System

Abstract

An object of a system according to an embodiment is to automatically determine the presence or absence of snoring and propose an appropriate solution.SOLUTION: A system includes a recording part, an analysis part, a diagnosis part, and a proposal part. The recording part records snoring. The analysis unit analyzes the data recorded by the recording unit. The diagnosis unit determines the presence or absence of snoring based on the result analyzed by the analysis unit. The proposal unit proposes a solution for eliminating the snoring based on the result determined by the diagnosis unit.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 had the problem that it is difficult for people to check for themselves whether they snore or not and find an appropriate solution.

[0005] The system according to the embodiment aims to automatically determine whether or not a person is snoring and to propose an appropriate solution. [Means for solving the problem]

[0006] The system according to the embodiment includes a recording unit, an analysis unit, a diagnosis unit, and a suggestion unit. The recording unit records snoring. The analysis unit analyzes the data recorded by the recording unit. The diagnosis unit determines whether snoring is occurring based on the analysis results obtained by the analysis unit. The suggestion unit suggests a solution to eliminate snoring based on the results obtained by the diagnosis unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically determine whether or not a person is snoring and propose appropriate solutions. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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) A snoring diagnosis system according to an embodiment of the present invention is a system in which a user records snoring using a smartphone while sleeping, the recording data is analyzed by a generating AI, the system determines whether the user is snoring, and further proposes solutions to cure snoring. As a result, the snoring diagnosis system can effectively diagnose the user's snoring and provide specific solutions to cure it.

[0029] A snoring diagnosis system according to an embodiment includes a recording unit, an analysis unit, a diagnosis unit, and a suggestion unit. The recording unit records snoring while a user sleeps. For example, the recording unit records snoring using a smartphone microphone. Alternatively, the recording unit can place the smartphone next to a pillow or bedside and continuously record audio throughout the night. For example, the user launches a dedicated app before going to sleep and sets the device to start recording. The analysis unit uses a generation AI to analyze the data recorded by the recording unit. For example, the generation AI analyzes the waveform and frequency components of the audio data to detect characteristic snoring patterns. The generation AI performs the analysis using a text generation AI (e.g., LLM) or a multimodal generation AI. The diagnosis unit determines whether or not a user is snoring based on the analysis results of the analysis unit. For example, based on the analysis results of the generation AI, the diagnosis unit provides specific information such as, "You snore. Your snoring frequency is five times per hour, and your average volume is 70 decibels." The suggestion unit proposes solutions to cure snoring based on the results of the diagnosis unit. Examples of such recommendations include improving lifestyle habits, changing sleeping posture, and recommending specific exercises and stretches. The generation AI analyzes the user's snoring patterns and causes and then proposes optimal solutions based on the analysis. For example, it provides specific advice such as, "We recommend sleeping on your side," or "It is effective to do some light stretching before bed." This allows the snoring diagnosis system according to the embodiment to effectively diagnose the user's snoring and provide specific solutions to treat it. For example, after the user implements the proposed solutions, the system records and analyzes the snoring again to confirm the effectiveness. The generation AI then provides continuous follow-up by reporting the degree of improvement and providing additional advice. For example, it provides feedback such as, "Your snoring frequency has decreased. We recommend that you continue to sleep on your side."

[0030] The recording unit can locate the source of snoring using sound source localization technology by placing multiple microphones in different positions in the room in addition to the smartphone's microphone. For example, the recording unit can locate the source of snoring by placing microphones in the four corners of the room and linking them with the smartphone's microphone. For example, the recording unit synchronizes the audio data from each microphone and calculates the location of snoring using a sound source localization algorithm. The recording unit also connects the smartphone's microphone to an external microphone via Bluetooth to collect data from multiple sound sources in real time. For example, a smartphone app integrates the data from each microphone and identifies the location of snoring. The recording unit also develops a dedicated device for locating the source of snoring using sound source localization technology and links it to the smartphone. For example, the device analyzes the acoustic characteristics of the room and identifies the location of snoring with high accuracy. This allows the source of snoring to be identified with high accuracy.

[0031] The recording unit can include environmental sounds in the recorded data and simultaneously record factors that affect snoring. For example, when recording with a smartphone microphone, the recording unit can also use sensors that measure room temperature and humidity to simultaneously record environmental data. For example, a temperature sensor and a humidity sensor can be connected to the smartphone and saved along with the recorded data. The recording unit can also add a function to record environmental sounds to the smartphone app and analyze factors that affect snoring. For example, the sound of an air conditioner or external noise can be recorded and compared with the snoring data. The recording unit can also develop a dedicated device to record environmental data and link it to the smartphone. For example, the device can measure room temperature, humidity, and noise levels and send the data to the smartphone app. This allows factors that affect snoring to be simultaneously recorded.

[0032] The recording unit can use the smartphone camera to record the user's sleeping position and analyze the relationship between snoring and sleeping position. The recording unit, for example, installs the smartphone camera next to the bed and records the user's sleeping position. For example, it analyzes video data from nighttime and identifies the relationship between snoring and sleeping position. The recording unit also adds an automatic sleeping position detection function to the smartphone app and integrates the snoring data. For example, it uses video analysis technology to detect the user's sleeping position in real time. The recording unit also links the smartphone camera with an external camera to record the user's sleeping position from multiple perspectives. For example, it installs cameras in different positions in the room and integrates and analyzes the sleeping position data. This makes it possible to analyze the effect of sleeping position on snoring.

[0033] The recording unit can use the smartphone's acceleration sensor to record the user's tossing and movements and analyze the relationship between snoring and movements. The recording unit, for example, uses the smartphone's acceleration sensor to record the user's tossing and movements. For example, the smartphone can be placed next to a pillow to detect nighttime movements and compare them with snoring data. The recording unit can also add a function to the smartphone app that analyzes acceleration sensor data to identify the relationship between snoring and movements. For example, it can analyze movement patterns and identify the timing of snoring. The recording unit can also link the smartphone's acceleration sensor with an external sensor to record the user's movements with high accuracy. For example, a sensor installed on the bed can send movement data to the smartphone and integrate it with snoring data. This makes it possible to analyze the impact of tossing and movements on snoring.

[0034] The analysis unit can use the generation AI to extract specific frequency components from snoring audio data and identify the cause of snoring. The analysis unit, for example, uses the generation AI to extract specific frequency components from snoring audio data. For example, if there are a lot of low-frequency components, this may be due to nasal stenosis. The analysis unit also performs frequency analysis of the audio data and develops an algorithm to identify the cause of snoring. For example, if there is a characteristic pattern in a specific frequency band, the cause is identified. The analysis unit also uses the generation AI to analyze the audio data and build a model to identify the cause of snoring. For example, it learns from past data and determines whether specific frequency components are related to the cause of snoring. This makes it possible to extract frequency components to identify the cause of snoring.

[0035] When the generation AI performs analysis, the analysis unit compares it with past snoring data and can detect changes in snoring patterns. For example, the generation AI compares past snoring data with current data to detect changes in snoring patterns. For example, the analysis unit analyzes changes in snoring frequency and volume. The analysis unit also learns past snoring data and develops an algorithm for the generation AI to detect changes in patterns. For example, it identifies changes in the timing and duration of snoring. The analysis unit also builds a system in which the generation AI integrates past and current data to detect changes in snoring patterns in real time. For example, it issues an alert if the snoring pattern changes. This makes it possible to detect changes in snoring patterns.

[0036] The analysis unit uses the generation AI to analyze snoring audio data in real time and notify the user the moment snoring occurs. For example, the analysis unit analyzes snoring audio data in real time using the generation AI and sends a notification to a smartphone the moment snoring occurs. For example, an alert is issued when snoring sounds are detected. The analysis unit also develops a generation AI algorithm that performs real-time analysis and builds a system that notifies the user the moment snoring occurs. For example, a smartphone app notifies the user by vibration or sound. The analysis unit also develops a device that uses the generation AI to analyze snoring audio data in real time and notifies the user the moment snoring occurs. For example, a smartwatch notifies the user by vibration. This allows the user to be notified the moment snoring occurs.

[0037] When the generation AI performs its analysis, the analysis unit integrates it with other health data, allowing it to perform a multifaceted analysis of the cause of snoring. For example, the analysis unit integrates snoring audio data with heart rate and breathing patterns to perform a multifaceted analysis of the cause of snoring. For example, it compares heart rate fluctuations with the timing of snoring. The analysis unit also collects other health data, and the generation AI integrates them to develop an algorithm for analyzing the cause of snoring. For example, it identifies the correlation between changes in breathing patterns and snoring. The analysis unit also builds a system in which the generation AI analyzes snoring audio data and other health data in real time to identify the cause of snoring from multiple angles. For example, it integrates and analyzes data from a smartwatch. This allows for a multifaceted analysis of the cause of snoring.

[0038] The diagnostic unit can provide a detailed explanation of the causes of snoring based on the results of the analysis by the generation AI in a format that is easy for the user to understand. For example, the diagnostic unit automatically generates a report to explain in detail the causes of snoring analyzed by the generation AI. For example, it illustrates the mechanism of snoring and specific causes. The diagnostic unit also develops an app that provides the causes of snoring in a format that is easy for the user to understand based on the analysis results by the generation AI. For example, it uses animations and infographics to explain. The diagnostic unit also automatically generates video content to explain in detail the causes of snoring analyzed by the generation AI. For example, it explains the causes of snoring and countermeasures in a video. This allows the causes of snoring to be explained in detail and provided in a format that is easy for the user to understand.

[0039] The diagnostic unit can make specific suggestions for improvements to the user's lifestyle and environment based on the diagnostic results. For example, the diagnostic unit makes specific suggestions for improvements to the user's lifestyle and environment based on the diagnostic results analyzed by the generation AI. For example, it recommends dietary improvements and exercise. The diagnostic unit also develops an app that suggests improvements to the user's lifestyle and environment based on the diagnostic results. For example, it displays specific improvement measures in list format. The diagnostic unit also automatically generates a customized plan to suggest improvements to the user's lifestyle and environment based on the diagnostic results analyzed by the generation AI. For example, it provides an individual improvement plan. This makes it possible to make specific suggestions for improvements to the lifestyle and environment.

[0040] The diagnosis unit provides individualized health advice to the user based on the diagnosis results, and can also address health issues other than snoring. The diagnosis unit provides individualized health advice to the user based on, for example, the diagnosis results analyzed by the generation AI. For example, it provides advice on diet, exercise, and stress management. The diagnosis unit also develops an app that provides individualized health advice to the user based on the diagnosis results. For example, it displays advice that addresses health issues other than snoring. The diagnosis unit also automatically generates a customized plan to provide individualized health advice to the user based on the diagnosis results analyzed by the generation AI. For example, it provides a comprehensive health management plan. This makes it possible to provide individualized health advice that addresses health issues other than snoring.

[0041] The diagnostic unit can introduce appropriate medical institutions and specialists to the user based on the diagnostic results. For example, the diagnostic unit introduces appropriate medical institutions and specialists to the user based on the diagnostic results analyzed by the generative AI. For example, it may recommend an otolaryngologist or sleep specialist. The diagnostic unit also develops an app that introduces appropriate medical institutions and specialists to the user based on the diagnostic results. For example, it displays a list of nearby medical institutions and specialists. The diagnostic unit also automatically generates a customized plan to introduce appropriate medical institutions and specialists to the user based on the diagnostic results analyzed by the generative AI. For example, it may provide an individual treatment plan. This allows the user to be introduced to appropriate medical institutions and specialists.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] In addition to the smartphone's microphone, the recording unit can also be equipped with a dedicated microphone for recording the user's breathing sounds. For example, the dedicated microphone is attached to the user's nose or mouth to record breathing sounds with high accuracy. The recording unit can also link the smartphone's microphone with the dedicated microphone to simultaneously record breathing sounds and snoring sounds. For example, a dedicated app can combine both sets of audio data and send it to the analysis unit. The recording unit can also be equipped with a wearable device for recording breathing sounds that can be linked to a smartphone. For example, the device can record the user's breathing sounds in real time and send the data to the smartphone. This allows for highly accurate analysis of the relationship between snoring and breathing sounds.

[0044] The analysis unit can use the generation AI to evaluate the user's sleep quality from snoring audio data. For example, it can analyze the frequency, volume, and duration of snoring to generate a score for sleep quality. The analysis unit can also integrate snoring audio data with other health data to evaluate sleep quality from multiple angles. For example, it can analyze heart rate and breathing pattern data to evaluate overall sleep quality. The analysis unit can also use the generation AI to learn from past data and provide advice to improve the user's sleep quality. For example, it can provide specific advice such as, "To increase deep sleep, we recommend taking time to relax before bed." This allows the system to evaluate the user's sleep quality through snoring analysis and suggest improvement measures.

[0045] The diagnostic unit can visualize the user's snoring patterns based on the results of the analysis by the generation AI. For example, it can display the frequency, volume, and duration of snoring in graphs and charts. The diagnostic unit can also develop a dedicated app to visualize snoring patterns, allowing users to intuitively understand their snoring status. For example, the app can display snoring data in real time, allowing users to check their snoring status. The diagnostic unit can also provide a web portal to visualize snoring patterns, allowing users to compare past data. For example, the web portal can save snoring data, allowing users to check changes in their snoring. This allows users to visualize their snoring patterns and take specific measures to improve them.

[0046] The suggestion unit can propose an individualized treatment plan based on the cause of the user's snoring. For example, if the cause is narrowing of the nasal cavity, the suggestion unit can recommend the use of nasal dilator tape. The suggestion unit can also provide a treatment plan tailored to the user's lifestyle and environment. For example, the suggestion unit can recommend the use of a humidifier to adjust the humidity in the bedroom. The suggestion unit can also recommend a consultation with a specialist based on the cause of the user's snoring. For example, the suggestion unit can provide specific advice such as, "We recommend that you see an otolaryngologist." This allows the user to receive an individualized treatment plan based on the cause of their snoring.

[0047] The analysis unit can use the generation AI to extract specific frequency components from snoring audio data and identify the cause of snoring. For example, the generation AI is used to extract specific frequency components from snoring audio data. For example, if there are a lot of low-frequency components, this may be due to nasal stenosis. The analysis unit also performs frequency analysis of the audio data and develops an algorithm to identify the cause of snoring. For example, if there is a characteristic pattern in a specific frequency band, the cause is identified. The analysis unit also uses the generation AI to analyze the audio data and build a model to identify the cause of snoring. For example, it learns from past data and determines whether specific frequency components are related to the cause of snoring. This makes it possible to extract frequency components to identify the cause of snoring.

[0048] When the generation AI performs analysis, the analysis unit compares it with past snoring data to detect changes in snoring patterns. For example, the generation AI compares past snoring data with current data to detect changes in snoring patterns. For example, it analyzes changes in snoring frequency and volume. The analysis unit also learns from past snoring data and develops an algorithm that allows the generation AI to detect changes in patterns. For example, it identifies changes in the timing and duration of snoring. The analysis unit also builds a system in which the generation AI integrates past and current data to detect changes in snoring patterns in real time. For example, it issues an alert if the snoring pattern changes. This makes it possible to detect changes in snoring patterns.

[0049] The diagnostic unit can explain the causes of snoring in detail based on the results of the analysis by the generation AI, providing the information in a format that is easy for users to understand. For example, it can automatically generate a report to explain in detail the causes of snoring analyzed by the generation AI. For example, it can illustrate the mechanism behind snoring and the specific causes. The diagnostic unit can also develop an app that provides the causes of snoring in a format that is easy for users to understand, based on the analysis results by the generation AI. For example, it can explain using animations and infographics. The diagnostic unit can also automatically generate video content to explain in detail the causes of snoring analyzed by the generation AI. For example, it can explain the causes of snoring and how to deal with them in a video. This allows the causes of snoring to be explained in detail and provided in a format that is easy for users to understand.

[0050] The diagnostic unit can make specific suggestions for improvements to the user's lifestyle and environment based on the diagnostic results. For example, it can make specific suggestions for improvements to the user's lifestyle and environment based on the diagnostic results analyzed by the generation AI. For example, it can recommend dietary improvements and exercise. The diagnostic unit can also develop an app that suggests improvements to the user's lifestyle and environment based on the diagnostic results. For example, it can display specific improvement measures in list format. The diagnostic unit can also automatically generate a customized plan to suggest improvements to the user's lifestyle and environment based on the diagnostic results analyzed by the generation AI. For example, it can provide an individual improvement plan. This makes it possible to make specific suggestions for improvements to the lifestyle and environment.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The recording unit records the user's snoring while they sleep. For example, snoring can be recorded using the microphone on a smartphone. Alternatively, the recording unit can be placed next to a pillow or bedside table to continuously record sounds throughout the night. Before going to sleep, the user launches the dedicated app and sets it to start recording. Step 2: The analysis unit uses the generation AI to analyze the data recorded by the recording unit. For example, the generation AI analyzes the waveform and frequency components of the audio data to detect characteristic patterns of snoring. The generation AI performs the analysis using text generation AI (e.g., LLM) or multimodal generation AI. Step 3: The diagnosis unit determines whether or not the person is snoring based on the results of the analysis by the analysis unit. For example, based on the results of the analysis by the generation AI, it provides specific information such as, "You are snoring. You snore five times per hour, and your average snoring volume is 70 decibels." Step 4: The suggestion unit proposes solutions to cure snoring based on the results determined by the diagnosis unit. Examples include improving lifestyle habits, changing sleeping posture, and recommending specific exercises and stretches. The generation AI analyzes the user's snoring patterns and causes and proposes the optimal solution based on that. For example, it provides specific advice such as, "We recommend sleeping on your side," or "It is effective to do some light stretching before going to bed."

[0053] (Example 2) A snoring diagnosis system according to an embodiment of the present invention is a system in which a user records snoring using a smartphone while sleeping, the recording data is analyzed by a generating AI, the system determines whether the user is snoring, and further proposes solutions to cure snoring. As a result, the snoring diagnosis system can effectively diagnose the user's snoring and provide specific solutions to cure it.

[0054] A snoring diagnosis system according to an embodiment includes a recording unit, an analysis unit, a diagnosis unit, and a suggestion unit. The recording unit records snoring while a user sleeps. For example, the recording unit records snoring using a smartphone microphone. Alternatively, the recording unit can place the smartphone next to a pillow or bedside and continuously record audio throughout the night. For example, the user launches a dedicated app before going to sleep and sets the device to start recording. The analysis unit uses a generation AI to analyze the data recorded by the recording unit. For example, the generation AI analyzes the waveform and frequency components of the audio data to detect characteristic snoring patterns. The generation AI performs the analysis using a text generation AI (e.g., LLM) or a multimodal generation AI. The diagnosis unit determines whether or not a user is snoring based on the analysis results of the analysis unit. For example, based on the analysis results of the generation AI, the diagnosis unit provides specific information such as, "You snore. Your snoring frequency is five times per hour, and your average volume is 70 decibels." The suggestion unit proposes solutions to cure snoring based on the results of the diagnosis unit. Examples of such recommendations include improving lifestyle habits, changing sleeping posture, and recommending specific exercises and stretches. The generation AI analyzes the user's snoring patterns and causes and then proposes optimal solutions based on the analysis. For example, it provides specific advice such as, "We recommend sleeping on your side," or "It is effective to do some light stretching before bed." This allows the snoring diagnosis system according to the embodiment to effectively diagnose the user's snoring and provide specific solutions to treat it. For example, after the user implements the proposed solutions, the system records and analyzes the snoring again to confirm the effectiveness. The generation AI then provides continuous follow-up by reporting the degree of improvement and providing additional advice. For example, it provides feedback such as, "Your snoring frequency has decreased. We recommend that you continue to sleep on your side."

[0055] The recording unit can locate the source of snoring using sound source localization technology by placing multiple microphones in different positions in the room in addition to the smartphone's microphone. For example, the recording unit can locate the source of snoring by placing microphones in the four corners of the room and linking them with the smartphone's microphone. For example, the recording unit synchronizes the audio data from each microphone and calculates the location of snoring using a sound source localization algorithm. The recording unit also connects the smartphone's microphone to an external microphone via Bluetooth to collect data from multiple sound sources in real time. For example, a smartphone app integrates the data from each microphone and identifies the location of snoring. The recording unit also develops a dedicated device for locating the source of snoring using sound source localization technology and links it to the smartphone. For example, the device analyzes the acoustic characteristics of the room and identifies the location of snoring with high accuracy. This allows the source of snoring to be identified with high accuracy.

[0056] The recording unit can include environmental sounds in the recorded data and simultaneously record factors that affect snoring. For example, when recording with a smartphone microphone, the recording unit can also use sensors that measure room temperature and humidity to simultaneously record environmental data. For example, a temperature sensor and a humidity sensor can be connected to the smartphone and saved along with the recorded data. The recording unit can also add a function to record environmental sounds to the smartphone app and analyze factors that affect snoring. For example, the sound of an air conditioner or external noise can be recorded and compared with the snoring data. The recording unit can also develop a dedicated device to record environmental data and link it to the smartphone. For example, the device can measure room temperature, humidity, and noise levels and send the data to the smartphone app. This allows factors that affect snoring to be simultaneously recorded.

[0057] The recording unit uses the emotion estimation function to record the user's emotional state before going to sleep and analyze the effect of that emotional state on snoring. The recording unit, for example, uses a smartphone's camera and microphone to analyze the user's facial expressions and voice before going to sleep and record the emotional state. For example, an emotion score is calculated by combining face recognition technology and voice analysis technology. The recording unit also works in conjunction with a smartwatch equipped with an emotion estimation function to record the user's heart rate and stress level. For example, the smartwatch sends the emotion data to the smartphone and integrates it with snoring data. The recording unit also develops a dedicated app for recording the user's emotional state before going to sleep and analyzes it together with the snoring data. For example, the app collects the user's emotional state in the form of a questionnaire and compares it with the snoring data. This makes it possible to analyze the effect of the emotional state on snoring.

[0058] The recording unit can use the smartphone camera to record the user's sleeping position and analyze the relationship between snoring and sleeping position. The recording unit, for example, installs the smartphone camera next to the bed and records the user's sleeping position. For example, it analyzes video data from nighttime and identifies the relationship between snoring and sleeping position. The recording unit also adds an automatic sleeping position detection function to the smartphone app and integrates the snoring data. For example, it uses video analysis technology to detect the user's sleeping position in real time. The recording unit also links the smartphone camera with an external camera to record the user's sleeping position from multiple perspectives. For example, it installs cameras in different positions in the room and integrates and analyzes the sleeping position data. This makes it possible to analyze the effect of sleeping position on snoring.

[0059] The recording unit can use the smartphone's acceleration sensor to record the user's tossing and movements and analyze the relationship between snoring and movements. The recording unit, for example, uses the smartphone's acceleration sensor to record the user's tossing and movements. For example, the smartphone can be placed next to a pillow to detect nighttime movements and compare them with snoring data. The recording unit can also add a function to the smartphone app that analyzes acceleration sensor data to identify the relationship between snoring and movements. For example, it can analyze movement patterns and identify the timing of snoring. The recording unit can also link the smartphone's acceleration sensor with an external sensor to record the user's movements with high accuracy. For example, a sensor installed on the bed can send movement data to the smartphone and integrate it with snoring data. This makes it possible to analyze the impact of tossing and movements on snoring.

[0060] The recording unit works in conjunction with a smartwatch equipped with an emotion estimation function to record the user's heart rate and stress level and analyze the correlation with snoring. For example, the recording unit uses a smartwatch equipped with an emotion estimation function to record the user's heart rate and stress level. For example, the smartwatch sends the data to a smartphone and integrates it with snoring data. The recording unit also analyzes the data from the smartwatch to identify the correlation between snoring and the heart rate and stress level. For example, it compares heart rate fluctuations with the timing of snoring. The recording unit also links the smartwatch with a smartphone app to record the user's emotional state in real time. For example, it analyzes whether snoring is more likely when stress levels are high. This makes it possible to analyze the impact of heart rate and stress level on snoring.

[0061] The analysis unit can use the generation AI to extract specific frequency components from snoring audio data and identify the cause of snoring. The analysis unit, for example, uses the generation AI to extract specific frequency components from snoring audio data. For example, if there are a lot of low-frequency components, this may be due to nasal stenosis. The analysis unit also performs frequency analysis of the audio data and develops an algorithm to identify the cause of snoring. For example, if there is a characteristic pattern in a specific frequency band, the cause is identified. The analysis unit also uses the generation AI to analyze the audio data and build a model to identify the cause of snoring. For example, it learns from past data and determines whether specific frequency components are related to the cause of snoring. This makes it possible to extract frequency components to identify the cause of snoring.

[0062] When the generation AI performs analysis, the analysis unit compares it with past snoring data and can detect changes in snoring patterns. For example, the generation AI compares past snoring data with current data to detect changes in snoring patterns. For example, the analysis unit analyzes changes in snoring frequency and volume. The analysis unit also learns past snoring data and develops an algorithm for the generation AI to detect changes in patterns. For example, it identifies changes in the timing and duration of snoring. The analysis unit also builds a system in which the generation AI integrates past and current data to detect changes in snoring patterns in real time. For example, it issues an alert if the snoring pattern changes. This makes it possible to detect changes in snoring patterns.

[0063] The analysis unit can use the emotion estimation function to estimate the user's emotional state from the snoring audio data and analyze the impact of that emotional state on snoring. For example, the analysis unit has the generation AI analyze the snoring audio data and estimate the user's emotional state. For example, it calculates an emotion score from the tone and rhythm of the audio. The analysis unit also uses the emotion estimation function to develop an algorithm that analyzes the user's emotional state from the snoring audio data. For example, it identifies the impact that stress and anxiety have on snoring. The analysis unit also builds a system in which the generation AI integrates the snoring audio data with the emotion data and analyzes the impact of emotion on snoring. For example, it identifies snoring patterns when the emotional state changes. This makes it possible to analyze the impact that the emotional state has on snoring.

[0064] The analysis unit uses the generation AI to analyze snoring audio data in real time and notify the user the moment snoring occurs. For example, the analysis unit analyzes snoring audio data in real time using the generation AI and sends a notification to a smartphone the moment snoring occurs. For example, an alert is issued when snoring sounds are detected. The analysis unit also develops a generation AI algorithm that performs real-time analysis and builds a system that notifies the user the moment snoring occurs. For example, a smartphone app notifies the user by vibration or sound. The analysis unit also develops a device that uses the generation AI to analyze snoring audio data in real time and notifies the user the moment snoring occurs. For example, a smartwatch notifies the user by vibration. This allows the user to be notified the moment snoring occurs.

[0065] When the generation AI performs its analysis, the analysis unit integrates it with other health data, allowing it to perform a multifaceted analysis of the cause of snoring. For example, the analysis unit integrates snoring audio data with heart rate and breathing patterns to perform a multifaceted analysis of the cause of snoring. For example, it compares heart rate fluctuations with the timing of snoring. The analysis unit also collects other health data, and the generation AI integrates them to develop an algorithm for analyzing the cause of snoring. For example, it identifies the correlation between changes in breathing patterns and snoring. The analysis unit also builds a system in which the generation AI analyzes snoring audio data and other health data in real time to identify the cause of snoring from multiple angles. For example, it integrates and analyzes data from a smartwatch. This allows for a multifaceted analysis of the cause of snoring.

[0066] The diagnostic unit can provide a detailed explanation of the causes of snoring based on the results of the analysis by the generation AI in a format that is easy for the user to understand. For example, the diagnostic unit automatically generates a report to explain in detail the causes of snoring analyzed by the generation AI. For example, it illustrates the mechanism of snoring and specific causes. The diagnostic unit also develops an app that provides the causes of snoring in a format that is easy for the user to understand based on the analysis results by the generation AI. For example, it uses animations and infographics to explain. The diagnostic unit also automatically generates video content to explain in detail the causes of snoring analyzed by the generation AI. For example, it explains the causes of snoring and countermeasures in a video. This allows the causes of snoring to be explained in detail and provided in a format that is easy for the user to understand.

[0067] The diagnostic unit can make specific suggestions for improvements to the user's lifestyle and environment based on the diagnostic results. For example, the diagnostic unit makes specific suggestions for improvements to the user's lifestyle and environment based on the diagnostic results analyzed by the generation AI. For example, it recommends dietary improvements and exercise. The diagnostic unit also develops an app that suggests improvements to the user's lifestyle and environment based on the diagnostic results. For example, it displays specific improvement measures in list format. The diagnostic unit also automatically generates a customized plan to suggest improvements to the user's lifestyle and environment based on the diagnostic results analyzed by the generation AI. For example, it provides an individual improvement plan. This makes it possible to make specific suggestions for improvements to the lifestyle and environment.

[0068] The diagnosis unit can use the emotion estimation function to collect the user's emotional reactions to the diagnosis results and provide feedback based on those reactions. The diagnosis unit, for example, uses the emotion estimation function to collect the user's emotional reactions to the diagnosis results. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The diagnosis unit also builds a system that provides feedback based on the user's emotional reactions to the diagnosis results. For example, if there are many positive reactions, it displays an encouraging message. The diagnosis unit also analyzes the user's emotional reactions to the diagnosis results based on the emotion estimation data and reflects the results in the feedback. For example, if there are many negative reactions, it re-suggests improvement measures. In this way, it is possible to collect the user's emotional reactions to the diagnosis results and provide feedback based on those reactions.

[0069] The diagnosis unit provides individualized health advice to the user based on the diagnosis results, and can also address health issues other than snoring. The diagnosis unit provides individualized health advice to the user based on, for example, the diagnosis results analyzed by the generation AI. For example, it provides advice on diet, exercise, and stress management. The diagnosis unit also develops an app that provides individualized health advice to the user based on the diagnosis results. For example, it displays advice that addresses health issues other than snoring. The diagnosis unit also automatically generates a customized plan to provide individualized health advice to the user based on the diagnosis results analyzed by the generation AI. For example, it provides a comprehensive health management plan. This makes it possible to provide individualized health advice that addresses health issues other than snoring.

[0070] The diagnostic unit can introduce appropriate medical institutions and specialists to the user based on the diagnostic results. For example, the diagnostic unit introduces appropriate medical institutions and specialists to the user based on the diagnostic results analyzed by the generative AI. For example, it may recommend an otolaryngologist or sleep specialist. The diagnostic unit also develops an app that introduces appropriate medical institutions and specialists to the user based on the diagnostic results. For example, it displays a list of nearby medical institutions and specialists. The diagnostic unit also automatically generates a customized plan to introduce appropriate medical institutions and specialists to the user based on the diagnostic results analyzed by the generative AI. For example, it may provide an individual treatment plan. This allows the user to be introduced to appropriate medical institutions and specialists.

[0071] The diagnosis unit can use the emotion estimation function to collect the user's emotional reactions to the diagnosis results and provide feedback based on those reactions. The diagnosis unit, for example, uses the emotion estimation function to collect the user's emotional reactions to the diagnosis results. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The diagnosis unit also builds a system that provides feedback based on the user's emotional reactions to the diagnosis results. For example, if there are many positive reactions, it displays an encouraging message. The diagnosis unit also analyzes the user's emotional reactions to the diagnosis results based on the emotion estimation data and reflects the results in the feedback. For example, if there are many negative reactions, it re-suggests improvement measures. In this way, it is possible to collect the user's emotional reactions to the diagnosis results and provide feedback based on those reactions.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] In addition to the smartphone's microphone, the recording unit can also be equipped with a dedicated microphone for recording the user's breathing sounds. For example, the dedicated microphone is attached to the user's nose or mouth to record breathing sounds with high accuracy. The recording unit can also link the smartphone's microphone with the dedicated microphone to simultaneously record breathing sounds and snoring sounds. For example, a dedicated app can combine both sets of audio data and send it to the analysis unit. The recording unit can also be equipped with a wearable device for recording breathing sounds that can be linked to a smartphone. For example, the device can record the user's breathing sounds in real time and send the data to the smartphone. This allows for highly accurate analysis of the relationship between snoring and breathing sounds.

[0074] The analysis unit can use the generation AI to evaluate the user's sleep quality from snoring audio data. For example, it can analyze the frequency, volume, and duration of snoring to generate a score for sleep quality. The analysis unit can also integrate snoring audio data with other health data to evaluate sleep quality from multiple angles. For example, it can analyze heart rate and breathing pattern data to evaluate overall sleep quality. The analysis unit can also use the generation AI to learn from past data and provide advice to improve the user's sleep quality. For example, it can provide specific advice such as, "To increase deep sleep, we recommend taking time to relax before bed." This allows the system to evaluate the user's sleep quality through snoring analysis and suggest improvement measures.

[0075] The diagnostic unit can visualize the user's snoring patterns based on the results of the analysis by the generation AI. For example, it can display the frequency, volume, and duration of snoring in graphs and charts. The diagnostic unit can also develop a dedicated app to visualize snoring patterns, allowing users to intuitively understand their snoring status. For example, the app can display snoring data in real time, allowing users to check their snoring status. The diagnostic unit can also provide a web portal to visualize snoring patterns, allowing users to compare past data. For example, the web portal can save snoring data, allowing users to check changes in their snoring. This allows users to visualize their snoring patterns and take specific measures to improve them.

[0076] The suggestion unit can propose an individualized treatment plan based on the cause of the user's snoring. For example, if the cause is narrowing of the nasal cavity, the suggestion unit can recommend the use of nasal dilator tape. The suggestion unit can also provide a treatment plan tailored to the user's lifestyle and environment. For example, the suggestion unit can recommend the use of a humidifier to adjust the humidity in the bedroom. The suggestion unit can also recommend a consultation with a specialist based on the cause of the user's snoring. For example, the suggestion unit can provide specific advice such as, "We recommend that you see an otolaryngologist." This allows the user to receive an individualized treatment plan based on the cause of their snoring.

[0077] The recording unit uses the emotion estimation function to record the user's emotional state before going to sleep and analyze the effect of that emotional state on snoring. For example, the recording unit uses a smartphone's camera and microphone to analyze the user's facial expressions and voice before going to sleep and record the emotional state. For example, facial recognition technology and voice analysis technology are combined to calculate an emotion score. The recording unit also works in conjunction with a smartwatch equipped with an emotion estimation function to record the user's heart rate and stress level. For example, the smartwatch sends the emotion data to the smartphone and integrates it with snoring data. The recording unit also develops a dedicated app to record the user's emotional state before going to sleep and analyzes it together with the snoring data. For example, the app collects the user's emotional state in the form of a questionnaire and compares it with the snoring data. This makes it possible to analyze the effect of the emotional state on snoring.

[0078] The analysis unit can use the generation AI to extract specific frequency components from snoring audio data and identify the cause of snoring. For example, the generation AI is used to extract specific frequency components from snoring audio data. For example, if there are a lot of low-frequency components, this may be due to nasal stenosis. The analysis unit also performs frequency analysis of the audio data and develops an algorithm to identify the cause of snoring. For example, if there is a characteristic pattern in a specific frequency band, the cause is identified. The analysis unit also uses the generation AI to analyze the audio data and build a model to identify the cause of snoring. For example, it learns from past data and determines whether specific frequency components are related to the cause of snoring. This makes it possible to extract frequency components to identify the cause of snoring.

[0079] When the generation AI performs analysis, the analysis unit compares it with past snoring data to detect changes in snoring patterns. For example, the generation AI compares past snoring data with current data to detect changes in snoring patterns. For example, it analyzes changes in snoring frequency and volume. The analysis unit also learns from past snoring data and develops an algorithm that allows the generation AI to detect changes in patterns. For example, it identifies changes in the timing and duration of snoring. The analysis unit also builds a system in which the generation AI integrates past and current data to detect changes in snoring patterns in real time. For example, it issues an alert if the snoring pattern changes. This makes it possible to detect changes in snoring patterns.

[0080] The diagnostic unit can explain the causes of snoring in detail based on the results of the analysis by the generation AI, providing the information in a format that is easy for users to understand. For example, it can automatically generate a report to explain in detail the causes of snoring analyzed by the generation AI. For example, it can illustrate the mechanism behind snoring and the specific causes. The diagnostic unit can also develop an app that provides the causes of snoring in a format that is easy for users to understand, based on the analysis results by the generation AI. For example, it can explain using animations and infographics. The diagnostic unit can also automatically generate video content to explain in detail the causes of snoring analyzed by the generation AI. For example, it can explain the causes of snoring and how to deal with them in a video. This allows the causes of snoring to be explained in detail and provided in a format that is easy for users to understand.

[0081] The diagnosis unit can use the emotion estimation function to collect the user's emotional reactions to the diagnosis results and provide feedback based on those reactions. For example, the emotion estimation function is used to collect the user's emotional reactions to the diagnosis results. For example, the user's facial expressions and voice are analyzed to calculate an emotion score. The diagnosis unit also builds a system that provides feedback based on the user's emotional reactions to the diagnosis results. For example, if there are many positive reactions, an encouraging message is displayed. The diagnosis unit also analyzes the user's emotional reactions to the diagnosis results based on the emotion estimation data and reflects the results in the feedback. For example, if there are many negative reactions, improvement measures are re-proposed. In this way, the user's emotional reactions to the diagnosis results can be collected and feedback can be provided based on those reactions.

[0082] The diagnostic unit can make specific suggestions for improvements to the user's lifestyle and environment based on the diagnostic results. For example, it can make specific suggestions for improvements to the user's lifestyle and environment based on the diagnostic results analyzed by the generation AI. For example, it can recommend dietary improvements and exercise. The diagnostic unit can also develop an app that suggests improvements to the user's lifestyle and environment based on the diagnostic results. For example, it can display specific improvement measures in list format. The diagnostic unit can also automatically generate a customized plan to suggest improvements to the user's lifestyle and environment based on the diagnostic results analyzed by the generation AI. For example, it can provide an individual improvement plan. This makes it possible to make specific suggestions for improvements to the lifestyle and environment.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The recording unit records the user's snoring while they sleep. For example, snoring can be recorded using the microphone on a smartphone. Alternatively, the recording unit can be placed next to a pillow or bedside table to continuously record sounds throughout the night. Before going to sleep, the user launches the dedicated app and sets it to start recording. Step 2: The analysis unit uses the generation AI to analyze the data recorded by the recording unit. For example, the generation AI analyzes the waveform and frequency components of the audio data to detect characteristic patterns of snoring. The generation AI performs the analysis using text generation AI (e.g., LLM) or multimodal generation AI. Step 3: The diagnosis unit determines whether or not the person is snoring based on the results of the analysis by the analysis unit. For example, based on the results of the analysis by the generation AI, it provides specific information such as, "You are snoring. You snore five times per hour, and your average snoring volume is 70 decibels." Step 4: The suggestion unit proposes solutions to cure snoring based on the results determined by the diagnosis unit. Examples include improving lifestyle habits, changing sleeping posture, and recommending specific exercises and stretches. The generation AI analyzes the user's snoring patterns and causes and proposes the optimal solution based on that. For example, it provides specific advice such as, "We recommend sleeping on your side," or "It is effective to do some light stretching before going to bed."

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, 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. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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]

[0152] 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 recording unit that records snoring, an analysis unit that analyzes the data recorded by the recording unit; a diagnosis unit that determines whether or not snoring occurs based on the results of the analysis by the analysis unit; a suggestion unit that suggests a solution to eliminate snoring based on the result determined by the diagnosis unit. A system characterized by:

2. The recording unit In addition to the smartphone microphone, multiple microphones are placed in different positions in the room and sound source localization technology is used to identify the source of snoring.

2. The system of claim 1.

3. The recording unit The recording data includes environmental sounds, and factors that affect the occurrence of snoring are also recorded.

2. The system of claim 1.

4. The recording unit The emotional state of the user before going to sleep is recorded, and the influence of the emotional state on the snoring is analyzed.

2. The system of claim 1.

5. The recording unit The smartphone camera is used to record the user's sleeping position, and the relationship between the occurrence of snoring and sleeping position is analyzed.

2. The system of claim 1.

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