system

Abstract

The system according to this embodiment aims to automate tasks and decisions by simulating multiple different personalities, perspectives, and skill sets of the user. [Solution] The system according to the embodiment comprises a data conversion unit, a learning unit, a simulation unit, and an automation unit. The data conversion unit converts the user's way of thinking, judgment criteria, work style, and user characteristics into data. The learning unit learns the user's past behavior and awareness based on the information converted into data by the data conversion unit. The simulation unit simulates multiple different personalities, perspectives, and skill sets of the user based on the information learned by the learning unit. The automation unit automates tasks and decisions based on the information simulated by the simulation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it has not been sufficiently carried out to simultaneously simulate a plurality of different personalities, viewpoints, and skill sets of a user and automate tasks and decisions.

[0005] The system according to the embodiment aims to simulate a plurality of different personalities, viewpoints, and skill sets of a user and automate tasks and decisions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data conversion unit, a learning unit, a simulation unit, and an automation unit. The data conversion unit converts the user's way of thinking, judgment criteria, work style, and individual characteristics into data. The learning unit learns the user's past behavior and awareness based on the information converted into data by the data conversion unit. The simulation unit simulates multiple different personalities, perspectives, and skill sets of the user based on the information learned by the learning unit. The automation unit automates tasks and decisions based on the information simulated by the simulation unit. [Effects of the Invention]

[0007] The system according to this embodiment can simulate multiple different personalities, perspectives, and skill sets of users, and automate tasks and decisions. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​meta-conscious agent system according to an embodiment of the present invention is a system that simulates multiple different personalities, perspectives, and skill sets of a user, and each "self" processes different tasks in different places and times. This system digitizes the user's way of thinking, judgment criteria, work style, and individuality, and the AI ​​agent learns the user's past actions and consciousness to simulate multiple different personalities, perspectives, and skill sets of the user. As a result, "diverse versions" of the user are generated, each capable of processing different tasks in different places and times. For example, when the user is busy, the AI ​​can have "one self" participate in a business meeting and "another self" focus on household chores. Because this system allows the user to maximize their time when they are busy, the AI ​​can have "one self" participate in a business meeting and "another self" focus on household chores. Furthermore, because the AI ​​learns the user's past actions and consciousness and automates tasks and decisions by simultaneously utilizing multiple "diverse versions" of the user, the user's life can be optimized to the fullest extent. For example, the AI ​​meta-conscious agent system digitizes the user's way of thinking, judgment criteria, work style, and individuality. For example, it learns the user's past behavior and consciousness, and simulates multiple different personalities, perspectives, and skill sets of the user. For instance, when the user is busy, the AI ​​can have "one version of themselves" attend a business meeting and "another version of themselves" focus on household chores. In this way, the AI ​​meta-conscious agent system can optimize the user's life to the fullest extent and achieve things that are physically impossible.

[0029] The AI ​​meta-conscious agent system according to this embodiment comprises a data conversion unit, a learning unit, a simulation unit, and an automation unit. The data conversion unit converts thinking, judgment criteria, work style, and user characteristics into data. For example, the data conversion unit can convert the user's thinking into data. For example, the data conversion unit can convert the user's problem-solving approach, values, and beliefs into data. The data conversion unit can also convert the user's judgment criteria into data. For example, the data conversion unit can convert the user's decision-making process and evaluation criteria into data. The data conversion unit can also convert the user's work style into data. For example, the data conversion unit can convert the user's task management method and communication style into data. The data conversion unit can also convert user characteristics into data. For example, the data conversion unit can convert the user's personal characteristics, behavioral patterns, and preferences into data. The learning unit learns the user's past behavior and consciousness based on the information converted into data by the data conversion unit. For example, the learning unit learns the user's past behavior. For example, the learning unit can analyze the user's past behavioral history and learn the user's behavioral patterns. Furthermore, the learning unit can also learn the user's past consciousness. For example, the learning unit can analyze changes in the user's past consciousness and learn the user's consciousness trends. The simulation unit simulates multiple different personalities, perspectives, and skill sets of the user based on the information learned by the learning unit. For example, the simulation unit can simulate multiple different personalities of the user, and each personality can handle different tasks. The simulation unit can also simulate multiple different perspectives of the user. For example, the simulation unit can simulate different perspectives of the user, and each perspective can handle different tasks. The simulation unit can also simulate multiple different skill sets of the user. For example, the simulation unit can simulate different skill sets of the user, and each skill set can handle different tasks.The automation unit automates tasks and decisions based on information simulated by the simulation unit. For example, the automation unit can automate tasks. For example, the automation unit can automate a user's tasks based on information simulated by the simulation unit. The automation unit can also automate decisions. For example, the automation unit can automate a user's decisions based on information simulated by the simulation unit. As a result, the AI ​​meta-conscious agent system according to the embodiment can simulate multiple different personalities, perspectives, and skill sets of the user, and each "self" can process different tasks in different places and times. Some or all of the above-described processing in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can automate tasks and decisions using an AI model that takes information simulated by the simulation unit as input and outputs tasks and decisions.

[0030] The Data Collection Department digitizes users' thinking, decision-making criteria, work styles, and individual characteristics. Specifically, to digitize users' problem-solving approaches, values, and beliefs, it collects and analyzes users' past decision-making and behavioral history. For example, it meticulously records what decisions users made in what situations and what values ​​guided their actions, and stores this data in a database. Furthermore, to digitize users' decision-making processes and evaluation criteria, it meticulously analyzes what criteria users use to evaluate things and what processes they follow to make decisions. This includes identifying the evaluation metrics and decision-making criteria used by users and recording them as data. In addition, to digitize users' task management methods and communication styles, it observes how users manage tasks and how they communicate with others, and records this data. For example, it meticulously records the task management tools, communication methods, frequency, and methods used by users. Furthermore, to digitize users' individual characteristics, behavioral patterns, and preferences, it collects users' behavioral history and preferences and stores this data in a database. This includes meticulously recording what behaviors users prefer and in what patterns they act. This allows the data processing unit to digitize diverse user information in detail and provide a foundation for building the user's meta-awareness based on this data.

[0031] The learning unit learns the user's past behavior and awareness based on the information digitized by the data digitization unit. Specifically, it uses machine learning algorithms to analyze the user's past behavioral history and learn the user's behavioral patterns. For example, it analyzes what actions the user took in different situations in the past and identifies the user's behavioral patterns based on this. This involves treating the user's behavioral history as time-series data and extracting behavioral trends and patterns based on this data. It also uses natural language processing techniques to analyze the user's past changes in awareness and learn the user's trends in awareness. For example, it analyzes what changes in awareness the user has experienced in the past and identifies the user's trends in awareness based on this. This involves analyzing the user's past statements and writings and identifying changes in awareness based on this data. Furthermore, the learning unit can learn the user's behavior and awareness changes in real time and update the user's meta-awareness based on this. For example, it can learn the changes in behavior and awareness when the user faces a new situation in real time and update the user's meta-awareness based on this. In this way, the learning unit can learn the user's past behavior and awareness in detail and provide a foundation for building the user's meta-awareness based on this.

[0032] The simulation unit simulates multiple different personalities, perspectives, and skill sets of a user based on information learned by the learning unit. Specifically, to simulate different personalities of a user, it constructs different personality models based on the learned information. For example, it simulates what kind of personality a user has in different situations and reproduces how each personality handles different tasks. This includes identifying and modeling the characteristics of different personalities based on the user's behavioral patterns and tendencies of consciousness. Furthermore, to simulate different perspectives of a user, it constructs different perspective models based on the learned information. For example, it simulates how a user perceives things from different perspectives and reproduces how each perspective handles different tasks. This includes identifying and modeling the characteristics of different perspectives based on the user's values ​​and beliefs. In addition, to simulate different skill sets of a user, it constructs different skill set models based on the learned information. For example, it simulates how a user handles tasks when they have different skill sets and reproduces how each skill set handles different tasks. This includes identifying and modeling the characteristics of different skill sets based on the user's past behavioral history and skill acquisition status. This allows the simulation unit to simulate multiple different personalities, perspectives, and skill sets of the user in detail, and to provide a foundation for building the user's meta-consciousness based on this.

[0033] The automation unit automates tasks and decisions based on information simulated by the simulation unit. Specifically, it uses AI models to automate user tasks based on simulated information. For example, it automates user tasks based on different personalities, perspectives, and skill sets simulated by the simulation unit. This includes identifying and automating the optimal task processing method based on the user's behavioral patterns and tendencies of consciousness. It also uses AI models to automate user decisions based on simulated information. For example, it automates user decision-making based on different personalities, perspectives, and skill sets simulated by the simulation unit. This includes identifying and automating the optimal decision-making method based on the user's judgment criteria and evaluation metrics. Furthermore, the automation unit can continuously improve the automation of tasks and decisions based on information updated in real time. For example, it learns changes in user behavior and consciousness in real time and updates the automation methods for tasks and decisions based on this. As a result, the automation unit can automate tasks and decisions in detail based on multiple different personalities, perspectives, and skill sets of the user, and provide a foundation for building the user's meta-consciousness.

[0034] The automation unit includes a meeting participation unit that allows the AI ​​to participate in business meetings. The meeting participation unit can, for example, participate in a business meeting on behalf of the user when the user is unable to attend. For example, the meeting participation unit can grasp the meeting agenda on behalf of the user and speak in accordance with the progress of the meeting. The meeting participation unit can also create meeting minutes on behalf of the user. For example, the meeting participation unit can record the content of the meeting and compile it into minutes. The meeting participation unit can also report the results of the meeting on behalf of the user. For example, the meeting participation unit can report the results of the meeting to the user and suggest necessary actions. This allows the AI ​​to participate in business meetings on behalf of the user when the user is busy. Some or all of the above processes in the meeting participation unit may be performed using AI, for example, or not using AI. For example, the meeting participation unit can input the meeting agenda and progress into a generating AI and have the generating AI perform tasks such as speaking in accordance with the progress of the meeting and creating meeting minutes.

[0035] The automation unit includes a chore-focusing unit that concentrates on household tasks. The chore-focusing unit can, for example, perform household tasks on behalf of the user when the user is doing them. For example, the chore-focusing unit can clean on behalf of the user. It can also cook on behalf of the user. For example, it can prepare ingredients and cook meals on behalf of the user. It can also do laundry on behalf of the user. For example, it can wash and dry laundry on behalf of the user. This allows the AI ​​to concentrate on household tasks when the user is busy. Some or all of the above-mentioned processes in the chore-focusing unit may be performed using AI, for example, or without AI. For example, the chore-focusing unit can input household tasks into a generation AI and have the generation AI perform the household tasks.

[0036] The data conversion unit improves the accuracy of data conversion by referring to the user's past behavior history during the data conversion process. For example, the data conversion unit can refer to the history of tasks the user has performed in the past and convert similar tasks into data. The data conversion unit can also analyze the user's past behavior patterns and propose the optimal data conversion method. For example, the data conversion unit can analyze the user's past behavior patterns and propose the optimal data conversion method. The data conversion unit can also analyze tasks the user has failed at in the past and perform data conversion that reflects improvements. For example, the data conversion unit can analyze tasks the user has failed at in the past and perform data conversion that reflects improvements. This improves the accuracy of data conversion by referring to the user's past behavior history. Some or all of the above processing in the data conversion unit may be performed using AI, for example, or without AI. For example, the data conversion unit can input the user's past behavior history into a generating AI and have the generating AI perform the data conversion accuracy improvement.

[0037] The data conversion unit customizes the content of the data conversion based on the user's current living situation and areas of interest. For example, the data conversion unit can prioritize the conversion of information related to projects the user is currently working on. The data conversion unit can also convert relevant information based on the user's areas of interest. For example, the data conversion unit can convert relevant information based on the user's areas of interest. The data conversion unit can also convert necessary information according to the user's living situation. For example, the data conversion unit can convert necessary information according to the user's living situation. This allows for more appropriate data conversion by customizing the content of the data conversion based on the user's current living situation and areas of interest. Some or all of the above processing in the data conversion unit may be performed using AI, for example, or without AI. For example, the data conversion unit can input the user's current living situation and areas of interest into a generating AI and have the generating AI perform the customization of the data conversion content.

[0038] The data conversion unit prioritizes the conversion of highly relevant data, taking into account the user's geographical location information. For example, the data conversion unit can prioritize the conversion of information related to the user's current location. The data conversion unit can also convert relevant data based on the user's geographical location information. For example, the data conversion unit can convert relevant data based on the user's geographical location information. Furthermore, if the user is on the move, the data conversion unit can convert information related to their destination. For example, if the user is on the move, the data conversion unit can convert information related to their destination. This allows for more appropriate data conversion by prioritizing the conversion of highly relevant data while considering the user's geographical location information. Some or all of the above processing in the data conversion unit may be performed using AI, for example, or without AI. For example, the data conversion unit can input the user's geographical location information into a generating AI and have the generating AI perform the priority conversion of highly relevant data.

[0039] The data conversion unit analyzes the user's social media activity and converts the relevant data into data. For example, the data conversion unit can convert information shared by the user on social media into data. The data conversion unit can also convert relevant data based on the user's social media activity. For example, the data conversion unit can convert relevant data based on the user's social media activity. The data conversion unit can also convert information that the user has shown interest in on social media. For example, the data conversion unit can convert information that the user has shown interest in on social media into data. This allows for more appropriate data conversion by analyzing the user's social media activity and converting the relevant data into data. Some or all of the above processing in the data conversion unit may be performed using AI, for example, or without AI. For example, the data conversion unit can input the user's social media activity into a generating AI and have the generating AI perform the data conversion of the relevant data.

[0040] The learning unit optimizes the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can analyze past learning data and select the optimal learning algorithm. The learning unit can also improve the learning algorithm based on past learning data. For example, the learning unit can improve the learning algorithm based on past learning data. The learning unit can also improve the accuracy of the learning algorithm by referring to past learning data. For example, the learning unit can improve the accuracy of the learning algorithm by referring to past learning data. As a result, the accuracy of learning is improved by optimizing the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0041] The learning unit customizes the learning content based on the user's behavior patterns during learning. For example, the learning unit can analyze the user's behavior patterns and propose the optimal learning content. The learning unit can also customize the learning content based on the user's behavior patterns. For example, the learning unit can customize the learning content based on the user's behavior patterns. The learning unit can also optimize the learning content by referring to the user's behavior patterns. For example, the learning unit can optimize the learning content by referring to the user's behavior patterns. This makes it possible to perform more appropriate learning by customizing the learning content based on the user's behavior patterns. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's behavior patterns into a generating AI and have the generating AI perform the customization of the learning content.

[0042] The learning unit selects training data while considering the user's geographical location information during training. For example, the learning unit can learn data related to the user's current location. The learning unit can also select relevant training data based on the user's geographical location information. For example, the learning unit can select relevant training data based on the user's geographical location information. Furthermore, if the user is on the move, the learning unit can learn data related to their destination. For example, if the user is on the move, the learning unit can learn data related to their destination. This allows for more appropriate training by selecting training data while considering the user's geographical location information. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's geographical location information into a generating AI and have the generating AI perform the selection of training data.

[0043] The learning unit analyzes the user's social media activity and learns relevant data during the learning process. For example, the learning unit can learn information shared by the user on social media. The learning unit can also learn relevant data based on the user's social media activity. For example, the learning unit can learn information that the user has shown interest in on social media. This allows for more appropriate learning by analyzing the user's social media activity and learning relevant data. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's social media activity into a generating AI and have the generating AI perform the learning of relevant data.

[0044] The simulation unit improves the accuracy of the simulation by referring to the user's past behavior history during the simulation. For example, the simulation unit can refer to the history of tasks the user has performed in the past and simulate similar tasks. The simulation unit can also analyze the user's past behavior patterns and propose the optimal simulation method. For example, the simulation unit can analyze the user's past behavior patterns and propose the optimal simulation method. The simulation unit can also analyze tasks the user has failed at in the past and perform simulations that reflect improvements. For example, the simulation unit can analyze tasks the user has failed at in the past and perform simulations that reflect improvements. By improving the accuracy of the simulation by referring to the user's past behavior history, more appropriate simulations become possible. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's past behavior history into a generating AI and have the generating AI perform the simulation accuracy improvement.

[0045] The simulation unit customizes the simulation content based on the user's current living situation and areas of interest during the simulation. For example, the simulation unit can perform simulations related to a project the user is currently working on. The simulation unit can also perform relevant simulations based on the user's areas of interest. For example, the simulation unit can perform relevant simulations based on the user's areas of interest. The simulation unit can also perform necessary simulations according to the user's living situation. For example, the simulation unit can perform necessary simulations according to the user's living situation. This allows for more appropriate simulations by customizing the simulation content based on the user's current living situation and areas of interest. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's current living situation and areas of interest into a generating AI and have the generating AI perform the customization of the simulation content.

[0046] The simulation unit adjusts the simulation content while considering the user's geographical location information. For example, the simulation unit can perform simulations related to the user's current location. It can also perform simulations related to the user's geographical location information. For example, the simulation unit can perform simulations related to the user's geographical location information. Furthermore, if the user is on the move, the simulation unit can perform simulations related to their destination. For example, if the user is on the move, the simulation unit can perform simulations related to their destination. By adjusting the simulation content while considering the user's geographical location information, a more appropriate simulation becomes possible. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's geographical location information into a generating AI and have the generating AI perform the adjustment of the simulation content.

[0047] The simulation unit analyzes the user's social media activity during the simulation and reflects the relevant data in the simulation. For example, the simulation unit can reflect information shared by the user on social media in the simulation. The simulation unit can also perform relevant simulations based on the user's social media activity. For example, the simulation unit can perform relevant simulations based on the user's social media activity. The simulation unit can also reflect information that the user has shown interest in on social media in the simulation. For example, the simulation unit can reflect information that the user has shown interest in on social media in the simulation. By analyzing the user's social media activity and reflecting the relevant data in the simulation, a more appropriate simulation becomes possible. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's social media activity into a generating AI and have the generating AI execute a simulation of the relevant data.

[0048] The automation unit improves the accuracy of automation by referring to the user's past behavior history during automation. For example, the automation unit can refer to the history of tasks the user has performed in the past and automate similar tasks. The automation unit can also analyze the user's past behavior patterns and propose the optimal automation method. For example, the automation unit can analyze the user's past behavior patterns and propose the optimal automation method. The automation unit can also analyze tasks the user has failed at in the past and perform automation that reflects improvements. For example, the automation unit can analyze tasks the user has failed at in the past and perform automation that reflects improvements. By improving the accuracy of automation by referring to the user's past behavior history, more appropriate automation becomes possible. Some or all of the above processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input the user's past behavior history into a generating AI and have the generating AI perform the automation accuracy improvement.

[0049] The automation unit customizes the content of the automation based on the user's current living situation and areas of interest. For example, the automation unit can automate tasks related to a project the user is currently working on. The automation unit can also automate related tasks based on the user's areas of interest. For example, the automation unit can automate related tasks based on the user's areas of interest. The automation unit can also automate tasks that are necessary depending on the user's living situation. For example, the automation unit can automate tasks that are necessary depending on the user's living situation. This allows for more appropriate automation by customizing the content of the automation based on the user's current living situation and areas of interest. Some or all of the above processing in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input the user's current living situation and areas of interest into a generating AI and have the generating AI perform the customization of the automation content.

[0050] The automation unit adjusts the content of the automation process by taking into account the user's geographical location information. For example, the automation unit can automate tasks related to the user's current location. The automation unit can also automate relevant tasks based on the user's geographical location information. For example, the automation unit can automate relevant tasks based on the user's geographical location information. Furthermore, if the user is on the move, the automation unit can automate tasks related to their destination. For example, if the user is on the move, the automation unit can automate tasks related to their destination. By adjusting the content of the automation process by taking into account the user's geographical location information, more appropriate automation becomes possible. Some or all of the above-described processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input the user's geographical location information into a generating AI and have the generating AI perform the adjustment of the automation content.

[0051] The automation unit analyzes the user's social media activity during automation and incorporates relevant data into the automation process. For example, the automation unit can incorporate information shared by the user on social media into the automation process. The automation unit can also automate related tasks based on the user's social media activity. For example, the automation unit can automate related tasks based on the user's social media activity. The automation unit can also incorporate information that the user has shown interest in on social media into the automation process. For example, the automation unit can incorporate information that the user has shown interest in on social media into the automation process. By analyzing the user's social media activity and incorporating relevant data into the automation process, more appropriate automation becomes possible. Some or all of the above-described processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input the user's social media activity into a generating AI and have the generating AI perform the automation of the related data.

[0052] The meeting participation unit, when a user joins a meeting, selects the optimal participation method by referring to the user's past meeting history. For example, the meeting participation unit can refer to the user's past meeting history and allow them to join similar meetings. The meeting participation unit can also analyze the user's past meeting history and suggest the optimal participation method. For example, the meeting participation unit can analyze the user's past meeting history and suggest the optimal participation method. For example, the meeting participation unit can analyze past meetings where the user failed and select a participation method that reflects improvements. For example, the meeting participation unit can analyze past meetings where the user failed and select a participation method that reflects improvements. By referring to the user's past meeting history and selecting the optimal participation method, more appropriate meeting participation becomes possible. Some or all of the above processing in the meeting participation unit may be performed using AI, for example, or without AI. For example, the meeting participation unit can input the user's past meeting history into a generating AI and have the generating AI select the optimal participation method.

[0053] The meeting participation unit selects the optimal participation method when a user joins a meeting, taking into account the user's geographical location. For example, the meeting participation unit can allow the user to join a meeting related to their current location. It can also allow the meeting participation unit to allow the user to join a meeting related to their destination if the user is on the move. By selecting the optimal participation method while considering the user's geographical location, more appropriate meeting participation becomes possible. Some or all of the above processing in the meeting participation unit may be performed using AI, for example, or without AI. For example, the meeting participation unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal participation method.

[0054] The household chore concentration unit selects the optimal concentration method by referring to the user's past household chore history when household chores are concentrated. For example, the household chore concentration unit can refer to the user's past household chore history and concentrate on similar chores. The household chore concentration unit can also analyze the user's past household chore history and suggest the optimal concentration method. For example, the household chore concentration unit can analyze the user's past household chore history and suggest the optimal concentration method. For example, the household chore concentration unit can analyze the user's past household chore failures and select a concentration method that reflects improvements. For example, the household chore concentration unit can analyze the user's past household chore failures and select a concentration method that reflects improvements. By selecting the optimal concentration method by referring to the user's past household chore history, more appropriate household chore concentration becomes possible. Some or all of the above processing in the household chore concentration unit may be performed using AI, for example, or without AI. For example, the household chore concentration unit can input the user's past household chore history into a generating AI and have the generating AI select the optimal concentration method.

[0055] The household chore concentration unit selects the optimal concentration method when household chores are being concentrated, taking into account the user's geographical location information. For example, the household chore concentration unit can concentrate on household chores related to the user's current location. The household chore concentration unit can also concentrate on household chores related to the user's geographical location information. For example, the household chore concentration unit can concentrate on household chores related to the user's geographical location information. Furthermore, if the user is on the move, the household chore concentration unit can concentrate on household chores related to the user's destination. For example, if the user is on the move, the household chore concentration unit can concentrate on household chores related to the user's destination. This allows for more appropriate household chore concentration by selecting the optimal concentration method while considering the user's geographical location information. Some or all of the above processing in the household chore concentration unit may be performed using AI, for example, or without AI. For example, the household chore concentration unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal concentration method.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The data processing unit can monitor the user's health status and adjust the data processing content based on that status. For example, if the user is fatigued, the data processing unit can prioritize processing tasks related to rest. If the user is healthy, the data processing unit can process more complex tasks. Furthermore, if the user is ill, the data processing unit can process tasks related to health management. This enables appropriate data processing according to the user's health status.

[0058] The learning unit can analyze the user's sleep patterns and suggest the optimal learning time. For example, if the user is a night owl, the learning unit can suggest studying at night. If the user is an early riser, the learning unit can suggest studying in the morning. Furthermore, if the user has irregular sleep patterns, the learning unit can adjust the learning timing to match the user's sleep patterns. This enables optimal learning based on the user's sleep patterns.

[0059] The simulation unit can refer to the user's eating history and perform simulations related to meals. For example, it can simulate a healthy meal plan based on data from meals the user has eaten in the past. Furthermore, if the user has an allergy to a specific food, it can simulate a meal plan that avoids that food. Additionally, if the user is on a diet, it can simulate a meal plan that takes calorie restriction into account. This enables the simulation of an appropriate meal plan based on the user's eating history.

[0060] The automation unit can refer to the user's exercise history and automate exercise-related tasks. For example, it can automate the creation of an optimal exercise plan based on data from the user's past exercises. Furthermore, if the user prefers a particular exercise, it can automate a plan that includes that exercise. Additionally, if the user is injured, it can automate an exercise plan related to rehabilitation. This enables the automation of appropriate exercise plans based on the user's exercise history.

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

[0062] Step 1: The data entry department digitizes thinking, judgment criteria, work methods, and user characteristics. For example, it digitizes the user's problem-solving approach, values, beliefs, decision-making process, evaluation criteria, task management methods, communication style, personal characteristics, behavioral patterns, and preferences. Step 2: The learning unit learns the user's past behavior and attitudes based on the information digitized by the data digitization unit. For example, it analyzes the user's past behavioral history to learn behavioral patterns, or analyzes past changes in attitudes to learn attitude trends. Step 3: The simulation unit simulates multiple different personalities, perspectives, and skill sets of the user based on the information learned by the learning unit. For example, it simulates different personalities, perspectives, and skill sets, each handling different tasks. Step 4: The automation unit automates tasks and decisions based on the information simulated by the simulation unit. For example, automation is performed using an AI model that takes simulated information as input and outputs tasks and decisions.

[0063] (Example of form 2) The AI ​​meta-conscious agent system according to an embodiment of the present invention is a system that simulates multiple different personalities, perspectives, and skill sets of a user, and each "self" processes different tasks in different places and times. This system digitizes the user's way of thinking, judgment criteria, work style, and individuality, and the AI ​​agent learns the user's past actions and consciousness to simulate multiple different personalities, perspectives, and skill sets of the user. As a result, "diverse versions" of the user are generated, each capable of processing different tasks in different places and times. For example, when the user is busy, the AI ​​can have "one self" participate in a business meeting and "another self" focus on household chores. Because this system allows the user to maximize their time when they are busy, the AI ​​can have "one self" participate in a business meeting and "another self" focus on household chores. Furthermore, because the AI ​​learns the user's past actions and consciousness and automates tasks and decisions by simultaneously utilizing multiple "diverse versions" of the user, the user's life can be optimized to the fullest extent. For example, the AI ​​meta-conscious agent system digitizes the user's way of thinking, judgment criteria, work style, and individuality. For example, it learns the user's past behavior and consciousness, and simulates multiple different personalities, perspectives, and skill sets of the user. For instance, when the user is busy, the AI ​​can have "one version of themselves" attend a business meeting and "another version of themselves" focus on household chores. In this way, the AI ​​meta-conscious agent system can optimize the user's life to the fullest extent and achieve things that are physically impossible.

[0064] The AI ​​meta-conscious agent system according to this embodiment comprises a data conversion unit, a learning unit, a simulation unit, and an automation unit. The data conversion unit converts thinking, judgment criteria, work style, and user characteristics into data. For example, the data conversion unit can convert the user's thinking into data. For example, the data conversion unit can convert the user's problem-solving approach, values, and beliefs into data. The data conversion unit can also convert the user's judgment criteria into data. For example, the data conversion unit can convert the user's decision-making process and evaluation criteria into data. The data conversion unit can also convert the user's work style into data. For example, the data conversion unit can convert the user's task management method and communication style into data. The data conversion unit can also convert user characteristics into data. For example, the data conversion unit can convert the user's personal characteristics, behavioral patterns, and preferences into data. The learning unit learns the user's past behavior and consciousness based on the information converted into data by the data conversion unit. For example, the learning unit learns the user's past behavior. For example, the learning unit can analyze the user's past behavioral history and learn the user's behavioral patterns. Furthermore, the learning unit can also learn the user's past consciousness. For example, the learning unit can analyze changes in the user's past consciousness and learn the user's consciousness trends. The simulation unit simulates multiple different personalities, perspectives, and skill sets of the user based on the information learned by the learning unit. For example, the simulation unit can simulate multiple different personalities of the user, and each personality can handle different tasks. The simulation unit can also simulate multiple different perspectives of the user. For example, the simulation unit can simulate different perspectives of the user, and each perspective can handle different tasks. The simulation unit can also simulate multiple different skill sets of the user. For example, the simulation unit can simulate different skill sets of the user, and each skill set can handle different tasks.The automation unit automates tasks and decisions based on information simulated by the simulation unit. For example, the automation unit can automate tasks. For example, the automation unit can automate a user's tasks based on information simulated by the simulation unit. The automation unit can also automate decisions. For example, the automation unit can automate a user's decisions based on information simulated by the simulation unit. As a result, the AI ​​meta-conscious agent system according to the embodiment can simulate multiple different personalities, perspectives, and skill sets of the user, and each "self" can process different tasks in different places and times. Some or all of the above-described processing in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can automate tasks and decisions using an AI model that takes information simulated by the simulation unit as input and outputs tasks and decisions.

[0065] The Data Collection Department digitizes users' thinking, decision-making criteria, work styles, and individual characteristics. Specifically, to digitize users' problem-solving approaches, values, and beliefs, it collects and analyzes users' past decision-making and behavioral history. For example, it meticulously records what decisions users made in what situations and what values ​​guided their actions, and stores this data in a database. Furthermore, to digitize users' decision-making processes and evaluation criteria, it meticulously analyzes what criteria users use to evaluate things and what processes they follow to make decisions. This includes identifying the evaluation metrics and decision-making criteria used by users and recording them as data. In addition, to digitize users' task management methods and communication styles, it observes how users manage tasks and how they communicate with others, and records this data. For example, it meticulously records the task management tools, communication methods, frequency, and methods used by users. Furthermore, to digitize users' individual characteristics, behavioral patterns, and preferences, it collects users' behavioral history and preferences and stores this data in a database. This includes meticulously recording what behaviors users prefer and in what patterns they act. This allows the data processing unit to digitize diverse user information in detail and provide a foundation for building the user's meta-awareness based on this data.

[0066] The learning unit learns the user's past behavior and awareness based on the information digitized by the data digitization unit. Specifically, it uses machine learning algorithms to analyze the user's past behavioral history and learn the user's behavioral patterns. For example, it analyzes what actions the user took in different situations in the past and identifies the user's behavioral patterns based on this. This involves treating the user's behavioral history as time-series data and extracting behavioral trends and patterns based on this data. It also uses natural language processing techniques to analyze the user's past changes in awareness and learn the user's trends in awareness. For example, it analyzes what changes in awareness the user has experienced in the past and identifies the user's trends in awareness based on this. This involves analyzing the user's past statements and writings and identifying changes in awareness based on this data. Furthermore, the learning unit can learn the user's behavior and awareness changes in real time and update the user's meta-awareness based on this. For example, it can learn the changes in behavior and awareness when the user faces a new situation in real time and update the user's meta-awareness based on this. In this way, the learning unit can learn the user's past behavior and awareness in detail and provide a foundation for building the user's meta-awareness based on this.

[0067] The simulation unit simulates multiple different personalities, perspectives, and skill sets of a user based on information learned by the learning unit. Specifically, to simulate different personalities of a user, it constructs different personality models based on the learned information. For example, it simulates what kind of personality a user has in different situations and reproduces how each personality handles different tasks. This includes identifying and modeling the characteristics of different personalities based on the user's behavioral patterns and tendencies of consciousness. Furthermore, to simulate different perspectives of a user, it constructs different perspective models based on the learned information. For example, it simulates how a user perceives things from different perspectives and reproduces how each perspective handles different tasks. This includes identifying and modeling the characteristics of different perspectives based on the user's values ​​and beliefs. In addition, to simulate different skill sets of a user, it constructs different skill set models based on the learned information. For example, it simulates how a user handles tasks when they have different skill sets and reproduces how each skill set handles different tasks. This includes identifying and modeling the characteristics of different skill sets based on the user's past behavioral history and skill acquisition status. This allows the simulation unit to simulate multiple different personalities, perspectives, and skill sets of the user in detail, and to provide a foundation for building the user's meta-consciousness based on this.

[0068] The automation unit automates tasks and decisions based on information simulated by the simulation unit. Specifically, it uses AI models to automate user tasks based on simulated information. For example, it automates user tasks based on different personalities, perspectives, and skill sets simulated by the simulation unit. This includes identifying and automating the optimal task processing method based on the user's behavioral patterns and tendencies of consciousness. It also uses AI models to automate user decisions based on simulated information. For example, it automates user decision-making based on different personalities, perspectives, and skill sets simulated by the simulation unit. This includes identifying and automating the optimal decision-making method based on the user's judgment criteria and evaluation metrics. Furthermore, the automation unit can continuously improve the automation of tasks and decisions based on information updated in real time. For example, it learns changes in user behavior and consciousness in real time and updates the automation methods for tasks and decisions based on this. As a result, the automation unit can automate tasks and decisions in detail based on multiple different personalities, perspectives, and skill sets of the user, and provide a foundation for building the user's meta-consciousness.

[0069] The automation unit includes a meeting participation unit that allows the AI ​​to participate in business meetings. The meeting participation unit can, for example, participate in a business meeting on behalf of the user when the user is unable to attend. For example, the meeting participation unit can grasp the meeting agenda on behalf of the user and speak in accordance with the progress of the meeting. The meeting participation unit can also create meeting minutes on behalf of the user. For example, the meeting participation unit can record the content of the meeting and compile it into minutes. The meeting participation unit can also report the results of the meeting on behalf of the user. For example, the meeting participation unit can report the results of the meeting to the user and suggest necessary actions. This allows the AI ​​to participate in business meetings on behalf of the user when the user is busy. Some or all of the above processes in the meeting participation unit may be performed using AI, for example, or not using AI. For example, the meeting participation unit can input the meeting agenda and progress into a generating AI and have the generating AI perform tasks such as speaking in accordance with the progress of the meeting and creating meeting minutes.

[0070] The automation unit includes a chore-focusing unit that concentrates on household tasks. The chore-focusing unit can, for example, perform household tasks on behalf of the user when the user is doing them. For example, the chore-focusing unit can clean on behalf of the user. It can also cook on behalf of the user. For example, it can prepare ingredients and cook meals on behalf of the user. It can also do laundry on behalf of the user. For example, it can wash and dry laundry on behalf of the user. This allows the AI ​​to concentrate on household tasks when the user is busy. Some or all of the above-mentioned processes in the chore-focusing unit may be performed using AI, for example, or without AI. For example, the chore-focusing unit can input household tasks into a generation AI and have the generation AI perform the household tasks.

[0071] The data processing unit estimates the user's emotions and determines the priority of data processing based on the estimated emotions. For example, if the user is feeling stressed, the data processing unit can prioritize tasks that help them relax. Similarly, if the user is excited, the data processing unit can prioritize tasks that require concentration. For example, if the user is tired, the data processing unit can prioritize tasks that help them rest. This allows for more appropriate data processing by determining the priority of data processing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data processing unit may be performed using AI, for example, or without AI. For example, the data processing unit can input user emotion data into a generating AI and have the generating AI determine the priority of data processing.

[0072] The data conversion unit improves the accuracy of data conversion by referring to the user's past behavior history during the data conversion process. For example, the data conversion unit can refer to the history of tasks the user has performed in the past and convert similar tasks into data. The data conversion unit can also analyze the user's past behavior patterns and propose the optimal data conversion method. For example, the data conversion unit can analyze the user's past behavior patterns and propose the optimal data conversion method. The data conversion unit can also analyze tasks the user has failed at in the past and perform data conversion that reflects improvements. For example, the data conversion unit can analyze tasks the user has failed at in the past and perform data conversion that reflects improvements. This improves the accuracy of data conversion by referring to the user's past behavior history. Some or all of the above processing in the data conversion unit may be performed using AI, for example, or without AI. For example, the data conversion unit can input the user's past behavior history into a generating AI and have the generating AI perform the data conversion accuracy improvement.

[0073] The data conversion unit customizes the content of the data conversion based on the user's current living situation and areas of interest. For example, the data conversion unit can prioritize the conversion of information related to projects the user is currently working on. The data conversion unit can also convert relevant information based on the user's areas of interest. For example, the data conversion unit can convert relevant information based on the user's areas of interest. The data conversion unit can also convert necessary information according to the user's living situation. For example, the data conversion unit can convert necessary information according to the user's living situation. This allows for more appropriate data conversion by customizing the content of the data conversion based on the user's current living situation and areas of interest. Some or all of the above processing in the data conversion unit may be performed using AI, for example, or without AI. For example, the data conversion unit can input the user's current living situation and areas of interest into a generating AI and have the generating AI perform the customization of the data conversion content.

[0074] The data processing unit estimates the user's emotions and adjusts the timing of data processing based on the estimated emotions. For example, if the user is relaxed, the data processing unit can delay the timing of data processing. For example, if the user is in a hurry, the data processing unit can speed up the timing of data processing. For example, if the user is focused, the data processing unit can adjust the timing of data processing. For example, if the user is focused, the data processing unit can adjust the timing of data processing. By adjusting the timing of data processing based on the user's emotions, data processing can be performed at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data processing unit may be performed using AI, for example, or without AI. For example, the data processing unit can input user emotion data into the generating AI and have the generating AI adjust the timing of the data processing.

[0075] The data conversion unit prioritizes the conversion of highly relevant data, taking into account the user's geographical location information. For example, the data conversion unit can prioritize the conversion of information related to the user's current location. The data conversion unit can also convert relevant data based on the user's geographical location information. For example, the data conversion unit can convert relevant data based on the user's geographical location information. Furthermore, if the user is on the move, the data conversion unit can convert information related to their destination. For example, if the user is on the move, the data conversion unit can convert information related to their destination. This allows for more appropriate data conversion by prioritizing the conversion of highly relevant data while considering the user's geographical location information. Some or all of the above processing in the data conversion unit may be performed using AI, for example, or without AI. For example, the data conversion unit can input the user's geographical location information into a generating AI and have the generating AI perform the priority conversion of highly relevant data.

[0076] The data conversion unit analyzes the user's social media activity and converts the relevant data into data. For example, the data conversion unit can convert information shared by the user on social media into data. The data conversion unit can also convert relevant data based on the user's social media activity. For example, the data conversion unit can convert relevant data based on the user's social media activity. The data conversion unit can also convert information that the user has shown interest in on social media. For example, the data conversion unit can convert information that the user has shown interest in on social media into data. This allows for more appropriate data conversion by analyzing the user's social media activity and converting the relevant data into data. Some or all of the above processing in the data conversion unit may be performed using AI, for example, or without AI. For example, the data conversion unit can input the user's social media activity into a generating AI and have the generating AI perform the data conversion of the relevant data.

[0077] The learning unit estimates the user's emotions and selects training data based on the estimated emotions. For example, if the user is relaxed, the learning unit can learn data related to relaxation. Similarly, if the user is excited, the learning unit can learn data related to excitement. For example, if the user is excited, the learning unit can learn data related to excitement. Similarly, if the user is tired, the learning unit can learn data related to rest. For example, if the user is tired, the learning unit can learn data related to rest. This allows for more appropriate learning by selecting training data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, or not using AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform the selection of training data.

[0078] The learning unit optimizes the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can analyze past learning data and select the optimal learning algorithm. The learning unit can also improve the learning algorithm based on past learning data. For example, the learning unit can improve the learning algorithm based on past learning data. The learning unit can also improve the accuracy of the learning algorithm by referring to past learning data. For example, the learning unit can improve the accuracy of the learning algorithm by referring to past learning data. As a result, the accuracy of learning is improved by optimizing the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0079] The learning unit customizes the learning content based on the user's behavior patterns during learning. For example, the learning unit can analyze the user's behavior patterns and propose the optimal learning content. The learning unit can also customize the learning content based on the user's behavior patterns. For example, the learning unit can customize the learning content based on the user's behavior patterns. The learning unit can also optimize the learning content by referring to the user's behavior patterns. For example, the learning unit can optimize the learning content by referring to the user's behavior patterns. This makes it possible to perform more appropriate learning by customizing the learning content based on the user's behavior patterns. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's behavior patterns into a generating AI and have the generating AI perform the customization of the learning content.

[0080] The learning unit estimates the user's emotions and adjusts the learning frequency based on the estimated emotions. For example, the learning unit can reduce the learning frequency if the user is relaxed, or increase it if the user is excited. For example, the learning unit can increase the learning frequency if the user is excited, or adjust it if the user is tired. By adjusting the learning frequency based on the user's emotions, learning can be performed at a more appropriate frequency. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, or not using AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI adjust the learning frequency.

[0081] The learning unit selects training data while considering the user's geographical location information during training. For example, the learning unit can learn data related to the user's current location. The learning unit can also select relevant training data based on the user's geographical location information. For example, the learning unit can select relevant training data based on the user's geographical location information. Furthermore, if the user is on the move, the learning unit can learn data related to their destination. For example, if the user is on the move, the learning unit can learn data related to their destination. This allows for more appropriate training by selecting training data while considering the user's geographical location information. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's geographical location information into a generating AI and have the generating AI perform the selection of training data.

[0082] The learning unit analyzes the user's social media activity and learns relevant data during the learning process. For example, the learning unit can learn information shared by the user on social media. The learning unit can also learn relevant data based on the user's social media activity. For example, the learning unit can learn information that the user has shown interest in on social media. This allows for more appropriate learning by analyzing the user's social media activity and learning relevant data. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's social media activity into a generating AI and have the generating AI perform the learning of relevant data.

[0083] The simulation unit estimates the user's emotions and adjusts the simulation content based on the estimated emotions. For example, if the user is relaxed, the simulation unit can perform simulations related to relaxation. Similarly, if the user is excited, the simulation unit can perform simulations related to excitement. For example, if the user is excited, the simulation unit can perform simulations related to excitement. Similarly, if the user is tired, the simulation unit can perform simulations related to rest. By adjusting the simulation content based on the user's emotions, a more appropriate simulation becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the simulation unit may be performed using AI, or not using AI. For example, the simulation unit can input user emotion data into the generative AI and have the generative AI adjust the simulation content.

[0084] The simulation unit improves the accuracy of the simulation by referring to the user's past behavior history during the simulation. For example, the simulation unit can refer to the history of tasks the user has performed in the past and simulate similar tasks. The simulation unit can also analyze the user's past behavior patterns and propose the optimal simulation method. For example, the simulation unit can analyze the user's past behavior patterns and propose the optimal simulation method. The simulation unit can also analyze tasks the user has failed at in the past and perform simulations that reflect improvements. For example, the simulation unit can analyze tasks the user has failed at in the past and perform simulations that reflect improvements. By improving the accuracy of the simulation by referring to the user's past behavior history, more appropriate simulations become possible. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's past behavior history into a generating AI and have the generating AI perform the simulation accuracy improvement.

[0085] The simulation unit customizes the simulation content based on the user's current living situation and areas of interest during the simulation. For example, the simulation unit can perform simulations related to a project the user is currently working on. The simulation unit can also perform relevant simulations based on the user's areas of interest. For example, the simulation unit can perform relevant simulations based on the user's areas of interest. The simulation unit can also perform necessary simulations according to the user's living situation. For example, the simulation unit can perform necessary simulations according to the user's living situation. This allows for more appropriate simulations by customizing the simulation content based on the user's current living situation and areas of interest. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's current living situation and areas of interest into a generating AI and have the generating AI perform the customization of the simulation content.

[0086] The simulation unit estimates the user's emotions and adjusts the timing of the simulation based on the estimated emotions. For example, if the user is relaxed, the simulation unit can delay the timing of the simulation. For example, if the user is in a hurry, the simulation unit can speed up the timing of the simulation. For example, if the user is focused, the simulation unit can adjust the timing of the simulation. For example, if the user is focused, the simulation unit can adjust the timing of the simulation. By adjusting the timing of the simulation based on the user's emotions, it becomes possible to perform the simulation at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the simulation timing.

[0087] The simulation unit adjusts the simulation content while considering the user's geographical location information. For example, the simulation unit can perform simulations related to the user's current location. It can also perform simulations related to the user's geographical location information. For example, the simulation unit can perform simulations related to the user's geographical location information. Furthermore, if the user is on the move, the simulation unit can perform simulations related to their destination. For example, if the user is on the move, the simulation unit can perform simulations related to their destination. By adjusting the simulation content while considering the user's geographical location information, a more appropriate simulation becomes possible. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's geographical location information into a generating AI and have the generating AI perform the adjustment of the simulation content.

[0088] The simulation unit analyzes the user's social media activity during the simulation and reflects the relevant data in the simulation. For example, the simulation unit can reflect information shared by the user on social media in the simulation. The simulation unit can also perform relevant simulations based on the user's social media activity. For example, the simulation unit can perform relevant simulations based on the user's social media activity. The simulation unit can also reflect information that the user has shown interest in on social media in the simulation. For example, the simulation unit can reflect information that the user has shown interest in on social media in the simulation. By analyzing the user's social media activity and reflecting the relevant data in the simulation, a more appropriate simulation becomes possible. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's social media activity into a generating AI and have the generating AI execute a simulation of the relevant data.

[0089] The automation unit estimates the user's emotions and adjusts the automated tasks based on the estimated emotions. For example, if the user is relaxed, the automation unit can automate tasks related to relaxation. Similarly, if the user is excited, the automation unit can automate tasks related to excitement. For example, if the user is excited, the automation unit can automate tasks related to excitement. Similarly, if the user is tired, the automation unit can automate tasks related to rest. For example, if the user is tired, the automation unit can automate tasks related to rest. This allows for more appropriate automation by adjusting the automated tasks based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the automation unit may be performed using AI, or not. For example, the automation unit can input user emotion data into the generative AI and have the generative AI adjust the automated tasks.

[0090] The automation unit improves the accuracy of automation by referring to the user's past behavior history during automation. For example, the automation unit can refer to the history of tasks the user has performed in the past and automate similar tasks. The automation unit can also analyze the user's past behavior patterns and propose the optimal automation method. For example, the automation unit can analyze the user's past behavior patterns and propose the optimal automation method. The automation unit can also analyze tasks the user has failed at in the past and perform automation that reflects improvements. For example, the automation unit can analyze tasks the user has failed at in the past and perform automation that reflects improvements. By improving the accuracy of automation by referring to the user's past behavior history, more appropriate automation becomes possible. Some or all of the above processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input the user's past behavior history into a generating AI and have the generating AI perform the automation accuracy improvement.

[0091] The automation unit customizes the content of the automation based on the user's current living situation and areas of interest. For example, the automation unit can automate tasks related to a project the user is currently working on. The automation unit can also automate related tasks based on the user's areas of interest. For example, the automation unit can automate related tasks based on the user's areas of interest. The automation unit can also automate tasks that are necessary depending on the user's living situation. For example, the automation unit can automate tasks that are necessary depending on the user's living situation. This allows for more appropriate automation by customizing the content of the automation based on the user's current living situation and areas of interest. Some or all of the above processing in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input the user's current living situation and areas of interest into a generating AI and have the generating AI perform the customization of the automation content.

[0092] The automation unit estimates the user's emotions and adjusts the timing of automation based on the estimated emotions. For example, if the user is relaxed, the automation unit can delay the timing of automation. For example, if the user is in a hurry, the automation unit can advance the timing of automation. For example, if the user is focused, the automation unit can adjust the timing of automation. For example, if the user is focused, the automation unit can adjust the timing of automation. By adjusting the timing of automation based on the user's emotions, automation can be performed at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input user emotion data into the generative AI and have the generative AI adjust the timing of automation.

[0093] The automation unit adjusts the content of the automation process by taking into account the user's geographical location information. For example, the automation unit can automate tasks related to the user's current location. The automation unit can also automate relevant tasks based on the user's geographical location information. For example, the automation unit can automate relevant tasks based on the user's geographical location information. Furthermore, if the user is on the move, the automation unit can automate tasks related to their destination. For example, if the user is on the move, the automation unit can automate tasks related to their destination. By adjusting the content of the automation process by taking into account the user's geographical location information, more appropriate automation becomes possible. Some or all of the above-described processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input the user's geographical location information into a generating AI and have the generating AI perform the adjustment of the automation content.

[0094] The automation unit analyzes the user's social media activity during automation and incorporates relevant data into the automation process. For example, the automation unit can incorporate information shared by the user on social media into the automation process. The automation unit can also automate related tasks based on the user's social media activity. For example, the automation unit can automate related tasks based on the user's social media activity. The automation unit can also incorporate information that the user has shown interest in on social media into the automation process. For example, the automation unit can incorporate information that the user has shown interest in on social media into the automation process. By analyzing the user's social media activity and incorporating relevant data into the automation process, more appropriate automation becomes possible. Some or all of the above-described processes in the automation unit may be performed using AI, for example, or without AI. For example, the automation unit can input the user's social media activity into a generating AI and have the generating AI perform the automation of the related data.

[0095] The meeting participation unit estimates the user's emotions and adjusts how the user participates in the meeting based on the estimated emotions. For example, if the user is nervous, the meeting participation unit can allow them to participate in the meeting in a relaxing environment. If the user is relaxed, the meeting participation unit can allow them to participate in the meeting in an environment that enhances their concentration. For example, if the user is relaxed, the meeting participation unit can allow them to participate in the meeting in an environment that enhances their concentration. For example, if the user is in a hurry, the meeting participation unit can allow them to participate in the meeting efficiently. For example, if the user is in a hurry, the meeting participation unit can allow them to participate in the meeting efficiently. In this way, by adjusting how the user participates in the meeting based on their emotions, more appropriate meeting participation becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the meeting participation unit may be performed using AI, for example, or not using AI. For example, the meeting participation unit can input the user's emotion data into the generative AI and have the generative AI perform the adjustment of how the user participates in the meeting.

[0096] The meeting participation unit, when a user joins a meeting, selects the optimal participation method by referring to the user's past meeting history. For example, the meeting participation unit can refer to the user's past meeting history and allow them to join similar meetings. The meeting participation unit can also analyze the user's past meeting history and suggest the optimal participation method. For example, the meeting participation unit can analyze the user's past meeting history and suggest the optimal participation method. For example, the meeting participation unit can analyze past meetings where the user failed and select a participation method that reflects improvements. For example, the meeting participation unit can analyze past meetings where the user failed and select a participation method that reflects improvements. By referring to the user's past meeting history and selecting the optimal participation method, more appropriate meeting participation becomes possible. Some or all of the above processing in the meeting participation unit may be performed using AI, for example, or without AI. For example, the meeting participation unit can input the user's past meeting history into a generating AI and have the generating AI select the optimal participation method.

[0097] The meeting participation unit estimates the user's emotions and adjusts the timing of meeting participation based on the estimated emotions. For example, if the user is relaxed, the meeting participation unit can delay the timing of meeting participation. For example, if the user is in a hurry, the meeting participation unit can advance the timing of meeting participation. For example, if the user is focused, the meeting participation unit can adjust the timing of meeting participation. For example, if the user is focused, the meeting participation unit can adjust the timing of meeting participation. This allows for meeting participation at a more appropriate time by adjusting the timing of meeting participation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the meeting participation unit may be performed using AI, for example, or without AI. For example, the meeting participation section can input user emotion data into a generating AI and have the AI ​​adjust the timing of meeting participation.

[0098] The meeting participation unit selects the optimal participation method when a user joins a meeting, taking into account the user's geographical location. For example, the meeting participation unit can allow the user to join a meeting related to their current location. It can also allow the meeting participation unit to allow the user to join a meeting related to their destination if the user is on the move. By selecting the optimal participation method while considering the user's geographical location, more appropriate meeting participation becomes possible. Some or all of the above processing in the meeting participation unit may be performed using AI, for example, or without AI. For example, the meeting participation unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal participation method.

[0099] The household chore concentration unit estimates the user's emotions and adjusts the method of concentration on household chores based on the estimated emotions. For example, if the user is relaxed, the household chore concentration unit can create a relaxing environment for the user to concentrate on household chores. For example, if the user is excited, the household chore concentration unit can create an environment that enhances concentration for the user to concentrate on household chores. For example, if the user is tired, the household chore concentration unit can create an environment that enhances concentration for the user to concentrate on household chores. For example, if the user is tired, the household chore concentration unit can create an environment that enhances concentration for the user to concentrate on household chores. In this way, by adjusting the method of concentration on household chores based on the user's emotions, more appropriate concentration on household chores becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the household chore concentration unit may be performed using AI, for example, or without AI. For example, the household chore concentration unit can input user emotional data into a generating AI and have the AI ​​adjust the way household chores are concentrated.

[0100] The household chore concentration unit selects the optimal concentration method by referring to the user's past household chore history when household chores are concentrated. For example, the household chore concentration unit can refer to the user's past household chore history and concentrate on similar chores. The household chore concentration unit can also analyze the user's past household chore history and suggest the optimal concentration method. For example, the household chore concentration unit can analyze the user's past household chore history and suggest the optimal concentration method. For example, the household chore concentration unit can analyze the user's past household chore failures and select a concentration method that reflects improvements. For example, the household chore concentration unit can analyze the user's past household chore failures and select a concentration method that reflects improvements. By selecting the optimal concentration method by referring to the user's past household chore history, more appropriate household chore concentration becomes possible. Some or all of the above processing in the household chore concentration unit may be performed using AI, for example, or without AI. For example, the household chore concentration unit can input the user's past household chore history into a generating AI and have the generating AI select the optimal concentration method.

[0101] The household chore concentration unit estimates the user's emotions and adjusts the timing of household chore concentration based on the estimated emotions. For example, if the user is relaxed, the household chore concentration unit can delay the timing of household chore concentration. Conversely, if the user is in a hurry, the household chore concentration unit can also advance the timing of household chore concentration. For example, if the user is in a hurry, the household chore concentration unit can advance the timing of household chore concentration. Conversely, if the user is concentrating, the household chore concentration unit can also adjust the timing of household chore concentration. For example, if the user is concentrating, the household chore concentration unit can adjust the timing of household chore concentration. This allows for more appropriate timing of household chore concentration by adjusting the timing of household chore concentration based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the household chore concentration unit may be performed using AI, for example, or without AI. For example, the household chore concentration unit can input user emotional data into a generating AI, which can then adjust the timing of household chore concentration.

[0102] The household chore concentration unit selects the optimal concentration method when household chores are being concentrated, taking into account the user's geographical location information. For example, the household chore concentration unit can concentrate on household chores related to the user's current location. The household chore concentration unit can also concentrate on household chores related to the user's geographical location information. For example, the household chore concentration unit can concentrate on household chores related to the user's geographical location information. Furthermore, if the user is on the move, the household chore concentration unit can concentrate on household chores related to the user's destination. For example, if the user is on the move, the household chore concentration unit can concentrate on household chores related to the user's destination. This allows for more appropriate household chore concentration by selecting the optimal concentration method while considering the user's geographical location information. Some or all of the above processing in the household chore concentration unit may be performed using AI, for example, or without AI. For example, the household chore concentration unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal concentration method.

[0103] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0104] The data processing unit can monitor the user's health status and adjust the data processing content based on that status. For example, if the user is fatigued, the data processing unit can prioritize processing tasks related to rest. If the user is healthy, the data processing unit can process more complex tasks. Furthermore, if the user is ill, the data processing unit can process tasks related to health management. This enables appropriate data processing according to the user's health status.

[0105] The learning unit can analyze the user's sleep patterns and suggest the optimal learning time. For example, if the user is a night owl, the learning unit can suggest studying at night. If the user is an early riser, the learning unit can suggest studying in the morning. Furthermore, if the user has irregular sleep patterns, the learning unit can adjust the learning timing to match the user's sleep patterns. This enables optimal learning based on the user's sleep patterns.

[0106] The simulation unit can refer to the user's eating history and perform simulations related to meals. For example, it can simulate a healthy meal plan based on data from meals the user has eaten in the past. Furthermore, if the user has an allergy to a specific food, it can simulate a meal plan that avoids that food. Additionally, if the user is on a diet, it can simulate a meal plan that takes calorie restriction into account. This enables the simulation of an appropriate meal plan based on the user's eating history.

[0107] The automation unit can refer to the user's exercise history and automate exercise-related tasks. For example, it can automate the creation of an optimal exercise plan based on data from the user's past exercises. Furthermore, if the user prefers a particular exercise, it can automate a plan that includes that exercise. Additionally, if the user is injured, it can automate an exercise plan related to rehabilitation. This enables the automation of appropriate exercise plans based on the user's exercise history.

[0108] The data generation unit can estimate the user's emotions and adjust the data generation content based on the estimated emotions. For example, if the user is stressed, tasks that promote relaxation can be prioritized in the data generation. If the user is excited, tasks that require concentration can be prioritized in the data generation. Furthermore, if the user is tired, tasks that promote rest can be prioritized in the data generation. This enables appropriate data generation based on the user's emotions.

[0109] The learning unit can estimate the user's emotions and adjust the learning content based on those estimated emotions. For example, if the user is relaxed, it can learn data related to relaxation. If the user is excited, it can learn data related to excitement. Furthermore, if the user is tired, it can learn data related to rest. This enables appropriate learning based on the user's emotions.

[0110] The simulation unit can estimate the user's emotions and adjust the simulation content based on those emotions. For example, if the user is relaxed, it can perform simulations related to relaxation. If the user is excited, it can perform simulations related to excitement. Furthermore, if the user is tired, it can perform simulations related to rest. This enables appropriate simulations based on the user's emotions.

[0111] The automation unit can estimate the user's emotions and adjust the automation content based on the estimated emotions. For example, if the user is relaxed, tasks related to relaxation can be automated. If the user is excited, tasks related to excitement can be automated. Furthermore, if the user is tired, tasks related to rest can be automated. This enables appropriate automation based on the user's emotions.

[0112] The meeting participation function can estimate the user's emotions and adjust how they participate in the meeting based on those emotions. For example, if a user is feeling nervous, they can participate in the meeting in a relaxing environment. If a user is relaxed, they can participate in an environment that enhances their concentration. Furthermore, if a user is in a hurry, they can participate in the meeting efficiently. This enables appropriate meeting participation based on the user's emotions.

[0113] The household chore concentration unit can estimate the user's emotions and adjust the method of concentration on household chores based on those emotions. For example, if the user is relaxed, it can concentrate on household chores in a relaxing environment. If the user is excited, it can concentrate on household chores in an environment that enhances concentration. Furthermore, if the user is tired, it can concentrate on household chores efficiently. This enables appropriate household chore concentration based on the user's emotions.

[0114] The following briefly describes the processing flow for example form 2.

[0115] Step 1: The data entry department digitizes thinking, judgment criteria, work methods, and user characteristics. For example, it digitizes the user's problem-solving approach, values, beliefs, decision-making process, evaluation criteria, task management methods, communication style, personal characteristics, behavioral patterns, and preferences. Step 2: The learning unit learns the user's past behavior and attitudes based on the information digitized by the data digitization unit. For example, it analyzes the user's past behavioral history to learn behavioral patterns, or analyzes past changes in attitudes to learn attitude trends. Step 3: The simulation unit simulates multiple different personalities, perspectives, and skill sets of the user based on the information learned by the learning unit. For example, it simulates different personalities, perspectives, and skill sets, each handling different tasks. Step 4: The automation unit automates tasks and decisions based on the information simulated by the simulation unit. For example, automation is performed using an AI model that takes simulated information as input and outputs tasks and decisions.

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0118] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] Each of the multiple elements described above, including the data conversion unit, learning unit, simulation unit, and automation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data conversion unit is implemented by the control unit 46A of the smart device 14 and converts the user's thoughts and judgment criteria into data. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's past actions and consciousness based on the data converted into data. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and simulates multiple different personalities, perspectives, and skill sets of the user based on the learned information. The automation unit is implemented by the control unit 46A of the smart device 14 and automates tasks and decisions based on the simulated information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0120] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0121] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0122] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0123] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0124] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0126] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0127] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0128] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0130] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0132] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0134] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements described above, including the data conversion unit, learning unit, simulation unit, and automation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data conversion unit is implemented by the control unit 46A of the smart glasses 214 and converts the user's thoughts and judgment criteria into data. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's past actions and consciousness based on the data converted into data. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and simulates multiple different personalities, perspectives, and skill sets of the user based on the learned information. The automation unit is implemented by the control unit 46A of the smart glasses 214 and automates tasks and decisions based on the simulated information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0136] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0137] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0139] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0140] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the data conversion unit, learning unit, simulation unit, and automation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data conversion unit is implemented by the control unit 46A of the headset terminal 314 and converts the user's thoughts and judgment criteria into data. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's past actions and consciousness based on the digitized information. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and simulates multiple different personalities, perspectives, and skill sets of the user based on the learned information. The automation unit is implemented by the control unit 46A of the headset terminal 314 and automates tasks and decisions based on the simulated information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0152] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0153] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0154] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0155] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0156] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0158] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0159] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0160] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0161] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0162] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0163] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0164] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0165] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0167] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0168] Each of the multiple elements described above, including the data conversion unit, learning unit, simulation unit, and automation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data conversion unit is implemented by the control unit 46A of the robot 414 and converts the user's thoughts and judgment criteria into data. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's past actions and consciousness based on the data converted into data. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and simulates multiple different personalities, perspectives, and skill sets of the user based on the learned information. The automation unit is implemented by the control unit 46A of the robot 414 and automates tasks and decisions based on the simulated information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0169] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0170] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0171] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0172] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0173] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0174] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0175] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0176] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0177] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0178] 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.

[0179] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0180] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0181] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0182] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0183] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0184] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0185] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0186] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0187] (Note 1) The Data Analysis Department digitizes ways of thinking, decision-making criteria, work styles, and user characteristics, A learning unit learns the user's past behavior and awareness based on the information digitized by the aforementioned data digitization unit, A simulation unit that simulates multiple different personalities, perspectives, and skill sets of a user based on the information learned by the learning unit, The system includes an automation unit that automates tasks and decisions based on information simulated by the simulation unit. A system characterized by the following features. (Note 2) The aforementioned automation unit, It includes a meeting participation section for business meetings. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned automation unit, Equipped with a dedicated room for household chores, allowing you to focus on household tasks. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned data conversion unit, We estimate the user's emotions and determine the priority of data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned data conversion unit, When digitizing data, the accuracy of the data is improved by referring to the user's past behavior history. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned data conversion unit, During data collection, the content of the data is customized based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned data conversion unit, We estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned data conversion unit, When digitizing data, the system prioritizes the creation of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned data conversion unit, During data entry, we analyze users' social media activity and digitize relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned learning unit, During learning, the learning content is customized based on the user's behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned learning unit, During training, the training data is selected taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned learning unit, During training, the system analyzes users' social media activity and learns from relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned simulation unit, It estimates the user's emotions and adjusts the simulation content based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned simulation unit, During simulations, the system improves the accuracy of the simulations by referencing the user's past behavior history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned simulation unit, During the simulation, the simulation content is customized based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned simulation unit, It estimates the user's emotions and adjusts the timing of the simulation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned simulation unit, During the simulation, the simulation content is adjusted to take into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned simulation unit, During the simulation, the system analyzes users' social media activity and incorporates relevant data into the simulation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned automation unit, It estimates the user's emotions and adjusts the automated process based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned automation unit, When automating processes, we improve the accuracy of the automation by referencing the user's past behavior history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned automation unit, During automation, the content of the automation is customized based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned automation unit, It estimates the user's emotions and adjusts the timing of automation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned automation unit, When automating processes, the automation content is adjusted to take into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned automation unit, During automation, analyze users' social media activity and incorporate relevant data into the automation process. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned meeting participants said, It estimates the user's emotions and adjusts how they participate in the meeting based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned meeting participants said, When a user joins a meeting, the system will refer to their past meeting history to select the most suitable method of participation. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned meeting participants said, It estimates the user's emotions and adjusts the timing of meeting participation based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned meeting participants said, When a user joins a meeting, the system will select the most suitable participation method by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned household chore centralization unit is It estimates the user's emotions and adjusts how household chores are concentrated based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned household chore centralization unit is When concentrating on household chores, the system selects the optimal method of concentration by referring to the user's past chore history. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned household chore centralization unit is It estimates the user's emotions and adjusts the timing of household chores based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned household chore centralization unit is When concentrating on household chores, the system selects the optimal concentration method by considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0188] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The Data Analysis Department digitizes ways of thinking, decision-making criteria, work styles, and user characteristics, A learning unit learns the user's past behavior and awareness based on the information digitized by the aforementioned data digitization unit, A simulation unit that simulates multiple different personalities, perspectives, and skill sets of the user based on the information learned by the learning unit, The system includes an automation unit that automates tasks and decisions based on information simulated by the simulation unit. A system characterized by the following features.

2. The aforementioned automation unit, It includes a meeting participation section for business meetings. The system according to feature 1.

3. The aforementioned automation unit, Equipped with a dedicated room for household chores, allowing you to focus on household tasks. The system according to feature 1.

4. The aforementioned data conversion unit, We estimate the user's emotions and determine the priority of data collection based on the estimated user emotions. The system according to feature 1.

5. The aforementioned data conversion unit, When digitizing data, the accuracy of the data is improved by referring to the user's past behavior history. The system according to feature 1.

6. The aforementioned data conversion unit, During data collection, the content of the data is customized based on the user's current lifestyle and areas of interest. The system according to feature 1.

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

8. The aforementioned data conversion unit, When digitizing data, the system prioritizes the creation of highly relevant data, taking into account the user's geographical location. The system according to feature 1.

9. The aforementioned data conversion unit, During data entry, we analyze users' social media activity and digitize relevant data. The system according to feature 1.

10. The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system according to feature 1.

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