System

Abstract

An object of a system according to an embodiment is to recognize a stress level of a user in real time and provide an appropriate countermeasure.SOLUTION: A system includes a vocal cord analysis unit, a stress determination unit, a scent releasing unit, and a visualization unit. The vocal cord analysis unit analyzes movement of vocal cords of a user. The stress determiner determines a stress level based on the data analyzed by the vocal cord analyzer. The scent releasing unit releases scent smoke based on the stress level determined by the stress determination unit. The visualization unit changes the color or amount of the scent smoke based on the stress level.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have not been sufficient in recognizing a user's stress level in real time and providing appropriate measures.

[0005] The system according to the embodiment aims to recognize the stress level of a user in real time and provide appropriate measures. [Means for solving the problem]

[0006] The system according to the embodiment includes a vocal cord analysis unit, a stress assessment unit, a fragrance emission unit, and a visualization unit. The vocal cord analysis unit analyzes the movement of the user's vocal cords. The stress assessment unit assesses the stress level based on the data analyzed by the vocal cord analysis unit. The fragrance emission unit emits fragrance smoke based on the stress level assessed by the stress assessment unit. The visualization unit changes the color or amount of the fragrance smoke based on the stress level. [Effects of the Invention]

[0007] The system according to the embodiment can recognize the stress level of a user in real time and provide appropriate measures. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The aroma humidifier system according to an embodiment of the present invention analyzes the movement of the user's vocal cords, emits fragrant smoke according to the user's stress level, and visualizes the stress level, thereby supporting the user's mental health.

[0029] The aroma humidifier system according to the embodiment includes a vocal cord analysis unit, a stress assessment unit, a fragrance emission unit, and a visualization unit. The vocal cord analysis unit analyzes the movement of a user's vocal cords. For example, it analyzes the tone, pitch, and rhythm of the user's voice when the user speaks. The vocal cord analysis unit can collect and analyze the user's voice data in real time. For example, it collects the user's voice using a microphone and analyzes the movement of the vocal cords using an analysis algorithm. The stress assessment unit determines the stress level based on the data analyzed by the vocal cord analysis unit. For example, if the voice has a high tone and a fast rhythm, it determines that the stress level is high. The stress assessment unit can also quantify the stress level based on the analyzed data. For example, it evaluates the stress level on a scale from 0 to 100. The fragrance emission unit emits fragrance smoke based on the stress level determined by the stress assessment unit. For example, if the stress level is high, it emits a relaxing lavender fragrance. The fragrance emission unit can adjust the type and intensity of the fragrance depending on the stress level. For example, if the stress level is low, it emits a refreshing mint fragrance. The visualization unit changes the color and amount of aromatic smoke based on the stress level. For example, if the stress level is high, a large amount of dark-colored smoke is emitted. The visualization unit can also dynamically adjust the color and amount of smoke to visually indicate the stress level. For example, if the stress level is low, a small amount of light-colored smoke is emitted. This allows the aroma humidifier system according to the embodiment to support the mental health of the user. For example, the user can grasp their stress level at a glance and select an appropriate relaxation method.

[0030] The vocal cord analysis unit can simultaneously analyze not only the movement of the vocal cords but also facial expressions and body movements to perform a comprehensive stress assessment. For example, the vocal cord analysis unit captures the user's facial expressions with a camera in addition to the movement of the vocal cords when they speak, and AI analyzes their expressions. For example, it analyzes the wrinkles between the eyebrows and the degree to which the corners of the mouth turn down to evaluate the stress level. The vocal cord analysis unit also detects the user's body movements with a sensor, and AI analyzes those movements. For example, it detects hand tremors and changes in posture and evaluates them as signs of stress. The vocal cord analysis unit also performs an integrated analysis of the movement of the vocal cords, facial expressions, and body movements to perform a comprehensive stress assessment. For example, if the voice is high-pitched, the face is tense, and the body is stiff, it determines that the stress level is high. This enables more accurate stress assessment by analyzing not only the movement of the vocal cords but also facial expressions and body movements.

[0031] The vocal cord analysis unit learns the user's past voice data and identifies individual stress patterns, enabling more accurate stress assessment. The vocal cord analysis unit, for example, collects the user's past voice data and uses AI to learn that data. For example, it analyzes past stress levels by correlating them with voice characteristics. The vocal cord analysis unit also identifies stress patterns for each user based on the past voice data. For example, it learns that specific tones and rhythms are signs of stress. The vocal cord analysis unit also analyzes the user's voice in real time based on the learned stress patterns and determines the stress level. For example, if a pattern matching past data is detected, it determines that the stress level is high. In this way, by learning past voice data, individual stress patterns can be identified, enabling more accurate stress assessment.

[0032] The vocal cord analysis unit can link the results of the vocal cord movement analysis with other health devices to perform comprehensive health management. For example, the vocal cord analysis unit links the results of the vocal cord movement analysis with a smartwatch and integrates them with heart rate and sleep data to perform comprehensive health management. For example, if the stress level is high, it checks for an increase in heart rate and a decrease in sleep quality. The vocal cord analysis unit also links with a fitness tracker to integrate exercise data and the results of the vocal cord movement analysis. For example, if lack of exercise is the cause of stress, it recommends exercise. The vocal cord analysis unit also links with other health devices to perform stress management based on comprehensive health data. For example, it integrates with dietary data and weight data to identify the cause of stress. This makes it possible to perform comprehensive health management by linking with other health devices.

[0033] The vocal cord analysis unit can combine the results of the vocal cord movement analysis with data on the user's workplace or home environment to evaluate the impact of stress due to environmental factors. For example, the vocal cord analysis unit combines the results of the vocal cord movement analysis with data on the workplace environment to identify workplace stress factors. For example, it analyzes the relationship between the frequency of meetings, workload, and stress level. The vocal cord analysis unit also integrates data on the home environment with the results of the vocal cord movement analysis to evaluate stress factors within the home. For example, it analyzes the relationship between noise levels at home and the burden of housework, and stress level. The vocal cord analysis unit also comprehensively evaluates the impact of stress due to environmental factors based on data on the workplace and home environment. For example, if stress is high both at work and at home, it suggests comprehensive stress management. In this way, by combining data on the workplace and home environment, the impact of stress due to environmental factors can be evaluated.

[0034] The fragrance emitting unit not only emits fragrance but also adjusts music and lighting at the same time, thereby providing a comprehensive relaxing environment. The fragrance emitting unit plays relaxing music while emitting fragrance, for example, according to the stress level. For example, if the stress level is high, gentle music is played. The fragrance emitting unit also adjusts lighting to provide a relaxing environment. For example, if the stress level is high, warm lighting is used. The fragrance emitting unit also adjusts the fragrance, music, and lighting in an integrated manner to provide a comprehensive relaxing environment. For example, if the stress level is high, a lavender fragrance, gentle music, and warm lighting are combined. In this way, by not only emitting fragrance but also adjusting music and lighting, a comprehensive relaxing environment can be provided.

[0035] The scent emitting unit can learn a user's past scent preferences or reactions and emit an individually optimized scent. For example, the scent emitting unit stores a user's past scent preferences and reactions in a database, and AI learns from that data. For example, it identifies scents that have had a high relaxing effect in the past. The scent emitting unit also emits a scent optimized for each user based on the learned data. For example, it may emit lavender for a particular user and mint for another user. The scent emitting unit also analyzes the user's scent preferences and reactions in real time and dynamically adjusts the individually optimized scent. For example, it may change the strength or type of scent depending on the user's reaction. This makes it possible to emit an individually optimized scent by learning the user's past scent preferences and reactions.

[0036] The scent emitting unit can provide breathing techniques or meditation guides to enhance the effects of aromatherapy simultaneously with the release of the scent. For example, the scent emitting unit can play audio guiding breathing techniques to enhance relaxation simultaneously with the release of the scent. For example, audio instructions are given on the timing of deep breathing. The scent emitting unit can also provide meditation guides to maximize the effects of the scent. For example, audio guidance is provided on the steps of meditation. The scent emitting unit can also link the breathing techniques or meditation guides with an app, allowing the user to relax at their own pace. For example, the content of the guide can be customized on the app. This can enhance the effects of aromatherapy by providing breathing techniques or meditation guides simultaneously with the release of the scent.

[0037] The visualization unit records fluctuations in a user's stress level over the long term and performs trend analysis, thereby identifying the root cause of stress. The visualization unit, for example, builds a system that records a user's stress level over the long term and performs trend analysis. For example, it displays daily fluctuations in stress level in a graph. The visualization unit also analyzes long-term stress data and identifies the root cause of stress. For example, if stress levels are high on specific days of the week or during specific times of the day, it identifies the cause. The visualization unit also provides stress management advice to the user based on the trend analysis. For example, it suggests relaxation methods during periods of high stress. In this way, the root cause of stress can be identified by recording fluctuations in stress levels over the long term and performing trend analysis.

[0038] The visualization unit can share the stress level visualization results with the user's family or medical professionals to support comprehensive stress management. The visualization unit, for example, adds a function to share the stress level visualization results with family members. For example, it allows family members to check the user's stress level on the app. The visualization unit also builds a system to share the stress level visualization results with medical professionals and receive professional advice. For example, a doctor may make a diagnosis based on the user's stress data. The visualization unit also works with family members and medical professionals to support comprehensive stress management. For example, it adds a function to send a notification to family members or doctors when the stress level is high. This makes it possible to share the stress level visualization results with family members and medical professionals, enabling comprehensive stress management.

[0039] The visualization unit can integrate the visualization results of the stress level with other health data to evaluate the overall health state. For example, the visualization unit integrates the visualization results of the stress level with heart rate data to evaluate the overall health state. For example, if the heart rate is high, the stress level will also be displayed as high. The visualization unit also integrates the visualization results of the stress level with sleep data to evaluate the relationship between sleep quality and stress. For example, if lack of sleep is the cause of stress, this relationship will be displayed. The visualization unit also integrates the visualization results of the stress level with other health data to build a system for evaluating the overall health state. For example, it integrates it with dietary data and exercise data to comprehensively evaluate the health state. In this way, by integrating the visualization results of the stress level with other health data, it is possible to evaluate the overall health state.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The aroma humidifier system can not only evaluate a user's stress level based on the analysis of the user's vocal cord movement, but also provide personalized health advice using the user's vocal characteristics. For example, if the tone or rhythm of the voice changes, the system can advise the user on appropriate rest or exercise. The vocal cord analysis unit can also record changes in the user's voice over the long term and analyze health trends. For example, if the voice tone continues to deteriorate, the system can recommend that the user consult a doctor. Furthermore, the vocal cord analysis unit can evaluate health indicators other than stress based on the user's vocal characteristics. For example, if the user frequently experiences hoarseness or shortness of breath, the system can advise the user to check the health of their respiratory system. In this way, the aroma humidifier system can support comprehensive health management using the user's vocal characteristics.

[0042] The aroma humidifier system can further evaluate the impact of stress caused by environmental factors based on the analysis results of the user's vocal cord movement, combining them with data on the user's workplace or home environment. For example, the analysis results of vocal cord movement can be combined with data on the workplace environment to identify workplace stress factors. For example, the correlation between the frequency of meetings, workload, and stress level can be analyzed. The vocal cord analysis unit also integrates data on the home environment with the analysis results of vocal cord movement to evaluate stress factors within the home. For example, the correlation between noise levels within the home, the burden of housework, and stress level can be analyzed. The vocal cord analysis unit also comprehensively evaluates the impact of stress caused by environmental factors based on data on the workplace and home environment. For example, if stress is high both at work and at home, the system can suggest comprehensive stress management. This allows the impact of stress caused by environmental factors to be evaluated by combining data on the workplace and home environment.

[0043] The aroma humidifier system can further learn the user's past voice data based on the analysis of the user's vocal cord movement and identify individual stress patterns, enabling more accurate stress assessment. For example, the system collects the user's past voice data and uses AI to learn from that data. For example, it associates past stress levels with voice characteristics and analyzes them. The vocal cord analysis unit also identifies each user's stress pattern based on the past voice data. For example, it learns that specific tones and rhythms are signs of stress. The vocal cord analysis unit then analyzes the user's voice in real time based on the learned stress patterns and determines the stress level. For example, if a pattern matching past data is detected, it determines that the stress level is high. This allows the system to learn past voice data and identify individual stress patterns, enabling more accurate stress assessment.

[0044] The aroma humidifier system can further evaluate the impact of stress caused by environmental factors based on the analysis results of the user's vocal cord movement, combining them with data on the user's workplace or home environment. For example, the analysis results of vocal cord movement can be combined with data on the workplace environment to identify workplace stress factors. For example, the correlation between the frequency of meetings, workload, and stress level can be analyzed. The vocal cord analysis unit also integrates data on the home environment with the analysis results of vocal cord movement to evaluate stress factors within the home. For example, the correlation between noise levels within the home, the burden of housework, and stress level can be analyzed. The vocal cord analysis unit also comprehensively evaluates the impact of stress caused by environmental factors based on data on the workplace and home environment. For example, if stress is high both at work and at home, the system can suggest comprehensive stress management. This allows the impact of stress caused by environmental factors to be evaluated by combining data on the workplace and home environment.

[0045] The aroma humidifier system can further learn the user's past voice data based on the analysis of the user's vocal cord movement and identify individual stress patterns, enabling more accurate stress assessment. For example, the system collects the user's past voice data and uses AI to learn from that data. For example, it associates past stress levels with voice characteristics and analyzes them. The vocal cord analysis unit also identifies each user's stress pattern based on the past voice data. For example, it learns that specific tones and rhythms are signs of stress. The vocal cord analysis unit then analyzes the user's voice in real time based on the learned stress patterns and determines the stress level. For example, if a pattern matching past data is detected, it determines that the stress level is high. This allows the system to learn past voice data and identify individual stress patterns, enabling more accurate stress assessment.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The vocal cord analysis unit analyzes the movement of the user's vocal cords. For example, it analyzes the tone, pitch, and rhythm of the user's voice when they speak. The vocal cord analysis unit can also collect and analyze the user's voice data in real time. For example, it collects the user's voice using a microphone and analyzes the movement of the vocal cords using an analysis algorithm. Step 2: The stress assessment unit determines the stress level based on the data analyzed by the vocal cord analysis unit. For example, if the voice has a high tone and a fast rhythm, it determines that the stress level is high. The stress assessment unit can also quantify the stress level based on the analyzed data. For example, it can evaluate the stress level on a scale from 0 to 100. Step 3: The scent release unit releases scented smoke based on the stress level determined by the stress determination unit. For example, if the stress level is high, it releases the relaxing scent of lavender. The scent release unit can also adjust the type and strength of the scent depending on the stress level. For example, if the stress level is low, it releases a refreshing mint scent. Step 4: The visualization unit changes the color and amount of the scented smoke based on the stress level. For example, if the stress level is high, a large amount of dark-colored smoke is emitted. The visualization unit can also dynamically adjust the color and amount of smoke to visually indicate the stress level. For example, if the stress level is low, a small amount of light-colored smoke is emitted.

[0048] (Example 2) The aroma humidifier system according to an embodiment of the present invention analyzes the movement of the user's vocal cords, emits fragrant smoke according to the user's stress level, and visualizes the stress level, thereby supporting the user's mental health.

[0049] The aroma humidifier system according to the embodiment includes a vocal cord analysis unit, a stress assessment unit, a fragrance emission unit, and a visualization unit. The vocal cord analysis unit analyzes the movement of a user's vocal cords. For example, it analyzes the tone, pitch, and rhythm of the user's voice when the user speaks. The vocal cord analysis unit can collect and analyze the user's voice data in real time. For example, it collects the user's voice using a microphone and analyzes the movement of the vocal cords using an analysis algorithm. The stress assessment unit determines the stress level based on the data analyzed by the vocal cord analysis unit. For example, if the voice has a high tone and a fast rhythm, it determines that the stress level is high. The stress assessment unit can also quantify the stress level based on the analyzed data. For example, it evaluates the stress level on a scale from 0 to 100. The fragrance emission unit emits fragrance smoke based on the stress level determined by the stress assessment unit. For example, if the stress level is high, it emits a relaxing lavender fragrance. The fragrance emission unit can adjust the type and intensity of the fragrance depending on the stress level. For example, if the stress level is low, it emits a refreshing mint fragrance. The visualization unit changes the color and amount of aromatic smoke based on the stress level. For example, if the stress level is high, a large amount of dark-colored smoke is emitted. The visualization unit can also dynamically adjust the color and amount of smoke to visually indicate the stress level. For example, if the stress level is low, a small amount of light-colored smoke is emitted. This allows the aroma humidifier system according to the embodiment to support the mental health of the user. For example, the user can grasp their stress level at a glance and select an appropriate relaxation method.

[0050] The vocal cord analysis unit can simultaneously analyze not only the movement of the vocal cords but also facial expressions and body movements to perform a comprehensive stress assessment. For example, the vocal cord analysis unit captures the user's facial expressions with a camera in addition to the movement of the vocal cords when they speak, and AI analyzes their expressions. For example, it analyzes the wrinkles between the eyebrows and the degree to which the corners of the mouth turn down to evaluate the stress level. The vocal cord analysis unit also detects the user's body movements with a sensor, and AI analyzes those movements. For example, it detects hand tremors and changes in posture and evaluates them as signs of stress. The vocal cord analysis unit also performs an integrated analysis of the movement of the vocal cords, facial expressions, and body movements to perform a comprehensive stress assessment. For example, if the voice is high-pitched, the face is tense, and the body is stiff, it determines that the stress level is high. This enables more accurate stress assessment by analyzing not only the movement of the vocal cords but also facial expressions and body movements.

[0051] The vocal cord analysis unit learns the user's past voice data and identifies individual stress patterns, enabling more accurate stress assessment. The vocal cord analysis unit, for example, collects the user's past voice data and uses AI to learn that data. For example, it analyzes past stress levels by correlating them with voice characteristics. The vocal cord analysis unit also identifies stress patterns for each user based on the past voice data. For example, it learns that specific tones and rhythms are signs of stress. The vocal cord analysis unit also analyzes the user's voice in real time based on the learned stress patterns and determines the stress level. For example, if a pattern matching past data is detected, it determines that the stress level is high. In this way, by learning past voice data, individual stress patterns can be identified, enabling more accurate stress assessment.

[0052] The vocal cord analysis unit can use the emotion estimation function to estimate an emotion from the tone or rhythm of the user's voice and evaluate the stress level based on that emotion. The vocal cord analysis unit, for example, analyzes the tone and rhythm of the user's voice and estimates the emotion using the emotion estimation function. For example, if the user speaks in a high-pitched voice and quickly, it estimates emotions such as tension or impatience. The vocal cord analysis unit also evaluates the stress level based on the estimated emotion. For example, if the user feels strong tension or impatience, it determines that the stress level is high. The vocal cord analysis unit also uses the emotion estimation function to analyze changes in the user's voice in real time and dynamically evaluate the stress level according to fluctuations in emotion. For example, if the emotion changes suddenly, it determines that the stress level is high. In this way, by using the emotion estimation function, it is possible to evaluate the stress level based on the user's emotion.

[0053] The vocal cord analysis unit can link the results of the vocal cord movement analysis with other health devices to perform comprehensive health management. For example, the vocal cord analysis unit links the results of the vocal cord movement analysis with a smartwatch and integrates them with heart rate and sleep data to perform comprehensive health management. For example, if the stress level is high, it checks for an increase in heart rate and a decrease in sleep quality. The vocal cord analysis unit also links with a fitness tracker to integrate exercise data and the results of the vocal cord movement analysis. For example, if lack of exercise is the cause of stress, it recommends exercise. The vocal cord analysis unit also links with other health devices to perform stress management based on comprehensive health data. For example, it integrates with dietary data and weight data to identify the cause of stress. This makes it possible to perform comprehensive health management by linking with other health devices.

[0054] The vocal cord analysis unit can combine the results of the vocal cord movement analysis with data on the user's workplace or home environment to evaluate the impact of stress due to environmental factors. For example, the vocal cord analysis unit combines the results of the vocal cord movement analysis with data on the workplace environment to identify workplace stress factors. For example, it analyzes the relationship between the frequency of meetings, workload, and stress level. The vocal cord analysis unit also integrates data on the home environment with the results of the vocal cord movement analysis to evaluate stress factors within the home. For example, it analyzes the relationship between noise levels at home and the burden of housework, and stress level. The vocal cord analysis unit also comprehensively evaluates the impact of stress due to environmental factors based on data on the workplace and home environment. For example, if stress is high both at work and at home, it suggests comprehensive stress management. In this way, by combining data on the workplace and home environment, the impact of stress due to environmental factors can be evaluated.

[0055] The fragrance emitting unit not only emits fragrance but also adjusts music and lighting at the same time, thereby providing a comprehensive relaxing environment. The fragrance emitting unit plays relaxing music while emitting fragrance, for example, according to the stress level. For example, if the stress level is high, gentle music is played. The fragrance emitting unit also adjusts lighting to provide a relaxing environment. For example, if the stress level is high, warm lighting is used. The fragrance emitting unit also adjusts the fragrance, music, and lighting in an integrated manner to provide a comprehensive relaxing environment. For example, if the stress level is high, a lavender fragrance, gentle music, and warm lighting are combined. In this way, by not only emitting fragrance but also adjusting music and lighting, a comprehensive relaxing environment can be provided.

[0056] The scent emitting unit can learn a user's past scent preferences or reactions and emit an individually optimized scent. For example, the scent emitting unit stores a user's past scent preferences and reactions in a database, and AI learns from that data. For example, it identifies scents that have had a high relaxing effect in the past. The scent emitting unit also emits a scent optimized for each user based on the learned data. For example, it may emit lavender for a particular user and mint for another user. The scent emitting unit also analyzes the user's scent preferences and reactions in real time and dynamically adjusts the individually optimized scent. For example, it may change the strength or type of scent depending on the user's reaction. This makes it possible to emit an individually optimized scent by learning the user's past scent preferences and reactions.

[0057] The scent emitting unit can use the emotion estimation function to adjust the type or intensity of the scent in real time based on the user's emotions. For example, the scent emitting unit uses the emotion estimation function to adjust the type of scent in real time based on the user's emotions. For example, if the user is nervous, it emits a scent that has a relaxing effect. The scent emitting unit also dynamically adjusts the intensity of the scent according to the user's emotions. For example, if the user is emotionally excited, it increases the intensity of the scent. The scent emitting unit also uses the emotion estimation function to adjust the type and intensity of the scent in real time according to changes in the user's emotions. For example, it readjusts the scent to an appropriate level if the user's emotions change. In this way, by using the emotion estimation function, it is possible to adjust the type and intensity of the scent based on the user's emotions.

[0058] The scent emitting unit can provide breathing techniques or meditation guides to enhance the effects of aromatherapy simultaneously with the release of the scent. For example, the scent emitting unit can play audio guiding breathing techniques to enhance relaxation simultaneously with the release of the scent. For example, audio instructions are given on the timing of deep breathing. The scent emitting unit can also provide meditation guides to maximize the effects of the scent. For example, audio guidance is provided on the steps of meditation. The scent emitting unit can also link the breathing techniques or meditation guides with an app, allowing the user to relax at their own pace. For example, the content of the guide can be customized on the app. This can enhance the effects of aromatherapy by providing breathing techniques or meditation guides simultaneously with the release of the scent.

[0059] The scent release unit can use the emotion estimation function to automatically generate a scent blend according to the user's emotion and provide an individually customized scent. The scent release unit, for example, uses the emotion estimation function to automatically generate a scent blend according to the user's emotion. For example, it combines lavender, which has a relaxing effect, with refreshing mint. The scent release unit also customizes the scent blend based on the user's emotion. For example, if the user is nervous, it blends in more of the scent that has a relaxing effect. The scent release unit also uses the emotion estimation function to adjust the scent blend in real time according to changes in the user's emotion. For example, it regenerates an appropriate scent blend when the user's emotion changes. In this way, by using the emotion estimation function, it is possible to automatically generate a scent blend according to the user's emotion and provide an individually customized scent.

[0060] The visualization unit records fluctuations in a user's stress level over the long term and performs trend analysis, thereby identifying the root cause of stress. The visualization unit, for example, builds a system that records a user's stress level over the long term and performs trend analysis. For example, it displays daily fluctuations in stress level in a graph. The visualization unit also analyzes long-term stress data and identifies the root cause of stress. For example, if stress levels are high on specific days of the week or during specific times of the day, it identifies the cause. The visualization unit also provides stress management advice to the user based on the trend analysis. For example, it suggests relaxation methods during periods of high stress. In this way, the root cause of stress can be identified by recording fluctuations in stress levels over the long term and performing trend analysis.

[0061] The visualization unit uses the emotion estimation function to visualize the stress level based on the user's emotions and display changes in emotions in real time. The visualization unit, for example, uses the emotion estimation function to visualize the stress level based on the user's emotions. For example, if the user is emotionally charged, the stress level is displayed as high. The visualization unit also analyzes changes in the user's emotions in real time and dynamically displays the stress level based on the results. For example, the stress level is updated the moment the emotion changes. The visualization unit also builds a system that displays fluctuations in the stress level in real time based on the emotion estimation data. For example, the stress level graph is dynamically updated in accordance with changes in emotion. As a result, by using the emotion estimation function, it is possible to visualize and display the stress level in real time based on the user's emotions.

[0062] The visualization unit can share the stress level visualization results with the user's family or medical professionals to support comprehensive stress management. The visualization unit, for example, adds a function to share the stress level visualization results with family members. For example, it allows family members to check the user's stress level on the app. The visualization unit also builds a system to share the stress level visualization results with medical professionals and receive professional advice. For example, a doctor may make a diagnosis based on the user's stress data. The visualization unit also works with family members and medical professionals to support comprehensive stress management. For example, it adds a function to send a notification to family members or doctors when the stress level is high. This makes it possible to share the stress level visualization results with family members and medical professionals, enabling comprehensive stress management.

[0063] The visualization unit can integrate the visualization results of the stress level with other health data to evaluate the overall health state. For example, the visualization unit integrates the visualization results of the stress level with heart rate data to evaluate the overall health state. For example, if the heart rate is high, the stress level will also be displayed as high. The visualization unit also integrates the visualization results of the stress level with sleep data to evaluate the relationship between sleep quality and stress. For example, if lack of sleep is the cause of stress, this relationship will be displayed. The visualization unit also integrates the visualization results of the stress level with other health data to build a system for evaluating the overall health state. For example, it integrates it with dietary data and exercise data to comprehensively evaluate the health state. In this way, by integrating the visualization results of the stress level with other health data, it is possible to evaluate the overall health state.

[0064] The visualization unit can use the emotion estimation function to provide stress management advice according to the user's emotions and propose individually customized stress countermeasures. The visualization unit, for example, uses the emotion estimation function to provide stress management advice according to the user's emotions. For example, if the user is tense, it suggests relaxation methods. The visualization unit also proposes individually customized stress countermeasures based on the user's emotion data. For example, it recommends meditation for a specific user and exercise for another user. The visualization unit also builds a system that proposes stress countermeasures in real time according to changes in the user's emotions based on the emotion estimation data. For example, it re-proposes appropriate countermeasures if the user's emotions change. In this way, by using the emotion estimation function, it is possible to provide stress management advice according to the user's emotions and propose customized stress countermeasures.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The aroma humidifier system can not only evaluate a user's stress level based on the analysis of the user's vocal cord movement, but also provide personalized health advice using the user's vocal characteristics. For example, if the tone or rhythm of the voice changes, the system can advise the user on appropriate rest or exercise. The vocal cord analysis unit can also record changes in the user's voice over the long term and analyze health trends. For example, if the voice tone continues to deteriorate, the system can recommend that the user consult a doctor. Furthermore, the vocal cord analysis unit can evaluate health indicators other than stress based on the user's vocal characteristics. For example, if the user frequently experiences hoarseness or shortness of breath, the system can advise the user to check the health of their respiratory system. In this way, the aroma humidifier system can support comprehensive health management using the user's vocal characteristics.

[0067] The aroma humidifier system can further estimate the user's emotions based on the analysis of the user's vocal cord movements and evaluate the stress level based on those emotions. For example, if the user's vocal tone or rhythm changes, the system estimates the user's emotions, and if the user is emotionally aroused, the system determines that the stress level is high. The vocal cord analysis unit can also analyze the user's emotional changes in real time and dynamically evaluate the stress level according to emotional fluctuations. For example, if the user's emotions change suddenly, the system determines that the stress level is high. Furthermore, the vocal cord analysis unit can suggest individually customized stress relief measures based on the user's emotional data. For example, if the user is tense, the system suggests relaxation methods, and if the user is calm, the system provides advice on maintaining that state. This makes it possible to use the emotion estimation function to evaluate the user's stress level based on their emotions and suggest individual stress relief measures.

[0068] The aroma humidifier system can further evaluate the impact of stress caused by environmental factors based on the analysis results of the user's vocal cord movement, combining them with data on the user's workplace or home environment. For example, the analysis results of vocal cord movement can be combined with data on the workplace environment to identify workplace stress factors. For example, the correlation between the frequency of meetings, workload, and stress level can be analyzed. The vocal cord analysis unit also integrates data on the home environment with the analysis results of vocal cord movement to evaluate stress factors within the home. For example, the correlation between noise levels within the home, the burden of housework, and stress level can be analyzed. The vocal cord analysis unit also comprehensively evaluates the impact of stress caused by environmental factors based on data on the workplace and home environment. For example, if stress is high both at work and at home, the system can suggest comprehensive stress management. This allows the impact of stress caused by environmental factors to be evaluated by combining data on the workplace and home environment.

[0069] The aroma humidifier system can further estimate the user's emotions based on the analysis of the user's vocal cord movement, and adjust the type or strength of the fragrance in real time based on the emotion. For example, the emotion estimation function can be used to adjust the type of fragrance in real time based on the user's emotions. For example, if the user is nervous, a fragrance with a relaxing effect can be emitted. The fragrance emission unit also dynamically adjusts the intensity of the fragrance according to the user's emotions. For example, if the user is emotionally excited, the fragrance emission unit can increase the intensity of the fragrance. The emotion estimation function can also be used to adjust the type and strength of the fragrance in real time according to changes in the user's emotions. For example, if the user's emotions change, the fragrance can be readjusted to an appropriate fragrance. In this way, the emotion estimation function can be used to adjust the type and strength of the fragrance based on the user's emotions.

[0070] The aroma humidifier system can further learn the user's past voice data based on the analysis of the user's vocal cord movement and identify individual stress patterns, enabling more accurate stress assessment. For example, the system collects the user's past voice data and uses AI to learn from that data. For example, it associates past stress levels with voice characteristics and analyzes them. The vocal cord analysis unit also identifies each user's stress pattern based on the past voice data. For example, it learns that specific tones and rhythms are signs of stress. The vocal cord analysis unit then analyzes the user's voice in real time based on the learned stress patterns and determines the stress level. For example, if a pattern matching past data is detected, it determines that the stress level is high. This allows the system to learn past voice data and identify individual stress patterns, enabling more accurate stress assessment.

[0071] The aroma humidifier system can further estimate the user's emotions based on the analysis of the user's vocal cord movements and evaluate the stress level based on those emotions. For example, if the user's vocal tone or rhythm changes, the system estimates the user's emotions, and if the user is emotionally aroused, the system determines that the stress level is high. The vocal cord analysis unit can also analyze the user's emotional changes in real time and dynamically evaluate the stress level according to emotional fluctuations. For example, if the user's emotions change suddenly, the system determines that the stress level is high. Furthermore, the vocal cord analysis unit can suggest individually customized stress relief measures based on the user's emotional data. For example, if the user is tense, the system suggests relaxation methods, and if the user is calm, the system provides advice on maintaining that state. This makes it possible to use the emotion estimation function to evaluate the user's stress level based on their emotions and suggest individual stress relief measures.

[0072] The aroma humidifier system can further evaluate the impact of stress caused by environmental factors based on the analysis results of the user's vocal cord movement, combining them with data on the user's workplace or home environment. For example, the analysis results of vocal cord movement can be combined with data on the workplace environment to identify workplace stress factors. For example, the correlation between the frequency of meetings, workload, and stress level can be analyzed. The vocal cord analysis unit also integrates data on the home environment with the analysis results of vocal cord movement to evaluate stress factors within the home. For example, the correlation between noise levels within the home, the burden of housework, and stress level can be analyzed. The vocal cord analysis unit also comprehensively evaluates the impact of stress caused by environmental factors based on data on the workplace and home environment. For example, if stress is high both at work and at home, the system can suggest comprehensive stress management. This allows the impact of stress caused by environmental factors to be evaluated by combining data on the workplace and home environment.

[0073] The aroma humidifier system can further estimate the user's emotions based on the analysis of the user's vocal cord movement, and adjust the type or strength of the fragrance in real time based on the emotion. For example, the emotion estimation function can be used to adjust the type of fragrance in real time based on the user's emotions. For example, if the user is nervous, a fragrance with a relaxing effect can be emitted. The fragrance emission unit also dynamically adjusts the intensity of the fragrance according to the user's emotions. For example, if the user is emotionally excited, the fragrance emission unit can increase the intensity of the fragrance. The emotion estimation function can also be used to adjust the type and strength of the fragrance in real time according to changes in the user's emotions. For example, if the user's emotions change, the fragrance can be readjusted to an appropriate fragrance. In this way, the emotion estimation function can be used to adjust the type and strength of the fragrance based on the user's emotions.

[0074] The aroma humidifier system can further learn the user's past voice data based on the analysis of the user's vocal cord movement and identify individual stress patterns, enabling more accurate stress assessment. For example, the system collects the user's past voice data and uses AI to learn from that data. For example, it associates past stress levels with voice characteristics and analyzes them. The vocal cord analysis unit also identifies each user's stress pattern based on the past voice data. For example, it learns that specific tones and rhythms are signs of stress. The vocal cord analysis unit then analyzes the user's voice in real time based on the learned stress patterns and determines the stress level. For example, if a pattern matching past data is detected, it determines that the stress level is high. This allows the system to learn past voice data and identify individual stress patterns, enabling more accurate stress assessment.

[0075] The aroma humidifier system can further estimate the user's emotions based on the analysis of the user's vocal cord movements and evaluate the stress level based on those emotions. For example, if the user's vocal tone or rhythm changes, the system estimates the user's emotions, and if the user is emotionally aroused, the system determines that the stress level is high. The vocal cord analysis unit can also analyze the user's emotional changes in real time and dynamically evaluate the stress level according to emotional fluctuations. For example, if the user's emotions change suddenly, the system determines that the stress level is high. Furthermore, the vocal cord analysis unit can suggest individually customized stress relief measures based on the user's emotional data. For example, if the user is tense, the system suggests relaxation methods, and if the user is calm, the system provides advice on maintaining that state. This makes it possible to use the emotion estimation function to evaluate the user's stress level based on their emotions and suggest individual stress relief measures.

[0076] The processing flow of the second embodiment will be briefly explained below.

[0077] Step 1: The vocal cord analysis unit analyzes the movement of the user's vocal cords. For example, it analyzes the tone, pitch, and rhythm of the user's voice when they speak. The vocal cord analysis unit can also collect and analyze the user's voice data in real time. For example, it collects the user's voice using a microphone and analyzes the movement of the vocal cords using an analysis algorithm. Step 2: The stress assessment unit determines the stress level based on the data analyzed by the vocal cord analysis unit. For example, if the voice has a high tone and a fast rhythm, it determines that the stress level is high. The stress assessment unit can also quantify the stress level based on the analyzed data. For example, it can evaluate the stress level on a scale from 0 to 100. Step 3: The scent release unit releases scented smoke based on the stress level determined by the stress determination unit. For example, if the stress level is high, it releases the relaxing scent of lavender. The scent release unit can also adjust the type and strength of the scent depending on the stress level. For example, if the stress level is low, it releases a refreshing mint scent. Step 4: The visualization unit changes the color and amount of the scented smoke based on the stress level. For example, if the stress level is high, a large amount of dark-colored smoke is emitted. The visualization unit can also dynamically adjust the color and amount of smoke to visually indicate the stress level. For example, if the stress level is low, a small amount of light-colored smoke is emitted.

[0078] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0079] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0080] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0081] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0082] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0083] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0084] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0085] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0086] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0087] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0088] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0089] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0090] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0091] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0092] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0093] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0094] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0096] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0097] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0098] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0099] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0100] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0101] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0102] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0103] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0104] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0108] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0111] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0112] 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.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0119] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0120] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0127] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0128] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0129] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0130] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0131] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0132] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0133] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0134] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0135] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0136] 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.

[0137] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0138] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0139] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0140] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0141] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0142] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0143] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0144] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0145] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a vocal cord analysis unit that analyzes the movement of the user's vocal cords; a stress determination unit that determines a stress level based on the data analyzed by the vocal cord analysis unit; a fragrance release unit that releases fragrance smoke based on the stress level determined by the stress determination unit; a visualization unit that changes the color or amount of the fragrance smoke based on the stress level. A system characterized by:

2. The vocal cord analysis unit Not only vocal chord movements but also facial expressions and body movements are analyzed simultaneously to perform a comprehensive stress assessment.

2. The system of claim 1.

3. The vocal cord analysis unit The results of vocal cord movement analysis will be linked to other health devices to provide comprehensive health management.

2. The system of claim 1.

4. a vocal cord analysis unit that analyzes the movement of the user's vocal cords; a stress determination unit that determines a stress level based on the data analyzed by the vocal cord analysis unit; a fragrance release unit that releases fragrance smoke based on the stress level determined by the stress determination unit; a visualization unit that changes the color or amount of the fragrance smoke based on the stress level. A system characterized by:

5. The vocal cord analysis unit Inferring emotions from the tone or rhythm of a user's voice and assessing stress levels based on those emotions 2. The system of claim 1.

6. The scent release unit is Adjusting scent type or intensity in real time based on user emotions 2. The system of claim 1.

7. The visualization unit Visualize stress levels based on user emotions and display emotional changes in real time 2. The system of claim 1.

8. The visualization unit Provides stress management advice tailored to the user's emotions and suggests individually tailored stress management strategies 2. The system of claim 1.

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