing training to improve the user's skills. Job change support involves supporting the user's career change. As a result, the simulation tool according to the embodiment can perform aptitude tests and cognitive ability measurements of the user, ask questions, generate future image videos, calculate learning plans and expenses, and support income increase.

[0030] The measurement unit performs aptitude tests and measures the user's cognitive abilities. For example, it can conduct personality assessments and vocational aptitude tests. It can also administer IQ tests and cognitive ability tests. Furthermore, it can measure the user's stress level and concentration. Specifically, for personality assessments, it asks questions to evaluate the user's personality traits. Standard personality assessment tools such as the Big Five personality traits and MBTI can be used. Vocational aptitude tests are administered to suggest the most suitable occupation based on the user's interests and skills. These include Holland's Career Interest Model and StrengthsFinder. IQ tests assess the user's cognitive abilities, using tools such as the Wechsler Adult Intelligence Scale and Raven's Progression Matrix. Cognitive ability tests assess cognitive functions such as the user's memory, attention, and problem-solving abilities. These include the Stroop test and the Trailmaking test. Furthermore, the measurement unit can measure the user's stress level using cortisol level measurements and self-report stress scales. Concentration levels are measured using tests such as sustained attention tests and visual attention tests. This allows the measurement unit to evaluate the user's diverse characteristics and abilities in detail and provide information tailored to their individual needs.

[0031] The questioning unit asks questions based on the data obtained by the measurement unit. The questioning unit can ask users questions in the form of a questionnaire, for example. It can also ask users questions in the form of an interview. Furthermore, the questioning unit can ask additional questions based on the user's answers. Specifically, in the questionnaire format, detailed questions are asked about the user's living situation and areas of interest. This includes questions about lifestyle, hobbies, health status, and work experience. In the interview format, face-to-face or online interviews are conducted to collect detailed information about the user. In the interview, questions are asked to delve deeper into the user's past experiences and future goals to collect more specific information. Additional questions are asked to obtain more detailed information based on the user's initial answers. For example, if a user shows interest in a particular occupation, further questions may be asked about skills and experience related to that occupation. Also, if a user has a high stress level, detailed questions may be asked about the causes and countermeasures. In this way, the questioning unit can collect information that is tailored to the diverse backgrounds and needs of users and provide individualized support.

[0032] The generation unit generates future imagery based on the answers obtained from the questioning unit. For example, the generation unit can generate simulation images. It can also generate future imagery using animation. Furthermore, the generation unit can generate customized future imagery based on the user's answers. Specifically, simulation images visualize scenarios of the user's future life and career. This includes images that realistically reproduce daily life and work environments based on the occupation and lifestyle chosen by the user. Animated images visually represent the user's image of the future, allowing the user to visualize future scenarios more concretely. Customized images visualize individual scenarios based on the user's answers. For example, if the user chooses a specific career path, the generation unit visualizes a future scenario along that career path, allowing the user to visually confirm the results of their choice. This allows the generation unit to help users concretely visualize future options and make better decisions.

[0033] The planning unit calculates learning plans and expenses based on the videos generated by the generation unit. For example, the planning unit can create a learning schedule. It can also select learning materials. Furthermore, the planning unit can calculate educational and living expenses. Specifically, as a learning schedule, it creates a learning plan for the user. This involves setting short-term and long-term learning goals based on the user's objectives and current skill level, and creating a learning schedule accordingly. For the selection of learning materials, it selects materials suitable for the user's learning content. This includes online courses, books, workshops, etc. For the calculation of educational expenses, it calculates the costs associated with the user's education. This includes tuition fees, material fees, and examination fees. For the calculation of living expenses, it calculates the expenses necessary for the user's living. This includes rent, food expenses, transportation expenses, and medical expenses. Furthermore, the planning unit considers the user's income and expenditure balance and creates a realistic plan. This allows the planning unit to provide concrete steps for the user to achieve their goals and support the execution of the plan.

[0034] The Support Department provides support for increasing income based on the plan calculated by the Planning Department. For example, the Support Department can suggest side jobs. It can also provide support for skill development. Furthermore, it can provide job change support. Specifically, as a side job suggestion, it provides users with suitable side job opportunities. This includes freelance work through online platforms and local part-time job information. Skill development support provides training to improve users' skills. This includes online courses, workshops, and expert coaching. Job change support assists users in changing careers. This includes resume writing assistance, interview preparation, and job postings. Furthermore, the Support Department monitors users' progress and provides advice and support as needed. This allows the Support Department to provide concrete support to users to execute their plans and increase their income.

[0035] The measurement unit can analyze the user's past aptitude test and cognitive ability measurement results and select the optimal measurement method. For example, the measurement unit can prioritize selecting measurement methods in which the user has shown high performance in the past. It can also avoid measurement methods that the user has struggled with in the past and suggest alternative methods. Furthermore, the measurement unit can adjust the difficulty level of the measurement based on the user's past results. For example, the measurement unit can store the user's past aptitude test and cognitive ability measurement results in a database and select the optimal measurement method using an analysis algorithm. This improves the accuracy of the measurement by selecting the optimal measurement method based on the user's past results. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input the results of past aptitude tests and cognitive ability measurements into a generating AI and have the generating AI select the optimal measurement method.

[0036] The measurement unit can customize measurement items based on the user's current lifestyle and areas of interest during measurement. For example, the measurement unit can add measurement items related to areas of interest that the user is currently interested in. The measurement unit can also select appropriate measurement items according to the user's lifestyle. Furthermore, the measurement unit can customize measurement items related to the user's occupation or hobbies. For example, the measurement unit collects data on the user's lifestyle and areas of interest and customizes the measurement items using an analysis algorithm. This allows for more accurate measurements by customizing measurement items according to the user's lifestyle and areas of interest. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the customization of measurement items.

[0037] The questioning unit can select the most appropriate question by referring to the user's past answer history when a question is asked. For example, the questioning unit can analyze the trends of questions the user has answered in the past and select relevant questions. It can also select questions that the user is likely to be interested in based on their past answer history. Furthermore, the questioning unit can avoid questions that the user has avoided in the past and suggest alternative questions. For example, the questioning unit can store the user's past answer history in a database and use an analysis algorithm to select the most appropriate question. This improves the accuracy of the questions by selecting the most appropriate question based on the user's past answer history. Some or all of the above processing in the questioning unit may be performed using AI, for example, or without AI. For example, the questioning unit can input the past answer history into a generating AI and have the generating AI perform the selection of the most appropriate question.

[0038] The questioning unit can customize the content of questions based on the user's current living situation and areas of interest. For example, the questioning unit can ask questions related to topics the user is currently interested in. The questioning unit can also select appropriate questions according to the user's living situation. Furthermore, the questioning unit can customize questions related to the user's occupation and hobbies. For example, the questioning unit can collect data on the user's living situation and areas of interest and customize the content of questions using an analysis algorithm. This allows for more appropriate questions to be asked by customizing the content according to the user's living situation and areas of interest. Some or all of the above processing in the questioning unit may be performed using AI, for example, or without AI. For example, the questioning unit can input data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the customization of the question content.

[0039] The generation unit can select the optimal future scenario by referring to the user's past data during generation. For example, the generation unit can select the optimal future scenario based on scenarios the user has shown interest in in the past. The generation unit can also prioritize the selection of successful scenarios from the user's past data. Furthermore, the generation unit can analyze the user's past data and select the most realistic future scenario. For example, the generation unit can store the user's past data in a database and use an analysis algorithm to select the optimal future scenario. This improves the accuracy of the scenarios by selecting the optimal future scenario based on the user's past data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input past data into a generation AI and have the generation AI perform the selection of the optimal future scenario.

[0040] The generation unit can customize future scenarios based on the user's current lifestyle and areas of interest during the generation process. For example, the generation unit can generate future scenarios related to areas of interest that the user is currently interested in. The generation unit can also select an appropriate future scenario according to the user's lifestyle. Furthermore, the generation unit can customize future scenarios related to the user's occupation or hobbies. For example, the generation unit collects data on the user's lifestyle and areas of interest and uses an analysis algorithm to customize future scenarios. This allows for the generation of more appropriate scenarios by customizing future scenarios according to the user's lifestyle and areas of interest. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the user's lifestyle and areas of interest into a generation AI and have the generation AI perform the customization of future scenarios.

[0041] The planning unit can select the optimal plan by referring to the user's past data when calculating the plan. For example, the planning unit can select the optimal plan based on the user's past successful learning plans. The planning unit can also avoid failed plans based on the user's past data. Furthermore, the planning unit can analyze the user's past data and select the most realistic plan. For example, the planning unit can store the user's past data in a database and select the optimal plan using an analysis algorithm. This improves the accuracy of the plan by selecting the optimal plan based on the user's past data. Some or all of the above processes in the planning unit may be performed using AI, for example, or not using AI. For example, the planning unit can input past data into a generating AI and have the generating AI perform the selection of the optimal plan.

[0042] The planning unit can customize the plan content based on the user's current living situation and areas of interest when calculating the plan. For example, the planning unit can propose a learning plan related to the user's current areas of interest. The planning unit can also propose an appropriate spending plan according to the user's living situation. Furthermore, the planning unit can customize the plan content related to the user's occupation and hobbies. For example, the planning unit collects data on the user's living situation and areas of interest and customizes the plan content using an analysis algorithm. This allows for the creation of a more appropriate plan by customizing the plan content according to the user's living situation and areas of interest. Some or all of the above processes in the planning unit may be performed using AI, for example, or not using AI. For example, the planning unit can input data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the customization of the plan content.

[0043] The support unit can select the optimal support method by referring to the user's past data during the support process. For example, the support unit can select the optimal support method based on the user's past successful methods for increasing income. The support unit can also avoid failed support methods based on the user's past data. Furthermore, the support unit can analyze the user's past data and select the most realistic support method. For example, the support unit can store the user's past data in a database and use an analysis algorithm to select the optimal support method. This improves the accuracy of support by selecting the optimal support method based on the user's past data. Some or all of the above processes in the support unit may be performed using AI, for example, or not. For example, the support unit can input past data into a generating AI and have the generating AI select the optimal support method.

[0044] The support unit can customize the support provided based on the user's current living situation and areas of interest. For example, the support unit can suggest ways to increase income related to the user's current areas of interest. The support unit can also suggest appropriate support based on the user's living situation. Furthermore, the support unit can customize support related to the user's occupation or hobbies. For example, the support unit can collect data on the user's living situation and areas of interest and customize the support using an analysis algorithm. This allows for more appropriate support by customizing the support according to the user's living situation and areas of interest. Some or all of the above processes in the support unit may be performed using AI, for example, or not. For example, the support unit can input data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the customization of the support.

[0045] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0046] The measurement unit monitors the user's health status and can adjust the timing of aptitude tests and cognitive ability measurements based on that status. For example, it can measure the user's heart rate and blood pressure, and if stress levels are high, it can conduct aptitude tests during a time when the user can relax. It can also measure cognitive ability after the user has had sufficient sleep. Furthermore, if the user is refreshed after exercise, it can conduct measurements at that time. By adjusting the timing of measurements according to the user's health status, more accurate results can be obtained.

[0047] The measurement unit can customize the measurement items based on the user's hobbies and interests. For example, if the user is interested in music, aptitude tests related to music can be added. Similarly, if the user is interested in sports, cognitive ability measurements related to sports can be performed. Furthermore, if the user is interested in art, measurement items related to art can be added. This allows for more accurate measurements by customizing the measurement items according to the user's hobbies and interests.

[0048] The questioning function can adjust the difficulty of questions by referring to the user's past answer history. For example, if a user has answered an easy question in the past, a more difficult question will be asked next time. Conversely, if a user has answered a difficult question in the past, an easier question can be asked next time. Furthermore, if a user has shown interest in a particular topic in the past, questions related to that topic can be added. This allows for more effective information gathering by adjusting the difficulty of questions based on the user's past answer history.

[0049] The generation unit can customize future image scenarios based on the user's current life situation. For example, if the user is currently a student, it can generate a scenario about their future career path. If the user is currently employed, it can also generate scenarios about promotions or job changes at work. Furthermore, if the user has a family, it can generate a scenario about their future with their family. By customizing the future image scenarios according to the user's current life situation, more realistic images are generated.

[0050] The planning department can monitor the progress of the learning plan by referring to the user's past data and adjust the plan as needed. For example, if the user did not follow the plan in the past, a more realistic plan will be suggested next time. Also, if the user exceeded the plan in the past, a more challenging plan can be suggested next time. Furthermore, if the user has been successful in a particular area in the past, plans related to that area can be prioritized. In this way, by monitoring the progress of the learning plan based on the user's past data and adjusting the plan as needed, more effective learning becomes possible.

[0051] The support team can customize income-increasing methods based on the user's current living situation and areas of interest. For example, if the user is currently a freelancer, they can suggest income-increasing methods tailored to freelancers. If the user is currently an employee, they can also suggest income-increasing methods related to promotion or changing jobs. Furthermore, if the user possesses specific skills, they can suggest income-increasing methods that utilize those skills. This allows for more appropriate support by customizing income-increasing methods according to the user's current living situation and areas of interest.

[0052] The following briefly describes the processing flow for example form 1.

[0053] Step 1: The measurement unit performs aptitude tests and measures the user's cognitive abilities. For example, it may perform personality assessments, vocational aptitude tests, IQ tests, cognitive ability tests, and measure stress levels and concentration. Step 2: The questioning unit asks questions based on the data obtained by the measurement unit. For example, it asks questions to the user in the form of a questionnaire or interview, and then asks additional questions based on the user's answers. Step 3: The generation unit generates a future image based on the answers obtained from the questioning unit. For example, it visualizes the user's future life and career scenarios using simulation videos or animations. Step 4: The planning unit calculates learning plans and expenses based on the videos generated by the generation unit. For example, it creates learning schedules, selects learning materials, and calculates educational and living expenses. Step 5: The support department provides support for increasing income based on the plan calculated by the planning department. For example, they may suggest side jobs, provide support for skill development, or assist with job changes.

[0054] (Example of form 2) The simulation tool according to an embodiment of the present invention is a simulation tool for alleviating future anxieties. This simulation tool addresses the difficulty many people face in accurately calculating the money they will need in the future, which requires them to clearly define the kind of life they want to live. The simulation tool visualizes the future, such as one year or three years from now, in video form. This helps users understand what will happen if they continue as they are and supports them in taking action toward their desired future. For example, the simulation tool allows users to complete "aptitude tests" and "cognitive ability measurements" and "skill registration." Then, by simply answering questions from the AI, an image of the future is generated. Users can not only enjoy the visualized future self but also use it as material to consider how to live their lives in the future and to draw a path toward that future. There are two elements: rails and train. The rails calculate necessary learning plans and expenses in conjunction with the services of various companies. Based on that plan, it makes suggestions to supplement any shortfalls by utilizing current assets. If income is insufficient compared to the plan, it supports income increase through job change support and suggestions for side jobs in conjunction with the services of various companies. The target audience is newly married couples around the age of 30 who face the challenge of not knowing how much money they need to save for the future. Using AI, we visualize the future several years from now, allowing users to consider their own lifestyle based on this visualization. Once a lifestyle is decided, we offer various suggestions to help users achieve it. We provide suggestions for asset management and increasing income, supporting users in living the life they desire. The generating AI uses the results of aptitude tests and cognitive ability measurements, along with answers to pre-set questions, to build the foundational data for generating future scenarios. Based on the analyzed data, the AI ​​simulates multiple possibilities for future life and career, and creates a video based on that data. In this way, the simulation tool can alleviate users' anxieties about the future and provide concrete countermeasures for achieving their desired future.

[0055] The simulation tool according to this embodiment comprises a measurement unit, a questioning unit, a generation unit, a planning unit, and a support unit. The measurement unit performs aptitude tests and cognitive ability measurements of the user. For example, the measurement unit can conduct personality assessments and vocational aptitude tests. The measurement unit can also conduct IQ tests and cognitive ability tests. Furthermore, the measurement unit can measure the user's stress level and concentration. For example, as a personality assessment, the measurement unit asks questions to evaluate the user's personality traits. A vocational aptitude test is conducted to evaluate the user's vocational aptitude. An IQ test is conducted to evaluate the user's cognitive ability. A cognitive ability test is conducted to evaluate the user's cognitive function. The questioning unit asks questions based on the data obtained by the measurement unit. For example, the questioning unit can ask questions to the user in the form of a questionnaire. The questioning unit can also ask questions to the user in the form of an interview. Furthermore, the questioning unit can ask additional questions based on the user's answers. For example, the questioning unit asks questions about the user's living situation and areas of interest in the form of a questionnaire. In the form of an interview, it asks questions to collect detailed information about the user. Additional questions are asked to gather more specific information based on the user's answers. The generation unit generates a future image based on the answers obtained by the questioning unit. The generation unit can, for example, generate a simulation image. It can also generate a future image using animation. Furthermore, the generation unit can generate a customized future image based on the user's answers. For example, the generation unit visualizes scenarios of the user's future life and career as a simulation image. Animated images visually represent the user's image of the future. Customized images visualize individual scenarios based on the user's answers. The planning unit calculates learning plans and expenses based on the images generated by the generation unit. The planning unit can, for example, create a learning schedule. It can also select learning materials. Furthermore, the planning unit can calculate expenses for education and living expenses. For example, the planning unit creates a learning plan for the user as a learning schedule.The selection of learning materials involves choosing materials suitable for the user's learning content. The calculation of educational expenses involves calculating the costs associated with the user's education. The calculation of living expenses involves calculating the costs necessary for the user's living expenses. The support department supports income increase based on the plan calculated by the planning department. The support department can, for example, suggest side jobs. The support department can also provide support for skill development. Furthermore, the support department can also provide job change support. For example, as a suggestion for a side job, the support department provides the user with a suitable side job opportunity. Skill development support involves providing training to improve the user's skills. Job change support involves supporting the user's career change. As a result, the simulation tool according to the embodiment can perform aptitude tests and cognitive ability measurements of the user, ask questions, generate future image videos, calculate learning plans and expenses, and support income increase.

[0056] The measurement unit performs aptitude tests and measures the user's cognitive abilities. For example, it can conduct personality assessments and vocational aptitude tests. It can also administer IQ tests and cognitive ability tests. Furthermore, it can measure the user's stress level and concentration. Specifically, for personality assessments, it asks questions to evaluate the user's personality traits. Standard personality assessment tools such as the Big Five personality traits and MBTI can be used. Vocational aptitude tests are administered to suggest the most suitable occupation based on the user's interests and skills. These include Holland's Career Interest Model and StrengthsFinder. IQ tests assess the user's cognitive abilities, using tools such as the Wechsler Adult Intelligence Scale and Raven's Progression Matrix. Cognitive ability tests assess cognitive functions such as the user's memory, attention, and problem-solving abilities. These include the Stroop test and the Trailmaking test. Furthermore, the measurement unit can measure the user's stress level using cortisol level measurements and self-report stress scales. Concentration levels are measured using tests such as sustained attention tests and visual attention tests. This allows the measurement unit to evaluate the user's diverse characteristics and abilities in detail and provide information tailored to their individual needs.

[0057] The questioning unit asks questions based on the data obtained by the measurement unit. The questioning unit can ask users questions in the form of a questionnaire, for example. It can also ask users questions in the form of an interview. Furthermore, the questioning unit can ask additional questions based on the user's answers. Specifically, in the questionnaire format, detailed questions are asked about the user's living situation and areas of interest. This includes questions about lifestyle, hobbies, health status, and work experience. In the interview format, face-to-face or online interviews are conducted to collect detailed information about the user. In the interview, questions are asked to delve deeper into the user's past experiences and future goals to collect more specific information. Additional questions are asked to obtain more detailed information based on the user's initial answers. For example, if a user shows interest in a particular occupation, further questions may be asked about skills and experience related to that occupation. Also, if a user has a high stress level, detailed questions may be asked about the causes and countermeasures. In this way, the questioning unit can collect information that is tailored to the diverse backgrounds and needs of users and provide individualized support.

[0058] The generation unit generates future imagery based on the answers obtained from the questioning unit. For example, the generation unit can generate simulation images. It can also generate future imagery using animation. Furthermore, the generation unit can generate customized future imagery based on the user's answers. Specifically, simulation images visualize scenarios of the user's future life and career. This includes images that realistically reproduce daily life and work environments based on the occupation and lifestyle chosen by the user. Animated images visually represent the user's image of the future, allowing the user to visualize future scenarios more concretely. Customized images visualize individual scenarios based on the user's answers. For example, if the user chooses a specific career path, the generation unit visualizes a future scenario along that career path, allowing the user to visually confirm the results of their choice. This allows the generation unit to help users concretely visualize future options and make better decisions.

[0059] The planning unit calculates learning plans and expenses based on the videos generated by the generation unit. For example, the planning unit can create a learning schedule. It can also select learning materials. Furthermore, the planning unit can calculate educational and living expenses. Specifically, as a learning schedule, it creates a learning plan for the user. This involves setting short-term and long-term learning goals based on the user's objectives and current skill level, and creating a learning schedule accordingly. For the selection of learning materials, it selects materials suitable for the user's learning content. This includes online courses, books, workshops, etc. For the calculation of educational expenses, it calculates the costs associated with the user's education. This includes tuition fees, material fees, and examination fees. For the calculation of living expenses, it calculates the expenses necessary for the user's living. This includes rent, food expenses, transportation expenses, and medical expenses. Furthermore, the planning unit considers the user's income and expenditure balance and creates a realistic plan. This allows the planning unit to provide concrete steps for the user to achieve their goals and support the execution of the plan.

[0060] The Support Department provides support for increasing income based on the plan calculated by the Planning Department. For example, the Support Department can suggest side jobs. It can also provide support for skill development. Furthermore, it can provide job change support. Specifically, as a side job suggestion, it provides users with suitable side job opportunities. This includes freelance work through online platforms and local part-time job information. Skill development support provides training to improve users' skills. This includes online courses, workshops, and expert coaching. Job change support assists users in changing careers. This includes resume writing assistance, interview preparation, and job postings. Furthermore, the Support Department monitors users' progress and provides advice and support as needed. This allows the Support Department to provide concrete support to users to execute their plans and increase their income.

[0061] The measurement unit can estimate the user's emotions and adjust the timing of aptitude tests and cognitive ability measurements based on the estimated emotions. For example, if the user is feeling stressed, the measurement unit can conduct aptitude tests during a time when the user is relaxed. The measurement unit can also conduct cognitive ability measurements when the user is concentrating. Furthermore, if the user is tired, the measurement unit can conduct aptitude tests after the user has rested. For example, the measurement unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expressions. This allows for more appropriate timing of measurements by adjusting the timing of aptitude tests and cognitive ability measurements according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the measurement unit may be performed using AI, or not. For example, the measurement unit can input image data of the user captured by the camera into a generating AI, which can then perform the estimation of the user's emotions.

[0062] The measurement unit can analyze the user's past aptitude test and cognitive ability measurement results and select the optimal measurement method. For example, the measurement unit can prioritize selecting measurement methods in which the user has shown high performance in the past. It can also avoid measurement methods that the user has struggled with in the past and suggest alternative methods. Furthermore, the measurement unit can adjust the difficulty level of the measurement based on the user's past results. For example, the measurement unit can store the user's past aptitude test and cognitive ability measurement results in a database and select the optimal measurement method using an analysis algorithm. This improves the accuracy of the measurement by selecting the optimal measurement method based on the user's past results. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input the results of past aptitude tests and cognitive ability measurements into a generating AI and have the generating AI select the optimal measurement method.

[0063] The measurement unit can customize measurement items based on the user's current lifestyle and areas of interest during measurement. For example, the measurement unit can add measurement items related to areas of interest that the user is currently interested in. The measurement unit can also select appropriate measurement items according to the user's lifestyle. Furthermore, the measurement unit can customize measurement items related to the user's occupation or hobbies. For example, the measurement unit collects data on the user's lifestyle and areas of interest and customizes the measurement items using an analysis algorithm. This allows for more accurate measurements by customizing measurement items according to the user's lifestyle and areas of interest. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the customization of measurement items.

[0064] The questioning unit can estimate the user's emotions and adjust the wording of the questions based on the estimated emotions. For example, if the user is nervous, the questioning unit will ask questions in a gentle tone. If the user is relaxed, the questioning unit can ask detailed questions. Furthermore, if the user is in a hurry, the questioning unit can ask concise questions. For example, the questioning unit can capture the user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expression. This allows for more appropriate questions to be asked by adjusting the wording of the questions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the questioning unit may be performed using AI, or not using AI. For example, the questioning unit can input image data of the user captured by the camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0065] The questioning unit can select the most appropriate question by referring to the user's past answer history when a question is asked. For example, the questioning unit can analyze the trends of questions the user has answered in the past and select relevant questions. It can also select questions that the user is likely to be interested in based on their past answer history. Furthermore, the questioning unit can avoid questions that the user has avoided in the past and suggest alternative questions. For example, the questioning unit can store the user's past answer history in a database and use an analysis algorithm to select the most appropriate question. This improves the accuracy of the questions by selecting the most appropriate question based on the user's past answer history. Some or all of the above processing in the questioning unit may be performed using AI, for example, or without AI. For example, the questioning unit can input the past answer history into a generating AI and have the generating AI perform the selection of the most appropriate question.

[0066] The questioning unit can customize the content of questions based on the user's current living situation and areas of interest. For example, the questioning unit can ask questions related to topics the user is currently interested in. The questioning unit can also select appropriate questions according to the user's living situation. Furthermore, the questioning unit can customize questions related to the user's occupation and hobbies. For example, the questioning unit can collect data on the user's living situation and areas of interest and customize the content of questions using an analysis algorithm. This allows for more appropriate questions to be asked by customizing the content according to the user's living situation and areas of interest. Some or all of the above processing in the questioning unit may be performed using AI, for example, or without AI. For example, the questioning unit can input data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the customization of the question content.

[0067] The generation unit can estimate the user's emotions and adjust the way future imagery is presented based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a video that progresses at a leisurely pace. If the user is in a hurry, the generation unit can also generate a video that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a video with visually stimulating effects. For example, the generation unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expressions. This allows for the generation of more appropriate videos by adjusting the way future imagery is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user image data captured by a camera into the generation AI, and have the generation AI adjust the way future image videos are represented.

[0068] The generation unit can select the optimal future scenario by referring to the user's past data during generation. For example, the generation unit can select the optimal future scenario based on scenarios the user has shown interest in in the past. The generation unit can also prioritize the selection of successful scenarios from the user's past data. Furthermore, the generation unit can analyze the user's past data and select the most realistic future scenario. For example, the generation unit can store the user's past data in a database and use an analysis algorithm to select the optimal future scenario. This improves the accuracy of the scenarios by selecting the optimal future scenario based on the user's past data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input past data into a generation AI and have the generation AI perform the selection of the optimal future scenario.

[0069] The generation unit can customize future scenarios based on the user's current lifestyle and areas of interest during the generation process. For example, the generation unit can generate future scenarios related to areas of interest that the user is currently interested in. The generation unit can also select an appropriate future scenario according to the user's lifestyle. Furthermore, the generation unit can customize future scenarios related to the user's occupation or hobbies. For example, the generation unit collects data on the user's lifestyle and areas of interest and uses an analysis algorithm to customize future scenarios. This allows for the generation of more appropriate scenarios by customizing future scenarios according to the user's lifestyle and areas of interest. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the user's lifestyle and areas of interest into a generation AI and have the generation AI perform the customization of future scenarios.

[0070] The planning unit can estimate the user's emotions and adjust the learning plan and expenditure calculation methods based on the estimated user emotions. For example, if the user is relaxed, the planning unit can propose a detailed learning plan. If the user is in a hurry, the planning unit can propose a concise learning plan. Furthermore, if the user is excited, the planning unit can propose a visually stimulating learning plan. For example, the planning unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expressions. This allows for the creation of more appropriate plans by adjusting the learning plan and expenditure calculation methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the planning unit may be performed using AI, for example, or without AI. For example, the planning department can input user image data captured by a camera into a generating AI, and have the AI ​​adjust learning plans and expense calculation methods.

[0071] The planning unit can select the optimal plan by referring to the user's past data when calculating the plan. For example, the planning unit can select the optimal plan based on the user's past successful learning plans. The planning unit can also avoid failed plans based on the user's past data. Furthermore, the planning unit can analyze the user's past data and select the most realistic plan. For example, the planning unit can store the user's past data in a database and select the optimal plan using an analysis algorithm. This improves the accuracy of the plan by selecting the optimal plan based on the user's past data. Some or all of the above processes in the planning unit may be performed using AI, for example, or not using AI. For example, the planning unit can input past data into a generating AI and have the generating AI perform the selection of the optimal plan.

[0072] The planning unit can customize the plan content based on the user's current living situation and areas of interest when calculating the plan. For example, the planning unit can propose a learning plan related to the user's current areas of interest. The planning unit can also propose an appropriate spending plan according to the user's living situation. Furthermore, the planning unit can customize the plan content related to the user's occupation and hobbies. For example, the planning unit collects data on the user's living situation and areas of interest and customizes the plan content using an analysis algorithm. This allows for the creation of a more appropriate plan by customizing the plan content according to the user's living situation and areas of interest. Some or all of the above processes in the planning unit may be performed using AI, for example, or not using AI. For example, the planning unit can input data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the customization of the plan content.

[0073] The support unit can estimate the user's emotions and adjust the method of supporting income increase based on the estimated user emotions. For example, if the user is relaxed, the support unit can suggest a detailed method of increasing income. If the user is in a hurry, the support unit can also suggest a concise method of increasing income. Furthermore, if the user is excited, the support unit can suggest a visually stimulating method of increasing income. For example, the support unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The emotion estimation algorithm calculates an emotion score based on changes in facial expression. This allows for more appropriate support by adjusting the method of supporting income increase according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support department can input user image data captured by a camera into a generating AI, and have the AI ​​adjust methods to support increased income.

[0074] The support unit can select the optimal support method by referring to the user's past data during the support process. For example, the support unit can select the optimal support method based on the user's past successful methods for increasing income. The support unit can also avoid failed support methods based on the user's past data. Furthermore, the support unit can analyze the user's past data and select the most realistic support method. For example, the support unit can store the user's past data in a database and use an analysis algorithm to select the optimal support method. This improves the accuracy of support by selecting the optimal support method based on the user's past data. Some or all of the above processes in the support unit may be performed using AI, for example, or not. For example, the support unit can input past data into a generating AI and have the generating AI select the optimal support method.

[0075] The support unit can customize the support provided based on the user's current living situation and areas of interest. For example, the support unit can suggest ways to increase income related to the user's current areas of interest. The support unit can also suggest appropriate support based on the user's living situation. Furthermore, the support unit can customize support related to the user's occupation or hobbies. For example, the support unit can collect data on the user's living situation and areas of interest and customize the support using an analysis algorithm. This allows for more appropriate support by customizing the support according to the user's living situation and areas of interest. Some or all of the above processes in the support unit may be performed using AI, for example, or not. For example, the support unit can input data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the customization of the support.

[0076] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0077] The measurement unit monitors the user's health status and can adjust the timing of aptitude tests and cognitive ability measurements based on that status. For example, it can measure the user's heart rate and blood pressure, and if stress levels are high, it can conduct aptitude tests during a time when the user can relax. It can also measure cognitive ability after the user has had sufficient sleep. Furthermore, if the user is refreshed after exercise, it can conduct measurements at that time. By adjusting the timing of measurements according to the user's health status, more accurate results can be obtained.

[0078] The questioning function can estimate the user's emotions and adjust the order of questions based on that estimation. For example, if the user is nervous, it can start with simple questions to help them relax. If the user is relaxed, it can ask important questions first. Furthermore, if the user is excited, it can ask questions that pique their interest first. By adjusting the order of questions according to the user's emotions, more effective information gathering becomes possible.

[0079] The generation unit can estimate the user's emotions and adjust the audio of the future image video based on those emotions. For example, if the user is relaxed, calming music can be used as the background. If the user is excited, energetic music can be used. Furthermore, if the user is sad, audio containing encouraging messages can be added. By adjusting the audio of the future image video according to the user's emotions, a more emotionally resonant video is generated.

[0080] The planning department can estimate the user's emotions and adjust the difficulty level of the learning plan based on those emotions. For example, if the user is stressed, it can start with easy tasks. If the user is relaxed, it can suggest more difficult tasks. Furthermore, if the user is excited, it can suggest challenging tasks. By adjusting the difficulty level of the learning plan according to the user's emotions, more effective learning becomes possible.

[0081] The support unit can estimate the user's emotions and adjust the methods of suggesting ways to increase income based on those emotions. For example, if the user is relaxed, it can suggest detailed methods for increasing income. If the user is in a hurry, it can suggest concise methods. Furthermore, if the user is excited, it can suggest visually stimulating methods for increasing income. By adjusting the methods of suggesting ways to increase income according to the user's emotions, more appropriate support can be provided.

[0082] The measurement unit can customize the measurement items based on the user's hobbies and interests. For example, if the user is interested in music, aptitude tests related to music can be added. Similarly, if the user is interested in sports, cognitive ability measurements related to sports can be performed. Furthermore, if the user is interested in art, measurement items related to art can be added. This allows for more accurate measurements by customizing the measurement items according to the user's hobbies and interests.

[0083] The questioning function can adjust the difficulty of questions by referring to the user's past answer history. For example, if a user has answered an easy question in the past, a more difficult question will be asked next time. Conversely, if a user has answered a difficult question in the past, an easier question can be asked next time. Furthermore, if a user has shown interest in a particular topic in the past, questions related to that topic can be added. This allows for more effective information gathering by adjusting the difficulty of questions based on the user's past answer history.

[0084] The generation unit can customize future image scenarios based on the user's current life situation. For example, if the user is currently a student, it can generate a scenario about their future career path. If the user is currently employed, it can also generate scenarios about promotions or job changes at work. Furthermore, if the user has a family, it can generate a scenario about their future with their family. By customizing the future image scenarios according to the user's current life situation, more realistic images are generated.

[0085] The planning department can monitor the progress of the learning plan by referring to the user's past data and adjust the plan as needed. For example, if the user did not follow the plan in the past, a more realistic plan will be suggested next time. Also, if the user exceeded the plan in the past, a more challenging plan can be suggested next time. Furthermore, if the user has been successful in a particular area in the past, plans related to that area can be prioritized. In this way, by monitoring the progress of the learning plan based on the user's past data and adjusting the plan as needed, more effective learning becomes possible.

[0086] The support team can customize income-increasing methods based on the user's current living situation and areas of interest. For example, if the user is currently a freelancer, they can suggest income-increasing methods tailored to freelancers. If the user is currently an employee, they can also suggest income-increasing methods related to promotion or changing jobs. Furthermore, if the user possesses specific skills, they can suggest income-increasing methods that utilize those skills. This allows for more appropriate support by customizing income-increasing methods according to the user's current living situation and areas of interest.

[0087] The following briefly describes the processing flow for example form 2.

[0088] Step 1: The measurement unit performs aptitude tests and measures the user's cognitive abilities. For example, it may perform personality assessments, vocational aptitude tests, IQ tests, cognitive ability tests, and measure stress levels and concentration. Step 2: The questioning unit asks questions based on the data obtained by the measurement unit. For example, it asks questions to the user in the form of a questionnaire or interview, and then asks additional questions based on the user's answers. Step 3: The generation unit generates a future image based on the answers obtained from the questioning unit. For example, it visualizes the user's future life and career scenarios using simulation videos or animations. Step 4: The planning unit calculates learning plans and expenses based on the videos generated by the generation unit. For example, it creates learning schedules, selects learning materials, and calculates educational and living expenses. Step 5: The support department provides support for increasing income based on the plan calculated by the planning department. For example, they may suggest side jobs, provide support for skill development, or assist with job changes.

[0089] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0090] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0091] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0092] Each of the multiple elements described above, including the measurement unit, questioning unit, generation unit, planning unit, and support unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the measurement unit uses the camera 42 of the smart device 14 to capture the user's facial expressions and estimates their emotions using an emotion estimation algorithm. The questioning unit uses the control unit 46A of the smart device 14 to ask the user questions in the form of a questionnaire or interview. The generation unit uses the specific processing unit 290 of the data processing unit 12 to generate future imagery. The planning unit uses the specific processing unit 290 of the data processing unit 12 to calculate learning plans and expenditures. The support unit uses the specific processing unit 290 of the data processing unit 12 to support increased income. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0093] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0094] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0095] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0096] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0097] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0098] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0099] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0100] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0101] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0102] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0103] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0104] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0107] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] Each of the multiple elements described above, including the measurement unit, questioning unit, generation unit, planning unit, and support unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the measurement unit uses the camera 42 of the smart glasses 214 to capture the user's facial expressions and estimates their emotions using an emotion estimation algorithm. The questioning unit uses the control unit 46A of the smart glasses 214 to ask the user questions in the form of a questionnaire or interview. The generation unit uses the specific processing unit 290 of the data processing unit 12 to generate future imagery. The planning unit uses the specific processing unit 290 of the data processing unit 12 to calculate learning plans and expenditures. The support unit uses the specific processing unit 290 of the data processing unit 12 to support increased income. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0109] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0110] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0111] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0112] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0113] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0115] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0116] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0117] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0118] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0119] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0121] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0123] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements described above, including the measurement unit, questioning unit, generation unit, planning unit, and support unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the measurement unit uses the camera 42 of the headset terminal 314 to capture the user's facial expressions and estimates their emotions using an emotion estimation algorithm. The questioning unit uses the control unit 46A of the headset terminal 314 to ask the user questions in the form of a questionnaire or interview. The generation unit uses the specific processing unit 290 of the data processing unit 12 to generate future imagery. The planning unit uses the specific processing unit 290 of the data processing unit 12 to calculate learning plans and expenditures. The support unit uses the specific processing unit 290 of the data processing unit 12 to support increased income. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0125] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0126] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0133] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0134] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0136] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0138] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0140] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements described above, including the measurement unit, questioning unit, generation unit, planning unit, and support unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the measurement unit uses the camera 42 of the robot 414 to capture the user's facial expressions and estimates their emotions using an emotion estimation algorithm. The questioning unit uses the control unit 46A of the robot 414 to ask the user questions in the form of a questionnaire or interview. The generation unit uses the specific processing unit 290 of the data processing unit 12 to generate future imagery. The planning unit uses the specific processing unit 290 of the data processing unit 12 to calculate learning plans and expenditures. The support unit uses the specific processing unit 290 of the data processing unit 12 to support increased income. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0142] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0143] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0144] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0145] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0146] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0147] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0148] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0149] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0150] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0151] 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.

[0152] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0153] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0154] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0155] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0156] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0157] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0158] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0159] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0160] (Note 1) A measurement unit that performs aptitude tests and measures cognitive abilities of users, An interrogation unit that asks questions based on the data obtained by the measurement unit, A generation unit that generates a future image based on the answers obtained by the aforementioned questioning unit, A planning unit calculates learning plans and expenditures based on the video generated by the generation unit, The system includes a support unit that supports revenue growth based on a plan calculated by the aforementioned planning unit. A system characterized by the following features. (Note 2) The aforementioned measuring unit is The system estimates the user's emotions and adjusts the timing of aptitude tests and cognitive ability measurements based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned measuring unit is The system analyzes the user's past aptitude test and cognitive ability measurement results to select the most suitable measurement method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned measuring unit is During measurement, the measurement items are customized based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned question section is, The system estimates the user's emotions and adjusts the wording of questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned question section is, When asking a question, the system selects the most appropriate question by referring to the user's past answer history. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned question section is, When asking questions, the questions are customized based on the user's current life situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is The system estimates the user's emotions and adjusts the way future imagery is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is During generation, the system selects the optimal future scenario by referencing the user's past data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is During generation, future scenarios are customized based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned planning department, It estimates the user's emotions and adjusts learning plans and spending calculation methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned planning department, When calculating a plan, the system selects the optimal plan by referring to the user's past data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned planning department, When calculating the plan, the plan content is customized based on the user's current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned support unit, It estimates user sentiment and adjusts how revenue increases are supported based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned support unit, During support, the system selects the optimal support method by referring to the user's past data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned support unit, During support, the support provided will be customized based on the user's current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A measurement unit that performs aptitude tests and measures cognitive abilities of users, An interrogation unit that asks questions based on the data obtained by the measurement unit, A generation unit that generates a future image based on the answers obtained by the aforementioned questioning unit, A planning unit calculates learning plans and expenditures based on the video generated by the generation unit, The system includes a support unit that supports revenue growth based on a plan calculated by the aforementioned planning unit. A system characterized by the following features.

2. The aforementioned measuring unit is The system estimates the user's emotions and adjusts the timing of aptitude tests and cognitive ability measurements based on those estimated emotions. The system according to feature 1.

3. The aforementioned measuring unit is The system analyzes the user's past aptitude test and cognitive ability measurement results to select the most suitable measurement method. The system according to feature 1.

4. The aforementioned measuring unit is During measurement, the measurement items are customized based on the user's current lifestyle and areas of interest. The system according to feature 1.

5. The aforementioned question section is, The system estimates the user's emotions and adjusts the wording of questions based on those estimated emotions. The system according to feature 1.

6. The aforementioned question section is, When asking a question, the system selects the most appropriate question by referring to the user's past answer history. The system according to feature 1.

7. The aforementioned question section is, When asking questions, the questions are customized based on the user's current life situation and areas of interest. The system according to feature 1.

8. The generating unit is The system estimates the user's emotions and adjusts the way future imagery is presented based on those estimated emotions. The system according to feature 1.

9. The generating unit is During generation, the system selects the optimal future scenario by referencing the user's past data. The system according to feature 1.

10. The generating unit is During generation, future scenarios are customized based on the user's current lifestyle and areas of interest. The system according to feature 1.

Images