system

Abstract

The system according to this embodiment aims to analyze the user's emotions in real time and provide appropriate feedback. [Solution] The system according to the embodiment comprises an emotion analysis unit, a feedback provision unit, a customization unit, and a suggestion unit. The emotion analysis unit analyzes the user's emotions in real time. The feedback provision unit provides positive messages and relaxation methods based on the data analyzed by the emotion analysis unit. The customization unit allows the user to customize the personality and speaking style of the AI ​​agent. The suggestion unit proposes a virtual coffee break to reduce feelings of loneliness during remote work.

Description

Technical Field

[0006] , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the user's emotions have not been sufficiently analyzed in real time to provide appropriate feedback, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the user's emotions in real time and provide appropriate feedback.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an emotion analysis unit, a feedback provision unit, a customization unit, and a suggestion unit. The emotion analysis unit analyzes the user's emotions in real time. The feedback provision unit provides positive messages and relaxation methods based on the data analyzed by the emotion analysis unit. The customization unit allows the user to customize the AI ​​agent's personality and speaking style. The suggestion unit proposes a virtual coffee break to reduce feelings of loneliness during remote work. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's emotions in real time and provide appropriate feedback. [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 reception 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 reception 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 ​​agent system according to an embodiment of the present invention is a system that provides positive messages and encouragement in response to various challenges and stresses faced in daily life. The AI ​​agent system has a function that allows the user to customize the AI ​​agent's personality and speaking style, and uses sensors from wearable devices and smartphones to analyze emotions in real time and provide immediate feedback. The AI ​​agent system also suggests relaxation methods and reduces stress. This mechanism reduces feelings of loneliness in adults working remotely and supports a stress-free environment. For example, when a user customizes the AI ​​agent's personality and speaking style, the user can set the AI ​​agent's personality and speaking style according to their preferences. For example, various options are available, such as a gentle personality, an energetic personality, a calm speaking style, or a cheerful speaking style. This allows the user to create an AI agent that suits them. Next, the system uses sensors from wearable devices and smartphones to analyze the user's emotions in real time. For example, it collects data such as heart rate, skin electrical activity, and voice tone, and the AI ​​analyzes this data to understand the user's emotional state. This allows for real-time analysis of various emotional states, such as when the user is stressed or relaxed. Furthermore, the AI ​​agent provides immediate feedback based on a real-time analysis of the user's emotional state. For example, if the user is feeling stressed, the AI ​​agent offers positive messages and words of encouragement. If the user is relaxed, the AI ​​agent provides advice to maintain that state. This ensures that the user always receives appropriate support. In addition, the AI ​​agent suggests relaxation techniques to reduce stress. For example, it can suggest various relaxation methods such as deep breathing, meditation, and light exercise. This allows users to find a relaxation method that suits them and reduce stress. This mechanism helps reduce feelings of loneliness in adults working remotely and supports a stress-free environment.For example, during remote work, an AI agent can suggest a virtual coffee break and engage in conversation with the user to reduce feelings of isolation. Furthermore, the AI ​​agent can support the user's mental health by providing positive messages and advice at appropriate times. In this way, the AI ​​agent system can analyze the user's emotions in real time and provide appropriate feedback and suggestions, thereby reducing stress and alleviating feelings of isolation during remote work.

[0029] The AI ​​agent system according to this embodiment comprises an emotion analysis unit, a feedback provision unit, a customization unit, and a suggestion unit. The emotion analysis unit analyzes the user's emotions in real time. The emotion analysis unit collects data by utilizing sensors from wearable devices or smartphones, for example. For example, the emotion analysis unit collects data such as heart rate, skin electrical activity, and voice tone, and the AI ​​analyzes this data to understand the user's emotional state. For example, the emotion analysis unit can estimate the user's stress level based on fluctuations in heart rate. The emotion analysis unit can also estimate the user's emotional state by analyzing changes in skin electrical activity. Furthermore, the emotion analysis unit can also estimate the user's emotional state by analyzing changes in voice tone. The feedback provision unit provides positive messages and relaxation methods based on the data analyzed by the emotion analysis unit. For example, if the user is feeling stressed, the feedback provision unit provides positive messages and words of encouragement. For example, the feedback provision unit can provide messages such as, "You're doing a great job." The feedback provision unit can also provide advice to maintain a relaxed state if the user is relaxed. For example, the feedback section can provide advice such as, "Just relax and continue." Furthermore, if the user is feeling anxious, the feedback section can also provide reassuring messages. For example, the feedback section can provide messages such as, "It's okay, you're not alone." The customization section allows users to customize the AI ​​agent's personality and speaking style. For example, the customization section can set the AI ​​agent's personality and speaking style to suit the user's preferences. For example, the customization section can offer various options such as a gentle personality, an energetic personality, a calm speaking style, or a cheerful speaking style. This allows users to create an AI agent that suits them. The suggestion section can suggest a virtual coffee break to reduce feelings of loneliness during remote work.The suggestion function can, for example, reduce feelings of loneliness by suggesting a virtual coffee break during remote work and engaging in conversation with the user. For instance, the suggestion function could make a suggestion such as, "Why don't you take a short break and have some coffee?" The suggestion function can also suggest relaxation techniques to help the user relax. For example, the suggestion function could make a suggestion such as, "Take a deep breath and relax." In this way, the AI ​​agent system according to the embodiment can reduce stress and alleviate feelings of loneliness during remote work by analyzing the user's emotions in real time and providing appropriate feedback and suggestions.

[0030] The emotion analysis unit analyzes users' emotions in real time. For example, it collects data using sensors from wearable devices and smartphones. Specifically, wearable devices collect biometric data such as heart rate, skin electrical activity, and body temperature, while smartphones detect changes in voice tone and facial expressions. This data is analyzed by AI to gain a detailed understanding of the user's emotional state. For instance, fluctuations in heart rate serve as an indicator of stress levels; a sudden increase in heart rate suggests the user is likely experiencing tension or anxiety. Changes in skin electrical activity indicate the user's state of excitement or relaxation, allowing for an estimation of their emotions. Furthermore, changes in voice tone can capture subtle shifts in the user's emotions; for example, a lower voice tone may indicate the user is feeling depressed. This data is collected in real time and rapidly analyzed by AI, enabling an immediate understanding of the user's emotional state. Additionally, the emotion analysis unit can accumulate past data and learn user emotional tendencies and patterns, enabling more accurate emotion analysis. For example, it is possible to understand and predict what emotions a user is likely to experience at specific times or in specific situations. This allows the emotion analysis department to understand the user's emotional state in real time and provide a foundation for giving appropriate feedback and suggestions.

[0031] The feedback department provides positive messages and relaxation techniques based on data analyzed by the emotion analysis department. Specifically, if a user is feeling stressed, the feedback department provides messages and relaxation techniques to calm the user. For example, it enhances the user's self-esteem by providing positive messages such as, "You're doing a great job." If a user is relaxed, it provides advice to maintain that state. For example, it helps the user stay relaxed by providing advice such as, "Keep relaxing and continuing." Furthermore, if a user is feeling anxious, it provides reassuring messages. For example, it reduces the user's anxiety by providing messages such as, "It's okay, you're not alone." By providing appropriate messages and advice according to the user's emotional state, the feedback department reduces user stress and supports mental stability. The feedback department also collects user feedback and evaluates the effectiveness of the messages and advice it provides. This allows the feedback department to provide optimal feedback tailored to the user's needs. For example, if a user shows a positive response to a particular message, repeating that message can help maintain the user's mental stability. In this way, the feedback department can provide appropriate feedback according to the user's emotional state and support their mental health.

[0032] The customization section allows users to customize the AI ​​agent's personality and speaking style. Specifically, users can set the AI ​​agent's personality and speaking style to their preferences. For example, various options are provided, such as a gentle personality, an energetic personality, a calm speaking style, or a cheerful speaking style. Users can choose the option that suits them best and customize the AI ​​agent. This allows users to create an AI agent that suits them, resulting in a more user-friendly and easy-to-use system. Furthermore, the customization section can collect user feedback and continuously improve the AI ​​agent's personality and speaking style. For example, if a user shows a positive reaction to a particular personality or speaking style, that personality or speaking style can be enhanced to improve user satisfaction. The customization section can also analyze the user's usage history and behavioral patterns and suggest the most suitable customization options for the user. This makes it easy for users to create the AI ​​agent that is best suited to them. In addition, the customization section can automatically adjust the AI ​​agent's personality and speaking style according to the user's emotional state. For example, if a user is feeling stressed, the AI ​​agent's speaking style can be changed to a calmer tone to help the user relax. This allows the customization department to respond flexibly to user needs and improve user satisfaction.

[0033] The proposal team proposes virtual coffee breaks to reduce feelings of loneliness during remote work. Specifically, the team proposes virtual coffee breaks during remote work and reduces feelings of loneliness by engaging in conversation with users. For example, they might suggest, "Why don't we take a short break and have some coffee?" to provide users with time to relax. The team can also suggest relaxation methods to help users relax. For example, they might suggest, "Let's take some deep breaths and relax," to support user relaxation. Furthermore, the team can make appropriate suggestions according to the user's emotional state. For example, if a user is feeling stressed, the team will suggest activities to reduce stress. In this way, the team can make appropriate suggestions according to the user's emotional state, reduce user stress, and alleviate feelings of loneliness during remote work. The team also collects user feedback and evaluates the effectiveness of the suggestions. This allows the team to make optimal suggestions that meet user needs. For example, if a user shows a positive response to a particular suggestion, repeating that suggestion can improve user satisfaction. Furthermore, the team can analyze users' usage history and behavioral patterns to make optimal suggestions for each user. This allows the proposal department to respond flexibly to user needs and improve user satisfaction.

[0034] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0035] The AI ​​agent system can also include a health management unit that collects user health data and provides feedback based on their health status. For example, the health management unit can collect data on the user's diet and exercise and suggest healthy lifestyle habits. It can also analyze the user's sleep data and provide advice to improve sleep quality. Furthermore, the health management unit can monitor the user's weight and blood pressure data and provide feedback to detect health risks early. This allows users to maintain their health and adopt better lifestyle habits.

[0036] The AI ​​agent system can also include an entertainment section that suggests entertainment content based on the user's hobbies and interests. For example, the entertainment section can suggest movies and music that the user likes. It can also suggest books based on the user's reading history. Furthermore, the entertainment section can suggest new games based on the user's gaming history. This allows users to enjoy entertainment that suits their hobbies and interests.

[0037] The AI ​​agent system can also include a learning support unit that collects user learning data and provides feedback based on learning progress. For example, the learning support unit can analyze the user's learning history and understand their learning progress. It can also suggest learning methods tailored to the user's learning style. Furthermore, the learning support unit can assist the user in creating a learning plan based on their learning goals. This allows the user to learn more efficiently.

[0038] The AI ​​agent system can also include a schedule management unit that manages the user's schedule and provides reminders based on that schedule. For example, the schedule management unit can synchronize the user's calendar and remind them of important appointments. It can also manage the user's tasks and notify them of tasks with approaching deadlines. Furthermore, the schedule management unit can suggest the best time management methods based on the user's schedule. This allows the user to manage their time efficiently.

[0039] The AI ​​agent system can also include a shopping support unit that collects user shopping data and suggests recommended products based on purchase history. For example, the shopping support unit can analyze the user's past purchase history and suggest related products. It can also suggest new products tailored to the user's preferences. Furthermore, the shopping support unit can provide advantageous sale information based on the user's purchasing patterns. This allows users to enjoy shopping more efficiently.

[0040] The following briefly describes the processing flow for example form 1.

[0041] Step 1: The emotion analysis unit analyzes the user's emotions in real time. The emotion analysis unit collects data using sensors from wearable devices and smartphones. For example, it collects data such as heart rate, skin electrical activity, and voice tone, and the AI ​​analyzes this data to understand the user's emotional state. Step 2: The feedback unit provides positive messages and relaxation techniques based on the data analyzed by the emotion analysis unit. For example, if the user is feeling stressed, it provides positive messages and words of encouragement. If the user is relaxed, it provides advice to maintain that state. Furthermore, if the user is feeling anxious, it provides messages that provide reassurance. Step 3: The customization section allows users to customize the AI ​​agent's personality and speaking style. For example, users can set the AI ​​agent's personality and speaking style to their liking. Various options are available, such as a gentle personality, an energetic personality, a calm speaking style, or a cheerful speaking style. Step 4: The proposal team proposes virtual coffee breaks to reduce feelings of loneliness during remote work. For example, they propose virtual coffee breaks during remote work, allowing users to converse with others to reduce feelings of loneliness. They also propose relaxation techniques to help users relax.

[0042] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that provides positive messages and encouragement in response to various challenges and stresses faced in daily life. The AI ​​agent system has a function that allows the user to customize the AI ​​agent's personality and speaking style, and uses sensors from wearable devices and smartphones to analyze emotions in real time and provide immediate feedback. The AI ​​agent system also suggests relaxation methods and reduces stress. This mechanism reduces feelings of loneliness in adults working remotely and supports a stress-free environment. For example, when a user customizes the AI ​​agent's personality and speaking style, the user can set the AI ​​agent's personality and speaking style according to their preferences. For example, various options are available, such as a gentle personality, an energetic personality, a calm speaking style, or a cheerful speaking style. This allows the user to create an AI agent that suits them. Next, the system uses sensors from wearable devices and smartphones to analyze the user's emotions in real time. For example, it collects data such as heart rate, skin electrical activity, and voice tone, and the AI ​​analyzes this data to understand the user's emotional state. This allows for real-time analysis of various emotional states, such as when the user is stressed or relaxed. Furthermore, the AI ​​agent provides immediate feedback based on a real-time analysis of the user's emotional state. For example, if the user is feeling stressed, the AI ​​agent offers positive messages and words of encouragement. If the user is relaxed, the AI ​​agent provides advice to maintain that state. This ensures that the user always receives appropriate support. In addition, the AI ​​agent suggests relaxation techniques to reduce stress. For example, it can suggest various relaxation methods such as deep breathing, meditation, and light exercise. This allows users to find a relaxation method that suits them and reduce stress. This mechanism helps reduce feelings of loneliness in adults working remotely and supports a stress-free environment.For example, during remote work, an AI agent can suggest a virtual coffee break and engage in conversation with the user to reduce feelings of isolation. Furthermore, the AI ​​agent can support the user's mental health by providing positive messages and advice at appropriate times. In this way, the AI ​​agent system can analyze the user's emotions in real time and provide appropriate feedback and suggestions, thereby reducing stress and alleviating feelings of isolation during remote work.

[0043] The AI ​​agent system according to this embodiment comprises an emotion analysis unit, a feedback provision unit, a customization unit, and a suggestion unit. The emotion analysis unit analyzes the user's emotions in real time. The emotion analysis unit collects data by utilizing sensors from wearable devices or smartphones, for example. For example, the emotion analysis unit collects data such as heart rate, skin electrical activity, and voice tone, and the AI ​​analyzes this data to understand the user's emotional state. For example, the emotion analysis unit can estimate the user's stress level based on fluctuations in heart rate. The emotion analysis unit can also estimate the user's emotional state by analyzing changes in skin electrical activity. Furthermore, the emotion analysis unit can also estimate the user's emotional state by analyzing changes in voice tone. The feedback provision unit provides positive messages and relaxation methods based on the data analyzed by the emotion analysis unit. For example, if the user is feeling stressed, the feedback provision unit provides positive messages and words of encouragement. For example, the feedback provision unit can provide messages such as, "You're doing a great job." The feedback provision unit can also provide advice to maintain a relaxed state if the user is relaxed. For example, the feedback section can provide advice such as, "Just relax and continue." Furthermore, if the user is feeling anxious, the feedback section can also provide reassuring messages. For example, the feedback section can provide messages such as, "It's okay, you're not alone." The customization section allows users to customize the AI ​​agent's personality and speaking style. For example, the customization section can set the AI ​​agent's personality and speaking style to suit the user's preferences. For example, the customization section can offer various options such as a gentle personality, an energetic personality, a calm speaking style, or a cheerful speaking style. This allows users to create an AI agent that suits them. The suggestion section can suggest a virtual coffee break to reduce feelings of loneliness during remote work.The suggestion function can, for example, reduce feelings of loneliness by suggesting a virtual coffee break during remote work and engaging in conversation with the user. For instance, the suggestion function could make a suggestion such as, "Why don't you take a short break and have some coffee?" The suggestion function can also suggest relaxation techniques to help the user relax. For example, the suggestion function could make a suggestion such as, "Take a deep breath and relax." In this way, the AI ​​agent system according to the embodiment can reduce stress and alleviate feelings of loneliness during remote work by analyzing the user's emotions in real time and providing appropriate feedback and suggestions.

[0044] The emotion analysis unit analyzes users' emotions in real time. For example, it collects data using sensors from wearable devices and smartphones. Specifically, wearable devices collect biometric data such as heart rate, skin electrical activity, and body temperature, while smartphones detect changes in voice tone and facial expressions. This data is analyzed by AI to gain a detailed understanding of the user's emotional state. For instance, fluctuations in heart rate serve as an indicator of stress levels; a sudden increase in heart rate suggests the user is likely experiencing tension or anxiety. Changes in skin electrical activity indicate the user's state of excitement or relaxation, allowing for an estimation of their emotions. Furthermore, changes in voice tone can capture subtle shifts in the user's emotions; for example, a lower voice tone may indicate the user is feeling depressed. This data is collected in real time and rapidly analyzed by AI, enabling an immediate understanding of the user's emotional state. Additionally, the emotion analysis unit can accumulate past data and learn user emotional tendencies and patterns, enabling more accurate emotion analysis. For example, it is possible to understand and predict what emotions a user is likely to experience at specific times or in specific situations. This allows the emotion analysis department to understand the user's emotional state in real time and provide a foundation for giving appropriate feedback and suggestions.

[0045] The feedback department provides positive messages and relaxation techniques based on data analyzed by the emotion analysis department. Specifically, if a user is feeling stressed, the feedback department provides messages and relaxation techniques to calm the user. For example, it enhances the user's self-esteem by providing positive messages such as, "You're doing a great job." If a user is relaxed, it provides advice to maintain that state. For example, it helps the user stay relaxed by providing advice such as, "Keep relaxing and continuing." Furthermore, if a user is feeling anxious, it provides reassuring messages. For example, it reduces the user's anxiety by providing messages such as, "It's okay, you're not alone." By providing appropriate messages and advice according to the user's emotional state, the feedback department reduces user stress and supports mental stability. The feedback department also collects user feedback and evaluates the effectiveness of the messages and advice it provides. This allows the feedback department to provide optimal feedback tailored to the user's needs. For example, if a user shows a positive response to a particular message, repeating that message can help maintain the user's mental stability. In this way, the feedback department can provide appropriate feedback according to the user's emotional state and support their mental health.

[0046] The customization section allows users to customize the AI ​​agent's personality and speaking style. Specifically, users can set the AI ​​agent's personality and speaking style to their preferences. For example, various options are provided, such as a gentle personality, an energetic personality, a calm speaking style, or a cheerful speaking style. Users can choose the option that suits them best and customize the AI ​​agent. This allows users to create an AI agent that suits them, resulting in a more user-friendly and easy-to-use system. Furthermore, the customization section can collect user feedback and continuously improve the AI ​​agent's personality and speaking style. For example, if a user shows a positive reaction to a particular personality or speaking style, that personality or speaking style can be enhanced to improve user satisfaction. The customization section can also analyze the user's usage history and behavioral patterns and suggest the most suitable customization options for the user. This makes it easy for users to create the AI ​​agent that is best suited to them. In addition, the customization section can automatically adjust the AI ​​agent's personality and speaking style according to the user's emotional state. For example, if a user is feeling stressed, the AI ​​agent's speaking style can be changed to a calmer tone to help the user relax. This allows the customization department to respond flexibly to user needs and improve user satisfaction.

[0047] The proposal team proposes virtual coffee breaks to reduce feelings of loneliness during remote work. Specifically, the team proposes virtual coffee breaks during remote work and reduces feelings of loneliness by engaging in conversation with users. For example, they might suggest, "Why don't we take a short break and have some coffee?" to provide users with time to relax. The team can also suggest relaxation methods to help users relax. For example, they might suggest, "Let's take some deep breaths and relax," to support user relaxation. Furthermore, the team can make appropriate suggestions according to the user's emotional state. For example, if a user is feeling stressed, the team will suggest activities to reduce stress. In this way, the team can make appropriate suggestions according to the user's emotional state, reduce user stress, and alleviate feelings of loneliness during remote work. The team also collects user feedback and evaluates the effectiveness of the suggestions. This allows the team to make optimal suggestions that meet user needs. For example, if a user shows a positive response to a particular suggestion, repeating that suggestion can improve user satisfaction. Furthermore, the team can analyze users' usage history and behavioral patterns to make optimal suggestions for each user. This allows the proposal department to respond flexibly to user needs and improve user satisfaction.

[0048] The emotion analysis unit can collect data using sensors from wearable devices and smartphones. For example, the emotion analysis unit can collect heart rate and skin electrical activity data using wearable devices. For example, the emotion analysis unit can monitor heart rate in real time using a smartwatch and estimate stress levels. It can also collect skin electrical activity data using a fitness tracker and estimate emotional states. Furthermore, the emotion analysis unit can collect voice tone data using smartphone sensors. For example, the emotion analysis unit can analyze voice tone using a smartphone microphone and estimate emotional states. This allows for accurate collection of user emotional data by utilizing sensors from wearable devices and smartphones. Some or all of the above-described processes in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input data acquired from sensors in wearable devices and smartphones into a generating AI and have the generating AI perform the estimation of emotional states.

[0049] The feedback unit can provide positive messages and relaxation techniques based on the emotional state analyzed in real time. For example, if the user is feeling stressed, the feedback unit can provide positive messages and words of encouragement. For example, the feedback unit can provide a message such as, "You're doing a great job." Also, if the user is relaxed, the feedback unit can provide advice to help maintain that state. For example, the feedback unit can provide advice such as, "Just keep relaxing." Furthermore, if the user is feeling anxious, the feedback unit can provide reassuring messages. For example, the feedback unit can provide a message such as, "It's okay, you're not alone." In this way, by providing feedback based on the emotional state in real time, the user's stress can be reduced. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input emotional data analyzed by the emotion analysis unit into a generating AI, and have the generating AI provide positive messages and relaxation techniques.

[0050] The customization section allows users to set the personality and speaking style of the AI ​​agent. For example, the customization section can provide various options such as a gentle personality, an energetic personality, a calm speaking style, or a cheerful speaking style. This allows users to create an AI agent that suits them. Some or all of the above-described processes in the customization section may be performed using AI, or not. For example, the customization section can have a generating AI perform the setting of the AI ​​agent's personality and speaking style based on the user's preferences. This allows users to customize the AI ​​agent to their liking.

[0051] The suggestion unit can propose virtual coffee breaks during remote work and engage in conversation with the user. For example, by proposing virtual coffee breaks during remote work and engaging in conversation with the user, the suggestion unit can reduce feelings of loneliness. For example, the suggestion unit can make a suggestion such as, "Why don't you take a short break and have some coffee?" The suggestion unit can also suggest relaxation methods to help the user relax. For example, the suggestion unit can make a suggestion such as, "Let's take some deep breaths and relax." This can reduce feelings of loneliness during remote work and support the user's mental health. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's emotional data into a generating AI and have the generating AI execute a virtual coffee break suggestion.

[0052] The emotion analysis unit can estimate the user's emotions and adjust the accuracy of the emotion analysis based on the estimated emotions. For example, if the user is stressed, the emotion analysis unit prioritizes analyzing heart rate and skin electrical activity data to improve the accuracy of the emotion analysis. For instance, it can analyze heart rate fluctuations in detail to estimate stress levels with high accuracy. It can also analyze changes in skin electrical activity in detail to estimate emotional states with high accuracy. Furthermore, if the user is relaxed, the emotion analysis unit can also perform emotion analysis by emphasizing voice tone and facial expression data. For example, it can analyze changes in voice tone in detail to estimate a relaxed state with high accuracy. It can also analyze changes in facial expression data in detail to estimate a relaxed state with high accuracy. In addition, if the user's emotions are unstable, the emotion analysis unit can improve the accuracy of the emotion analysis by integrating data from multiple sensors. For example, it can integrate and analyze heart rate, skin electrical activity, voice tone, and facial expression data to estimate emotional states with high accuracy. This allows for more accurate sentiment analysis by adjusting the accuracy of the sentiment analysis based on the user's emotions. Some or all of the above-described processes in the sentiment analysis unit may be performed using AI, for example, or without AI. For example, the sentiment analysis unit can input data acquired from multiple sensors into a generating AI and have the generating AI perform the adjustment of the sentiment analysis accuracy.

[0053] The emotion analysis unit can optimize its analysis algorithm by referring to the user's past emotional data during emotion analysis. For example, the emotion analysis unit can identify times and situations in which the user is likely to feel stressed based on the user's past emotional data and adjust the analysis algorithm accordingly. For example, the emotion analysis unit can analyze past emotional data and predict stress levels in specific times and situations. The emotion analysis unit can also extract specific patterns from the user's past emotional data to improve the accuracy of emotion analysis. For example, the emotion analysis unit can identify specific patterns based on past emotional data and predict emotional states based on those patterns. Furthermore, the emotion analysis unit can refer to the user's past emotional data and optimize the analysis algorithm to provide appropriate feedback in response to changes in emotion. For example, the emotion analysis unit can adjust the algorithm to provide feedback in response to changes in emotion based on past emotional data. This improves the accuracy of emotion analysis by referring to past emotional data. Some or all of the above processes in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input past emotional data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0054] The emotion analysis unit can predict emotional states by considering the user's lifestyle patterns and daily behavioral data during emotion analysis. For example, the emotion analysis unit can analyze the user's lifestyle patterns and predict emotional states during specific time periods or activities. For example, the emotion analysis unit can predict emotional states during specific time periods or activities based on the user's lifestyle patterns. The emotion analysis unit can also predict changes in emotions based on the user's daily behavioral data and provide feedback at appropriate times. For example, the emotion analysis unit can predict changes in emotions based on daily behavioral data and provide feedback at appropriate times. Furthermore, the emotion analysis unit can adjust its analysis algorithm to improve the accuracy of emotional state predictions by considering the user's lifestyle patterns and behavioral data. For example, the emotion analysis unit can adjust its algorithm to improve the accuracy of emotional state predictions based on lifestyle patterns and behavioral data. This improves the accuracy of emotional state predictions by considering lifestyle patterns and behavioral data. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input lifestyle patterns and behavioral data into a generating AI and have the generating AI perform emotional state predictions.

[0055] The sentiment analysis unit can estimate the user's emotions and adjust the frequency of sentiment analysis based on the estimated emotions. For example, if the user is stressed, the sentiment analysis unit can increase the frequency of sentiment analysis to provide real-time feedback. For example, if the stress level is high, the sentiment analysis unit can increase the frequency of sentiment analysis to provide real-time feedback. The sentiment analysis unit can also decrease the frequency of sentiment analysis when the user is relaxed and provide feedback only when necessary. For example, if the user remains relaxed, the sentiment analysis unit can decrease the frequency of sentiment analysis and provide feedback only when necessary. Furthermore, if the user's emotions are unstable, the sentiment analysis unit can adjust the frequency of sentiment analysis to provide feedback at the appropriate time. For example, if the emotions are unstable, the sentiment analysis unit can adjust the frequency of sentiment analysis to provide feedback at the appropriate time. In this way, by adjusting the frequency of sentiment analysis based on the user's emotions, feedback can be provided at the appropriate time. Some or all of the above processing in the sentiment analysis unit may be performed using AI, for example, or without using AI. For example, the emotion analysis unit can input emotion data into a generating AI and have the generating AI adjust the frequency of emotion analysis.

[0056] The sentiment analysis unit can analyze emotional states by considering the user's geographical location information during sentiment analysis. For example, if the user is in a specific location, the sentiment analysis unit can analyze emotional states related to that location. The sentiment analysis unit can also predict emotional states by comparing the user's geographical location information with past emotional data. Furthermore, if the user is on the move, the sentiment analysis unit can update geographical location information in real time and analyze emotional states. For example, if the user is on the move, the sentiment analysis unit can update geographical location information in real time and analyze emotional states. This improves the accuracy of emotional state analysis by considering geographical location information. Some or all of the above processing in the sentiment analysis unit may be performed using AI, for example, or without AI. For example, the sentiment analysis unit can input geographical location information into a generating AI and have the generating AI perform the emotional state analysis.

[0057] The sentiment analysis unit can analyze a user's social media activity and understand their emotional state during sentiment analysis. For example, the sentiment analysis unit can analyze the content of a user's social media posts to understand their emotional state. The sentiment analysis unit can also predict a user's emotional state based on the frequency and content of their social media activity. Furthermore, the sentiment analysis unit can analyze a user's social media interaction patterns to understand their emotional state. This improves the accuracy of understanding emotional states by analyzing social media activity. Some or all of the above processing in the sentiment analysis unit may be performed using AI, for example, or without AI. For example, the sentiment analysis unit can input social media activity data into a generating AI and have the generating AI perform the emotional state assessment.

[0058] The feedback provider can estimate the user's emotions and adjust the content of the feedback based on those emotions. For example, if the user is feeling stressed, the feedback provider can provide positive messages and words of encouragement. For instance, it could provide a message such as, "You're doing a great job." If the user is relaxed, the feedback provider can also provide advice to help them maintain that state. For example, it could provide advice such as, "Just stay relaxed and keep going." Furthermore, if the user is feeling anxious, the feedback provider can provide reassuring messages. For example, it could provide a message such as, "It's okay, you're not alone." This allows for more appropriate feedback to be provided by adjusting the content based on the user's emotions. Some or all of the above processing in the feedback provider may be performed using AI, or not. For example, the feedback provider can input emotion data into a generating AI and have the generating AI adjust the content of the feedback.

[0059] The feedback provider can provide the most suitable message by referring to the user's past feedback history when providing feedback. For example, the feedback provider can provide the most suitable message based on the feedback the user has received in the past. For example, the feedback provider can analyze the past feedback history and provide a message appropriate to a specific situation. The feedback provider can also suggest a message appropriate to a specific situation based on the user's past feedback history. For example, the feedback provider can suggest a message appropriate to a specific situation based on the past feedback history. Furthermore, the feedback provider can analyze the user's past feedback history and provide a message tailored to their emotional state. For example, the feedback provider can provide a message tailored to their emotional state based on the past feedback history. This allows the provider to provide the most suitable message by referring to the past feedback history. Some or all of the above processing in the feedback provider may be performed using AI, for example, or without AI. For example, the feedback provider can input the past feedback history into a generating AI and have the generating AI perform the task of providing the most suitable message.

[0060] The feedback provider can adjust the timing of feedback based on the user's current activity status. For example, if the user is working, the feedback provider can provide feedback at an appropriate time. For example, the feedback provider can provide feedback to a user who is working at an appropriate time. The feedback provider can also provide feedback suggesting relaxation methods if the user is on a break. For example, the feedback provider can provide feedback suggesting relaxation methods to a user who is on a break. Furthermore, the feedback provider can provide concise feedback if the user is on the move. For example, the feedback provider can provide concise feedback to a user who is on the move. In this way, by adjusting the timing of feedback based on the current activity status, feedback can be provided at an appropriate time. Some or all of the above processing in the feedback provider may be performed using AI, for example, or not using AI. For example, the feedback provider can input current activity status data into a generating AI and have the generating AI perform the adjustment of the feedback timing.

[0061] The feedback provider can estimate the user's emotions and adjust the format of the feedback based on the estimated emotions. For example, if the user is feeling stressed, the feedback provider can provide simple, visual feedback. For example, the feedback provider can provide simple, visual feedback to a user who is feeling stressed. The feedback provider can also provide detailed feedback if the user is relaxed. For example, the feedback provider can provide detailed feedback to a relaxed user. Furthermore, if the user is feeling anxious, the feedback provider can provide reassuring feedback. For example, the feedback provider can provide reassuring feedback to an anxious user. This allows for more appropriate feedback to be provided by adjusting the format of the feedback based on the user's emotions. Some or all of the above processing in the feedback provider may be performed using AI, for example, or without AI. For example, the feedback provider can input emotion data into a generating AI and have the generating AI perform the adjustment of the feedback format.

[0062] The feedback delivery unit can provide the most appropriate message when providing feedback, taking into account the user's geographical location. For example, if the user is in a specific location, the feedback delivery unit can provide a message relevant to that location. The feedback delivery unit can also provide the most appropriate message by comparing the user's geographical location with their past feedback history. Furthermore, if the user is on the move, the feedback delivery unit can update their geographical location in real time and provide the most appropriate message. This allows the system to provide the most appropriate message by considering the user's geographical location. Some or all of the above processing in the feedback delivery unit may be performed using AI, for example, or without AI. For example, the feedback delivery unit can input geographical location information into a generating AI and have the generating AI provide the most appropriate message.

[0063] The feedback provision unit can analyze the user's social media activity and provide relevant messages when providing feedback. For example, the feedback provision unit can analyze the content of the user's social media posts and provide relevant messages. The feedback provision unit can also provide optimal messages based on the frequency and content of the user's social media activity. Furthermore, the feedback provision unit can analyze the user's social media interaction patterns and provide relevant messages. In this way, relevant messages can be provided by analyzing social media activity. Some or all of the above processing in the feedback provision unit may be performed using AI, for example, or without AI. For example, the feedback provision unit can input social media activity data into a generating AI and have the generating AI perform the provision of relevant messages.

[0064] The customization unit can estimate the user's emotions and suggest customization options based on those emotions. For example, if the user is feeling stressed, the customization unit can suggest options for a relaxing personality or speaking style. The customization unit can also suggest options to maintain a relaxed state if the user is relaxed. Furthermore, if the user is feeling anxious, the customization unit can suggest options for a reassuring personality or speaking style. This allows for more appropriate customization by suggesting options based on the user's emotions. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input emotion data into a generating AI and have the generating AI execute the customization option suggestion.

[0065] The customization unit can suggest optimal settings by referring to the user's past customization history during the customization process. For example, the customization unit can suggest optimal customization options based on the personality and speaking style previously set by the user. For example, the customization unit can analyze past customization history and suggest settings appropriate for a specific situation. The customization unit can also suggest settings appropriate for a specific situation based on the user's past customization history. For example, the customization unit can suggest settings appropriate for a specific situation based on past customization history. Furthermore, the customization unit can analyze the user's past customization history and suggest settings that match their emotional state. For example, the customization unit can suggest settings that match their emotional state based on past customization history. This allows the customization unit to suggest optimal settings by referring to past customization history. Some or all of the above processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input past customization history into a generating AI and have the generating AI suggest optimal settings.

[0066] The customization unit can provide customization options based on the user's current living situation and areas of interest during the customization process. For example, the customization unit can analyze the user's current living situation and provide the optimal customization option. For example, the customization unit can provide the optimal customization option based on the current living situation. The customization unit can also provide relevant customization options based on the user's areas of interest. For example, the customization unit can provide relevant customization options based on the user's areas of interest. Furthermore, the customization unit can provide customization options considering the user's living situation and areas of interest. For example, the customization unit can provide customization options considering the user's living situation and areas of interest. This allows for more appropriate customization by providing customization options based on the user's current living situation and areas of interest. Some or all of the above-described processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the task of providing customization options.

[0067] The customization unit can estimate the user's emotions and determine customization priorities based on those emotions. For example, if the user is feeling stressed, the customization unit can prioritize suggesting relaxing customization options. For example, if the user is feeling stressed, the customization unit can prioritize suggesting relaxing customization options. For example, if the user is relaxed, the customization unit can prioritize suggesting customization options to maintain that state. For example, if the user is relaxed, the customization unit can prioritize suggesting customization options to maintain that state. Furthermore, if the user is feeling anxious, the customization unit can prioritize suggesting customization options to provide a sense of security. For example, if the user is feeling anxious, the customization unit can prioritize suggesting customization options to provide a sense of security. This allows for more appropriate customization by determining customization priorities based on the user's emotions. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input emotion data into a generating AI and have the generating AI perform the customization priority determination.

[0068] The customization unit can suggest optimal settings by considering the user's geographical location during customization. For example, if the user is in a specific location, the customization unit can suggest customization options related to that location. The customization unit can also suggest optimal settings by comparing the user's geographical location with their past customization history. Furthermore, if the user is on the move, the customization unit can update their geographical location in real time and suggest optimal settings. This allows the customization unit to suggest optimal settings by considering geographical location. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input geographical location information into a generating AI and have the generating AI suggest optimal settings.

[0069] The customization unit can analyze the user's social media activity during customization and suggest relevant settings. For example, the customization unit can analyze the content of the user's social media posts and suggest relevant customization options. The customization unit can also suggest optimal customization settings based on the frequency and content of the user's social media activity. Furthermore, the customization unit can analyze the user's social media interaction patterns and suggest relevant settings. In this way, relevant settings can be suggested by analyzing social media activity. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input social media activity data into a generating AI and have the generating AI suggest relevant settings.

[0070] The suggestion unit can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is feeling stressed, the suggestion unit can suggest relaxation techniques or positive messages. For example, if the user is feeling stressed, the suggestion unit can suggest relaxation techniques or positive messages. The suggestion unit can also suggest advice to help the user maintain a relaxed state. For example, if the user is feeling relaxed, the suggestion unit can suggest advice to help the user maintain that state. Furthermore, if the user is feeling anxious, the suggestion unit can offer suggestions to provide reassurance. For example, if the suggestion unit is feeling anxious, the suggestion unit can offer suggestions to provide reassurance. By adjusting the content of suggestions based on the user's emotions, more appropriate suggestions become possible. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input emotion data into a generating AI and have the generating AI adjust the content of the suggestions.

[0071] The suggestion unit can provide the most suitable suggestion by referring to the user's past suggestion history. For example, the suggestion unit can provide the most suitable suggestion based on the content of suggestions the user has received in the past. For example, the suggestion unit can analyze the past suggestion history and provide suggestions tailored to a specific situation. The suggestion unit can also make suggestions tailored to a specific situation based on the user's past suggestion history. For example, the suggestion unit can make suggestions tailored to a specific situation based on the past suggestion history. Furthermore, the suggestion unit can analyze the user's past suggestion history and provide suggestions tailored to their emotional state. For example, the suggestion unit can provide suggestions tailored to their emotional state based on the past suggestion history. In this way, the optimal suggestion can be provided by referring to the past suggestion history. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI. For example, the suggestion unit can input the past suggestion history into a generation AI and have the generation AI perform the task of providing the optimal suggestion.

[0072] The suggestion unit can provide suggestions based on the user's current living situation and areas of interest. For example, the suggestion unit can analyze the user's current living situation and provide the most suitable suggestion. For example, the suggestion unit can provide the most suitable suggestion based on the user's current living situation. The suggestion unit can also provide relevant suggestions based on the user's areas of interest. For example, the suggestion unit can provide relevant suggestions based on the user's areas of interest. Furthermore, the suggestion unit can provide suggestions considering the user's living situation and areas of interest. For example, the suggestion unit can provide suggestions considering the user's living situation and areas of interest. This makes it possible to provide more appropriate suggestions by providing suggestions based on the user's current living situation and areas of interest. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the task of providing suggestions.

[0073] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the user is feeling stressed, the suggestion unit can prioritize suggestions for relaxation. For example, if the user is feeling stressed, the suggestion unit can prioritize suggestions for relaxation. For example, if the user is relaxed, the suggestion unit can prioritize suggestions for maintaining that state. Furthermore, if the user is feeling anxious, the suggestion unit can prioritize suggestions that provide a sense of security. For example, if the suggestion unit is feeling anxious, the suggestion unit can prioritize suggestions that provide a sense of security. This allows for more appropriate suggestions by determining the priority of suggestions based on the user's emotions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input emotion data into a generating AI and have the generating AI perform the suggestion priority determination.

[0074] The suggestion unit can provide optimal suggestions by considering the user's geographical location information when making suggestions. For example, if the user is in a specific location, the suggestion unit can make suggestions related to that location. For example, the suggestion unit can make suggestions related to a user who is in a specific location. The suggestion unit can also provide optimal suggestions by comparing the user's geographical location information with past suggestion history. For example, the suggestion unit can provide optimal suggestions by comparing the geographical location information with past suggestion history. Furthermore, if the user is on the move, the suggestion unit can update the geographical location information in real time and provide optimal suggestions. For example, the suggestion unit can update the geographical location information in real time while the user is on the move and provide optimal suggestions. This allows the suggestion unit to provide optimal suggestions by considering geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input geographical location information into a generation AI and have the generation AI perform the task of providing optimal suggestions.

[0075] The suggestion unit can analyze the user's social media activity and provide relevant suggestions when making suggestions. For example, the suggestion unit can analyze the content of the user's social media posts and provide relevant suggestions. The suggestion unit can also provide optimal suggestions based on the frequency and content of the user's social media activity. Furthermore, the suggestion unit can analyze the user's social media interaction patterns and provide relevant suggestions. In this way, relevant suggestions can be provided by analyzing social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input social media activity data into a generating AI and have the generating AI perform the task of providing relevant suggestions.

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

[0077] The AI ​​agent system can also include a health management unit that collects user health data and provides feedback based on their health status. For example, the health management unit can collect data on the user's diet and exercise and suggest healthy lifestyle habits. It can also analyze the user's sleep data and provide advice to improve sleep quality. Furthermore, the health management unit can monitor the user's weight and blood pressure data and provide feedback to detect health risks early. This allows users to maintain their health and adopt better lifestyle habits.

[0078] The AI ​​agent system can also include an entertainment section that suggests entertainment content based on the user's hobbies and interests. For example, the entertainment section can suggest movies and music that the user likes. It can also suggest books based on the user's reading history. Furthermore, the entertainment section can suggest new games based on the user's gaming history. This allows users to enjoy entertainment that suits their hobbies and interests.

[0079] The AI ​​agent system can also include a learning support unit that collects user learning data and provides feedback based on learning progress. For example, the learning support unit can analyze the user's learning history and understand their learning progress. It can also suggest learning methods tailored to the user's learning style. Furthermore, the learning support unit can assist the user in creating a learning plan based on their learning goals. This allows the user to learn more efficiently.

[0080] The AI ​​agent system can also include a schedule management unit that manages the user's schedule and provides reminders based on that schedule. For example, the schedule management unit can synchronize the user's calendar and remind them of important appointments. It can also manage the user's tasks and notify them of tasks with approaching deadlines. Furthermore, the schedule management unit can suggest the best time management methods based on the user's schedule. This allows the user to manage their time efficiently.

[0081] The AI ​​agent system can also include a shopping support unit that collects user shopping data and suggests recommended products based on purchase history. For example, the shopping support unit can analyze the user's past purchase history and suggest related products. It can also suggest new products tailored to the user's preferences. Furthermore, the shopping support unit can provide advantageous sale information based on the user's purchasing patterns. This allows users to enjoy shopping more efficiently.

[0082] An AI agent system can include a music provider that estimates the user's emotions and selects music based on those emotions. For example, if the user is feeling stressed, the music provider can suggest relaxing music. If the user is relaxed, the music provider can suggest music to maintain that state. Furthermore, if the user wants to feel energized, the music provider can suggest energetic music. This allows users to enjoy music that matches their emotions.

[0083] The AI ​​agent system can be equipped with a fitness support unit that estimates the user's emotions and suggests exercises based on those emotions. For example, if the user is feeling stressed, the fitness support unit can suggest relaxing yoga or stretching. If the user wants to feel more energetic, it can suggest energetic exercises. Furthermore, if the user is relaxed, it can suggest light exercises to maintain that state. This allows users to exercise in a way that matches their emotions.

[0084] The AI ​​agent system can include a nutrition management unit that estimates the user's emotions and suggests meals based on those emotions. For example, if the user is feeling stressed, the nutrition management unit can suggest a relaxing meal. It can also suggest an energetic meal if the user wants to feel more energetic. Furthermore, if the user is relaxed, the nutrition management unit can suggest a balanced meal to maintain that state. This allows users to enjoy meals tailored to their emotions.

[0085] An AI agent system can include a travel support unit that estimates the user's emotions and proposes travel plans based on those emotions. For example, if the user is feeling stressed, the travel support unit can suggest a relaxing travel destination. If the user wants to feel more energetic, it can suggest an active travel plan. Furthermore, if the user is relaxed, it can suggest a calm travel plan to maintain that state. This allows users to enjoy a trip tailored to their emotions.

[0086] An AI agent system can be equipped with a hobby support unit that estimates the user's emotions and suggests hobby activities based on those emotions. For example, if the user is feeling stressed, the hobby support unit can suggest relaxing hobby activities. It can also suggest energetic hobby activities if the user wants to feel more energetic. Furthermore, if the user is relaxed, the hobby support unit can suggest calming hobby activities to maintain that state. This allows users to enjoy hobby activities that match their emotions.

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

[0088] Step 1: The emotion analysis unit analyzes the user's emotions in real time. The emotion analysis unit collects data using sensors from wearable devices and smartphones. For example, it collects data such as heart rate, skin electrical activity, and voice tone, and the AI ​​analyzes this data to understand the user's emotional state. Step 2: The feedback unit provides positive messages and relaxation techniques based on the data analyzed by the emotion analysis unit. For example, if the user is feeling stressed, it provides positive messages and words of encouragement. If the user is relaxed, it provides advice to maintain that state. Furthermore, if the user is feeling anxious, it provides messages that provide reassurance. Step 3: The customization section allows users to customize the AI ​​agent's personality and speaking style. For example, users can set the AI ​​agent's personality and speaking style to their liking. Various options are available, such as a gentle personality, an energetic personality, a calm speaking style, or a cheerful speaking style. Step 4: The proposal team proposes virtual coffee breaks to reduce feelings of loneliness during remote work. For example, they propose virtual coffee breaks during remote work, allowing users to converse with others to reduce feelings of loneliness. They also propose relaxation techniques to help users relax.

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

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

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

[0092] Each of the multiple elements described above, including the emotion analysis unit, feedback provision unit, customization unit, and suggestion unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the emotion analysis unit is implemented by collecting data such as heart rate, skin electrical activity, and voice tone using the sensors of the smart device 14, and analyzing this data with the specific processing unit 290 of the data processing unit 12. The feedback provision unit is implemented, for example, with the specific processing unit 290 of the data processing unit 12, and provides positive messages and relaxation methods based on the data analyzed by the emotion analysis unit. The customization unit is implemented, for example, with the control unit 46A of the smart device 14, and allows the user to set the personality and speaking style of the AI ​​agent. The suggestion unit is implemented, for example, with the specific processing unit 290 of the data processing unit 12, and suggests virtual coffee breaks and relaxation methods to reduce feelings of loneliness during remote work. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0108] Each of the multiple elements described above, including the emotion analysis unit, feedback provision unit, customization unit, and suggestion unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the emotion analysis unit is implemented by collecting data such as heart rate, skin electrical activity, and voice tone using the sensors of the smart glasses 214, and analyzing this data with the specific processing unit 290 of the data processing unit 12. The feedback provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and provides positive messages and relaxation methods based on the data analyzed by the emotion analysis unit. The customization unit is implemented, for example, by the control unit 46A of the smart glasses 214, and allows the user to set the personality and speaking style of the AI ​​agent. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and suggests virtual coffee breaks and relaxation methods to reduce feelings of loneliness during remote work. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0124] Each of the multiple elements described above, including the emotion analysis unit, feedback provision unit, customization unit, and suggestion unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the emotion analysis unit is implemented by collecting data such as heart rate, skin electrical activity, and voice tone using the sensors of the headset terminal 314, and analyzing this data with the specific processing unit 290 of the data processing unit 12. The feedback provision unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides positive messages and relaxation methods based on the data analyzed by the emotion analysis unit. The customization unit is implemented in the control unit 46A of the headset terminal 314 and allows the user to set the personality and speaking style of the AI ​​agent. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12 and suggests virtual coffee breaks and relaxation methods to reduce feelings of loneliness during remote work. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0141] Each of the multiple elements described above, including the emotion analysis unit, feedback provision unit, customization unit, and suggestion unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the emotion analysis unit is implemented by collecting data such as heart rate, skin electrical activity, and voice tone using the sensors of the robot 414, and analyzing this data with the specific processing unit 290 of the data processing unit 12. The feedback provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and provides positive messages and relaxation methods based on the data analyzed by the emotion analysis unit. The customization unit is implemented, for example, by the control unit 46A of the robot 414, and allows the user to set the personality and speaking style of the AI ​​agent. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and suggests virtual coffee breaks and relaxation methods to reduce feelings of loneliness during remote work. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

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

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

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

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

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

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

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

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

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

[0151] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

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

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

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

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

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

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

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

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

[0160] (Note 1) The emotion analysis department analyzes user emotions in real time, A feedback provision unit provides positive messages and relaxation methods based on data analyzed by the aforementioned emotion analysis unit, A customization section where users can customize the AI ​​agent's personality and speaking style, It includes a proposal department that suggests virtual coffee breaks to reduce feelings of loneliness during remote work. A system characterized by the following features. (Note 2) The aforementioned emotion analysis unit, We collect data using sensors in wearable devices and smartphones. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned feedback provision unit, It provides positive messages and relaxation techniques based on emotional states analyzed in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned customization unit is Users can set the AI ​​agent's personality and speaking style. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Suggest virtual coffee breaks during remote work and engage in conversations with users. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned emotion analysis unit, It estimates the user's emotions and adjusts the accuracy of the emotion analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned emotion analysis unit, During sentiment analysis, the analysis algorithm is optimized by referencing the user's past sentiment data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned emotion analysis unit, When performing sentiment analysis, we predict the emotional state by considering the user's lifestyle patterns and daily behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned emotion analysis unit, It estimates the user's emotions and adjusts the frequency of emotion analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned emotion analysis unit, When analyzing emotions, the system takes the user's geographical location into consideration when analyzing their emotional state. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned emotion analysis unit, During sentiment analysis, the user's social media activity is analyzed to understand their emotional state. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned feedback provision unit, It estimates the user's emotions and adjusts the content of the feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned feedback provision unit, When providing feedback, we refer to the user's past feedback history to deliver the most appropriate message. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned feedback provision unit, When providing feedback, the timing of the feedback will be adjusted based on the user's current activity level. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned feedback provision unit, It estimates the user's emotions and adjusts the feedback format based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned feedback provision unit, When providing feedback, we will deliver the most appropriate message considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned feedback provision unit, When providing feedback, we analyze the user's social media activity and deliver relevant messages. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned customization unit is It estimates the user's emotions and suggests customization options based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned customization unit is During customization, the system will refer to the user's past customization history to suggest the optimal settings. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned customization unit is During customization, we provide customization options based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned customization unit is It estimates the user's emotions and determines the priority of customization based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned customization unit is During customization, we suggest optimal settings that take into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned customization unit is During customization, the system analyzes the user's social media activity and suggests relevant settings. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and adjusts the content of the suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making a proposal, we refer to the user's past proposal history to provide the most suitable proposal. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making suggestions, provide options based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When making a proposal, we provide the most suitable suggestions by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making suggestions, we analyze the user's social media activity and provide relevant recommendations. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

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

Claims

1. The emotion analysis department analyzes user emotions in real time, A feedback provision unit provides positive messages and relaxation methods based on data analyzed by the aforementioned emotion analysis unit, A customization section where users can customize the personality and speaking style of the AI ​​agent, It includes a proposal department that suggests virtual coffee breaks to reduce feelings of loneliness during remote work. A system characterized by the following features.

2. The aforementioned emotion analysis unit, We collect data using sensors in wearable devices and smartphones. The system according to feature 1.

3. The aforementioned feedback provision unit, It provides positive messages and relaxation techniques based on emotional states analyzed in real time. The system according to feature 1.

4. The aforementioned customization unit is Users can set the AI ​​agent's personality and speaking style. The system according to feature 1.

5. The aforementioned proposal section is, Suggest virtual coffee breaks during remote work and engage in conversations with users. The system according to feature 1.

6. The aforementioned emotion analysis unit, It estimates the user's emotions and adjusts the accuracy of the emotion analysis based on the estimated user emotions. The system according to feature 1.

7. The aforementioned emotion analysis unit, During sentiment analysis, the analysis algorithm is optimized by referencing the user's past sentiment data. The system according to feature 1.

8. The aforementioned emotion analysis unit, When performing sentiment analysis, we predict the emotional state by considering the user's lifestyle patterns and daily behavioral data. The system according to feature 1.

Images