system

Abstract

The system according to this embodiment aims to simulate life events based on an individual's behavioral data and present multiple options. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a simulation unit, and a presentation unit. The data collection unit collects individual behavioral data. The analysis unit analyzes the data collected by the data collection unit. The simulation unit simulates life events based on the analysis results obtained by the analysis unit. The presentation unit presents multiple options based on the simulation results obtained by the simulation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including 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 prior art, there was a problem that it was difficult to predict future careers and life events.

[0005] The system according to the embodiment aims to simulate life events based on an individual's behavioral data and present multiple options.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a simulation unit, and a presentation unit. The data collection unit collects individual behavioral data. The analysis unit analyzes the data collected by the data collection unit. The simulation unit simulates life events based on the analysis results obtained by the analysis unit. The presentation unit presents multiple options based on the simulation results obtained by the simulation unit. [Effects of the Invention]

[0007] The system according to this embodiment can simulate life events based on an individual's behavioral data and present multiple options. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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 career path prediction system according to an embodiment of the present invention is a system that uses AI to predict an individual's life events and provides a career path outlook. This career path prediction system collects and analyzes an individual's behavioral data, simulates life events, and presents multiple options to help individuals foresee their future career paths and support their self-growth. For example, the career path prediction system collects an individual's behavioral data. In this process, it collects detailed data including elements such as the individual's abilities, qualifications, experience, and ambition. For example, it collects data such as work history, educational background, skill set, and interests. This allows the career path prediction system to understand the individual's characteristics. Next, the career path prediction system analyzes the collected data. Based on the collected data, the career path prediction system predicts the individual's life events. For example, the career path prediction system can analyze past data and predict the timing of marriage and childbirth. It can also predict the timing of career changes and retirement. This allows the career path prediction system to enable individuals to foresee future life events. Furthermore, based on the simulation results, the career path prediction system presents the individual with multiple options. For example, the career path prediction system performs a career simulation and proposes the optimal path. Specifically, the career path prediction system suggests the optimal career path based on an individual's abilities and qualifications. It also suggests the most suitable resources and networks to support self-development. For example, it provides support in areas such as education, training, mentorship, and fundraising. This allows individuals to support their self-growth and foresee their future career paths. This career path prediction system targets job seekers and individuals engaged in career development. Its aim is to provide career-related information and support, including job searching and skill development. Through AI-powered scenario generation and suggested goal-achievement steps, the system can address future uncertainties and enable appropriate preparation.This allows the career path prediction system to provide an outlook on an individual's career path and support their personal growth.

[0029] The career path prediction system according to this embodiment comprises a data collection unit, an analysis unit, a simulation unit, and a presentation unit. The data collection unit collects an individual's behavioral data. This includes, but is not limited to, examples such as travel history, purchase history, and social media activity. The data collection unit also collects data such as an individual's occupational history, educational background, skill set, and interests. For example, the data collection unit can collect occupational history such as past job duties, length of employment, and job title. The data collection unit can also collect educational data such as highest level of education, major, and degree obtained. Furthermore, the data collection unit can collect skill set data such as technical skills, soft skills, and qualifications. For example, the data collection unit can collect interest data such as an individual's hobbies, areas of interest, and activities. The analysis unit analyzes the data collected by the data collection unit. The analysis unit predicts the timing of marriage and childbirth based on the collected data. For example, the analysis unit can predict the timing of marriage and childbirth by considering factors such as age, income, and living situation. The analysis unit can also predict the timing of career changes and retirement. For example, the analysis unit can predict the timing of career changes and retirement by considering factors such as age, work experience, and health status. The simulation unit simulates life events based on the analysis results obtained by the analysis unit. The simulation unit, for example, performs a career simulation and proposes the optimal path. For example, the simulation unit can propose the optimal career path by considering factors such as income, job content, and future growth potential. The presentation unit presents multiple options based on the simulation results obtained by the simulation unit. For example, the presentation unit proposes the optimal resources and networks to support self-development. For example, the presentation unit can propose educational resources, professional networks, support groups, etc. Thus, the career path prediction system according to the embodiment can provide an outlook on an individual's career path and support self-development.

[0030] The data collection unit collects individual behavioral data. This data includes, but is not limited to, travel history, purchase history, and social media activity. Specifically, travel history is obtained from GPS data and transportation usage history, and purchase history is collected from credit card statements and online shopping history. Social media activity is collected as data such as posts, likes, shares, and comments, and is used to understand an individual's interests and preferences. The data collection unit also collects data such as an individual's work history, education, skill set, and interests. Work history includes past job duties, employment period, position, and number of job changes, and this data is obtained from resumes, work histories, and company HR databases. Educational data includes highest level of education, major, degree obtained, and period of enrollment, and is collected from educational institution databases and self-reported information. Skill sets include technical skills, soft skills, qualifications, and training history, and this data is obtained from qualification certificates, training completion certificates, and self-reported information. Interest and preference data includes an individual's hobbies, areas of interest, and activity history, collected from social media activity, surveys, and participation history in hobby-related communities. This allows the data collection unit to gather detailed behavioral data from diverse data sources and comprehensively obtain the information necessary for predicting an individual's career path. Furthermore, the data collection unit centrally manages this data and builds a database to maintain data integrity and up-to-dateness. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit predicts the timing of marriage and childbirth based on the collected data. Specifically, it can predict the timing of marriage and childbirth by considering factors such as age, income, living situation, and past behavioral patterns. For example, it can analyze general trends in marriage and childbirth based on age and income data to predict the timing of individual life events. The analysis unit can also predict the timing of career changes and retirement. For example, it can predict the timing of career changes and retirement by considering factors such as age, work experience, health status, and work environment. The analysis unit uses AI to analyze this data and utilizes pattern recognition and machine learning algorithms to predict an individual's future behavior. For example, the AI ​​calculates the probability of marriage and childbirth at a specific age or income level based on past data and predicts the timing of an individual's life events. The AI ​​can also evaluate the risks of career changes and retirement based on work experience and health status data and suggest the optimal timing. Furthermore, the analysis unit can utilize past data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past career path data, the system can predict fluctuations in risks in specific occupations or industries and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and security of the system.

[0032] The simulation unit simulates life events based on the analysis results obtained by the analysis unit. For example, the simulation unit performs career simulations and proposes the optimal career path. Specifically, it can propose the optimal career path by considering factors such as income, job content, future growth potential, and personal interests. For example, the simulation unit simulates income trends, changes in job content, and future growth potential to propose the most suitable career path for an individual. The simulation unit also performs life event simulations and can propose career plans that take into account the timing of events such as marriage, childbirth, and retirement. For example, by simulating the timing of marriage and childbirth and proposing a career plan that matches these, it is possible to balance an individual's life events with their career. The simulation unit uses AI to perform these simulations and identifies the most likely career path by simulating multiple scenarios. This allows the simulation unit to propose the optimal career path for an individual with high accuracy and support their self-growth. Furthermore, the simulation unit can continuously modify the simulation results based on real-time updated data to respond to the latest situations. For example, if income or job content changes rapidly, the simulation unit immediately incorporates the new data and updates the simulation results. Furthermore, the simulation unit can propose more accurate career paths by considering regional characteristics and past career path data. This allows the simulation unit to consistently provide highly accurate career paths based on the latest information, supporting quick and appropriate responses.

[0033] The presentation unit presents multiple options based on the simulation results obtained by the simulation unit. Specifically, it proposes optimal resources and networks to support self-development. For example, the presentation unit can propose educational resources, professional networks, and support groups. Educational resources include online courses, vocational schools, and university programs, providing information to support individual skill development and career changes. Professional networks include events and communities to strengthen connections with industry professionals and colleagues, supporting career development. Support groups include mentoring programs, career counseling, and self-development seminars, supporting individual growth. The presentation unit customizes these resources to suit individual needs and goals, providing the optimal choice. For example, it can suggest the best online course for acquiring specific skills or introduce networking events with relevant industry professionals to individuals considering a career change. Furthermore, the presentation unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it evaluates the quality and applicability of resources based on feedback from users who have used the suggested resources, and reflects this in future suggestions. The presentation unit can also reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications, but also via email, social media, and dedicated apps. This allows the display unit to provide users with information quickly and reliably, supporting their self-growth.

[0034] The data collection unit can collect data such as occupational history, educational background, skill sets, and interests. For example, the data collection unit can collect occupational history such as past job duties, employment period, and job title. The data collection unit can also collect educational data such as highest level of education, major, and degree obtained. The data collection unit can also collect skill sets such as technical skills, soft skills, and qualifications. The data collection unit can also collect interest data such as personal hobbies, areas of interest, and activities. By collecting data such as occupational history, educational background, skill sets, and interests, it is possible to understand an individual's characteristics. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input an individual's occupational history data into a generating AI and have the generating AI perform an analysis of the occupational history.

[0035] The analysis unit can predict the timing of marriage and childbirth based on the collected data. The analysis unit predicts the timing of marriage and childbirth by considering factors such as age, income, and living situation. The analysis unit can also predict the timing of marriage and childbirth by analyzing past data. The analysis unit can also predict the timing of marriage and childbirth by analyzing patterns of an individual's life events. This allows for foresight into an individual's life events by predicting the timing of marriage and childbirth. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the prediction of the timing of marriage and childbirth.

[0036] The analysis unit can predict the timing of career changes and retirement. The analysis unit predicts the timing of career changes and retirement by considering factors such as age, work experience, and health status. The analysis unit can also predict the timing of career changes and retirement by analyzing past data. The analysis unit can also predict the timing of career changes and retirement by analyzing patterns in an individual's career path. This allows for foresight into an individual's career path by predicting the timing of career changes and retirement. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected data into a generating AI and have the generating AI perform the prediction of the timing of career changes and retirement.

[0037] The simulation unit can perform career simulations and propose optimal career paths. The simulation unit proposes optimal career paths by considering factors such as income, job content, and future growth potential. The simulation unit can also propose optimal career paths based on an individual's abilities and qualifications. The simulation unit can also set up career simulation scenarios and propose optimal career paths. In this way, by performing career simulations and proposing optimal career paths, individuals can gain insight into their career paths. Some or all of the above processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input collected data into a generating AI and have the generating AI execute a career simulation.

[0038] The suggestion unit can propose optimal resources and networks to support self-development. For example, the suggestion unit can propose educational resources, professional networks, support groups, etc. The suggestion unit can also propose optimal resources and networks based on an individual's abilities and qualifications. For example, the suggestion unit can propose resources and networks to support self-development. In this way, it can support individual growth by proposing optimal resources and networks to support self-development. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input collected data into a generating AI and have the generating AI execute the proposal of optimal resources and networks.

[0039] The data collection unit can analyze the user's past behavioral data and select the optimal collection method. For example, the data collection unit can collect data from devices that the user has frequently used in the past. The data collection unit can also determine the optimal collection timing based on the user's past behavioral patterns. For example, the data collection unit can prioritize the collection of data related to specific activities from the user's past behavioral data. This allows for efficient data collection by analyzing the user's past behavioral data and selecting the optimal collection method. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral data into a generating AI and have the generating AI select the optimal collection method.

[0040] The data collection unit can filter behavioral data based on the user's current lifestyle and areas of interest. For example, the data collection unit can prioritize collecting data related to projects the user is currently working on. The data collection unit can also filter and collect relevant data based on the user's current interests. The data collection unit can also select and collect appropriate data according to the user's lifestyle (e.g., work, family, hobbies). This allows for the collection of highly relevant data by filtering data based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current lifestyle and areas of interest into a generating AI and have the generating AI perform data filtering.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting behavioral data. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. For example, if the user is traveling, the data collection unit can prioritize the collection of data related to the travel destination. For example, if the user is at home, the data collection unit can prioritize the collection of behavioral data at home. In this way, by collecting data while considering the user's geographical location, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0042] The data collection unit can analyze a user's social media activity and collect relevant data when collecting behavioral data. For example, the data collection unit can collect relevant behavioral data based on information shared by the user on social media. The data collection unit can also analyze a user's social media activity patterns and determine the optimal timing for data collection. The data collection unit can also collect relevant data based on a user's areas of interest on social media. This allows for the collection of highly relevant data by analyzing and collecting data based on the user's social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the behavioral data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. For example, the analysis unit can perform an analysis with an appropriate level of detail on data with moderate importance. In this way, by adjusting the level of detail of the analysis based on the importance of the behavioral data, the analysis can be performed efficiently. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the behavioral data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of behavioral data during analysis. For example, the analysis unit can apply a career path prediction algorithm to occupational history data. For example, the analysis unit can also apply an education background analysis algorithm to educational data. For example, the analysis unit can also apply a skill matching algorithm to skill set data. By applying different analysis algorithms depending on the category of behavioral data, more accurate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of behavioral data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0045] The analysis unit can determine the priority of analysis based on the timing of behavioral data collection during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent data. Alternatively, it may prioritize the analysis of current data while referencing past data. It may also prioritize the analysis of data collected during a specific period. This allows for efficient analysis by determining the priority of analysis based on the timing of behavioral data collection. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input the timing of behavioral data collection into a generating AI and have the generating AI determine the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the behavioral data during the analysis. For example, the analysis unit may prioritize the analysis of data with high relevance. For example, the analysis unit may also analyze data with moderate relevance next. For example, the analysis unit may also analyze data with low relevance last. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the behavioral data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the behavioral data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0047] The simulation unit can improve the accuracy of the simulation by considering the interrelationships of behavioral data during the simulation. For example, the simulation unit can perform the simulation by considering the interrelationships between occupational history and educational background. For example, the simulation unit can also perform the simulation by considering the interrelationships between skill sets and interests. For example, the simulation unit can perform the simulation by considering the interrelationships between past behavioral data and current behavioral data. This improves the accuracy of the simulation by considering the interrelationships of behavioral data. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without using AI. For example, the simulation unit can input the interrelationships of behavioral data into a generating AI and have the generating AI perform the simulation accuracy improvement.

[0048] The simulation unit can perform simulations while considering the attribute information of the submitter of the behavioral data. For example, the simulation unit can perform simulations while considering the submitter's age. For example, the simulation unit can also perform simulations while considering the submitter's gender. For example, the simulation unit can also perform simulations while considering the submitter's occupation. This allows for more personalized simulation results by considering the submitter's attribute information. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the submitter's attribute information into a generating AI and have the generating AI perform the simulation.

[0049] The simulation unit can perform simulations while considering the geographical distribution of behavioral data. For example, if a user lives in a specific region, the simulation unit can perform simulations related to that region. For example, if a user lives in multiple regions, the simulation unit can perform simulations related to each region. For example, if a user plans to travel, the simulation unit can perform simulations related to the destination region. By considering the geographical distribution of behavioral data, more realistic simulation results can be provided. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the geographical distribution of behavioral data into a generating AI and have the generating AI perform the simulation.

[0050] The simulation unit can improve the accuracy of the simulation by referring to relevant literature on behavioral data during the simulation. For example, the simulation unit can perform the simulation by referring to relevant academic papers. For example, the simulation unit can also perform the simulation by referring to relevant industry reports. For example, the simulation unit can also perform the simulation by referring to relevant market research data. This improves the accuracy of the simulation by referring to relevant literature. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input relevant literature on behavioral data into a generating AI and have the generating AI perform the simulation.

[0051] The presentation unit can adjust the level of detail in the presentation based on the importance of the simulation results. For example, the presentation unit can provide detailed information for simulation results of high importance. For example, the presentation unit can also provide simplified information for simulation results of low importance. For example, the presentation unit can provide information of moderate importance for simulation results of moderate importance. This allows for efficient information provision by adjusting the level of detail in the presentation based on the importance of the simulation results. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the importance of the simulation results into a generating AI and have the generating AI perform the adjustment of the level of detail in the presentation.

[0052] The presentation unit can apply different presentation algorithms depending on the category of the simulation results during presentation. For example, the presentation unit can apply a career path presentation algorithm to career-related simulation results. For example, the presentation unit can also apply a life event presentation algorithm to life event-related simulation results. For example, the presentation unit can apply a health management presentation algorithm to health-related simulation results. By applying different presentation algorithms depending on the category of the simulation results, more appropriate information can be provided. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the category of the simulation results into a generating AI and have the generating AI execute the application of different presentation algorithms.

[0053] The presentation unit can determine the presentation priority based on the timing of simulation result collection. For example, the presentation unit may prioritize the presentation of the most recent simulation results. Alternatively, it may prioritize the presentation of current simulation results while referencing past simulation results. It may also prioritize the presentation of simulation results collected during a specific period. This allows for efficient information provision by determining the presentation priority based on the timing of simulation result collection. Some or all of the above processing in the presentation unit may be performed using AI, or without AI. For example, the presentation unit can input the timing of simulation result collection into a generating AI and have the generating AI determine the presentation priority.

[0054] The presentation unit can adjust the order of presentation based on the relevance of the simulation results. For example, the presentation unit may prioritize the presentation of simulation results with high relevance. For example, it may also present simulation results with moderate relevance next. For example, it may also present simulation results with low relevance last. By adjusting the order of presentation based on the relevance of the simulation results, more appropriate information can be provided. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the relevance of the simulation results into a generating AI and have the generating AI perform the adjustment of the presentation order.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The career path prediction system can also include a health management unit that collects and analyzes the user's health data. This unit collects data such as the user's exercise history, diet, and sleep patterns. This allows the career path prediction system to propose career paths that take the user's health into consideration. For example, the health management unit can detect a user's lack of exercise and suggest a lifestyle that incorporates exercise. It can also analyze the user's diet and suggest improvements to nutritional balance. Furthermore, it can analyze the user's sleep patterns and provide advice to improve sleep quality. This enables the career path prediction system to comprehensively manage the user's health and propose more appropriate career paths.

[0057] The career path prediction system may further include a hobby analysis unit that suggests career paths based on the user's hobbies and interests. The hobby analysis unit, for example, collects data on the user's hobby activity history and areas of interest. This allows the career path prediction system to suggest career paths that take the user's hobbies and interests into account. For example, if the user is interested in music, the hobby analysis unit can suggest music-related career paths. Similarly, if the user is interested in sports, it can suggest sports-related career paths. Furthermore, if the user is interested in art, it can suggest art-related career paths. This enables the career path prediction system to suggest career paths that reflect the user's hobbies and interests.

[0058] The career path prediction system may also include a network analysis unit that analyzes the user's social network. The network analysis unit collects data, for example, on the user's social media connections and workplace relationships. This allows the career path prediction system to propose career paths that take the user's social network into account. For instance, the network analysis unit can analyze the user's social media connections and suggest valuable connections for their career. It can also analyze the user's workplace relationships and provide advice to help them advance their career. Furthermore, the network analysis unit can suggest events and groups to expand the user's social network. This enables the career path prediction system to propose career paths that leverage the user's social network.

[0059] The career path prediction system may also include a lifestyle analysis unit that collects and analyzes user lifestyle data. The lifestyle analysis unit collects data such as the user's daily habits, hobbies, interests, and health status. This allows the career path prediction system to propose career paths that take the user's lifestyle into account. For example, the lifestyle analysis unit can analyze the user's daily habits and propose a career path that allows them to maintain a healthy lifestyle. It can also propose career paths that consider the user's hobbies and interests. Furthermore, it can propose career paths that consider the user's health status. This enables the career path prediction system to comprehensively manage the user's lifestyle and propose more appropriate career paths.

[0060] The career path prediction system may further include a geographic analysis unit that collects and analyzes the user's geographical movement data. The geographic analysis unit, for example, collects data on the user's movement history and current place of residence. This allows the career path prediction system to propose career paths that take the user's geographical factors into account. For example, if the user lives in an urban area, the geographic analysis unit can propose a career path within an urban area. Similarly, if the user lives in a rural area, the geographic analysis unit can propose a career path within a rural area. Furthermore, if the user plans to emigrate abroad, the geographic analysis unit can propose a career path in their destination country. This enables the career path prediction system to propose career paths that reflect the user's geographical factors.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects individual behavioral data. This data includes travel history, purchase history, social media activity, work history, educational background, skill set, interests, etc. For example, the data collection unit can collect data such as past job duties, length of employment, job title, highest level of education, major, degree obtained, technical skills, soft skills, qualifications, hobbies, areas of interest, and activities. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, based on the collected data, the analysis unit predicts the timing of marriage and childbirth, career changes, and retirement. By considering factors such as age, income, living situation, work experience, and health status, the timing of these life events can be predicted. Step 3: The simulation unit simulates life events based on the analysis results obtained by the analysis unit. For example, the simulation unit performs a career simulation and proposes an optimal career path, taking into account factors such as income, job content, and future growth potential. Step 4: The presentation unit presents multiple options based on the simulation results obtained by the simulation unit. For example, the presentation unit suggests optimal resources and networks to support self-construction. These may include educational resources, professional networks, support groups, etc.

[0063] (Example of form 2) The career path prediction system according to an embodiment of the present invention is a system that uses AI to predict an individual's life events and provides a career path outlook. This career path prediction system collects and analyzes an individual's behavioral data, simulates life events, and presents multiple options to help individuals foresee their future career paths and support their self-growth. For example, the career path prediction system collects an individual's behavioral data. In this process, it collects detailed data including elements such as the individual's abilities, qualifications, experience, and ambition. For example, it collects data such as work history, educational background, skill set, and interests. This allows the career path prediction system to understand the individual's characteristics. Next, the career path prediction system analyzes the collected data. Based on the collected data, the career path prediction system predicts the individual's life events. For example, the career path prediction system can analyze past data and predict the timing of marriage and childbirth. It can also predict the timing of career changes and retirement. This allows the career path prediction system to enable individuals to foresee future life events. Furthermore, based on the simulation results, the career path prediction system presents the individual with multiple options. For example, the career path prediction system performs a career simulation and proposes the optimal path. Specifically, the career path prediction system suggests the optimal career path based on an individual's abilities and qualifications. It also suggests the most suitable resources and networks to support self-development. For example, it provides support in areas such as education, training, mentorship, and fundraising. This allows individuals to support their self-growth and foresee their future career paths. This career path prediction system targets job seekers and individuals engaged in career development. Its aim is to provide career-related information and support, including job searching and skill development. Through AI-powered scenario generation and suggested goal-achievement steps, the system can address future uncertainties and enable appropriate preparation.This allows the career path prediction system to provide an outlook on an individual's career path and support their personal growth.

[0064] The career path prediction system according to this embodiment comprises a data collection unit, an analysis unit, a simulation unit, and a presentation unit. The data collection unit collects an individual's behavioral data. This includes, but is not limited to, examples such as travel history, purchase history, and social media activity. The data collection unit also collects data such as an individual's occupational history, educational background, skill set, and interests. For example, the data collection unit can collect occupational history such as past job duties, length of employment, and job title. The data collection unit can also collect educational data such as highest level of education, major, and degree obtained. Furthermore, the data collection unit can collect skill set data such as technical skills, soft skills, and qualifications. For example, the data collection unit can collect interest data such as an individual's hobbies, areas of interest, and activities. The analysis unit analyzes the data collected by the data collection unit. The analysis unit predicts the timing of marriage and childbirth based on the collected data. For example, the analysis unit can predict the timing of marriage and childbirth by considering factors such as age, income, and living situation. The analysis unit can also predict the timing of career changes and retirement. For example, the analysis unit can predict the timing of career changes and retirement by considering factors such as age, work experience, and health status. The simulation unit simulates life events based on the analysis results obtained by the analysis unit. The simulation unit, for example, performs a career simulation and proposes the optimal path. For example, the simulation unit can propose the optimal career path by considering factors such as income, job content, and future growth potential. The presentation unit presents multiple options based on the simulation results obtained by the simulation unit. For example, the presentation unit proposes the optimal resources and networks to support self-development. For example, the presentation unit can propose educational resources, professional networks, support groups, etc. Thus, the career path prediction system according to the embodiment can provide an outlook on an individual's career path and support self-development.

[0065] The data collection unit collects individual behavioral data. This data includes, but is not limited to, travel history, purchase history, and social media activity. Specifically, travel history is obtained from GPS data and transportation usage history, and purchase history is collected from credit card statements and online shopping history. Social media activity is collected as data such as posts, likes, shares, and comments, and is used to understand an individual's interests and preferences. The data collection unit also collects data such as an individual's work history, education, skill set, and interests. Work history includes past job duties, employment period, position, and number of job changes, and this data is obtained from resumes, work histories, and company HR databases. Educational data includes highest level of education, major, degree obtained, and period of enrollment, and is collected from educational institution databases and self-reported information. Skill sets include technical skills, soft skills, qualifications, and training history, and this data is obtained from qualification certificates, training completion certificates, and self-reported information. Interest and preference data includes an individual's hobbies, areas of interest, and activity history, collected from social media activity, surveys, and participation history in hobby-related communities. This allows the data collection unit to gather detailed behavioral data from diverse data sources and comprehensively obtain the information necessary for predicting an individual's career path. Furthermore, the data collection unit centrally manages this data and builds a database to maintain data integrity and up-to-dateness. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0066] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit predicts the timing of marriage and childbirth based on the collected data. Specifically, it can predict the timing of marriage and childbirth by considering factors such as age, income, living situation, and past behavioral patterns. For example, it can analyze general trends in marriage and childbirth based on age and income data to predict the timing of individual life events. The analysis unit can also predict the timing of career changes and retirement. For example, it can predict the timing of career changes and retirement by considering factors such as age, work experience, health status, and work environment. The analysis unit uses AI to analyze this data and utilizes pattern recognition and machine learning algorithms to predict an individual's future behavior. For example, the AI ​​calculates the probability of marriage and childbirth at a specific age or income level based on past data and predicts the timing of an individual's life events. The AI ​​can also evaluate the risks of career changes and retirement based on work experience and health status data and suggest the optimal timing. Furthermore, the analysis unit can utilize past data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past career path data, the system can predict fluctuations in risks in specific occupations or industries and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and security of the system.

[0067] The simulation unit simulates life events based on the analysis results obtained by the analysis unit. For example, the simulation unit performs career simulations and proposes the optimal career path. Specifically, it can propose the optimal career path by considering factors such as income, job content, future growth potential, and personal interests. For example, the simulation unit simulates income trends, changes in job content, and future growth potential to propose the most suitable career path for an individual. The simulation unit also performs life event simulations and can propose career plans that take into account the timing of events such as marriage, childbirth, and retirement. For example, by simulating the timing of marriage and childbirth and proposing a career plan that matches these, it is possible to balance an individual's life events with their career. The simulation unit uses AI to perform these simulations and identifies the most likely career path by simulating multiple scenarios. This allows the simulation unit to propose the optimal career path for an individual with high accuracy and support their self-growth. Furthermore, the simulation unit can continuously modify the simulation results based on real-time updated data to respond to the latest situations. For example, if income or job content changes rapidly, the simulation unit immediately incorporates the new data and updates the simulation results. Furthermore, the simulation unit can propose more accurate career paths by considering regional characteristics and past career path data. This allows the simulation unit to consistently provide highly accurate career paths based on the latest information, supporting quick and appropriate responses.

[0068] The presentation unit presents multiple options based on the simulation results obtained by the simulation unit. Specifically, it proposes optimal resources and networks to support self-development. For example, the presentation unit can propose educational resources, professional networks, and support groups. Educational resources include online courses, vocational schools, and university programs, providing information to support individual skill development and career changes. Professional networks include events and communities to strengthen connections with industry professionals and colleagues, supporting career development. Support groups include mentoring programs, career counseling, and self-development seminars, supporting individual growth. The presentation unit customizes these resources to suit individual needs and goals, providing the optimal choice. For example, it can suggest the best online course for acquiring specific skills or introduce networking events with relevant industry professionals to individuals considering a career change. Furthermore, the presentation unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it evaluates the quality and applicability of resources based on feedback from users who have used the suggested resources, and reflects this in future suggestions. The presentation unit can also reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications, but also via email, social media, and dedicated apps. This allows the display unit to provide users with information quickly and reliably, supporting their self-growth.

[0069] The data collection unit can collect data such as occupational history, educational background, skill sets, and interests. For example, the data collection unit can collect occupational history such as past job duties, employment period, and job title. The data collection unit can also collect educational data such as highest level of education, major, and degree obtained. The data collection unit can also collect skill sets such as technical skills, soft skills, and qualifications. The data collection unit can also collect interest data such as personal hobbies, areas of interest, and activities. By collecting data such as occupational history, educational background, skill sets, and interests, it is possible to understand an individual's characteristics. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input an individual's occupational history data into a generating AI and have the generating AI perform an analysis of the occupational history.

[0070] The analysis unit can predict the timing of marriage and childbirth based on the collected data. The analysis unit predicts the timing of marriage and childbirth by considering factors such as age, income, and living situation. The analysis unit can also predict the timing of marriage and childbirth by analyzing past data. The analysis unit can also predict the timing of marriage and childbirth by analyzing patterns of an individual's life events. This allows for foresight into an individual's life events by predicting the timing of marriage and childbirth. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the prediction of the timing of marriage and childbirth.

[0071] The analysis unit can predict the timing of career changes and retirement. The analysis unit predicts the timing of career changes and retirement by considering factors such as age, work experience, and health status. The analysis unit can also predict the timing of career changes and retirement by analyzing past data. The analysis unit can also predict the timing of career changes and retirement by analyzing patterns in an individual's career path. This allows for foresight into an individual's career path by predicting the timing of career changes and retirement. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected data into a generating AI and have the generating AI perform the prediction of the timing of career changes and retirement.

[0072] The simulation unit can perform career simulations and propose optimal career paths. The simulation unit proposes optimal career paths by considering factors such as income, job content, and future growth potential. The simulation unit can also propose optimal career paths based on an individual's abilities and qualifications. The simulation unit can also set up career simulation scenarios and propose optimal career paths. In this way, by performing career simulations and proposing optimal career paths, individuals can gain insight into their career paths. Some or all of the above processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input collected data into a generating AI and have the generating AI execute a career simulation.

[0073] The suggestion unit can propose optimal resources and networks to support self-development. For example, the suggestion unit can propose educational resources, professional networks, support groups, etc. The suggestion unit can also propose optimal resources and networks based on an individual's abilities and qualifications. For example, the suggestion unit can propose resources and networks to support self-development. In this way, it can support individual growth by proposing optimal resources and networks to support self-development. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input collected data into a generating AI and have the generating AI execute the proposal of optimal resources and networks.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect behavioral data when the user is relaxed. For example, if the user is concentrating, the data collection unit can also collect detailed behavioral data at that time. For example, if the user is tired, the data collection unit can also collect behavioral data after resting. By adjusting the timing of behavioral data collection based on the user's emotions, more appropriate data can be collected. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the user's emotion data into the generative AI and have the generative AI adjust the timing of behavioral data collection.

[0075] The data collection unit can analyze the user's past behavioral data and select the optimal collection method. For example, the data collection unit can collect data from devices that the user has frequently used in the past. The data collection unit can also determine the optimal collection timing based on the user's past behavioral patterns. For example, the data collection unit can prioritize the collection of data related to specific activities from the user's past behavioral data. This allows for efficient data collection by analyzing the user's past behavioral data and selecting the optimal collection method. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavioral data into a generating AI and have the generating AI select the optimal collection method.

[0076] The data collection unit can filter behavioral data based on the user's current lifestyle and areas of interest. For example, the data collection unit can prioritize collecting data related to projects the user is currently working on. The data collection unit can also filter and collect relevant data based on the user's current interests. The data collection unit can also select and collect appropriate data according to the user's lifestyle (e.g., work, family, hobbies). This allows for the collection of highly relevant data by filtering data based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current lifestyle and areas of interest into a generating AI and have the generating AI perform data filtering.

[0077] The data collection unit can estimate the user's emotions and determine the priority of behavioral data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit will prioritize collecting data related to stress reduction. For example, if the user is relaxed, the data collection unit may also prioritize collecting behavioral data related to relaxation. For example, if the user is excited, the data collection unit may also prioritize collecting behavioral data related to excitement. This allows for the priority collection of important data by determining the priority of behavioral data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of behavioral data.

[0078] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting behavioral data. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. For example, if the user is traveling, the data collection unit can prioritize the collection of data related to the travel destination. For example, if the user is at home, the data collection unit can prioritize the collection of behavioral data at home. In this way, by collecting data while considering the user's geographical location, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0079] The data collection unit can analyze a user's social media activity and collect relevant data when collecting behavioral data. For example, the data collection unit can collect relevant behavioral data based on information shared by the user on social media. The data collection unit can also analyze a user's social media activity patterns and determine the optimal timing for data collection. The data collection unit can also collect relevant data based on a user's areas of interest on social media. This allows for the collection of highly relevant data by analyzing and collecting data based on the user's social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI collect relevant data.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple and easy-to-understand analysis result. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis result. For example, if the user is excited, the analysis unit can also provide a visually appealing analysis result. By adjusting the presentation of the analysis based on the user's emotions, more appropriate analysis results can be provided. 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the behavioral data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. For example, the analysis unit can perform an analysis with an appropriate level of detail on data with moderate importance. In this way, by adjusting the level of detail of the analysis based on the importance of the behavioral data, the analysis can be performed efficiently. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the behavioral data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of behavioral data during analysis. For example, the analysis unit can apply a career path prediction algorithm to occupational history data. For example, the analysis unit can also apply an education background analysis algorithm to educational data. For example, the analysis unit can also apply a skill matching algorithm to skill set data. By applying different analysis algorithms depending on the category of behavioral data, more accurate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of behavioral data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0083] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis result. For example, if the user is excited, the analysis unit can also provide a visually appealing analysis result. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0084] The analysis unit can determine the priority of analysis based on the timing of behavioral data collection during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent data. Alternatively, it may prioritize the analysis of current data while referencing past data. It may also prioritize the analysis of data collected during a specific period. This allows for efficient analysis by determining the priority of analysis based on the timing of behavioral data collection. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input the timing of behavioral data collection into a generating AI and have the generating AI determine the analysis priority.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the behavioral data during the analysis. For example, the analysis unit may prioritize the analysis of data with high relevance. For example, the analysis unit may also analyze data with moderate relevance next. For example, the analysis unit may also analyze data with low relevance last. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the behavioral data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the behavioral data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0086] The simulation unit can estimate the user's emotions and adjust the simulation criteria based on the estimated user emotions. For example, if the user is relaxed, the simulation unit can perform a detailed simulation. For example, if the user is in a hurry, the simulation unit can perform a simplified simulation. For example, if the user is excited, the simulation unit can perform a visually appealing simulation. By adjusting the simulation criteria based on the user's emotions, more appropriate simulation results can be provided. 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 simulation unit may be performed using AI, for example, or not using AI. For example, the simulation unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the simulation criteria.

[0087] The simulation unit can improve the accuracy of the simulation by considering the interrelationships of behavioral data during the simulation. For example, the simulation unit can perform the simulation by considering the interrelationships between occupational history and educational background. For example, the simulation unit can also perform the simulation by considering the interrelationships between skill sets and interests. For example, the simulation unit can perform the simulation by considering the interrelationships between past behavioral data and current behavioral data. This improves the accuracy of the simulation by considering the interrelationships of behavioral data. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without using AI. For example, the simulation unit can input the interrelationships of behavioral data into a generating AI and have the generating AI perform the simulation accuracy improvement.

[0088] The simulation unit can perform simulations while considering the attribute information of the submitter of the behavioral data. For example, the simulation unit can perform simulations while considering the submitter's age. For example, the simulation unit can also perform simulations while considering the submitter's gender. For example, the simulation unit can also perform simulations while considering the submitter's occupation. This allows for more personalized simulation results by considering the submitter's attribute information. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the submitter's attribute information into a generating AI and have the generating AI perform the simulation.

[0089] The simulation unit can estimate the user's emotions and adjust the order in which the simulation results are displayed based on the estimated emotions. For example, if the user is relaxed, the simulation unit can display detailed results first. If the user is in a hurry, the simulation unit can also display concise results first. If the user is excited, the simulation unit can also display visually appealing results first. By adjusting the display order of the simulation results based on the user's emotions, more appropriate information can be provided. 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 simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input user emotion data into the generative AI and have the generative AI adjust the display order of the simulation results.

[0090] The simulation unit can perform simulations while considering the geographical distribution of behavioral data. For example, if a user lives in a specific region, the simulation unit can perform simulations related to that region. For example, if a user lives in multiple regions, the simulation unit can perform simulations related to each region. For example, if a user plans to travel, the simulation unit can perform simulations related to the destination region. By considering the geographical distribution of behavioral data, more realistic simulation results can be provided. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the geographical distribution of behavioral data into a generating AI and have the generating AI perform the simulation.

[0091] The simulation unit can improve the accuracy of the simulation by referring to relevant literature on behavioral data during the simulation. For example, the simulation unit can perform the simulation by referring to relevant academic papers. For example, the simulation unit can also perform the simulation by referring to relevant industry reports. For example, the simulation unit can also perform the simulation by referring to relevant market research data. This improves the accuracy of the simulation by referring to relevant literature. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input relevant literature on behavioral data into a generating AI and have the generating AI perform the simulation.

[0092] The presentation unit can estimate the user's emotions and adjust the presentation's presentation style based on the estimated emotions. For example, if the user is stressed, the presentation unit can provide a simple and easy-to-understand presentation style. For example, if the user is relaxed, the presentation unit can also provide a presentation style that includes detailed information. For example, if the user is excited, the presentation unit can also provide a visually appealing presentation style. By adjusting the presentation style based on the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative 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 presentation unit may be performed using AI, or not using AI. For example, the presentation unit can input user emotion data into the generative AI and have the generative AI adjust the presentation style.

[0093] The presentation unit can adjust the level of detail in the presentation based on the importance of the simulation results. For example, the presentation unit can provide detailed information for simulation results of high importance. For example, the presentation unit can also provide simplified information for simulation results of low importance. For example, the presentation unit can provide information of moderate importance for simulation results of moderate importance. This allows for efficient information provision by adjusting the level of detail in the presentation based on the importance of the simulation results. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the importance of the simulation results into a generating AI and have the generating AI perform the adjustment of the level of detail in the presentation.

[0094] The presentation unit can apply different presentation algorithms depending on the category of the simulation results during presentation. For example, the presentation unit can apply a career path presentation algorithm to career-related simulation results. For example, the presentation unit can also apply a life event presentation algorithm to life event-related simulation results. For example, the presentation unit can apply a health management presentation algorithm to health-related simulation results. By applying different presentation algorithms depending on the category of the simulation results, more appropriate information can be provided. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the category of the simulation results into a generating AI and have the generating AI execute the application of different presentation algorithms.

[0095] The presentation unit can estimate the user's emotions and adjust the length of the presentation based on the estimated emotions. For example, if the user is in a hurry, the presentation unit can provide a short, concise presentation. If the user is relaxed, the presentation unit can provide a longer presentation containing detailed information. If the user is excited, the presentation unit can provide a visually appealing presentation. By adjusting the length of the presentation based on the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input user emotion data into a generative AI and have the generative AI adjust the length of the presentation.

[0096] The presentation unit can determine the presentation priority based on the timing of simulation result collection. For example, the presentation unit may prioritize the presentation of the most recent simulation results. Alternatively, it may prioritize the presentation of current simulation results while referencing past simulation results. It may also prioritize the presentation of simulation results collected during a specific period. This allows for efficient information provision by determining the presentation priority based on the timing of simulation result collection. Some or all of the above processing in the presentation unit may be performed using AI, or without AI. For example, the presentation unit can input the timing of simulation result collection into a generating AI and have the generating AI determine the presentation priority.

[0097] The presentation unit can adjust the order of presentation based on the relevance of the simulation results. For example, the presentation unit may prioritize the presentation of simulation results with high relevance. For example, it may also present simulation results with moderate relevance next. For example, it may also present simulation results with low relevance last. By adjusting the order of presentation based on the relevance of the simulation results, more appropriate information can be provided. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the relevance of the simulation results into a generating AI and have the generating AI perform the adjustment of the presentation order.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The career path prediction system can also include a health management unit that collects and analyzes the user's health data. This unit collects data such as the user's exercise history, diet, and sleep patterns. This allows the career path prediction system to propose career paths that take the user's health into consideration. For example, the health management unit can detect a user's lack of exercise and suggest a lifestyle that incorporates exercise. It can also analyze the user's diet and suggest improvements to nutritional balance. Furthermore, it can analyze the user's sleep patterns and provide advice to improve sleep quality. This enables the career path prediction system to comprehensively manage the user's health and propose more appropriate career paths.

[0100] The career path prediction system may further include a hobby analysis unit that suggests career paths based on the user's hobbies and interests. The hobby analysis unit, for example, collects data on the user's hobby activity history and areas of interest. This allows the career path prediction system to suggest career paths that take the user's hobbies and interests into account. For example, if the user is interested in music, the hobby analysis unit can suggest music-related career paths. Similarly, if the user is interested in sports, it can suggest sports-related career paths. Furthermore, if the user is interested in art, it can suggest art-related career paths. This enables the career path prediction system to suggest career paths that reflect the user's hobbies and interests.

[0101] The career path prediction system may also include a network analysis unit that analyzes the user's social network. The network analysis unit collects data, for example, on the user's social media connections and workplace relationships. This allows the career path prediction system to propose career paths that take the user's social network into account. For instance, the network analysis unit can analyze the user's social media connections and suggest valuable connections for their career. It can also analyze the user's workplace relationships and provide advice to help them advance their career. Furthermore, the network analysis unit can suggest events and groups to expand the user's social network. This enables the career path prediction system to propose career paths that leverage the user's social network.

[0102] The career path prediction system may further include an emotion analysis unit that estimates the user's emotions and proposes career paths based on those estimated emotions. The emotion analysis unit estimates emotions from, for example, the user's facial expressions, voice, and text data. This allows the career path prediction system to propose career paths that take into account the user's emotional state. For example, if the user is feeling stressed, the emotion analysis unit can propose career paths that help reduce stress. If the user is relaxed, the emotion analysis unit can also propose career paths that allow them to work in a relaxed state. Furthermore, if the user is excited, the emotion analysis unit can also propose career paths that help maintain that excitement. This allows the career path prediction system to propose career paths that reflect the user's emotional state.

[0103] The career path prediction system may also include a lifestyle analysis unit that collects and analyzes user lifestyle data. The lifestyle analysis unit collects data such as the user's daily habits, hobbies, interests, and health status. This allows the career path prediction system to propose career paths that take the user's lifestyle into account. For example, the lifestyle analysis unit can analyze the user's daily habits and propose a career path that allows them to maintain a healthy lifestyle. It can also propose career paths that consider the user's hobbies and interests. Furthermore, it can propose career paths that consider the user's health status. This enables the career path prediction system to comprehensively manage the user's lifestyle and propose more appropriate career paths.

[0104] The career path prediction system may further include an emotion presentation unit that estimates the user's emotions and presents career path options based on those estimated emotions. For example, the emotion presentation unit prioritizes and presents career path options according to the user's emotional state. This allows the career path prediction system to provide career path options that take the user's emotional state into consideration. For instance, if the user is stressed, the emotion presentation unit can prioritize career paths that help reduce stress. Similarly, if the user is relaxed, the emotion presentation unit can prioritize career paths that allow them to work in a relaxed state. Furthermore, if the user is excited, the emotion presentation unit can prioritize career paths that help maintain that excitement. This enables the career path prediction system to provide career path options that reflect the user's emotional state.

[0105] The career path prediction system may further include an emotional feedback unit that estimates the user's emotions and provides career path feedback based on those emotions. The emotional feedback unit, for example, adjusts the career path feedback content according to the user's emotional state. This allows the career path prediction system to provide career path feedback that takes the user's emotional state into account. For example, if the user is feeling stressed, the emotional feedback unit can provide feedback that helps reduce stress. If the user is relaxed, the emotional feedback unit can provide feedback that allows them to work in a relaxed state. Furthermore, if the user is excited, the emotional feedback unit can provide feedback that helps them maintain their excitement. This allows the career path prediction system to provide career path feedback that reflects the user's emotional state.

[0106] The career path prediction system may further include an emotion monitoring unit that estimates the user's emotions and monitors the progress of the career path based on the estimated emotions. The emotion monitoring unit, for example, monitors the progress of the career path in real time according to the user's emotional state. This allows the career path prediction system to understand the progress of the career path while taking the user's emotional state into account. For example, if the user is feeling stressed, the emotion monitoring unit can monitor progress that helps reduce stress. Also, if the user is relaxed, the emotion monitoring unit can monitor progress that allows them to work in a relaxed state. Furthermore, if the user is excited, the emotion monitoring unit can monitor progress that allows them to maintain their excitement. This allows the career path prediction system to understand the progress of the career path that reflects the user's emotional state.

[0107] The career path prediction system may further include an emotion goal setting unit that estimates the user's emotions and sets career path goals based on the estimated emotions. The emotion goal setting unit sets career path goals according to the user's emotional state, for example. This allows the career path prediction system to set career path goals that take the user's emotional state into consideration. For example, if the user is feeling stressed, the emotion goal setting unit can set goals that help reduce stress. Also, if the user is relaxed, the emotion goal setting unit can set goals that can be achieved in a relaxed state. Furthermore, if the user is excited, the emotion goal setting unit can set goals that can maintain that excitement. This allows the career path prediction system to set career path goals that reflect the user's emotional state.

[0108] The career path prediction system may further include a geographic analysis unit that collects and analyzes the user's geographical movement data. The geographic analysis unit, for example, collects data on the user's movement history and current place of residence. This allows the career path prediction system to propose career paths that take the user's geographical factors into account. For example, if the user lives in an urban area, the geographic analysis unit can propose a career path within an urban area. Similarly, if the user lives in a rural area, the geographic analysis unit can propose a career path within a rural area. Furthermore, if the user plans to emigrate abroad, the geographic analysis unit can propose a career path in their destination country. This enables the career path prediction system to propose career paths that reflect the user's geographical factors.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The data collection unit collects individual behavioral data. This data includes travel history, purchase history, social media activity, work history, educational background, skill set, interests, etc. For example, the data collection unit can collect data such as past job duties, length of employment, job title, highest level of education, major, degree obtained, technical skills, soft skills, qualifications, hobbies, areas of interest, and activities. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, based on the collected data, the analysis unit predicts the timing of marriage and childbirth, career changes, and retirement. By considering factors such as age, income, living situation, work experience, and health status, the timing of these life events can be predicted. Step 3: The simulation unit simulates life events based on the analysis results obtained by the analysis unit. For example, the simulation unit performs a career simulation and proposes an optimal career path, taking into account factors such as income, job content, and future growth potential. Step 4: The presentation unit presents multiple options based on the simulation results obtained by the simulation unit. For example, the presentation unit suggests optimal resources and networks to support self-construction. These may include educational resources, professional networks, support groups, etc.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the data collection unit, analysis unit, simulation unit, and presentation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14 and collects personal behavioral data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and simulates life events. The presentation unit is implemented by the control unit 46A of the smart device 14 and presents multiple options based on the simulation results. 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.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the data collection unit, analysis unit, simulation unit, and presentation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and collects personal behavioral data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and simulates life events. The presentation unit is implemented by the control unit 46A of the smart glasses 214 and presents multiple options based on the simulation results. 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.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the data collection unit, analysis unit, simulation unit, and presentation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 and collects individual behavioral data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and simulates life events. The presentation unit is implemented by the control unit 46A of the headset terminal 314 and presents multiple options based on the simulation results. 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.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the data collection unit, analysis unit, simulation unit, and presentation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the robot 414 and collects individual behavioral data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and simulates life events. The presentation unit is implemented by the control unit 46A of the robot 414 and presents multiple options based on the simulation results. 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A data collection unit that collects individual behavioral data, An analysis unit analyzes the data collected by the aforementioned collection unit, A simulation unit that simulates life events based on the analysis results obtained by the aforementioned analysis unit, A presentation unit presents multiple options based on the simulation results obtained by the simulation unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data such as work history, educational background, skill set, and interests. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected data, we predict the timing of marriage and childbirth. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Predicting the timing of career changes and retirement The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned simulation unit, We conduct career simulations and propose the optimal career path. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned display unit is, To support self-building, we propose the optimal resources and networks. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze users' past behavioral data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting behavioral data, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of behavioral data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting behavioral data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting behavioral data, analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the behavioral data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned simulation unit, It estimates the user's emotions and adjusts the simulation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned simulation unit, During simulation, consider the interrelationships of behavioral data to improve the accuracy of the simulation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned simulation unit, During the simulation, the attribute information of the person submitting the behavioral data will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned simulation unit, It estimates the user's emotions and adjusts the order in which the simulation results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned simulation unit, During the simulation, the geographical distribution of behavioral data will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned simulation unit, During simulations, we improve the accuracy of the simulations by referring to relevant literature on behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is, It estimates the user's emotions and adjusts the presentation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned display unit is, When presenting the results, adjust the level of detail based on the importance of the simulation results. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is, When presenting the results, different presentation algorithms are applied depending on the category of the simulation results. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned display unit is, It estimates the user's emotions and adjusts the length of the presentation based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned display unit is, When presenting the results, prioritize them based on when the simulation results were collected. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned display unit is, When presenting the results, adjust the order of presentation based on the relevance of the simulation results. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 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 data collection unit that collects individual behavioral data, An analysis unit analyzes the data collected by the aforementioned collection unit, A simulation unit that simulates life events based on the analysis results obtained by the aforementioned analysis unit, A presentation unit presents multiple options based on the simulation results obtained by the simulation unit, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is We collect data such as work history, educational background, skill set, and interests. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected data, we predict the timing of marriage and childbirth. The system according to feature 1.

4. The aforementioned analysis unit, Predicting the timing of career changes and retirement The system according to feature 1.

5. The aforementioned simulation unit, We conduct career simulations and propose the optimal career path. The system according to feature 1.

6. The aforementioned display unit is, To support self-building, we propose the optimal resources and networks. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze users' past behavioral data and select the optimal data collection method. The system according to feature 1.

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