system

Abstract

The system according to this embodiment aims to promote human-centered activities using environmental sounds and scents. [Solution] The system according to the embodiment comprises a detection unit, a generation unit, and a control unit. The detection unit detects the user's state. The generation unit generates ambient sounds and scents based on the information detected by the detection unit. The control unit controls the ambient sounds and scents generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a risk that the subjective thinking of humans may be hindered as the instructions of AI are materialized.

[0005] The system according to the embodiment aims to promote human-centered activities by using environmental sounds and scents.

Means for Solving the Problems

[0006] The system according to the embodiment includes a detection unit, a generation unit, and a control unit. The detection unit detects the state of the user. The generation unit generates environmental sounds and scents based on the information detected by the detection unit. The control unit controls the environmental sounds and scents generated by the generation unit.

Effects of the Invention

[0007] The system according to this embodiment can promote human-centered activities using environmental sounds and scents. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 appealing to the five senses according to an embodiment of the present invention proposes an AI agent that appeals to the five senses in order to solve the problem that language-based AI agents hinder human thought. In this system, when the user inputs "I want to go from XX to XX," the generating AI creates a video showing how to get from the current location to the destination. This video starts navigation according to the orientation of the user's smartphone, and the screen moves in accordance with the walking speed. This results in a simple structure that can be easily used by children and the elderly alike, making it enjoyable for everyone. Furthermore, since the viewpoint of the smartphone is pointed at all axes, there is no chance of getting lost, and walking safety is ensured because the smartphone is held horizontally. First, the user inputs "I want to go from XX to XX." At this time, the user only needs to input the starting point and destination. For example, the user inputs "I want to go from my home to the station." This information is input to the generating AI. Next, the generating AI analyzes the input information and creates a video showing how to get from the current location to the destination. The generating AI calculates the optimal route based on map data and generates a video along that route. For example, if the user inputs a route from home to the station, a video along that route will be generated. The generated video starts navigating in accordance with the orientation of the user's smartphone. For example, if the user is pointing their smartphone north, the video will also start navigating in the north direction. This allows the user to receive navigation that is aligned with the direction they are facing. Furthermore, the video screen moves in accordance with the user's walking speed. For example, if the user is walking slowly, the video will also progress slowly. This allows the user to receive navigation at their own pace. This mechanism results in a simple structure that is easy for children and the elderly to use, making it enjoyable for everyone. Users can receive navigation intuitively without having to perform complex operations. Also, since the viewpoint of the smartphone is the axis of all directions, there is no chance of getting lost, and walking safety is ensured because the smartphone is held horizontally. For example, if the user is walking with their smartphone held horizontally, the video will also be displayed horizontally, allowing the user to walk safely.This allows the AI ​​agent system, which appeals to the five senses, to promote human-centered activities by generating and controlling ambient sounds and scents according to the user's state.

[0029] The AI ​​agent system appealing to the five senses according to this embodiment comprises a detection unit, a generation unit, and a control unit. The detection unit detects the user's state. The user's state includes, but is not limited to, heart rate, body temperature, and facial expression. The detection unit detects the user's heart rate using, for example, a heart rate sensor. The detection unit can also detect the user's body temperature using a body temperature sensor. Furthermore, the detection unit can detect the user's facial expression using facial expression recognition technology. For example, the detection unit monitors the user's heart rate in real time using a heart rate sensor and issues an alert if an abnormality is detected. The body temperature sensor periodically measures the user's body temperature and notifies if an abnormality is detected. Facial expression recognition technology analyzes the user's facial expression and detects changes in emotion. The generation unit uses generation AI to generate ambient sounds and scents based on the information detected by the detection unit. The generated ambient sounds and scents include, but are not limited to, natural sounds and relaxing scents. For example, the generation unit uses generation AI to generate natural sounds corresponding to the user's state. Furthermore, the generation unit can use generation AI to generate relaxing scents. In addition, the generation unit can use generation AI to generate music tailored to the user's state. For example, the generation unit uses generation AI to generate relaxing nature sounds when the user's heart rate is high. The generation AI uses technologies such as deep learning and GAN (Generative Adversarial Network) to generate ambient sounds and scents. The control unit controls the ambient sounds and scents generated by the generation unit. For example, the control unit adjusts the intensity of the generated ambient sounds and scents according to the user's state. The control unit can also adjust the generated ambient sounds and scents to match the user's environment. Furthermore, the control unit can adjust the type of generated ambient sounds and scents based on the user's emotions. For example, the control unit increases the intensity of relaxing nature sounds when the user's heart rate is high. The control unit adjusts the concentration of the generated scents to match the user's environment. The control unit changes the type of generated music based on the user's emotions.As a result, the AI ​​agent system that appeals to the five senses according to the embodiment can promote human-centered activities by generating and controlling environmental sounds and scents according to the user's state.

[0030] The detection unit detects the user's state. This state includes, but is not limited to, heart rate, body temperature, and facial expression. For example, the detection unit can detect the user's heart rate using a heart rate sensor. The heart rate sensor, by contacting the user's skin, can monitor the rhythm and fluctuations of the heartbeat in real time. This allows for understanding the user's stress level and relaxation state. The detection unit can also detect the user's body temperature using a body temperature sensor. The body temperature sensor measures the temperature of the user's skin surface and tracks changes in body temperature in real time. This allows for early detection of changes in the user's health and physical condition. Furthermore, the detection unit can detect the user's facial expression using facial recognition technology. Facial recognition technology uses a camera to capture the user's face and analyzes changes in facial expression using a deep learning algorithm. This allows for real-time understanding of the user's emotional state and psychological changes. For example, the detection unit can monitor the user's heart rate in real time using a heart rate sensor and issue an alert if an abnormality is detected. The body temperature sensor periodically measures the user's body temperature and notifies the user if an abnormality is detected. Facial recognition technology analyzes the user's facial expressions and detects changes in emotion. This allows the detection unit to monitor the user's state from multiple angles and collect information in real time. Furthermore, the detection unit can centrally manage this information and collaborate with other systems and departments. For example, collected data can be stored on a cloud server and made accessible by the generation and control units. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. As a result, the detection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0031] The generation unit uses a generation AI to generate ambient sounds and scents based on information detected by the detection unit. The generated ambient sounds and scents include, but are not limited to, natural sounds and relaxing scents. The generation AI uses technologies such as deep learning and GAN (Generative Opposite Network) to generate ambient sounds and scents. Specifically, the generation AI receives data such as the user's heart rate, body temperature, and facial expressions as input, and generates optimal ambient sounds and scents based on this data. For example, the generation unit uses the generation AI to generate natural sounds that correspond to the user's state. These natural sounds include birdsong, the babbling of a stream, and the sound of wind, and are used to enhance the user's relaxation. The generation unit can also use the generation AI to generate relaxing scents. Essential oils such as lavender, chamomile, and mint are used for these scents, which are used to reduce user stress and enhance relaxation. Furthermore, the generation unit can also use the generation AI to generate music that corresponds to the user's state. The music includes genres such as classical, jazz, and ambient, and the most suitable music is selected based on the user's heart rate, body temperature, and facial expression. For example, the generation unit uses a generation AI to generate relaxing natural sounds when the user's heart rate is high. The generation AI uses technologies such as deep learning and GAN (Generative Adversarial Network) to generate ambient sounds and scents. This allows the generation unit to generate optimal ambient sounds and scents according to the user's state, enhancing the user's relaxation effect. Furthermore, the generation unit can adjust the generated ambient sounds and scents in real time. For example, it can adjust the type and intensity of the generated ambient sounds and scents according to changes in the user's heart rate, body temperature, and facial expression. This allows the generation unit to provide optimal ambient sounds and scents according to the user's state, maximizing the user's relaxation effect.

[0032] The control unit controls the ambient sounds and scents generated by the generation unit. For example, the control unit adjusts the intensity of the generated ambient sounds and scents according to the user's state. The control unit can also adjust the generated ambient sounds and scents to suit the user's environment. Specifically, the control unit monitors data such as the user's heart rate, body temperature, and facial expression in real time, and adjusts the intensity and type of generated ambient sounds and scents based on this data. For example, if the user's heart rate is high, the control unit increases the intensity of relaxing natural sounds. This can lower the user's heart rate and enhance relaxation. The control unit also adjusts the concentration of the generated scents according to the user's environment. For example, if the user is in a large room, increasing the scent concentration allows the scent to spread throughout the entire room. Conversely, if the user is in a small room, lowering the scent concentration prevents the scent from being too strong. Furthermore, the control unit can also change the type of music generated based on the user's emotions. For example, if the user is stressed, relaxing music can be played, and if the user is relaxed, more upbeat music can be played. This allows the control unit to provide optimal ambient sounds and scents according to the user's state and environment, maximizing the user's relaxation effect. Furthermore, the control unit can collect user feedback and evaluate the effects of the generated ambient sounds and scents. For example, it can collect whether the user felt relaxed through a questionnaire, and use this data to improve the algorithms of the generation and control units. This allows the control unit to provide optimal ambient sounds and scents according to the user's state and environment, maximizing the user's relaxation effect.

[0033] The generation unit generates ambient sounds and scents using a generation AI. For example, the generation unit uses the generation AI to generate natural sounds that correspond to the user's state. The generation AI uses technologies such as deep learning and GAN (Generative Adversarial Network) to generate ambient sounds and scents. For example, the generation unit uses the generation AI to generate relaxing natural sounds when the user's heart rate is high. The generation AI takes the user's state as input and outputs ambient sounds and scents. For example, the generation AI receives information such as the user's heart rate, body temperature, and facial expression as input and generates the optimal ambient sounds and scents based on that information. The generation AI can also adjust the generated ambient sounds and scents in real time. For example, if the user's heart rate changes, the generation AI automatically adjusts the type and intensity of the generated ambient sounds and scents. This improves the accuracy of ambient sound and scent generation when using the generation AI.

[0034] The control unit can control the ambient sounds and scents generated according to the user's state. For example, the control unit may increase the intensity of relaxing nature sounds when the user's heart rate is high. The control unit can also adjust the concentration of the generated scents to suit the user's environment. The control unit can also change the type of music generated based on the user's emotions. For example, the control unit may increase the intensity of relaxing nature sounds when the user's heart rate is high. The control unit may adjust the concentration of the generated scents to suit the user's environment. The control unit may change the type of music generated based on the user's emotions. This allows for the provision of more appropriate ambient sounds and scents through control according to the user's state. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can control ambient sounds and scents using an AI model that takes ambient sounds and scents generated by the generation unit as input and outputs control according to the user's state.

[0035] The detection unit can detect a decrease in the user's walking speed and concentration. For example, the detection unit detects the user's walking speed using a walking speed sensor. The detection unit can also monitor eye movements and the frequency of work interruptions to detect a decrease in concentration. For example, the detection unit monitors the user's walking speed in real time using a walking speed sensor and issues an alert if an abnormality is detected. The detection unit analyzes eye movements to detect a decrease in concentration. The detection unit records the frequency of work interruptions to detect a decrease in concentration. As a result, by detecting the user's walking speed and decrease in concentration, appropriate ambient sounds and scents can be provided. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the user's walking speed data into a generating AI and cause the generating AI to execute a process to detect abnormalities in walking speed.

[0036] The generation unit can generate music and scents tailored to the user's state using a generation AI. For example, the generation unit can use the generation AI to generate relaxing music when the user's heart rate is high. The generation AI uses technologies such as deep learning and GAN (Generative Adversarial Network) to generate music and scents. For example, the generation unit can use the generation AI to generate a refreshing scent when the user's body temperature is high. The generation AI takes the user's state as input and outputs music and scents. For example, the generation AI receives information such as the user's heart rate, body temperature, and facial expression as input and generates the optimal music and scent based on that information. The generation AI can also adjust the generated music and scents in real time. For example, if the user's heart rate changes, the generation AI automatically adjusts the type and intensity of the generated music and scents. As a result, using the generation AI improves the accuracy of generating music and scents tailored to the user's state.

[0037] The control unit can adjust the generated music and scent to suit the user's environment. For example, the control unit can adjust the volume of the generated music to suit the user's environment. The control unit can also adjust the concentration of the generated scent to suit the user's environment. The control unit can also change the type of generated music and scent based on the user's environment. For example, the control unit can adjust the volume of the generated music to suit the user's environment. The control unit can adjust the concentration of the generated scent to suit the user's environment. The control unit can change the type of generated music and scent based on the user's environment. This allows for the provision of more appropriate music and scent through adjustments tailored to the user's environment. Some or all of the above-described processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can adjust the music and scent using an AI model that takes the music and scent generated by the generation unit as input and outputs adjustments according to the user's environment.

[0038] The detection unit can analyze the user's past behavioral history and select the optimal detection method. For example, the detection unit can detect information that has a relaxing effect during times when the user previously felt the need to relax. The detection unit can also detect information that helps maintain concentration during times when the user previously felt the need to concentrate. The detection unit can also detect information that has a refreshing effect during times when the user previously felt tired. For example, the detection unit can detect information that has a relaxing effect during times when the user previously felt the need to relax. The detection unit can detect information that helps maintain concentration during times when the user previously felt the need to concentrate. The detection unit can detect information that has a refreshing effect during times when the user previously felt tired. By analyzing past behavioral history, the optimal detection method can be selected. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the user's past behavioral history data into a generating AI and have the generating AI select the optimal detection method.

[0039] The detection unit can filter information based on the user's current activity status and environment upon detection. For example, if the user is in the office, the detection unit can prioritize detecting information that helps maintain concentration. If the user is at home, the detection unit can also prioritize detecting information that has a relaxing effect. If the user is out, the detection unit can also prioritize detecting information that has a refreshing effect. This allows for the provision of more appropriate information through filtering based on the current activity status and environment. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the user's current activity status data into a generating AI and have the generating AI perform the filtering.

[0040] The detection unit can prioritize detecting highly relevant information by considering the user's geographical location information. For example, if the user is in a park, the detection unit will prioritize detecting natural sounds and scents. If the user is in a cafe, the detection unit can also prioritize detecting relaxing music and scents. If the user is in an office, the detection unit can also prioritize detecting music and scents that help maintain concentration. For example, if the user is in a park, the detection unit will prioritize detecting natural sounds and scents. If the user is in a cafe, the detection unit will prioritize detecting relaxing music and scents. If the user is in an office, the detection unit will prioritize detecting music and scents that help maintain concentration. This allows the system to provide highly relevant information by considering geographical location information. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the user's geographical location data into a generating AI and have the generating AI perform the detection of highly relevant information.

[0041] The detection unit can analyze the user's social media activity and detect relevant information upon detection. For example, if a user posts on social media that they want to relax, the detection unit can detect information that has a relaxing effect. If a user posts on social media that they want to concentrate, the detection unit can also detect information that helps maintain concentration. If a user posts on social media that they are tired, the detection unit can also detect information that has a refreshing effect. For example, if a user posts on social media that they want to relax, the detection unit can detect information that has a relaxing effect. If a user posts on social media that they want to concentrate, the detection unit can detect information that helps maintain concentration. If a user posts on social media that they are tired, the detection unit can detect information that has a refreshing effect. In this way, relevant information can be provided by analyzing social media activity. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the user's social media activity data into a generating AI and have the generating AI perform the detection of relevant information.

[0042] The generation unit can analyze the user's past preferences during generation to generate optimal ambient sounds and scents. For example, the generation unit can generate relaxing ambient sounds and scents during times when the user previously felt the need to relax. The generation unit can also generate ambient sounds and scents to maintain concentration during times when the user previously felt the need to concentrate. The generation unit can also generate refreshing ambient sounds and scents during times when the user previously felt tired. For example, the generation unit can generate relaxing ambient sounds and scents during times when the user previously felt the need to relax. The generation unit can generate ambient sounds and scents to maintain concentration during times when the user previously felt the need to concentrate. The generation unit can generate refreshing ambient sounds and scents during times when the user previously felt tired. In this way, by analyzing past preferences, the optimal ambient sounds and scents can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past preference data into a generation AI and have the generation AI perform the generation of optimal ambient sounds and scents.

[0043] The generation unit can customize ambient sounds and scents based on the user's current activity status during generation. For example, if the user is in the office, the generation unit can generate ambient sounds and scents to help maintain concentration. If the user is at home, the generation unit can also generate ambient sounds and scents that promote relaxation. If the user is out, the generation unit can also generate ambient sounds and scents that promote refreshment. For example, if the user is in the office, the generation unit can generate ambient sounds and scents to help maintain concentration. If the user is at home, the generation unit can generate ambient sounds and scents that promote relaxation. If the user is out, the generation unit can generate ambient sounds and scents that promote refreshment. This allows for the provision of more appropriate ambient sounds and scents through customization based on the user's current activity status. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's current activity status data into a generation AI and have the generation AI perform the customization of ambient sounds and scents.

[0044] The generation unit can generate optimal ambient sounds and scents by considering the user's geographical location information during the generation process. For example, if the user is in a park, the generation unit will generate natural sounds and scents. If the user is in a cafe, the generation unit can also generate relaxing music and scents. If the user is in an office, the generation unit can also generate music and scents to help maintain concentration. For example, if the user is in a park, the generation unit will generate natural sounds and scents. If the user is in a cafe, the generation unit will generate relaxing music and scents. If the user is in an office, the generation unit will generate music and scents to help maintain concentration. This allows for the provision of optimal ambient sounds and scents by considering geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location data into a generation AI and have the generation AI generate optimal ambient sounds and scents.

[0045] The generation unit can analyze a user's social media activity during generation and generate relevant ambient sounds and scents. For example, if a user posts on social media that they want to relax, the generation unit will generate relaxing ambient sounds and scents. If a user posts on social media that they want to concentrate, the generation unit can also generate ambient sounds and scents to help maintain concentration. If a user posts on social media that they are tired, the generation unit can also generate refreshing ambient sounds and scents. For example, if a user posts on social media that they want to relax, the generation unit will generate relaxing ambient sounds and scents. If a user posts on social media that they want to concentrate, the generation unit will generate ambient sounds and scents to help maintain concentration. If a user posts on social media that they are tired, the generation unit will generate refreshing ambient sounds and scents. In this way, by analyzing social media activity, relevant ambient sounds and scents can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI perform the generation of relevant ambient sounds and scents.

[0046] The control unit can analyze the user's past responses during control and select the optimal control method. For example, the control unit can adjust the intensity of relaxing ambient sounds and scents during times when the user previously felt relaxed. The control unit can also adjust the intensity of ambient sounds and scents to maintain concentration during times when the user previously felt focused. The control unit can also adjust the intensity of refreshing ambient sounds and scents during times when the user previously felt tired. For example, the control unit can adjust the intensity of relaxing ambient sounds and scents during times when the user previously felt relaxed. The control unit can adjust the intensity of ambient sounds and scents to maintain concentration during times when the user previously felt focused. The control unit can adjust the intensity of refreshing ambient sounds and scents during times when the user previously felt tired. By analyzing past responses, the control unit can provide the optimal control method. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the user's past response data into a generating AI and have the generating AI select the optimal control method.

[0047] The control unit can customize the control of ambient sounds and scents based on the user's current activity status during operation. For example, if the user is in an office, the control unit can adjust the intensity of ambient sounds and scents to help maintain concentration. If the user is at home, the control unit can also adjust the intensity of ambient sounds and scents to promote relaxation. If the user is out, the control unit can also adjust the intensity of ambient sounds and scents to promote refreshment. For example, if the user is in an office, the control unit can adjust the intensity of ambient sounds and scents to help maintain concentration. If the user is at home, the control unit can adjust the intensity of ambient sounds and scents to promote relaxation. If the user is out, the control unit can adjust the intensity of ambient sounds and scents to promote refreshment. This allows for the provision of more appropriate ambient sounds and scents through customization based on the current activity status. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the user's current activity status data into a generating AI and have the generating AI perform the control of ambient sounds and scents.

[0048] The control unit can select the optimal control method by considering the user's geographical location information during control. For example, if the user is in a park, the control unit can prioritize the control of natural sounds and scents. If the user is in a cafe, the control unit can also prioritize the control of relaxing music and scents. If the user is in an office, the control unit can also prioritize the control of music and scents that help maintain concentration. For example, if the user is in a park, the control unit can prioritize the control of natural sounds and scents. If the user is in a cafe, the control unit can prioritize the control of relaxing music and scents. If the user is in an office, the control unit can prioritize the control of music and scents that help maintain concentration. This allows for the provision of the optimal control method by considering geographical location information. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the user's geographical location information data into a generating AI and have the generating AI select the optimal control method.

[0049] The control unit can analyze the user's social media activity during control and control relevant ambient sounds and scents. For example, if the user posts on social media that they want to relax, the control unit can control ambient sounds and scents that have a relaxing effect. If the user posts on social media that they want to concentrate, the control unit can also control ambient sounds and scents that help maintain concentration. If the user posts on social media that they are tired, the control unit can also control ambient sounds and scents that have a refreshing effect. For example, if the user posts on social media that they want to relax, the control unit can control ambient sounds and scents that have a relaxing effect. If the user posts on social media that they want to concentrate, the control unit can control ambient sounds and scents that help maintain concentration. If the user posts on social media that they are tired, the control unit can control ambient sounds and scents that have a refreshing effect. In this way, relevant ambient sounds and scents can be provided by analyzing social media activity. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the user's social media activity data into a generating AI and have the generating AI perform the control of relevant ambient sounds and scents.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The acquisition unit can acquire the user's past behavioral history and estimate the user's preferences based on that history. For example, it can estimate that the user prefers relaxing music and scents during times when they previously felt the need to relax. It can also estimate that the user prefers music and scents that help maintain concentration during times when they previously felt the need to concentrate. Furthermore, it can estimate that the user prefers refreshing music and scents during times when they previously felt tired. By estimating the user's preferences based on their past behavioral history, the system can provide more appropriate music and scents.

[0052] The service provider can acquire the user's geographical location information and provide appropriate content based on that information. For example, if the user is in a park, the service provider can provide natural sounds and scents. If the user is in a cafe, the service provider can provide relaxing music and scents. Furthermore, if the user is in an office, the service provider can provide music and scents to help maintain concentration. In this way, by providing appropriate content based on geographical location information, a more comfortable environment can be provided.

[0053] The notification unit can acquire users' social media activity and send appropriate notifications based on that activity. For example, if a user posts on social media that they want to relax, the notification unit can send a notification that promotes relaxation. Similarly, if a user posts on social media that they want to concentrate, the notification unit can send a notification to help them maintain focus. Furthermore, if a user posts on social media that they are tired, the notification unit can send a notification that promotes refreshment. This allows for more relevant information to be provided by sending appropriate notifications based on social media activity.

[0054] The recording unit can acquire the user's current activity status and record the user's behavioral history based on that status. For example, if the user is in the office, the recording unit can record actions taken to maintain concentration. If the user is at home, the recording unit can also record actions that promote relaxation. Furthermore, if the user is out, the recording unit can record actions that promote refreshment. By recording the user's behavioral history based on their current activity status, it becomes possible to analyze more appropriate behavioral patterns.

[0055] The analysis unit can acquire users' past responses and analyze user behavior based on those responses. For example, it can analyze relaxing behaviors that occurred during times when users previously felt relaxed. It can also analyze behaviors that help maintain concentration that occurred during times when users previously felt focused. Furthermore, it can analyze refreshing behaviors that occurred during times when users previously felt tired. By analyzing user behavior based on past responses, it becomes possible to understand more appropriate behavioral patterns.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The detection unit detects the user's state. The user's state includes heart rate, body temperature, and facial expression. For example, a heart rate sensor is used to detect the user's heart rate, a body temperature sensor is used to detect the user's body temperature, and facial expression recognition technology is used to detect the user's facial expression. Step 2: The generation unit generates ambient sounds and scents based on the information detected by the detection unit. The generated ambient sounds and scents include natural sounds and relaxing scents. The generation unit uses a generation AI to generate natural sounds and relaxing scents that are appropriate for the user's state. Step 3: The control unit controls the ambient sounds and scents generated by the generation unit. The control unit adjusts the intensity of the generated ambient sounds and scents according to the user's state and adjusts the generated ambient sounds and scents to suit the user's environment. Furthermore, the control unit adjusts the type of ambient sounds and scents generated based on the user's emotions.

[0058] (Example of form 2) The AI ​​agent system appealing to the five senses according to an embodiment of the present invention proposes an AI agent that appeals to the five senses in order to solve the problem that language-based AI agents hinder human thought. In this system, when the user inputs "I want to go from XX to XX," the generating AI creates a video showing how to get from the current location to the destination. This video starts navigation according to the orientation of the user's smartphone, and the screen moves in accordance with the walking speed. This results in a simple structure that can be easily used by children and the elderly alike, making it enjoyable for everyone. Furthermore, since the viewpoint of the smartphone is pointed at all axes, there is no chance of getting lost, and walking safety is ensured because the smartphone is held horizontally. First, the user inputs "I want to go from XX to XX." At this time, the user only needs to input the starting point and destination. For example, the user inputs "I want to go from my home to the station." This information is input to the generating AI. Next, the generating AI analyzes the input information and creates a video showing how to get from the current location to the destination. The generating AI calculates the optimal route based on map data and generates a video along that route. For example, if the user inputs a route from home to the station, a video along that route will be generated. The generated video starts navigating in accordance with the orientation of the user's smartphone. For example, if the user is pointing their smartphone north, the video will also start navigating in the north direction. This allows the user to receive navigation that is aligned with the direction they are facing. Furthermore, the video screen moves in accordance with the user's walking speed. For example, if the user is walking slowly, the video will also progress slowly. This allows the user to receive navigation at their own pace. This mechanism results in a simple structure that is easy for children and the elderly to use, making it enjoyable for everyone. Users can receive navigation intuitively without having to perform complex operations. Also, since the viewpoint of the smartphone is the axis of all directions, there is no chance of getting lost, and walking safety is ensured because the smartphone is held horizontally. For example, if the user is walking with their smartphone held horizontally, the video will also be displayed horizontally, allowing the user to walk safely.This allows the AI ​​agent system, which appeals to the five senses, to promote human-centered activities by generating and controlling ambient sounds and scents according to the user's state.

[0059] The AI ​​agent system appealing to the five senses according to this embodiment comprises a detection unit, a generation unit, and a control unit. The detection unit detects the user's state. The user's state includes, but is not limited to, heart rate, body temperature, and facial expression. The detection unit detects the user's heart rate using, for example, a heart rate sensor. The detection unit can also detect the user's body temperature using a body temperature sensor. Furthermore, the detection unit can detect the user's facial expression using facial expression recognition technology. For example, the detection unit monitors the user's heart rate in real time using a heart rate sensor and issues an alert if an abnormality is detected. The body temperature sensor periodically measures the user's body temperature and notifies if an abnormality is detected. Facial expression recognition technology analyzes the user's facial expression and detects changes in emotion. The generation unit uses generation AI to generate ambient sounds and scents based on the information detected by the detection unit. The generated ambient sounds and scents include, but are not limited to, natural sounds and relaxing scents. For example, the generation unit uses generation AI to generate natural sounds corresponding to the user's state. Furthermore, the generation unit can use generation AI to generate relaxing scents. In addition, the generation unit can use generation AI to generate music tailored to the user's state. For example, the generation unit uses generation AI to generate relaxing nature sounds when the user's heart rate is high. The generation AI uses technologies such as deep learning and GAN (Generative Adversarial Network) to generate ambient sounds and scents. The control unit controls the ambient sounds and scents generated by the generation unit. For example, the control unit adjusts the intensity of the generated ambient sounds and scents according to the user's state. The control unit can also adjust the generated ambient sounds and scents to match the user's environment. Furthermore, the control unit can adjust the type of generated ambient sounds and scents based on the user's emotions. For example, the control unit increases the intensity of relaxing nature sounds when the user's heart rate is high. The control unit adjusts the concentration of the generated scents to match the user's environment. The control unit changes the type of generated music based on the user's emotions.As a result, the AI ​​agent system that appeals to the five senses according to the embodiment can promote human-centered activities by generating and controlling environmental sounds and scents according to the user's state.

[0060] The detection unit detects the user's state. This state includes, but is not limited to, heart rate, body temperature, and facial expression. For example, the detection unit can detect the user's heart rate using a heart rate sensor. The heart rate sensor, by contacting the user's skin, can monitor the rhythm and fluctuations of the heartbeat in real time. This allows for understanding the user's stress level and relaxation state. The detection unit can also detect the user's body temperature using a body temperature sensor. The body temperature sensor measures the temperature of the user's skin surface and tracks changes in body temperature in real time. This allows for early detection of changes in the user's health and physical condition. Furthermore, the detection unit can detect the user's facial expression using facial recognition technology. Facial recognition technology uses a camera to capture the user's face and analyzes changes in facial expression using a deep learning algorithm. This allows for real-time understanding of the user's emotional state and psychological changes. For example, the detection unit can monitor the user's heart rate in real time using a heart rate sensor and issue an alert if an abnormality is detected. The body temperature sensor periodically measures the user's body temperature and notifies the user if an abnormality is detected. Facial recognition technology analyzes the user's facial expressions and detects changes in emotion. This allows the detection unit to monitor the user's state from multiple angles and collect information in real time. Furthermore, the detection unit can centrally manage this information and collaborate with other systems and departments. For example, collected data can be stored on a cloud server and made accessible by the generation and control units. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. As a result, the detection unit can collect data efficiently and effectively, improving the overall performance of the system.

[0061] The generation unit uses a generation AI to generate ambient sounds and scents based on information detected by the detection unit. The generated ambient sounds and scents include, but are not limited to, natural sounds and relaxing scents. The generation AI uses technologies such as deep learning and GAN (Generative Opposite Network) to generate ambient sounds and scents. Specifically, the generation AI receives data such as the user's heart rate, body temperature, and facial expressions as input, and generates optimal ambient sounds and scents based on this data. For example, the generation unit uses the generation AI to generate natural sounds that correspond to the user's state. These natural sounds include birdsong, the babbling of a stream, and the sound of wind, and are used to enhance the user's relaxation. The generation unit can also use the generation AI to generate relaxing scents. Essential oils such as lavender, chamomile, and mint are used for these scents, which are used to reduce user stress and enhance relaxation. Furthermore, the generation unit can also use the generation AI to generate music that corresponds to the user's state. The music includes genres such as classical, jazz, and ambient, and the most suitable music is selected based on the user's heart rate, body temperature, and facial expression. For example, the generation unit uses a generation AI to generate relaxing natural sounds when the user's heart rate is high. The generation AI uses technologies such as deep learning and GAN (Generative Adversarial Network) to generate ambient sounds and scents. This allows the generation unit to generate optimal ambient sounds and scents according to the user's state, enhancing the user's relaxation effect. Furthermore, the generation unit can adjust the generated ambient sounds and scents in real time. For example, it can adjust the type and intensity of the generated ambient sounds and scents according to changes in the user's heart rate, body temperature, and facial expression. This allows the generation unit to provide optimal ambient sounds and scents according to the user's state, maximizing the user's relaxation effect.

[0062] The control unit controls the ambient sounds and scents generated by the generation unit. For example, the control unit adjusts the intensity of the generated ambient sounds and scents according to the user's state. The control unit can also adjust the generated ambient sounds and scents to suit the user's environment. Specifically, the control unit monitors data such as the user's heart rate, body temperature, and facial expression in real time, and adjusts the intensity and type of generated ambient sounds and scents based on this data. For example, if the user's heart rate is high, the control unit increases the intensity of relaxing natural sounds. This can lower the user's heart rate and enhance relaxation. The control unit also adjusts the concentration of the generated scents according to the user's environment. For example, if the user is in a large room, increasing the scent concentration allows the scent to spread throughout the entire room. Conversely, if the user is in a small room, lowering the scent concentration prevents the scent from being too strong. Furthermore, the control unit can also change the type of music generated based on the user's emotions. For example, if the user is stressed, relaxing music can be played, and if the user is relaxed, more upbeat music can be played. This allows the control unit to provide optimal ambient sounds and scents according to the user's state and environment, maximizing the user's relaxation effect. Furthermore, the control unit can collect user feedback and evaluate the effects of the generated ambient sounds and scents. For example, it can collect whether the user felt relaxed through a questionnaire, and use this data to improve the algorithms of the generation and control units. This allows the control unit to provide optimal ambient sounds and scents according to the user's state and environment, maximizing the user's relaxation effect.

[0063] The generation unit generates ambient sounds and scents using a generation AI. For example, the generation unit uses the generation AI to generate natural sounds that correspond to the user's state. The generation AI uses technologies such as deep learning and GAN (Generative Adversarial Network) to generate ambient sounds and scents. For example, the generation unit uses the generation AI to generate relaxing natural sounds when the user's heart rate is high. The generation AI takes the user's state as input and outputs ambient sounds and scents. For example, the generation AI receives information such as the user's heart rate, body temperature, and facial expression as input and generates the optimal ambient sounds and scents based on that information. The generation AI can also adjust the generated ambient sounds and scents in real time. For example, if the user's heart rate changes, the generation AI automatically adjusts the type and intensity of the generated ambient sounds and scents. This improves the accuracy of ambient sound and scent generation when using the generation AI.

[0064] The control unit can control the ambient sounds and scents generated according to the user's state. For example, the control unit may increase the intensity of relaxing nature sounds when the user's heart rate is high. The control unit can also adjust the concentration of the generated scents to suit the user's environment. The control unit can also change the type of music generated based on the user's emotions. For example, the control unit may increase the intensity of relaxing nature sounds when the user's heart rate is high. The control unit may adjust the concentration of the generated scents to suit the user's environment. The control unit may change the type of music generated based on the user's emotions. This allows for the provision of more appropriate ambient sounds and scents through control according to the user's state. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can control ambient sounds and scents using an AI model that takes ambient sounds and scents generated by the generation unit as input and outputs control according to the user's state.

[0065] The detection unit can detect a decrease in the user's walking speed and concentration. For example, the detection unit detects the user's walking speed using a walking speed sensor. The detection unit can also monitor eye movements and the frequency of work interruptions to detect a decrease in concentration. For example, the detection unit monitors the user's walking speed in real time using a walking speed sensor and issues an alert if an abnormality is detected. The detection unit analyzes eye movements to detect a decrease in concentration. The detection unit records the frequency of work interruptions to detect a decrease in concentration. As a result, by detecting the user's walking speed and decrease in concentration, appropriate ambient sounds and scents can be provided. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the user's walking speed data into a generating AI and cause the generating AI to execute a process to detect abnormalities in walking speed.

[0066] The generation unit can generate music and scents tailored to the user's state using a generation AI. For example, the generation unit can use the generation AI to generate relaxing music when the user's heart rate is high. The generation AI uses technologies such as deep learning and GAN (Generative Adversarial Network) to generate music and scents. For example, the generation unit can use the generation AI to generate a refreshing scent when the user's body temperature is high. The generation AI takes the user's state as input and outputs music and scents. For example, the generation AI receives information such as the user's heart rate, body temperature, and facial expression as input and generates the optimal music and scent based on that information. The generation AI can also adjust the generated music and scents in real time. For example, if the user's heart rate changes, the generation AI automatically adjusts the type and intensity of the generated music and scents. As a result, using the generation AI improves the accuracy of generating music and scents tailored to the user's state.

[0067] The control unit can adjust the generated music and scent to suit the user's environment. For example, the control unit can adjust the volume of the generated music to suit the user's environment. The control unit can also adjust the concentration of the generated scent to suit the user's environment. The control unit can also change the type of generated music and scent based on the user's environment. For example, the control unit can adjust the volume of the generated music to suit the user's environment. The control unit can adjust the concentration of the generated scent to suit the user's environment. The control unit can change the type of generated music and scent based on the user's environment. This allows for the provision of more appropriate music and scent through adjustments tailored to the user's environment. Some or all of the above-described processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can adjust the music and scent using an AI model that takes the music and scent generated by the generation unit as input and outputs adjustments according to the user's environment.

[0068] The detection unit can estimate the user's emotions and adjust the type of information it detects based on the estimated emotions. For example, if the user is stressed, the detection unit will prioritize detecting information that has a relaxing effect. If the user is concentrating, the detection unit can also prioritize detecting information that helps maintain concentration. If the user is tired, the detection unit can also prioritize detecting information that has a refreshing effect. For example, if the user is stressed, the detection unit will prioritize detecting information that has a relaxing effect. If the user is concentrating, the detection unit will prioritize detecting information that helps maintain concentration. If the user is tired, the detection unit will prioritize detecting information that has a refreshing effect. By adjusting the type of information according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0069] The detection unit can analyze the user's past behavioral history and select the optimal detection method. For example, the detection unit can detect information that has a relaxing effect during times when the user previously felt the need to relax. The detection unit can also detect information that helps maintain concentration during times when the user previously felt the need to concentrate. The detection unit can also detect information that has a refreshing effect during times when the user previously felt tired. For example, the detection unit can detect information that has a relaxing effect during times when the user previously felt the need to relax. The detection unit can detect information that helps maintain concentration during times when the user previously felt the need to concentrate. The detection unit can detect information that has a refreshing effect during times when the user previously felt tired. By analyzing past behavioral history, the optimal detection method can be selected. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the user's past behavioral history data into a generating AI and have the generating AI select the optimal detection method.

[0070] The detection unit can filter information based on the user's current activity status and environment upon detection. For example, if the user is in the office, the detection unit can prioritize detecting information that helps maintain concentration. If the user is at home, the detection unit can also prioritize detecting information that has a relaxing effect. If the user is out, the detection unit can also prioritize detecting information that has a refreshing effect. This allows for the provision of more appropriate information through filtering based on the current activity status and environment. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the user's current activity status data into a generating AI and have the generating AI perform the filtering.

[0071] The detection unit can estimate the user's emotions and determine the priority of information to detect based on the estimated user emotions. For example, if the user is stressed, the detection unit will prioritize detecting information that has a relaxing effect. If the user is concentrating, the detection unit can also prioritize detecting information that helps maintain concentration. If the user is tired, the detection unit can also prioritize detecting information that has a refreshing effect. For example, if the user is stressed, the detection unit will prioritize detecting information that has a relaxing effect. If the user is concentrating, the detection unit will prioritize detecting information that helps maintain concentration. If the user is tired, the detection unit will prioritize detecting information that has a refreshing effect. By determining the priority of information according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0072] The detection unit can prioritize detecting highly relevant information by considering the user's geographical location information. For example, if the user is in a park, the detection unit will prioritize detecting natural sounds and scents. If the user is in a cafe, the detection unit can also prioritize detecting relaxing music and scents. If the user is in an office, the detection unit can also prioritize detecting music and scents that help maintain concentration. For example, if the user is in a park, the detection unit will prioritize detecting natural sounds and scents. If the user is in a cafe, the detection unit will prioritize detecting relaxing music and scents. If the user is in an office, the detection unit will prioritize detecting music and scents that help maintain concentration. This allows the system to provide highly relevant information by considering geographical location information. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the user's geographical location data into a generating AI and have the generating AI perform the detection of highly relevant information.

[0073] The detection unit can analyze the user's social media activity and detect relevant information upon detection. For example, if a user posts on social media that they want to relax, the detection unit can detect information that has a relaxing effect. If a user posts on social media that they want to concentrate, the detection unit can also detect information that helps maintain concentration. If a user posts on social media that they are tired, the detection unit can also detect information that has a refreshing effect. For example, if a user posts on social media that they want to relax, the detection unit can detect information that has a relaxing effect. If a user posts on social media that they want to concentrate, the detection unit can detect information that helps maintain concentration. If a user posts on social media that they are tired, the detection unit can detect information that has a refreshing effect. In this way, relevant information can be provided by analyzing social media activity. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the user's social media activity data into a generating AI and have the generating AI perform the detection of relevant information.

[0074] The generation unit can estimate the user's emotions and adjust the types of ambient sounds and scents it generates based on the estimated emotions. For example, if the user is stressed, the generation unit can generate relaxing ambient sounds and scents. If the user is concentrating, the generation unit can also generate ambient sounds and scents to help maintain concentration. If the user is tired, the generation unit can also generate refreshing ambient sounds and scents. For example, if the user is stressed, the generation unit can generate relaxing ambient sounds and scents. If the user is concentrating, the generation unit can generate ambient sounds and scents to help maintain concentration. If the user is tired, the generation unit can generate refreshing ambient sounds and scents. By adjusting the types of ambient sounds and scents according to the user's emotions, more appropriate ambient sounds and scents can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the generation AI, which can then generate environmental sounds and scents based on those emotions.

[0075] The generation unit can analyze the user's past preferences during generation to generate optimal ambient sounds and scents. For example, the generation unit can generate relaxing ambient sounds and scents during times when the user previously felt the need to relax. The generation unit can also generate ambient sounds and scents to maintain concentration during times when the user previously felt the need to concentrate. The generation unit can also generate refreshing ambient sounds and scents during times when the user previously felt tired. For example, the generation unit can generate relaxing ambient sounds and scents during times when the user previously felt the need to relax. The generation unit can generate ambient sounds and scents to maintain concentration during times when the user previously felt the need to concentrate. The generation unit can generate refreshing ambient sounds and scents during times when the user previously felt tired. In this way, by analyzing past preferences, the optimal ambient sounds and scents can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past preference data into a generation AI and have the generation AI perform the generation of optimal ambient sounds and scents.

[0076] The generation unit can customize ambient sounds and scents based on the user's current activity status during generation. For example, if the user is in the office, the generation unit can generate ambient sounds and scents to help maintain concentration. If the user is at home, the generation unit can also generate ambient sounds and scents that promote relaxation. If the user is out, the generation unit can also generate ambient sounds and scents that promote refreshment. For example, if the user is in the office, the generation unit can generate ambient sounds and scents to help maintain concentration. If the user is at home, the generation unit can generate ambient sounds and scents that promote relaxation. If the user is out, the generation unit can generate ambient sounds and scents that promote refreshment. This allows for the provision of more appropriate ambient sounds and scents through customization based on the user's current activity status. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's current activity status data into a generation AI and have the generation AI perform the customization of ambient sounds and scents.

[0077] The generation unit can estimate the user's emotions and determine the priority of ambient sounds and scents to generate based on the estimated emotions. For example, if the user is stressed, the generation unit will prioritize generating ambient sounds and scents with a relaxing effect. If the user is concentrating, the generation unit can also prioritize generating ambient sounds and scents that help maintain concentration. If the user is tired, the generation unit can also prioritize generating ambient sounds and scents that have a refreshing effect. For example, if the user is stressed, the generation unit will prioritize generating ambient sounds and scents with a relaxing effect. If the user is concentrating, the generation unit will prioritize generating ambient sounds and scents that help maintain concentration. If the user is tired, the generation unit will prioritize generating ambient sounds and scents that have a refreshing effect. By determining the priority of ambient sounds and scents according to the user's emotions, it is possible to provide more appropriate ambient sounds and scents. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI determine the priority of environmental sounds and scents based on those emotions.

[0078] The generation unit can generate optimal ambient sounds and scents by considering the user's geographical location information during the generation process. For example, if the user is in a park, the generation unit will generate natural sounds and scents. If the user is in a cafe, the generation unit can also generate relaxing music and scents. If the user is in an office, the generation unit can also generate music and scents to help maintain concentration. For example, if the user is in a park, the generation unit will generate natural sounds and scents. If the user is in a cafe, the generation unit will generate relaxing music and scents. If the user is in an office, the generation unit will generate music and scents to help maintain concentration. This allows for the provision of optimal ambient sounds and scents by considering geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location data into a generation AI and have the generation AI generate optimal ambient sounds and scents.

[0079] The generation unit can analyze a user's social media activity during generation and generate relevant ambient sounds and scents. For example, if a user posts on social media that they want to relax, the generation unit will generate relaxing ambient sounds and scents. If a user posts on social media that they want to concentrate, the generation unit can also generate ambient sounds and scents to help maintain concentration. If a user posts on social media that they are tired, the generation unit can also generate refreshing ambient sounds and scents. For example, if a user posts on social media that they want to relax, the generation unit will generate relaxing ambient sounds and scents. If a user posts on social media that they want to concentrate, the generation unit will generate ambient sounds and scents to help maintain concentration. If a user posts on social media that they are tired, the generation unit will generate refreshing ambient sounds and scents. In this way, by analyzing social media activity, relevant ambient sounds and scents can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI perform the generation of relevant ambient sounds and scents.

[0080] The control unit can estimate the user's emotions and adjust the intensity of ambient sounds and scents based on the estimated emotions. For example, if the user is stressed, the control unit can increase the intensity of relaxing ambient sounds and scents. If the user is concentrating, the control unit can also adjust the intensity of ambient sounds and scents to maintain concentration. If the user is tired, the control unit can also increase the intensity of refreshing ambient sounds and scents. For example, if the user is stressed, the control unit can increase the intensity of relaxing ambient sounds and scents. If the user is concentrating, the control unit can adjust the intensity of ambient sounds and scents to maintain concentration. If the user is tired, the control unit can increase the intensity of refreshing ambient sounds and scents. This allows for the provision of more appropriate ambient sounds and scents through intensity adjustments according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input user emotion data into a generating AI and cause the generating AI to adjust the intensity of ambient sounds and scents based on the emotion.

[0081] The control unit can analyze the user's past responses during control and select the optimal control method. For example, the control unit can adjust the intensity of relaxing ambient sounds and scents during times when the user previously felt relaxed. The control unit can also adjust the intensity of ambient sounds and scents to maintain concentration during times when the user previously felt focused. The control unit can also adjust the intensity of refreshing ambient sounds and scents during times when the user previously felt tired. For example, the control unit can adjust the intensity of relaxing ambient sounds and scents during times when the user previously felt relaxed. The control unit can adjust the intensity of ambient sounds and scents to maintain concentration during times when the user previously felt focused. The control unit can adjust the intensity of refreshing ambient sounds and scents during times when the user previously felt tired. By analyzing past responses, the control unit can provide the optimal control method. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the user's past response data into a generating AI and have the generating AI select the optimal control method.

[0082] The control unit can customize the control of ambient sounds and scents based on the user's current activity status during operation. For example, if the user is in an office, the control unit can adjust the intensity of ambient sounds and scents to help maintain concentration. If the user is at home, the control unit can also adjust the intensity of ambient sounds and scents to promote relaxation. If the user is out, the control unit can also adjust the intensity of ambient sounds and scents to promote refreshment. For example, if the user is in an office, the control unit can adjust the intensity of ambient sounds and scents to help maintain concentration. If the user is at home, the control unit can adjust the intensity of ambient sounds and scents to promote relaxation. If the user is out, the control unit can adjust the intensity of ambient sounds and scents to promote refreshment. This allows for the provision of more appropriate ambient sounds and scents through customization based on the current activity status. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the user's current activity status data into a generating AI and have the generating AI perform the control of ambient sounds and scents.

[0083] The control unit can estimate the user's emotions and determine the priority of ambient sounds and scents to control based on the estimated emotions. For example, if the user is stressed, the control unit will prioritize ambient sounds and scents that have a relaxing effect. If the user is concentrating, the control unit can also prioritize ambient sounds and scents that help maintain concentration. If the user is tired, the control unit can also prioritize ambient sounds and scents that have a refreshing effect. For example, if the user is stressed, the control unit will prioritize ambient sounds and scents that have a relaxing effect. If the user is concentrating, the control unit will prioritize ambient sounds and scents that help maintain concentration. If the user is tired, the control unit will prioritize ambient sounds and scents that have a refreshing effect. By determining the priority of ambient sounds and scents according to the user's emotions, more appropriate ambient sounds and scents can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input user emotion data into a generating AI and have the generating AI determine the priority of environmental sounds and scents based on those emotions.

[0084] The control unit can select the optimal control method by considering the user's geographical location information during control. For example, if the user is in a park, the control unit can prioritize the control of natural sounds and scents. If the user is in a cafe, the control unit can also prioritize the control of relaxing music and scents. If the user is in an office, the control unit can also prioritize the control of music and scents that help maintain concentration. For example, if the user is in a park, the control unit can prioritize the control of natural sounds and scents. If the user is in a cafe, the control unit can prioritize the control of relaxing music and scents. If the user is in an office, the control unit can prioritize the control of music and scents that help maintain concentration. This allows for the provision of the optimal control method by considering geographical location information. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the user's geographical location information data into a generating AI and have the generating AI select the optimal control method.

[0085] The control unit can analyze the user's social media activity during control and control relevant ambient sounds and scents. For example, if the user posts on social media that they want to relax, the control unit can control ambient sounds and scents that have a relaxing effect. If the user posts on social media that they want to concentrate, the control unit can also control ambient sounds and scents that help maintain concentration. If the user posts on social media that they are tired, the control unit can also control ambient sounds and scents that have a refreshing effect. For example, if the user posts on social media that they want to relax, the control unit can control ambient sounds and scents that have a relaxing effect. If the user posts on social media that they want to concentrate, the control unit can control ambient sounds and scents that help maintain concentration. If the user posts on social media that they are tired, the control unit can control ambient sounds and scents that have a refreshing effect. In this way, relevant ambient sounds and scents can be provided by analyzing social media activity. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the user's social media activity data into a generating AI and have the generating AI perform the control of relevant ambient sounds and scents.

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

[0087] The judgment unit can estimate the user's emotions and determine the user's preferences based on those estimated emotions. For example, if the user is stressed, the judgment unit can determine that they prefer music or scents that have a relaxing effect. Similarly, if the user is concentrating, the judgment unit can determine that they prefer music or scents that help maintain concentration. Furthermore, if the user is tired, the judgment unit can determine that they prefer music or scents that have a refreshing effect. By determining preferences in line with the user's emotions, the system can provide more appropriate music and scents.

[0088] The service provider can estimate the user's emotions and provide appropriate content based on those estimates. For example, if a user is stressed, the service provider can provide relaxing videos or music. If a user is concentrating, the service provider can provide videos or music to help maintain concentration. Furthermore, if a user is tired, the service provider can provide refreshing videos or music. By providing content tailored to the user's emotions, a more appropriate entertainment experience can be provided.

[0089] The notification unit can estimate the user's emotions and provide appropriate notifications based on those estimates. For example, if the user is stressed, the notification unit can provide relaxing notifications. If the user is concentrating, the notification unit can provide notifications to help maintain concentration. Furthermore, if the user is tired, the notification unit can provide refreshing notifications. This allows for more appropriate information to be provided by responding to the user's emotions.

[0090] The recording unit can estimate the user's emotions and record the user's behavioral history based on those emotions. For example, if the user is stressed, the recording unit can record behaviors that promote relaxation. Similarly, if the user is concentrating, the recording unit can record behaviors that help maintain that concentration. Furthermore, if the user is tired, the recording unit can record behaviors that promote refreshment. This allows for a more appropriate analysis of behavioral patterns by recording behavioral history in accordance with the user's emotions.

[0091] The analysis unit can estimate the user's emotions and analyze their behavior based on those estimated emotions. For example, if a user is stressed, the analysis unit can analyze behaviors that promote relaxation. Similarly, if a user is concentrating, the analysis unit can analyze behaviors that help maintain that concentration. Furthermore, if a user is tired, the analysis unit can analyze behaviors that promote refreshment. This allows for a more appropriate understanding of behavioral patterns by analyzing behaviors in accordance with the user's emotions.

[0092] The acquisition unit can acquire the user's past behavioral history and estimate the user's preferences based on that history. For example, it can estimate that the user prefers relaxing music and scents during times when they previously felt the need to relax. It can also estimate that the user prefers music and scents that help maintain concentration during times when they previously felt the need to concentrate. Furthermore, it can estimate that the user prefers refreshing music and scents during times when they previously felt tired. By estimating the user's preferences based on their past behavioral history, the system can provide more appropriate music and scents.

[0093] The service provider can acquire the user's geographical location information and provide appropriate content based on that information. For example, if the user is in a park, the service provider can provide natural sounds and scents. If the user is in a cafe, the service provider can provide relaxing music and scents. Furthermore, if the user is in an office, the service provider can provide music and scents to help maintain concentration. In this way, by providing appropriate content based on geographical location information, a more comfortable environment can be provided.

[0094] The notification unit can acquire users' social media activity and send appropriate notifications based on that activity. For example, if a user posts on social media that they want to relax, the notification unit can send a notification that promotes relaxation. Similarly, if a user posts on social media that they want to concentrate, the notification unit can send a notification to help them maintain focus. Furthermore, if a user posts on social media that they are tired, the notification unit can send a notification that promotes refreshment. This allows for more relevant information to be provided by sending appropriate notifications based on social media activity.

[0095] The recording unit can acquire the user's current activity status and record the user's behavioral history based on that status. For example, if the user is in the office, the recording unit can record actions taken to maintain concentration. If the user is at home, the recording unit can also record actions that promote relaxation. Furthermore, if the user is out, the recording unit can record actions that promote refreshment. By recording the user's behavioral history based on their current activity status, it becomes possible to analyze more appropriate behavioral patterns.

[0096] The analysis unit can acquire users' past responses and analyze user behavior based on those responses. For example, it can analyze relaxing behaviors that occurred during times when users previously felt relaxed. It can also analyze behaviors that help maintain concentration that occurred during times when users previously felt focused. Furthermore, it can analyze refreshing behaviors that occurred during times when users previously felt tired. By analyzing user behavior based on past responses, it becomes possible to understand more appropriate behavioral patterns.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The detection unit detects the user's state. The user's state includes heart rate, body temperature, and facial expression. For example, a heart rate sensor is used to detect the user's heart rate, a body temperature sensor is used to detect the user's body temperature, and facial expression recognition technology is used to detect the user's facial expression. Step 2: The generation unit generates ambient sounds and scents based on the information detected by the detection unit. The generated ambient sounds and scents include natural sounds and relaxing scents. The generation unit uses a generation AI to generate natural sounds and relaxing scents that are appropriate for the user's state. Step 3: The control unit controls the ambient sounds and scents generated by the generation unit. The control unit adjusts the intensity of the generated ambient sounds and scents according to the user's state and adjusts the generated ambient sounds and scents to suit the user's environment. Furthermore, the control unit adjusts the type of ambient sounds and scents generated based on the user's emotions.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0100] 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.

[0101] 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.

[0102] Each of the multiple elements described above, including the detection unit, generation unit, and control unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the detection unit detects the user's state using the heart rate sensor, body temperature sensor, and facial recognition technology of the smart device 14. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates ambient sounds and scents using generation AI. The control unit is implemented in the control unit 46A of the smart device 14 and controls the generated ambient sounds and scents. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements, including the detection unit, generation unit, and control unit described above, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the detection unit detects the user's state using the heart rate sensor, body temperature sensor, and facial recognition technology of the smart glasses 214. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates ambient sounds and scents using generation AI. The control unit is implemented in the control unit 46A of the smart glasses 214 and controls the generated ambient sounds and scents. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the detection unit, generation unit, and control unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the detection unit detects the user's state using the heart rate sensor, body temperature sensor, and facial recognition technology of the headset terminal 314. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates ambient sounds and scents using generation AI. The control unit is implemented in the control unit 46A of the headset terminal 314 and controls the generated ambient sounds and scents. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

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

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

[0146] In 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.

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

[0148] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0150] The data processing system 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.

[0151] Each of the multiple elements described above, including the detection unit, generation unit, and control unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the detection unit detects the user's state using the robot 414's heart rate sensor, body temperature sensor, and facial recognition technology. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates ambient sounds and scents using generation AI. The control unit is implemented, for example, by the control unit 46A of the robot 414, and controls the generated ambient sounds and scents. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] (Note 1) A detection unit that detects the user's state, A generation unit that generates ambient sounds and scents based on information detected by the aforementioned detection unit, The system includes a control unit that controls the ambient sounds and scents generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is Generative AI generates ambient sounds and scents. The system described in Appendix 1, characterized by the features described herein. (Note 3) The control unit, Control ambient sounds and scents generated according to the user's state. The system described in Appendix 1, characterized by the features described herein. (Note 4) The detection unit, It detects changes in the user's walking speed and concentration level. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is The AI ​​generates music and scents tailored to the user's state. The system described in Appendix 1, characterized by the features described herein. (Note 6) The control unit, The generated music and scents are adjusted to suit the user's environment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The detection unit, It estimates the user's emotions and adjusts the type of information detected based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The detection unit, Analyze the user's past behavior history and select the optimal detection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The detection unit, When detection occurs, filtering is performed based on the user's current activity status and environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The detection unit, It estimates the user's emotions and determines the priority of information to detect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The detection unit, When detection occurs, the system prioritizes detecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The detection unit, Upon detection, the system analyzes the user's social media activity and identifies relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the types of ambient sounds and scents generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, the system analyzes the user's past preferences to generate optimal ambient sounds and scents. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, ambient sounds and scents are customized based on the user's current activity status. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and determines the priority of ambient sounds and scents to generate based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, the system takes the user's geographical location into consideration to generate optimal ambient sounds and scents. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, the system analyzes the user's social media activity to generate relevant ambient sounds and scents. The system described in Appendix 1, characterized by the features described herein. (Note 19) The control unit, It estimates the user's emotions and adjusts the intensity of ambient sounds and scents based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The control unit, During control, the system analyzes the user's past responses to select the optimal control method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The control unit, During control, the system customizes the control of ambient sounds and scents based on the user's current activity status. The system described in Appendix 1, characterized by the features described herein. (Note 22) The control unit, It estimates the user's emotions and determines the priority of ambient sounds and scents to control based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The control unit, During control, the optimal control method is selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The control unit, During control, the system analyzes the user's social media activity and controls relevant ambient sounds and scents. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A detection unit that detects the user's state, A generation unit that generates ambient sounds and scents based on information detected by the aforementioned detection unit, The system includes a control unit that controls the ambient sounds and scents generated by the generation unit. A system characterized by the following features.

2. The generating unit is Generative AI generates ambient sounds and scents. The system according to feature 1.

3. The control unit, Control ambient sounds and scents generated according to the user's state. The system according to feature 1.

4. The detection unit is It detects changes in the user's walking speed and concentration level. The system according to feature 1.

5. The generating unit is The AI ​​generates music and scents tailored to the user's state. The system according to feature 1.

6. The control unit, The generated music and scents are adjusted to suit the user's environment. The system according to feature 1.

7. The detection unit is It estimates the user's emotions and adjusts the type of information detected based on the estimated user emotions. The system according to feature 1.

8. The detection unit is Analyze the user's past behavior history and select the optimal detection method. The system according to feature 1.

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