System

Abstract

An object of a system according to an embodiment is to generate a twin AI on the basis of user information and utilize the twin dictionary in various fields.SOLUTION: A system includes a AI acquisition part, a generation part, and a twin data generation part. The data collection unit collects data of a user. The generation unit analyzes the data collected by the data collection unit. The twin AI generator generates a twin AI based on the binary data analyzed by the generator.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately generate and utilize twin AI based on user data, and there is room for improvement.

[0005] The system according to the embodiment aims to generate twin AIs based on user data and utilize them in various fields. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a generation unit, and a twin AI generation unit. The data collection unit collects user data. The generation unit analyzes the data collected by the data collection unit. The twin AI generation unit generates twin AI based on the data analyzed by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment generates twin AI based on user data and can be used in a variety of fields. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The twin AI generation system according to an embodiment of the present invention is a system that collects user data, analyzes the data, and generates twin AI. As a result, the twin AI generation system generates twin AI based on user data, which can be used in a variety of industries.

[0029] The twin AI generation system according to the embodiment includes a data collection unit, a generation unit, and a twin AI generation unit. The data collection unit collects user data. For example, the user enters their own data through a Google Form or a questionnaire. The data collection unit can also collect data such as the user's profile information, health information, work history, hobbies, and preferences. The generation unit analyzes the data collected by the data collection unit. For example, the generation AI analyzes the user data using data mining technology. The generation AI can also analyze the data using a machine learning algorithm. The generation AI also performs analysis to generate a twin AI based on the user data. The twin AI generation unit generates a twin AI based on the data analyzed by the generation unit. For example, the generation AI generates a twin AI using digital twin technology. The generation AI can also generate a twin AI using a simulation model. The generation AI also receives prompts for generating a twin AI based on the user data and generates the twin AI based on the prompts. This allows the twin AI generation system to collect and analyze user data and generate a twin AI. For example, twin AI can be used in various industries, such as self-analysis, the medical field, and business. Self-analysis provides users with strengths and weaknesses that they may not be aware of, as well as areas for improvement and solutions to problems they are facing. In the medical field, information such as medical records is input, and generative AI analyzes the data to help diagnose diseases and symptoms. In companies, Twin AI is used as a human resources development support tool to understand the characteristics and status of employees.

[0030] The data collection unit may add voice input and image recognition functions to enable users to provide data through voice or images. For example, the data collection unit may add a voice input function to enable users to provide data through voice. For example, a user may enter profile information by dictation. The data collection unit may also add an image recognition function to enable users to provide data through images. For example, a user may take a photo and provide the image as data. The data collection unit may also capture an image using a smartphone camera and analyze the image data using a dedicated app. For example, the app may automatically correct the image and perform character recognition. The data collection unit may also scan handwritten notes by a user and convert the image data into text data. For example, the app may use OCR technology to recognize handwritten characters and convert them into digital text. This improves user convenience by enabling users to provide data through voice or images.

[0031] The data collection unit integrates data from different data sources, and the generation unit can generate twin AI based on that data. The data collection unit, for example, collects data from social media, and the generation AI generates twin AI based on that data. For example, it analyzes users' posts and like history. The data collection unit also collects data from wearable devices, and the generation AI generates twin AI based on that data. For example, it analyzes the user's heart rate and step count data. The data collection unit also collects sensor data, and the generation AI generates twin AI based on that data. For example, it analyzes the user's environmental data (temperature, humidity, air pressure, etc.). The data collection unit also integrates data from multiple data sources, and the generation AI generates twin AI based on that data. For example, it integrates and analyzes social media data, wearable device data, and sensor data. This allows for the generation of more detailed twin AI by integrating data from different data sources.

[0032] The generation unit can automatically complete part of the data based on the data entered by the user to generate a more detailed twin AI. For example, the generation unit automatically completes hobbies and interests based on profile information entered by the user. For example, if the user enters that they like sports, the generation AI suggests specific sports. The generation unit also automatically completes health status based on health information entered by the user. For example, if the user enters the results of a past health check, the generation AI estimates their current health status. The generation unit also automatically completes career paths based on work history entered by the user. For example, if the user enters their past work experience, the generation AI suggests future career paths. The generation unit also automatically completes part of the data based on the data entered by the user to generate a twin AI. For example, the generation AI estimates missing information based on the data entered by the user to generate a twin AI. This allows a more detailed twin AI to be generated by automatically completing part of the data.

[0033] The data collection unit collects the user's behavioral history and location information, and the generation unit can integrate this data to generate the twin AI. The data collection unit, for example, collects the user's behavioral history, and the generation AI generates the twin AI based on that data. For example, it analyzes the user's web browsing history. The data collection unit also collects the user's location information, and the generation AI generates the twin AI based on that data. For example, it analyzes the user's GPS data. The data collection unit also collects the user's app usage history, and the generation AI generates the twin AI based on that data. For example, it analyzes the types and frequency of apps used by the user. The data collection unit also integrates the user's behavioral history and location information, and the generation AI generates the twin AI based on that data. For example, it analyzes the user's movement patterns and behavior patterns. By collecting behavioral history and location information, a more detailed twin AI can be generated.

[0034] The twin AI generation unit analyzes the user's lifestyle habits and behavioral patterns, and the generation unit can suggest specific improvement measures. The twin AI generation unit, for example, analyzes the user's lifestyle habits and suggests specific improvement measures. For example, it analyzes sleep patterns and provides advice for better sleep. The twin AI generation unit also analyzes the user's eating habits and suggests healthy meals. For example, it analyzes the balance of meals and the amount of nutrients consumed. The twin AI generation unit also analyzes the user's exercise habits and suggests an appropriate exercise plan. For example, it analyzes the frequency and intensity of exercise. The twin AI generation unit also analyzes the user's behavioral patterns and suggests efficient time management. For example, it analyzes daily routines and schedules. In this way, by analyzing lifestyle habits and behavioral patterns, specific improvement measures can be suggested.

[0035] The twin AI generation unit adapts the self-analysis results to different languages ​​and cultural spheres, allowing the generation unit to obtain feedback from a global perspective. For example, the twin AI generation unit translates the self-analysis results into different languages ​​and collects feedback from a global perspective. For example, it supports multiple languages ​​such as English, French, and Chinese. The twin AI generation unit also adapts the self-analysis results to different cultural spheres and collects feedback that takes cultural background into consideration. For example, it takes into account cultural characteristics such as those of Asian countries and Western countries. The twin AI generation unit also adapts the self-analysis results to different regions and collects region-specific feedback. For example, it collects different feedback between urban and rural areas. The twin AI generation unit also adapts the self-analysis results to different industries and occupations and collects industry-specific feedback. For example, it collects different feedback between the IT industry and the manufacturing industry. In this way, by adapting to different languages ​​and cultural spheres, feedback from a global perspective can be obtained.

[0036] The Twin AI generation unit converts the self-analysis results into a visual note or mind map, making it easier to understand visually. For example, the Twin AI generation unit converts the self-analysis results into a visual note, providing it in a format that is visually easy for the user to understand. For example, it shows important points using diagrams or icons. The Twin AI generation unit also converts the self-analysis results into a mind map, providing it in a format that makes it easy for the user to organize information. For example, it displays the self-analysis results in a tree structure. The Twin AI generation unit also converts the self-analysis results into infographics, providing it in a format that is intuitively easy for the user to understand. For example, it visualizes the information using graphs and charts. The Twin AI generation unit also converts the self-analysis results into a presentation format, providing it in a format that makes it easy for the user to explain to others. For example, it organizes the information in a slide format. In this way, by converting it into a visual note or mind map, the self-analysis results can be made easier to understand visually.

[0037] The generation unit can improve the accuracy of diagnosis by comparing it with past diagnostic data when analyzing medical data. The generation unit, for example, analyzes past diagnostic data and compares it with current diagnostic data to improve the accuracy of diagnosis. For example, it compares past cases with current symptoms to make a more accurate diagnosis. The generation unit also analyzes past treatment data and compares it with current treatment data to improve the accuracy of treatment. For example, it compares past treatment methods with current treatment methods. The generation unit also analyzes past patient data and compares it with current patient data to more accurately understand the patient's condition. For example, it compares past health checkup results with current health checkup results. The generation unit also analyzes past medical data and compares it with current medical data to improve the quality of medical care. For example, it compares past medical records with current medical records. In this way, by comparing it with past diagnostic data, the accuracy of diagnosis can be improved.

[0038] The twin AI generation unit analyzes the patient's lifestyle habits and environmental factors, and the generation unit can make preventive medical recommendations. The twin AI generation unit, for example, analyzes the patient's lifestyle habits and makes preventive medical recommendations. For example, it analyzes dietary and exercise habits and makes recommendations for healthy lifestyles. The twin AI generation unit also analyzes the patient's environmental factors and makes preventive medical recommendations. For example, it analyzes the living environment and work environment and makes recommendations for reducing health risks. The twin AI generation unit also integrates and analyzes the patient's lifestyle habits and environmental factors and makes preventive medical recommendations. For example, it comprehensively analyzes diet, exercise, living environment, work environment, etc. The twin AI generation unit also analyzes the patient's health data and makes preventive medical recommendations. For example, it analyzes health checkup results and medical history to predict health risks. This makes it possible to make preventive medical recommendations by analyzing lifestyle habits and environmental factors.

[0039] The generation unit can share medical data with different medical institutions and research institutions and integrate that data to provide diagnostic support. For example, the generation unit shares medical data with different medical institutions, and the generation AI integrates that data to provide diagnostic support. For example, the generation unit analyzes data from multiple hospitals to provide more accurate diagnoses. The generation unit also shares medical data with research institutions, and the generation AI integrates that data to provide diagnostic support. For example, the generation unit analyzes data from universities and research institutes. The generation unit also shares medical data with corporate research departments, and the generation AI integrates that data to provide diagnostic support. For example, the generation unit analyzes data from pharmaceutical companies. The generation unit also shares medical data with international medical institutions, and the generation AI integrates that data to provide diagnostic support. For example, the generation unit analyzes international medical databases. This allows the accuracy of diagnostic support to be improved by integrating data from different medical institutions and research institutions.

[0040] The generation unit can convert the diagnostic results into visual notes or infographics and provide them in a format that is easy for patients to understand. For example, the generation unit converts the diagnostic results into visual notes and provides them in a format that is easy for patients to understand visually. For example, important points are shown using diagrams or icons. The generation unit also converts the diagnostic results into infographics and provides them in a format that is easy for patients to understand intuitively. For example, the information is visualized using graphs or charts. The generation unit also converts the diagnostic results into a presentation format and provides them in a format that is easy for patients to explain to others. For example, the information is organized in a slide format. The generation unit also converts the diagnostic results into a video format and provides them in a format that is easy for patients to understand visually. For example, the information is visualized using animation. In this way, the diagnostic results can be provided visually, making it easier for patients to understand.

[0041] The generation unit can compare an employee's past performance data with current data and analyze long-term growth trends. For example, the generation unit compares an employee's past performance data with current data, and the generation AI analyzes long-term growth trends. For example, it compares past project results with current work performance. The generation unit also compares an employee's past evaluation data with current evaluation data, and the generation AI analyzes growth trends. For example, it compares past evaluation scores with current evaluation scores. The generation unit also compares an employee's past skill data with current skill data, and the generation AI analyzes skill improvement. For example, it compares past skill sets with current skill sets. The generation unit also compares an employee's past behavioral data with current behavioral data, and the generation AI analyzes changes in behavior. For example, it compares past behavioral patterns with current behavioral patterns. In this way, by comparing past and current performance data, long-term growth trends can be analyzed.

[0042] The Twin AI generation unit analyzes an employee's skill set and interests, and the generation unit can make specific suggestions for skill improvement. The Twin AI generation unit, for example, analyzes an employee's skill set and makes specific suggestions for skill improvement. For example, it suggests the next skill that an employee should acquire based on the skills they possess. The Twin AI generation unit also analyzes an employee's interests and makes suggestions for skill improvement based on those interests. For example, it suggests skills related to areas in which the employee is interested. The Twin AI generation unit also integrates and analyzes an employee's skill set and interests to make suggestions for skill improvement. For example, it suggests an optimal skill improvement plan taking into account the employee's skills and interests. The Twin AI generation unit also compares an employee's past skill data with their current skill data to make suggestions for skill improvement. For example, it compares their past skill set with their current skill set and suggests skill improvement. In this way, specific suggestions for skill improvement can be made by analyzing skill sets and interests.

[0043] The generation unit can adapt the human resource development support tool to different industries and job types, making proposals specialized for each industry and job type. For example, the generation unit adapts the human resource development support tool to different industries, and the generation AI makes industry-specific proposals. For example, different skill sets are proposed for the IT industry and the manufacturing industry. The generation unit also adapts the human resource development support tool to different job types, and the generation AI makes job-specific proposals. For example, different career paths are proposed for engineers and marketing personnel. The generation unit also adapts the human resource development support tool to different regions, and the generation AI makes region-specific proposals. For example, different skill development plans are proposed for urban and rural areas. The generation unit also adapts the human resource development support tool to different corporate cultures, and the generation AI makes culture-specific proposals. For example, different training plans are proposed for startup companies and large companies. This makes it possible to provide more effective human resource development support by making proposals specialized for different industries and job types.

[0044] The generation unit can convert the human resource development support tool into a visual note or a mind map to make it easier to understand visually. For example, the generation unit converts the human resource development support tool into a visual note to provide it in a format that is easy for employees to understand visually. For example, important points are indicated using diagrams or icons. The generation unit can also convert the human resource development support tool into a mind map to provide it in a format that is easy for employees to organize information. For example, a skill development plan is displayed in a tree structure. The generation unit can also convert the human resource development support tool into an infographic to provide it in a format that is easy for employees to understand intuitively. For example, information is visualized using graphs and charts. The generation unit can also convert the human resource development support tool into a presentation format to provide it in a format that is easy for employees to explain to others. For example, information is organized in a slide format. In this way, by converting it into a visual note or a mind map, the human resource development support tool can be made easier to understand visually.

[0045] The generation unit can analyze the usage history of the additional functions and recommend the optimal functions to the user. For example, the generation unit analyzes the usage history of the additional functions, and the generation AI recommends the optimal functions to the user. For example, the generation unit suggests functions that the user uses frequently based on past usage history. The generation unit also analyzes the user's usage history, and the generation AI suggests functions based on the user's needs. For example, if the user frequently uses a specific function, the generation unit suggests additional functions related to that function. The generation unit also analyzes the user's usage history, and the generation AI suggests functions based on the user's behavioral patterns. For example, if the user uses a specific function at a specific time of day, the generation unit suggests functions suitable for that time of day. The generation unit also analyzes the user's usage history, and the generation AI suggests functions based on the user's preferences. For example, if the user prefers to use functions in a specific category, the generation AI suggests functions related to that category. In this way, the optimal functions can be recommended to the user by analyzing the usage history.

[0046] The generation unit can adapt the additional functions to different platforms and devices, and provide functions specialized for each platform and device. For example, the generation unit adapts the additional functions to different platforms, and the generation AI provides platform-specific functions. For example, different user interfaces are provided for iOS and Android. The generation unit also adapts the additional functions to different devices, and the generation AI provides device-specific functions. For example, different functions are provided for smartphones and tablets. The generation unit also adapts the additional functions to different operating systems, and the generation AI provides OS-specific functions. For example, different functions are provided for Windows and macOS. The generation unit also adapts the additional functions to different browsers, and the generation AI provides browser-specific functions. For example, different functions are provided for Chrome and Firefox. This allows for improved user convenience by providing functions specialized for different platforms and devices.

[0047] The generation unit can convert the additional functions into visual notes or infographics and provide them in a format that is easy for the user to understand. For example, the generation unit converts the additional functions into visual notes and provides them in a format that is easy for the user to visually understand. For example, important points are indicated using diagrams or icons. The generation unit can also convert the additional functions into infographics and provide them in a format that is easy for the user to intuitively understand. For example, the information is visualized using graphs or charts. The generation unit can also convert the additional functions into a presentation format and provide them in a format that is easy for the user to explain to others. For example, the information is organized in a slide format. The generation unit can also convert the additional functions into a video format and provide them in a format that is easy for the user to visually understand. For example, the information is visualized using animation. In this way, by converting them into visual notes or infographics, the additional functions can be made visually easy to understand.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The data collection unit may add voice input and image recognition functions to enable users to provide data through voice or images. For example, a user may enter profile information by dictation. The data collection unit may also add image recognition functions to enable users to provide data through images. For example, a user may take a photo and provide the image as data. The data collection unit may also capture an image using a smartphone camera and analyze the image data using a dedicated app. For example, the app may automatically correct the image and perform character recognition. The data collection unit may also scan handwritten notes by a user and convert the image data into text data. For example, the app may use OCR technology to recognize handwritten characters and convert them into digital text. This improves user convenience by enabling users to provide data through voice or images.

[0050] The data collection unit integrates data from different data sources, and the generation unit can generate twin AI based on that data. For example, data is collected from social media, and the generation AI generates twin AI based on that data. For example, it analyzes users' posts and like history. The data collection unit also collects data from wearable devices, and the generation AI generates twin AI based on that data. For example, it analyzes the user's heart rate and step count data. The data collection unit also collects sensor data, and the generation AI generates twin AI based on that data. For example, it analyzes the user's environmental data (temperature, humidity, air pressure, etc.). The data collection unit also integrates data from multiple data sources, and the generation AI generates twin AI based on that data. For example, it integrates and analyzes social media data, wearable device data, and sensor data. This allows for the generation of more detailed twin AI by integrating data from different data sources.

[0051] The generation unit can automatically complete part of the data based on the data entered by the user to generate a more detailed twin AI. For example, the generation AI automatically completes hobbies and interests based on the profile information entered by the user. For example, if the user enters that they like sports, the generation AI will suggest specific sports. The generation unit also automatically completes the health status based on the health information entered by the user. For example, if the user enters the results of a past health check, the generation AI will estimate their current health status. The generation unit also automatically completes the career path based on the work history entered by the user. For example, if the user enters their past work experience, the generation AI will suggest their future career path. The generation unit also automatically completes part of the data based on the data entered by the user to generate a twin AI. For example, the generation AI estimates missing information based on the data entered by the user to generate a twin AI. This allows a more detailed twin AI to be generated by automatically completing part of the data.

[0052] The data collection unit collects the user's behavioral history and location information, and the generation unit can integrate this data to generate a twin AI. For example, the user's behavioral history is collected, and the generation AI generates a twin AI based on that data. For example, the user's web browsing history is analyzed. The data collection unit also collects the user's location information, and the generation AI generates a twin AI based on that data. For example, the user's GPS data is analyzed. The data collection unit also collects the user's app usage history, and the generation AI generates a twin AI based on that data. For example, the type and frequency of apps used by the user is analyzed. The data collection unit also integrates the user's behavioral history and location information, and the generation AI generates a twin AI based on that data. For example, the user's movement patterns and behavior patterns are analyzed. By collecting behavioral history and location information, a more detailed twin AI can be generated.

[0053] The twin AI generation unit adapts the self-analysis results to different languages ​​and cultural spheres, allowing the generation unit to obtain feedback from a global perspective. For example, the twin AI generation unit translates the self-analysis results into different languages ​​and collects feedback from a global perspective. For example, it supports multiple languages ​​such as English, French, and Chinese. The twin AI generation unit also adapts the self-analysis results to different cultural spheres and collects feedback that takes cultural background into consideration. For example, it takes into account cultural characteristics such as those of Asian countries and Western countries. The twin AI generation unit also adapts the self-analysis results to different regions and collects region-specific feedback. For example, it collects different feedback between urban and rural areas. The twin AI generation unit also adapts the self-analysis results to different industries and occupations and collects industry-specific feedback. For example, it collects different feedback between the IT industry and the manufacturing industry. In this way, by adapting to different languages ​​and cultural spheres, feedback from a global perspective can be obtained.

[0054] The Twin AI generation unit converts the self-analysis results into a visual note or mind map, making them easier to understand visually. For example, the generation unit converts the self-analysis results into a visual note, providing them in a format that is visually easy for the user to understand. For example, important points are indicated using diagrams and icons. The Twin AI generation unit also converts the self-analysis results into a mind map, providing them in a format that makes it easy for the user to organize information. For example, the self-analysis results are displayed in a tree structure. The Twin AI generation unit also converts the self-analysis results into infographics, providing them in a format that is intuitively easy for the user to understand. For example, the information is visualized using graphs and charts. The Twin AI generation unit also converts the self-analysis results into a presentation format, providing them in a format that makes it easy for the user to explain to others. For example, the information is organized in a slide format. In this way, by converting them into a visual note or mind map, the self-analysis results can be made easier to understand visually.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The data collection unit collects user data. For example, users enter their own data through Google Forms or questionnaires. The data collection unit can also collect data such as user profile information, health information, work history, hobbies, and preferences. Step 2: The generation unit analyzes the data collected by the data collection unit. For example, the generation AI analyzes the user's data using data mining techniques and machine learning algorithms. The generation AI also performs analysis to generate twin AIs based on the user's data. Step 3: The twin AI generation unit generates the twin AI based on the data analyzed by the generation unit. For example, the generation AI generates the twin AI using digital twin technology or a simulation model. The generation AI also receives a prompt for generating the twin AI based on the user's data, and generates the twin AI based on the prompt.

[0057] (Example 2) The twin AI generation system according to an embodiment of the present invention is a system that collects user data, analyzes the data, and generates twin AI. As a result, the twin AI generation system generates twin AI based on user data, which can be used in a variety of industries.

[0058] The twin AI generation system according to the embodiment includes a data collection unit, a generation unit, and a twin AI generation unit. The data collection unit collects user data. For example, the user enters their own data through a Google Form or a questionnaire. The data collection unit can also collect data such as the user's profile information, health information, work history, hobbies, and preferences. The generation unit analyzes the data collected by the data collection unit. For example, the generation AI analyzes the user data using data mining technology. The generation AI can also analyze the data using a machine learning algorithm. The generation AI also performs analysis to generate a twin AI based on the user data. The twin AI generation unit generates a twin AI based on the data analyzed by the generation unit. For example, the generation AI generates a twin AI using digital twin technology. The generation AI can also generate a twin AI using a simulation model. The generation AI also receives prompts for generating a twin AI based on the user data and generates the twin AI based on the prompts. This allows the twin AI generation system to collect and analyze user data and generate a twin AI. For example, twin AI can be used in various industries, such as self-analysis, the medical field, and business. Self-analysis provides users with strengths and weaknesses that they may not be aware of, as well as areas for improvement and solutions to problems they are facing. In the medical field, information such as medical records is input, and generative AI analyzes the data to help diagnose diseases and symptoms. In companies, Twin AI is used as a human resources development support tool to understand the characteristics and status of employees.

[0059] The data collection unit can estimate emotions in real time while collecting user data and dynamically generate questions based on the user's emotions. For example, when a user enters data into a Google form, the data collection unit uses a generation AI to estimate emotions in real time and dynamically generate questions based on the user's emotions. For example, if the user is feeling stressed, a question to help them relax is added. The data collection unit also captures the user's facial expressions with a camera and analyzes their emotions using an emotion estimation algorithm. For example, an emotion score is calculated based on changes in facial expressions. The data collection unit also records the user's voice and estimates their emotions using voice analysis technology. For example, the tone and speed of the voice are analyzed to calculate an emotion score. The data collection unit also collects the user's biometric data (heart rate and electrodermal activity) using a sensor and analyzes their emotions using an emotion estimation algorithm. For example, an emotion score is calculated based on heart rate fluctuations. This enables more appropriate data collection by dynamically generating questions based on the user's emotions.

[0060] The data collection unit may add voice input and image recognition functions to enable users to provide data through voice or images. For example, the data collection unit may add a voice input function to enable users to provide data through voice. For example, a user may enter profile information by dictation. The data collection unit may also add an image recognition function to enable users to provide data through images. For example, a user may take a photo and provide the image as data. The data collection unit may also capture an image using a smartphone camera and analyze the image data using a dedicated app. For example, the app may automatically correct the image and perform character recognition. The data collection unit may also scan handwritten notes by a user and convert the image data into text data. For example, the app may use OCR technology to recognize handwritten characters and convert them into digital text. This improves user convenience by enabling users to provide data through voice or images.

[0061] The data collection unit integrates data from different data sources, and the generation unit can generate twin AI based on that data. The data collection unit, for example, collects data from social media, and the generation AI generates twin AI based on that data. For example, it analyzes users' posts and like history. The data collection unit also collects data from wearable devices, and the generation AI generates twin AI based on that data. For example, it analyzes the user's heart rate and step count data. The data collection unit also collects sensor data, and the generation AI generates twin AI based on that data. For example, it analyzes the user's environmental data (temperature, humidity, air pressure, etc.). The data collection unit also integrates data from multiple data sources, and the generation AI generates twin AI based on that data. For example, it integrates and analyzes social media data, wearable device data, and sensor data. This allows for the generation of more detailed twin AI by integrating data from different data sources.

[0062] The generation unit can automatically complete part of the data based on the data entered by the user to generate a more detailed twin AI. For example, the generation unit automatically completes hobbies and interests based on profile information entered by the user. For example, if the user enters that they like sports, the generation AI suggests specific sports. The generation unit also automatically completes health status based on health information entered by the user. For example, if the user enters the results of a past health check, the generation AI estimates their current health status. The generation unit also automatically completes career paths based on work history entered by the user. For example, if the user enters their past work experience, the generation AI suggests future career paths. The generation unit also automatically completes part of the data based on the data entered by the user to generate a twin AI. For example, the generation AI estimates missing information based on the data entered by the user to generate a twin AI. This allows a more detailed twin AI to be generated by automatically completing part of the data.

[0063] The data collection unit collects the user's behavioral history and location information, and the generation unit can integrate this data to generate the twin AI. The data collection unit, for example, collects the user's behavioral history, and the generation AI generates the twin AI based on that data. For example, it analyzes the user's web browsing history. The data collection unit also collects the user's location information, and the generation AI generates the twin AI based on that data. For example, it analyzes the user's GPS data. The data collection unit also collects the user's app usage history, and the generation AI generates the twin AI based on that data. For example, it analyzes the types and frequency of apps used by the user. The data collection unit also integrates the user's behavioral history and location information, and the generation AI generates the twin AI based on that data. For example, it analyzes the user's movement patterns and behavior patterns. By collecting behavioral history and location information, a more detailed twin AI can be generated.

[0064] The data collection unit can use the emotion estimation function to analyze the user's emotions in real time when entering data and generate questions that elicit positive emotions. For example, when the user enters data, the data collection unit uses a generation AI to analyze the user's emotions in real time and generate questions that elicit positive emotions. For example, if the user is tired, a question to help them relax is added. The data collection unit also captures the user's facial expressions with a camera and analyzes their emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on changes in facial expressions and generates questions that elicit positive emotions. The data collection unit also records the user's voice and estimates their emotions using voice analysis technology. For example, it analyzes the tone and speed of the voice and generates questions that elicit positive emotions. The data collection unit also collects the user's biometric data (heart rate and electrodermal activity) with a sensor and analyzes their emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on fluctuations in heart rate and generates questions that elicit positive emotions. This generates questions that elicit positive emotions, improving the user's data entry experience.

[0065] The twin AI generation unit can analyze a user's emotions in real time and provide self-analysis results based on the emotions. For example, if a user is feeling stressed, the twin AI generation unit can suggest the cause of the stress and countermeasures. The twin AI generation unit can also capture the user's facial expressions with a camera and analyze their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expressions and provide self-analysis results. The twin AI generation unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, it can analyze the tone and speed of the voice and provide self-analysis results. The twin AI generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and analyze their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on heart rate fluctuations and provide self-analysis results. This allows the user to deepen their self-understanding by providing self-analysis results based on emotions.

[0066] The twin AI generation unit analyzes the user's lifestyle habits and behavioral patterns, and the generation unit can suggest specific improvement measures. The twin AI generation unit, for example, analyzes the user's lifestyle habits and suggests specific improvement measures. For example, it analyzes sleep patterns and provides advice for better sleep. The twin AI generation unit also analyzes the user's eating habits and suggests healthy meals. For example, it analyzes the balance of meals and the amount of nutrients consumed. The twin AI generation unit also analyzes the user's exercise habits and suggests an appropriate exercise plan. For example, it analyzes the frequency and intensity of exercise. The twin AI generation unit also analyzes the user's behavioral patterns and suggests efficient time management. For example, it analyzes daily routines and schedules. In this way, by analyzing lifestyle habits and behavioral patterns, specific improvement measures can be suggested.

[0067] The twin AI generation unit adapts the self-analysis results to different languages ​​and cultural spheres, allowing the generation unit to obtain feedback from a global perspective. For example, the twin AI generation unit translates the self-analysis results into different languages ​​and collects feedback from a global perspective. For example, it supports multiple languages ​​such as English, French, and Chinese. The twin AI generation unit also adapts the self-analysis results to different cultural spheres and collects feedback that takes cultural background into consideration. For example, it takes into account cultural characteristics such as those of Asian countries and Western countries. The twin AI generation unit also adapts the self-analysis results to different regions and collects region-specific feedback. For example, it collects different feedback between urban and rural areas. The twin AI generation unit also adapts the self-analysis results to different industries and occupations and collects industry-specific feedback. For example, it collects different feedback between the IT industry and the manufacturing industry. In this way, by adapting to different languages ​​and cultural spheres, feedback from a global perspective can be obtained.

[0068] The Twin AI generation unit converts the self-analysis results into a visual note or mind map, making it easier to understand visually. For example, the Twin AI generation unit converts the self-analysis results into a visual note, providing it in a format that is visually easy for the user to understand. For example, it shows important points using diagrams or icons. The Twin AI generation unit also converts the self-analysis results into a mind map, providing it in a format that makes it easy for the user to organize information. For example, it displays the self-analysis results in a tree structure. The Twin AI generation unit also converts the self-analysis results into infographics, providing it in a format that is intuitively easy for the user to understand. For example, it visualizes the information using graphs and charts. The Twin AI generation unit also converts the self-analysis results into a presentation format, providing it in a format that makes it easy for the user to explain to others. For example, it organizes the information in a slide format. In this way, by converting it into a visual note or mind map, the self-analysis results can be made easier to understand visually.

[0069] The twin AI generation unit uses an emotion estimation function to collect the user's emotional responses to the self-analysis results, and the generation unit can improve the accuracy of the analysis results based on that data. For example, the twin AI generation unit collects the user's emotional responses to the self-analysis results in real time and improves the accuracy of the analysis results based on that data. For example, it prioritizes the adoption of analysis results with a high number of positive responses. The twin AI generation unit also captures the user's facial expressions with a camera and analyzes their emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on changes in facial expressions to improve the accuracy of the analysis results. The twin AI generation unit also records the user's voice and estimates their emotions using voice analysis technology. For example, it analyzes the tone and speed of the voice to improve the accuracy of the analysis results. The twin AI generation unit also collects the user's biometric data (heart rate and electrodermal activity) with a sensor and analyzes their emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on heart rate fluctuations to improve the accuracy of the analysis results. In this way, by collecting emotional responses, the accuracy of the analysis results can be improved.

[0070] The twin AI generation unit analyzes the patient's emotions and can provide diagnosis results and treatment suggestions based on the emotions. For example, the twin AI generation unit analyzes the patient's emotions in real time and provides diagnosis results based on the emotions. For example, if the patient is feeling anxious, it can suggest treatment methods to reduce the anxiety. The twin AI generation unit also captures the patient's facial expressions with a camera and analyzes the emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on changes in facial expressions and provides a diagnosis result. The twin AI generation unit also records the patient's voice and estimates the emotion using voice analysis technology. For example, it analyzes the tone and speed of the voice and provides a diagnosis result. The twin AI generation unit also collects the patient's biometric data (heart rate and electrodermal activity) with a sensor and analyzes the emotion using an emotion estimation algorithm. For example, it calculates an emotion score based on heart rate fluctuations and provides a diagnosis result. This allows for the provision of emotion-based diagnosis results and treatment suggestions, thereby improving the effectiveness of patient treatment.

[0071] The generation unit can improve the accuracy of diagnosis by comparing it with past diagnostic data when analyzing medical data. The generation unit, for example, analyzes past diagnostic data and compares it with current diagnostic data to improve the accuracy of diagnosis. For example, it compares past cases with current symptoms to make a more accurate diagnosis. The generation unit also analyzes past treatment data and compares it with current treatment data to improve the accuracy of treatment. For example, it compares past treatment methods with current treatment methods. The generation unit also analyzes past patient data and compares it with current patient data to more accurately understand the patient's condition. For example, it compares past health checkup results with current health checkup results. The generation unit also analyzes past medical data and compares it with current medical data to improve the quality of medical care. For example, it compares past medical records with current medical records. In this way, by comparing it with past diagnostic data, the accuracy of diagnosis can be improved.

[0072] The twin AI generation unit analyzes the patient's lifestyle habits and environmental factors, and the generation unit can make preventive medical recommendations. The twin AI generation unit, for example, analyzes the patient's lifestyle habits and makes preventive medical recommendations. For example, it analyzes dietary and exercise habits and makes recommendations for healthy lifestyles. The twin AI generation unit also analyzes the patient's environmental factors and makes preventive medical recommendations. For example, it analyzes the living environment and work environment and makes recommendations for reducing health risks. The twin AI generation unit also integrates and analyzes the patient's lifestyle habits and environmental factors and makes preventive medical recommendations. For example, it comprehensively analyzes diet, exercise, living environment, work environment, etc. The twin AI generation unit also analyzes the patient's health data and makes preventive medical recommendations. For example, it analyzes health checkup results and medical history to predict health risks. This makes it possible to make preventive medical recommendations by analyzing lifestyle habits and environmental factors.

[0073] The generation unit can share medical data with different medical institutions and research institutions and integrate that data to provide diagnostic support. For example, the generation unit shares medical data with different medical institutions, and the generation AI integrates that data to provide diagnostic support. For example, the generation unit analyzes data from multiple hospitals to provide more accurate diagnoses. The generation unit also shares medical data with research institutions, and the generation AI integrates that data to provide diagnostic support. For example, the generation unit analyzes data from universities and research institutes. The generation unit also shares medical data with corporate research departments, and the generation AI integrates that data to provide diagnostic support. For example, the generation unit analyzes data from pharmaceutical companies. The generation unit also shares medical data with international medical institutions, and the generation AI integrates that data to provide diagnostic support. For example, the generation unit analyzes international medical databases. This allows the accuracy of diagnostic support to be improved by integrating data from different medical institutions and research institutions.

[0074] The generation unit can convert the diagnostic results into visual notes or infographics and provide them in a format that is easy for patients to understand. For example, the generation unit converts the diagnostic results into visual notes and provides them in a format that is easy for patients to understand visually. For example, important points are shown using diagrams or icons. The generation unit also converts the diagnostic results into infographics and provides them in a format that is easy for patients to understand intuitively. For example, the information is visualized using graphs or charts. The generation unit also converts the diagnostic results into a presentation format and provides them in a format that is easy for patients to explain to others. For example, the information is organized in a slide format. The generation unit also converts the diagnostic results into a video format and provides them in a format that is easy for patients to understand visually. For example, the information is visualized using animation. In this way, the diagnostic results can be provided visually, making it easier for patients to understand.

[0075] The Twin AI generation unit analyzes employees' emotions and can propose career paths and training plans based on their emotions. For example, the Twin AI generation unit analyzes employees' emotions in real time and proposes career paths based on their emotions. For example, it proposes tasks that motivate employees. The Twin AI generation unit also captures employees' facial expressions with a camera and analyzes their emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on changes in facial expressions and proposes career paths. The Twin AI generation unit also records employees' voices and estimates their emotions using voice analysis technology. For example, it analyzes the tone and speed of their voices and proposes career paths. The Twin AI generation unit also collects employees' biometric data (heart rate and electrodermal activity) with sensors and analyzes their emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on heart rate fluctuations and proposes career paths. This makes it possible to promote employee growth by proposing career paths and training plans based on their emotions.

[0076] The generation unit can compare an employee's past performance data with current data and analyze long-term growth trends. For example, the generation unit compares an employee's past performance data with current data, and the generation AI analyzes long-term growth trends. For example, it compares past project results with current work performance. The generation unit also compares an employee's past evaluation data with current evaluation data, and the generation AI analyzes growth trends. For example, it compares past evaluation scores with current evaluation scores. The generation unit also compares an employee's past skill data with current skill data, and the generation AI analyzes skill improvement. For example, it compares past skill sets with current skill sets. The generation unit also compares an employee's past behavioral data with current behavioral data, and the generation AI analyzes changes in behavior. For example, it compares past behavioral patterns with current behavioral patterns. In this way, by comparing past and current performance data, long-term growth trends can be analyzed.

[0077] The Twin AI generation unit analyzes an employee's skill set and interests, and the generation unit can make specific suggestions for skill improvement. The Twin AI generation unit, for example, analyzes an employee's skill set and makes specific suggestions for skill improvement. For example, it suggests the next skill that an employee should acquire based on the skills they possess. The Twin AI generation unit also analyzes an employee's interests and makes suggestions for skill improvement based on those interests. For example, it suggests skills related to areas in which the employee is interested. The Twin AI generation unit also integrates and analyzes an employee's skill set and interests to make suggestions for skill improvement. For example, it suggests an optimal skill improvement plan taking into account the employee's skills and interests. The Twin AI generation unit also compares an employee's past skill data with their current skill data to make suggestions for skill improvement. For example, it compares their past skill set with their current skill set and suggests skill improvement. In this way, specific suggestions for skill improvement can be made by analyzing skill sets and interests.

[0078] The generation unit can adapt the human resource development support tool to different industries and job types, making proposals specialized for each industry and job type. For example, the generation unit adapts the human resource development support tool to different industries, and the generation AI makes industry-specific proposals. For example, different skill sets are proposed for the IT industry and the manufacturing industry. The generation unit also adapts the human resource development support tool to different job types, and the generation AI makes job-specific proposals. For example, different career paths are proposed for engineers and marketing personnel. The generation unit also adapts the human resource development support tool to different regions, and the generation AI makes region-specific proposals. For example, different skill development plans are proposed for urban and rural areas. The generation unit also adapts the human resource development support tool to different corporate cultures, and the generation AI makes culture-specific proposals. For example, different training plans are proposed for startup companies and large companies. This makes it possible to provide more effective human resource development support by making proposals specialized for different industries and job types.

[0079] The generation unit can convert the human resource development support tool into a visual note or a mind map to make it easier to understand visually. For example, the generation unit converts the human resource development support tool into a visual note to provide it in a format that is easy for employees to understand visually. For example, important points are indicated using diagrams or icons. The generation unit can also convert the human resource development support tool into a mind map to provide it in a format that is easy for employees to organize information. For example, a skill development plan is displayed in a tree structure. The generation unit can also convert the human resource development support tool into an infographic to provide it in a format that is easy for employees to understand intuitively. For example, information is visualized using graphs and charts. The generation unit can also convert the human resource development support tool into a presentation format to provide it in a format that is easy for employees to explain to others. For example, information is organized in a slide format. In this way, by converting it into a visual note or a mind map, the human resource development support tool can be made easier to understand visually.

[0080] The generation unit can collect employees' emotional responses using an emotion estimation function and improve the accuracy of human resource development support based on the collected data. For example, the generation unit collects employees' emotional responses in real time and improves the accuracy of human resource development support based on the collected data. For example, it prioritizes the adoption of proposals with a high number of positive responses. The generation unit also captures employees' facial expressions with a camera and analyzes their emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on changes in facial expressions, thereby improving the accuracy of human resource development support. The generation unit also records employees' voices and estimates their emotions using voice analysis technology. For example, it analyzes the tone and speed of their voices, thereby improving the accuracy of human resource development support. The generation unit also collects employees' biometric data (heart rate and electrodermal activity) using a sensor and analyzes their emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on heart rate fluctuations, thereby improving the accuracy of human resource development support. In this way, the collection of emotional responses can improve the accuracy of human resource development support.

[0081] The twin AI generation unit analyzes the user's emotions and can suggest additional functions and services based on the emotions. For example, the twin AI generation unit analyzes the user's emotions in real time and suggests additional functions based on the emotions. For example, if the user is feeling stressed, it suggests a function to help the user relax. The twin AI generation unit also captures the user's facial expressions with a camera and analyzes their emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on changes in facial expressions and suggests additional functions. The twin AI generation unit also records the user's voice and estimates their emotions using voice analysis technology. For example, it analyzes the tone and speed of the voice and suggests additional functions. The twin AI generation unit also collects the user's biometric data (heart rate and electrodermal activity) with a sensor and analyzes their emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on heart rate fluctuations and suggests additional functions. This makes it possible to improve user satisfaction by suggesting additional functions and services based on emotions.

[0082] The generation unit can analyze the usage history of the additional functions and recommend the optimal functions to the user. For example, the generation unit analyzes the usage history of the additional functions, and the generation AI recommends the optimal functions to the user. For example, the generation unit suggests functions that the user uses frequently based on past usage history. The generation unit also analyzes the user's usage history, and the generation AI suggests functions based on the user's needs. For example, if the user frequently uses a specific function, the generation unit suggests additional functions related to that function. The generation unit also analyzes the user's usage history, and the generation AI suggests functions based on the user's behavioral patterns. For example, if the user uses a specific function at a specific time of day, the generation unit suggests functions suitable for that time of day. The generation unit also analyzes the user's usage history, and the generation AI suggests functions based on the user's preferences. For example, if the user prefers to use functions in a specific category, the generation AI suggests functions related to that category. In this way, the optimal functions can be recommended to the user by analyzing the usage history.

[0083] The generation unit can adapt the additional functions to different platforms and devices, and provide functions specialized for each platform and device. For example, the generation unit adapts the additional functions to different platforms, and the generation AI provides platform-specific functions. For example, different user interfaces are provided for iOS and Android. The generation unit also adapts the additional functions to different devices, and the generation AI provides device-specific functions. For example, different functions are provided for smartphones and tablets. The generation unit also adapts the additional functions to different operating systems, and the generation AI provides OS-specific functions. For example, different functions are provided for Windows and macOS. The generation unit also adapts the additional functions to different browsers, and the generation AI provides browser-specific functions. For example, different functions are provided for Chrome and Firefox. This allows for improved user convenience by providing functions specialized for different platforms and devices.

[0084] The generation unit can convert the additional functions into visual notes or infographics and provide them in a format that is easy for the user to understand. For example, the generation unit converts the additional functions into visual notes and provides them in a format that is easy for the user to visually understand. For example, important points are indicated using diagrams or icons. The generation unit can also convert the additional functions into infographics and provide them in a format that is easy for the user to intuitively understand. For example, the information is visualized using graphs or charts. The generation unit can also convert the additional functions into a presentation format and provide them in a format that is easy for the user to explain to others. For example, the information is organized in a slide format. The generation unit can also convert the additional functions into a video format and provide them in a format that is easy for the user to visually understand. For example, the information is visualized using animation. In this way, by converting them into visual notes or infographics, the additional functions can be made visually easy to understand.

[0085] The generation unit can use the emotion estimation function to collect the user's emotional reactions to the additional functions and improve the accuracy of the functions based on the collected data. For example, the generation unit collects the user's emotional reactions to the additional functions in real time and improves the accuracy of the functions based on the collected data. For example, the generation unit prioritizes the adoption of functions with a high number of positive reactions. The generation unit also captures the user's facial expressions with a camera and analyzes the emotions using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on changes in facial expressions and improves the accuracy of the functions. The generation unit also records the user's voice and estimates the emotions using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice and improves the accuracy of the functions. The generation unit also collects the user's biometric data (heart rate and electrodermal activity) with a sensor and analyzes the emotions using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on heart rate fluctuations and improves the accuracy of the functions. In this way, the generation unit can improve the accuracy of the additional functions by collecting emotional reactions.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The data collection unit can estimate emotions in real time while collecting user data and dynamically generate questions based on the user's emotions. For example, if the user is feeling stressed, it can add questions to help them relax. The data collection unit also captures the user's facial expressions with a camera and analyzes their emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on changes in facial expressions. The data collection unit also records the user's voice and estimates their emotions using voice analysis technology. For example, it analyzes the tone and speed of the voice and calculates an emotion score. The data collection unit also collects the user's biometric data (heart rate and electrodermal activity) with a sensor and analyzes their emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on fluctuations in heart rate. This allows for more appropriate data collection by dynamically generating questions based on the user's emotions.

[0088] The data collection unit may add voice input and image recognition functions to enable users to provide data through voice or images. For example, a user may enter profile information by dictation. The data collection unit may also add image recognition functions to enable users to provide data through images. For example, a user may take a photo and provide the image as data. The data collection unit may also capture an image using a smartphone camera and analyze the image data using a dedicated app. For example, the app may automatically correct the image and perform character recognition. The data collection unit may also scan handwritten notes by a user and convert the image data into text data. For example, the app may use OCR technology to recognize handwritten characters and convert them into digital text. This improves user convenience by enabling users to provide data through voice or images.

[0089] The data collection unit integrates data from different data sources, and the generation unit can generate twin AI based on that data. For example, data is collected from social media, and the generation AI generates twin AI based on that data. For example, it analyzes users' posts and like history. The data collection unit also collects data from wearable devices, and the generation AI generates twin AI based on that data. For example, it analyzes the user's heart rate and step count data. The data collection unit also collects sensor data, and the generation AI generates twin AI based on that data. For example, it analyzes the user's environmental data (temperature, humidity, air pressure, etc.). The data collection unit also integrates data from multiple data sources, and the generation AI generates twin AI based on that data. For example, it integrates and analyzes social media data, wearable device data, and sensor data. This allows for the generation of more detailed twin AI by integrating data from different data sources.

[0090] The generation unit can automatically complete part of the data based on the data entered by the user to generate a more detailed twin AI. For example, the generation AI automatically completes hobbies and interests based on the profile information entered by the user. For example, if the user enters that they like sports, the generation AI will suggest specific sports. The generation unit also automatically completes the health status based on the health information entered by the user. For example, if the user enters the results of a past health check, the generation AI will estimate their current health status. The generation unit also automatically completes the career path based on the work history entered by the user. For example, if the user enters their past work experience, the generation AI will suggest their future career path. The generation unit also automatically completes part of the data based on the data entered by the user to generate a twin AI. For example, the generation AI estimates missing information based on the data entered by the user to generate a twin AI. This allows a more detailed twin AI to be generated by automatically completing part of the data.

[0091] The data collection unit collects the user's behavioral history and location information, and the generation unit can integrate this data to generate a twin AI. For example, the user's behavioral history is collected, and the generation AI generates a twin AI based on that data. For example, the user's web browsing history is analyzed. The data collection unit also collects the user's location information, and the generation AI generates a twin AI based on that data. For example, the user's GPS data is analyzed. The data collection unit also collects the user's app usage history, and the generation AI generates a twin AI based on that data. For example, the type and frequency of apps used by the user is analyzed. The data collection unit also integrates the user's behavioral history and location information, and the generation AI generates a twin AI based on that data. For example, the user's movement patterns and behavior patterns are analyzed. By collecting behavioral history and location information, a more detailed twin AI can be generated.

[0092] The data collection unit can use the emotion estimation function to analyze the user's emotions in real time when entering data and generate questions that elicit positive emotions. For example, if the user is tired, it adds questions to help them relax. The data collection unit also captures the user's facial expressions with a camera and analyzes their emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on changes in facial expressions and generates questions that elicit positive emotions. The data collection unit also records the user's voice and estimates their emotions using voice analysis technology. For example, it analyzes the tone and speed of the voice and generates questions that elicit positive emotions. The data collection unit also collects the user's biometric data (heart rate and electrodermal activity) with a sensor and analyzes their emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on fluctuations in heart rate and generates questions that elicit positive emotions. This generates questions that elicit positive emotions, improving the user's data entry experience.

[0093] The Twin AI generation unit can analyze the user's emotions in real time and provide self-analysis results based on their emotions. For example, if the user is feeling stressed, it will suggest the cause of the stress and countermeasures. The Twin AI generation unit also captures the user's facial expressions with a camera and analyzes their emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on changes in facial expressions and provides self-analysis results. The Twin AI generation unit also records the user's voice and estimates their emotions using voice analysis technology. For example, it analyzes the tone and speed of the voice and provides self-analysis results. The Twin AI generation unit also collects the user's biometric data (heart rate and electrodermal activity) with a sensor and analyzes their emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on heart rate fluctuations and provides self-analysis results. This allows the user to deepen their self-understanding by providing self-analysis results based on their emotions.

[0094] The twin AI generation unit adapts the self-analysis results to different languages ​​and cultural spheres, allowing the generation unit to obtain feedback from a global perspective. For example, the twin AI generation unit translates the self-analysis results into different languages ​​and collects feedback from a global perspective. For example, it supports multiple languages ​​such as English, French, and Chinese. The twin AI generation unit also adapts the self-analysis results to different cultural spheres and collects feedback that takes cultural background into consideration. For example, it takes into account cultural characteristics such as those of Asian countries and Western countries. The twin AI generation unit also adapts the self-analysis results to different regions and collects region-specific feedback. For example, it collects different feedback between urban and rural areas. The twin AI generation unit also adapts the self-analysis results to different industries and occupations and collects industry-specific feedback. For example, it collects different feedback between the IT industry and the manufacturing industry. In this way, by adapting to different languages ​​and cultural spheres, feedback from a global perspective can be obtained.

[0095] The Twin AI generation unit converts the self-analysis results into a visual note or mind map, making them easier to understand visually. For example, the generation unit converts the self-analysis results into a visual note, providing them in a format that is visually easy for the user to understand. For example, important points are indicated using diagrams and icons. The Twin AI generation unit also converts the self-analysis results into a mind map, providing them in a format that makes it easy for the user to organize information. For example, the self-analysis results are displayed in a tree structure. The Twin AI generation unit also converts the self-analysis results into infographics, providing them in a format that is intuitively easy for the user to understand. For example, the information is visualized using graphs and charts. The Twin AI generation unit also converts the self-analysis results into a presentation format, providing them in a format that makes it easy for the user to explain to others. For example, the information is organized in a slide format. In this way, by converting them into a visual note or mind map, the self-analysis results can be made easier to understand visually.

[0096] The twin AI generation unit uses an emotion estimation function to collect the user's emotional responses to the self-analysis results, and the generation unit can improve the accuracy of the analysis results based on that data. For example, the unit can collect the user's emotional responses to the self-analysis results in real time and improve the accuracy of the analysis results based on that data. For example, it can prioritize analysis results with a high number of positive responses. The twin AI generation unit also captures the user's facial expressions with a camera and analyzes their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on changes in facial expressions to improve the accuracy of the analysis results. The twin AI generation unit also records the user's voice and estimates their emotions using voice analysis technology. For example, it can analyze the tone and speed of the voice to improve the accuracy of the analysis results. The twin AI generation unit also collects the user's biometric data (heart rate and electrodermal activity) with a sensor and analyzes their emotions using an emotion estimation algorithm. For example, it can calculate an emotion score based on heart rate fluctuations to improve the accuracy of the analysis results. In this way, by collecting emotional responses, the accuracy of the analysis results can be improved.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The data collection unit collects user data. For example, users enter their own data through Google Forms or questionnaires. The data collection unit can also collect data such as user profile information, health information, work history, hobbies, and preferences. Step 2: The generation unit analyzes the data collected by the data collection unit. For example, the generation AI analyzes the user's data using data mining techniques and machine learning algorithms. The generation AI also performs analysis to generate twin AIs based on the user's data. Step 3: The twin AI generation unit generates the twin AI based on the data analyzed by the generation unit. For example, the generation AI generates the twin AI using digital twin technology or a simulation model. The generation AI also receives a prompt for generating the twin AI based on the user's data, and generates the twin AI based on the prompt.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0135] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0139] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0140] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0143] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0147] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0149] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0150] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0151] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0152] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0153] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0154] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0155] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0156] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0157] 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.

[0158] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0159] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0160] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0161] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0162] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0163] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0164] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0165] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection unit that collects user data; a generation unit that analyzes the data collected by the data collection unit; a twin AI generation unit that generates twin AI based on the data analyzed by the generation unit. A system characterized by:

2. The data collection unit Add voice input and image recognition capabilities, allowing users to provide data through voice or image input.

2. The system of claim 1.

3. The generation unit Automatically completes some of the data based on the data entered by the user to generate a more detailed twin AI 2. The system of claim 1.

4. The twin AI generation unit Analyzes user emotions in real time and provides self-analysis results based on emotions 2. The system of claim 1.

5. The twin AI generation unit The emotion of the patient is analyzed, and the generation unit Providing emotionally informed diagnostic results and treatment recommendations 2. The system of claim 1.

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