System

Abstract

To provide a system according to an embodiment that provides optimal sound contents matching an environment of a user or wellness situation thereof.SOLUTION: A system according to an embodiment comprises: a collection unit; an analysis unit; a generation unit; and a providing unit. The collection unit is configured to collect environment data and wellness data . The analysis unit is configured to analyze data collected by the collection unit. The generation unit is configured to generate sound contents on the basis of an analysis result obtained by the analysis unit. The providing unit is configured to provide the sound content generated by the generation unit for a user.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have not adequately provided optimal sound content according to a user's environment and health condition, and there is room for improvement.

[0005] The system according to the embodiment aims to provide optimal sound content according to the user's environment and health condition. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects environmental data and health data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates sound content based on the analysis result obtained by the analysis unit. The provision unit provides the sound content generated by the generation unit to a user. Effect of the Invention

[0007] The system according to the embodiment can provide optimal sound content according to the user's environment and health condition. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A sleep promotion system according to an embodiment of the present invention is a system that generates sound content that promotes sleep optimized according to the user's environment and health condition. This system collects the user's environmental data (room temperature, humidity, noise level, etc.) and health data (heart rate, breathing rate, body movement, etc.), and generates optimal sound content by analyzing it with AI. The generated sound content is provided to the user, and the user sleeps while listening to the sound. Data during sleep is also collected and analyzed by AI. The analysis results are fed back, and further personalized sound content is generated the next day. This allows the user to get a high-quality sleep while listening to optimal sound content every night. In addition, if the user dislikes wearable devices, it is also possible to develop a compatible pillow in collaboration with a bedding manufacturer. This pillow has a built-in sensor that collects the user's health data. This makes it possible to collect the user's health data and provide optimal sound content without using a wearable device. For example, the temperature, humidity, and noise level of the user's room are measured by a sensor, and health data such as heart rate, breathing rate, and body movement are collected. AI analyzes these data and generates optimal sound content for the user. The generated sound content is provided to the user through a smartphone or speaker. The user sleeps while listening to the sound. Data during sleep is also collected and analyzed by AI. The analysis results are fed back, and further personalized sound content is generated the next day. This allows the user to get a high-quality sleep while listening to the optimal sound content every night. In addition, if the user dislikes wearable devices, it is also possible to collaborate with bedding manufacturers to develop a compatible pillow. This pillow has a built-in sensor that collects the user's health data. This makes it possible to collect the user's health data and provide the optimal sound content without using a wearable device. This allows the sleep promotion system to provide the optimal sound content according to the user's environment and health condition, promoting high-quality sleep.

[0029] The sleep promotion system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects environmental data and health data. The environmental data includes, for example, room temperature, humidity, noise level, etc., but is not limited to such examples. The health data includes, for example, heart rate, respiratory rate, body movement, etc., but is not limited to such examples. The collection unit, for example, measures the temperature of the room with a sensor and collects data. The collection unit can also measure the humidity of the room using a humidity sensor and collect data. Furthermore, the collection unit can measure the noise level with a microphone and collect data. For example, the collection unit measures the temperature of the room with a temperature sensor and collects data. The humidity of the room can also be measured using a humidity sensor and collected data. The noise level can also be measured with a microphone and collected data. The analysis unit analyzes the data collected by the collection unit. The analysis is performed, for example, using statistical analysis of data or a machine learning algorithm, but is not limited to such examples. For example, the analysis unit performs statistical analysis on the collected temperature data and analyzes the fluctuation of the temperature in the room. The collected humidity data can be analyzed using a machine learning algorithm to analyze the humidity fluctuation in the room. The collected noise level data can be statistically analyzed to analyze the noise level fluctuation in the room. The generation unit generates sound content based on the analysis result obtained by the analysis unit. The sound content includes, for example, relaxing music, natural sounds, white noise, etc., but is not limited to such examples. For example, the generation unit generates relaxing music based on the analysis result. Natural sounds can also be generated based on the analysis result. White noise can also be generated based on the analysis result. The provision unit provides the sound content generated by the generation unit to the user. The provision is performed, for example, through a smartphone app, a speaker, headphones, etc., but is not limited to such examples. For example, the provision unit provides the generated sound content to the user through a smartphone app. The sound content can also be provided through a speaker. The sound content can also be provided through headphones.As a result, the sleep promotion system according to the embodiment can provide optimal sound content according to the user's environment and health condition, and promote high-quality sleep.

[0030] The collection unit collects environmental data and health data. The environmental data includes, for example, room temperature, humidity, noise level, etc., but is not limited to such examples. Specifically, the collection unit measures the temperature of the room with a temperature sensor and collects data. The temperature sensor is installed in multiple locations in the room, and can grasp the uniformity and fluctuation of the temperature in detail. In addition, the humidity sensor can be used to measure the humidity of the room and collect data. The humidity sensor monitors the air quality of the room and grasps the dryness and humidity state in real time. Furthermore, the collection unit can measure the noise level with a microphone and collect data. The microphone identifies the noise source in the room and records the fluctuation of the noise level. In this way, the collection unit can collect environmental data such as the room temperature, humidity, and noise level in detail and accurately grasp the user's sleep environment. The health data includes, for example, heart rate, respiratory rate, body movement, etc., but is not limited to such examples. The collection unit measures the user's heart rate with a heart rate sensor and collects data. The heart rate sensor monitors the fluctuation of the user's heart rate in real time and grasps the stress and relaxation state. The respiration rate can also be measured by a respiration sensor to collect data. The respiration sensor records the user's breathing pattern in detail and analyzes the depth and rhythm of breathing. Furthermore, the body movement can also be measured by an acceleration sensor to collect data. The acceleration sensor detects the user's body movement and records the movement and posture changes during sleep. This allows the collection unit to collect detailed health data such as the user's heart rate, respiration rate, and body movement, and accurately grasp the user's health condition. The collected environmental data and health data are transmitted to a central database so that the analysis unit can access them. This allows the collection unit to efficiently and effectively collect data and improve the performance of the entire system.

[0031] The analysis unit analyzes the data collected by the collection unit. The analysis is performed, for example, using statistical analysis of data or a machine learning algorithm, but is not limited to such examples. Specifically, the analysis unit statistically analyzes the collected temperature data and analyzes the temperature fluctuations in the room. For the analysis of the temperature data, methods such as time series analysis and histogram analysis are used to detect the fluctuation pattern of the room temperature and abnormal values. In addition, the analysis unit can analyze the humidity fluctuations in the room by analyzing the collected humidity data with a machine learning algorithm. The machine learning algorithm learns past data and builds a model that predicts future humidity fluctuations. Furthermore, the analysis unit can statistically analyze the collected noise level data and analyze the noise level fluctuations in the room. For the analysis of the noise level, methods such as frequency analysis and spectrum analysis are used to identify the noise source and grasp the noise level fluctuation pattern. Based on these analysis results, the analysis unit can evaluate the state of the user's sleep environment and identify areas for improvement. Furthermore, the analysis unit analyzes the collected health data and evaluates the health state of the user. For example, the analysis unit statistically analyzes the heart rate data and analyzes the user's heart rate fluctuations. The heart rate is analyzed using techniques such as time series analysis and Fourier transform to detect the rhythm of the heart rate and abnormal values. The breathing rate data can also be analyzed using a machine learning algorithm to analyze the user's breathing pattern. The machine learning algorithm learns from past data and builds a model to predict future breathing patterns. Furthermore, the body movement data can be statistically analyzed to analyze changes in the user's body movement and posture. The body movement is analyzed using techniques such as time series analysis and clustering to grasp the change patterns of movement and posture during sleep. This allows the analysis unit to quickly and accurately analyze the collected data and grasp the user's sleeping environment and health condition in real time.

[0032] The generating unit generates sound content based on the analysis result obtained by the analyzing unit. The sound content includes, for example, relaxing music, natural sounds, white noise, and the like, but is not limited to such examples. Specifically, the generating unit generates relaxing music based on the analysis result. The relaxing music adjusts the tempo and rhythm according to the user's heart rate and breathing rate to maximize the relaxing effect. In addition, natural sounds can be generated based on the analysis result. The natural sounds include, for example, the sound of waves, the sound of wind, and the chirping of birds, and are selected according to the user's preferences and environment. Furthermore, white noise can be generated based on the analysis result. White noise includes sounds in a certain frequency band, blocks out external noise, and supports the user's sleep. The generating unit can generate these sound contents in real time and provide the optimal sound for the user's sleep environment. Furthermore, the generating unit can adjust the sound content based on the user's feedback to maximize the effect. For example, if the user likes relaxing music, the generating unit increases the variety of relaxing music and provides it according to the user's preferences. In addition, if the user likes natural sounds, the generating unit adjusts the type and playback pattern of the natural sounds to enhance the user's relaxing effect. This enables the generation unit to generate optimal sound content based on the analysis results, thereby promoting sleep for the user.

[0033] The providing unit provides the sound content generated by the generating unit to the user. The provision is performed, for example, through a smartphone app, a speaker, headphones, etc., but is not limited to such examples. Specifically, the providing unit provides the generated sound content to the user through a smartphone app. The smartphone app has a function of selecting and playing sound content according to the user's preferences and environment. The sound content can also be provided through a speaker. The speaker spreads sound throughout the room to improve the user's sleep environment. Furthermore, the sound content can also be provided through headphones. The headphones block external noise and provide the user with a relaxing environment in which he or she can concentrate. The providing unit can provide the user with optimal sound content through these devices and promote high-quality sleep. Furthermore, the providing unit can collect user feedback and continuously improve the accuracy and effect of the provided content. For example, if the user likes a particular sound content, the providing unit increases the playback frequency of that content to increase the user's satisfaction. The providing unit can also reliably transmit information using multiple communication means. For example, important information can be reliably delivered by using not only smartphone notifications but also voice calls, SMS, emails, etc. This enables the providing unit to quickly and reliably provide sound content to the user, thereby promoting high-quality sleep.

[0034] The collection unit can collect environmental data such as the temperature, humidity, and noise level of the room. The environmental data includes, for example, temperature, humidity, and noise level, but is not limited to such examples. For example, the collection unit measures the temperature of the room with a sensor and collects data. It is also possible to measure the humidity of the room using a humidity sensor and collect data. It is also possible to measure the noise level with a microphone and collect data. For example, the collection unit measures the temperature of the room with a temperature sensor and collects data. It is also possible to measure the humidity of the room using a humidity sensor and collect data. It is also possible to measure the noise level with a microphone and collect data. In this way, the user's sleep environment can be optimized by collecting environmental data. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit inputs temperature data acquired by a temperature sensor to the generation AI, and causes the generation AI to execute analysis of the temperature data.

[0035] The collection unit can collect health data such as heart rate, respiratory rate, and body movement. The health data includes, for example, heart rate, respiratory rate, and body movement, but is not limited to such examples. For example, the collection unit measures the heart rate with a sensor and collects data. The respiratory rate can also be measured with a sensor and collected data. The body movement can also be measured with a sensor and collected data. For example, the collection unit measures the heart rate with a heart rate sensor and collects data. The respiratory rate can also be measured with a respiratory sensor and collected data. The body movement can also be measured with a body movement sensor and collected data. As a result, by collecting health data, sound content based on the user's health condition can be provided. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit inputs heart rate data acquired by a heart rate sensor to the generation AI, and causes the generation AI to execute analysis of the heart rate data.

[0036] The analysis unit can evaluate the quality of the user's sleep based on the collected data. The quality of sleep includes, for example, a sleep score, an analysis of sleep stages, and the like, but is not limited to such examples. For example, the analysis unit statistically analyzes the collected data to evaluate the quality of the user's sleep. The collected data can also be analyzed using a machine learning algorithm to evaluate the quality of the user's sleep. For example, the analysis unit statistically analyzes the collected heart rate data to evaluate the quality of the user's sleep. The collected respiratory rate data can also be analyzed using a machine learning algorithm to evaluate the quality of the user's sleep. The collected body movement data can also be statistically analyzed to evaluate the quality of the user's sleep. As a result, more appropriate sound content can be provided by evaluating the quality of the user's sleep. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to the generation AI and cause the generation AI to execute the evaluation of the quality of sleep.

[0037] The generation unit can generate sound content based on the analysis result. Sound content includes, for example, relaxing music, natural sounds, white noise, etc., but is not limited to such examples. The generation unit, for example, generates relaxing music based on the analysis result. Natural sounds can also be generated based on the analysis result. White noise can also be generated based on the analysis result. For example, the generation unit generates relaxing music based on the analysis result. Natural sounds can also be generated based on the analysis result. White noise can also be generated based on the analysis result. Thereby, by generating sound content based on the analysis result, it is possible to provide an optimal sound to the user. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the analysis result to the generation AI and causes the generation AI to generate the sound content.

[0038] The providing unit can provide sound content through a smartphone or speaker of the user. The provision is performed, for example, through a smartphone app, a speaker, headphones, etc., but is not limited to such examples. The providing unit, for example, provides the generated sound content to the user through a smartphone app. The sound content can also be provided through a speaker. The sound content can also be provided through headphones. For example, the providing unit provides the generated sound content to the user through a smartphone app. The sound content can also be provided through a speaker. The sound content can also be provided through headphones. As a result, convenience is improved by providing the sound content through the user's smartphone or speaker. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit inputs user data acquired through a smartphone app to the generating AI, and causes the generating AI to execute a method for providing the sound content.

[0039] The collection unit can collect data using a sensor built into the pillow. The sensor includes, for example, a pressure sensor, a temperature sensor, a position sensor, and the like, but is not limited to such examples. The collection unit, for example, collects data using a pressure sensor built into the pillow. The temperature sensor can also be used to collect temperature data of the pillow. The position sensor can also be used to collect position data of the pillow. For example, the collection unit collects data using a pressure sensor built into the pillow. The temperature sensor can also be used to collect temperature data of the pillow. The position sensor can also be used to collect position data of the pillow. As a result, by using the sensor built into the pillow, data can be collected without using a wearable device. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data acquired by a sensor built into the pillow to the generation AI and have the generation AI execute analysis of the data.

[0040] The collection unit can analyze the user's past sleep data and select the optimal data collection method. The past sleep data includes, for example, past sleep scores, records of sleep stages, and the like, but is not limited to such examples. The collection unit, for example, identifies the most effective data collection time period from the user's past sleep data. The frequency of data collection can also be adjusted based on the user's past sleep data. The user's past sleep data can be analyzed and a data collection method can be selected based on a specific pattern. For example, the collection unit identifies the most effective data collection time period from the user's past sleep data. The frequency of data collection can also be adjusted based on the user's past sleep data. The user's past sleep data can also be analyzed and a data collection method can be selected based on a specific pattern. In this way, the optimal data collection method can be selected by analyzing the past sleep data. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the past sleep data to the generation AI and cause the generation AI to select the data collection method.

[0041] The collection unit can perform filtering based on the user's current living situation and stress level when collecting data. Living situations include, for example, daily activity levels, eating patterns, stress levels, etc., but are not limited to such examples. For example, the collection unit reduces the frequency of data collection when the user is stressed at work. When the user is relaxed, detailed data can also be collected. The timing of data collection can also be adjusted according to the user's living situation. For example, the collection unit reduces the frequency of data collection when the user is stressed at work. When the user is relaxed, detailed data can also be collected. The timing of data collection can also be adjusted according to the user's living situation. As a result, more appropriate data can be collected by filtering data based on the user's living situation and stress level. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's living situation data to the generation AI and cause the generation AI to filter the data.

[0042] The collection unit can prioritize collecting highly relevant data in consideration of the geographical location information of the user when collecting data. The geographical location information includes, but is not limited to, for example, GPS data, Wi-Fi location information, and the like. For example, when the user is at home, the collection unit prioritizes collecting environmental data such as room temperature and humidity. When the user is traveling, the collection unit can also prioritize collecting health data such as heart rate and respiratory rate. The type of data to be collected can also be adjusted based on the geographical location information of the user. For example, when the user is at home, the collection unit prioritizes collecting environmental data such as room temperature and humidity. When the user is traveling, the collection unit can also prioritize collecting health data such as heart rate and respiratory rate. The type of data to be collected can also be adjusted based on the geographical location information of the user. As a result, more appropriate data can be collected by preferentially collecting highly relevant data based on the geographical location information of the user. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute the determination of the priority order of data.

[0043] The collection unit can analyze the user's social media activity at the time of data collection and collect related data. Social media activity includes, for example, the content of posts, the number of likes, comments, etc., but is not limited to such examples. For example, when the user feels stressed on social media, the collection unit preferentially collects health data such as heart rate and respiratory rate. When the user is relaxing on social media, the collection unit can also preferentially collect environmental data. The type of data to be collected can also be adjusted based on the user's social media activity. For example, when the user feels stressed on social media, the collection unit preferentially collects health data such as heart rate and respiratory rate. When the user is relaxing on social media, the collection unit can also preferentially collect environmental data. The type of data to be collected can also be adjusted based on the user's social media activity. In this way, more appropriate data can be collected by collecting related data based on the user's social media activity. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data to the generation AI and cause the generation AI to execute the data collection method.

[0044] The analysis unit can improve the accuracy of the analysis by referring to past analysis data during the analysis. The past analysis data includes, for example, past analysis results, data sets, etc., but is not limited to such examples. The analysis unit corrects the current analysis result, for example, based on the user's past analysis data. The parameters of the analysis algorithm can also be optimized by referring to the past analysis data. The accuracy of the analysis can also be improved by using the past analysis data. For example, the analysis unit corrects the current analysis result based on the user's past analysis data. The parameters of the analysis algorithm can also be optimized by referring to the past analysis data. The accuracy of the analysis can also be improved by using the past analysis data. As a result, the accuracy of the analysis can be improved by referring to the past analysis data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the past analysis data to the generation AI and cause the generation AI to execute the improvement of the accuracy of the analysis.

[0045] The analysis unit can customize the analysis method based on the health condition and lifestyle of the user during analysis. The health condition includes, for example, heart rate, blood pressure, body temperature, etc., but is not limited to such examples. The lifestyle includes, for example, eating patterns, exercise habits, sleep patterns, etc., but is not limited to such examples. The analysis unit adjusts the analysis method, for example, based on the health condition of the user. The analysis method can also be customized based on the lifestyle of the user. The analysis method can also be optimized based on the health data of the user. For example, the analysis unit adjusts the analysis method based on the health condition of the user. The analysis method can also be customized based on the lifestyle of the user. The analysis method can also be optimized based on the health data of the user. As a result, by customizing the analysis method based on the health condition and lifestyle of the user, more appropriate analysis results can be obtained. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit inputs the health data of the user to the generation AI and causes the generation AI to execute customization of the analysis method.

[0046] The analysis unit can perform the analysis while considering the geographical location information of the user. The geographical location information includes, for example, GPS data, Wi-Fi location information, and the like, but is not limited to such examples. For example, when the user is at home, the analysis unit performs the analysis with emphasis on environmental data. When the user is traveling, the analysis unit can also perform the analysis with emphasis on health data. The analysis method can also be adjusted based on the geographical location information of the user. For example, when the user is at home, the analysis unit performs the analysis with emphasis on environmental data. When the user is traveling, the analysis unit can also perform the analysis with emphasis on health data. The analysis method can also be adjusted based on the geographical location information of the user. As a result, a more appropriate analysis result can be obtained by performing the analysis based on the geographical location information of the user. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the geographical location information of the user to the generation AI and cause the generation AI to adjust the analysis method.

[0047] The analysis unit can improve the accuracy of the analysis by referring to the related literature of the user during the analysis. The related literature includes, for example, academic papers, technical reports, patent documents, etc., but is not limited to such examples. The analysis unit can improve the accuracy of the analysis by referring to, for example, related literature on the health condition of the user. The analysis method can also be optimized by referring to related literature on the lifestyle habits of the user. The analysis result can also be corrected by referring to related literature on the environmental data of the user. For example, the analysis unit can improve the accuracy of the analysis by referring to related literature on the health condition of the user. The analysis method can also be optimized by referring to related literature on the lifestyle habits of the user. The analysis result can also be corrected by referring to related literature on the environmental data of the user. As a result, the accuracy of the analysis can be improved by referring to the related literature of the user. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the related literature to the generation AI and cause the generation AI to execute the improvement of the accuracy of the analysis.

[0048] The generation unit can generate an optimal sound by referring to the user's past sound history when generating sound content. The past sound history includes, for example, a playback history, an evaluation history, and a frequency of use, but is not limited to such examples. The generation unit generates an optimal sound, for example, based on the user's past sound history. The generation unit can also adjust the characteristics of the sound content by referring to the user's past sound history. The generation unit can also generate the most effective sound by analyzing the user's past sound history. For example, the generation unit generates an optimal sound based on the user's past sound history. The generation unit can also adjust the characteristics of the sound content by referring to the user's past sound history. The generation unit can also generate the most effective sound by analyzing the user's past sound history. In this way, the generation unit can generate the optimal sound by referring to the user's past sound history. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the past sound history to the generation AI and cause the generation AI to generate the sound content.

[0049] The generation unit can customize the characteristics of the sound based on the current health condition of the user when generating the sound content. The health condition includes, for example, heart rate, blood pressure, body temperature, etc., but is not limited to such examples. The generation unit adjusts the rhythm of the sound based on, for example, the heart rate and breathing rate of the user. The frequency of the sound can also be adjusted based on the health condition of the user. The characteristics of the sound content can also be customized based on the health data of the user. For example, the generation unit adjusts the rhythm of the sound based on the heart rate and breathing rate of the user. The frequency of the sound can also be adjusted based on the health condition of the user. The characteristics of the sound content can also be customized based on the health data of the user. As a result, more effective sound can be provided by customizing the characteristics of the sound based on the health condition of the user. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the health data of the user to the generation AI and cause the generation AI to customize the characteristics of the sound content.

[0050] The generation unit can generate optimal sounds in consideration of the geographical location information of the user when generating sound content. The geographical location information includes, but is not limited to, for example, GPS data, Wi-Fi location information, and the like. For example, when the user is at home, the generation unit generates a sound with a high relaxing effect. When the user is traveling, the generation unit can also generate a sound with a high stress reduction effect. The characteristics of the sound content can also be adjusted based on the geographical location information of the user. For example, when the user is at home, the generation unit generates a sound with a high relaxing effect. When the user is traveling, the generation unit can also generate a sound with a high stress reduction effect. The characteristics of the sound content can also be adjusted based on the geographical location information of the user. As a result, a more effective sound can be provided by generating optimal sounds based on the geographical location information of the user. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit inputs the geographical location information of the user to the generation AI and causes the generation AI to generate the sound content.

[0051] The generation unit can adjust the characteristics of the sound by analyzing the social media activity of the user when generating the sound content. The social media activity includes, for example, the content of the post, the number of likes, and comments, but is not limited to such examples. For example, when the user is relaxing on social media, the generation unit generates a sound with a high relaxing effect. When the user is feeling stressed on social media, the generation unit can also generate a sound with a high stress reduction effect. The characteristics of the sound content can also be adjusted based on the social media activity of the user. For example, when the user is relaxing on social media, the generation unit generates a sound with a high relaxing effect. When the user is feeling stressed on social media, the generation unit can also generate a sound with a high stress reduction effect. The characteristics of the sound content can also be adjusted based on the social media activity of the user. As a result, a more appropriate sound can be provided by adjusting the characteristics of the sound based on the social media activity of the user. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the social media activity data of the user to the generation AI and cause the generation AI to adjust the characteristics of the sound content.

[0052] The providing unit can select the optimal providing method by referring to the user's past usage history when providing sound content. The past usage history includes, for example, playback history, evaluation history, and usage frequency, but is not limited to such examples. The providing unit selects the optimal providing method, for example, based on the user's past usage history. The timing of providing the sound content can also be adjusted by referring to the user's past usage history. The most effective providing method can also be selected by analyzing the user's past usage history. For example, the providing unit selects the optimal providing method based on the user's past usage history. The timing of providing the sound content can also be adjusted by referring to the user's past usage history. The most effective providing method can also be selected by analyzing the user's past usage history. In this way, the optimal providing method can be selected by referring to the user's past usage history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the past usage history to the generating AI and cause the generating AI to select the providing method.

[0053] The providing unit can customize the providing means based on the current device status of the user when providing sound content. The device status includes, for example, a remaining battery level, a connection status, a type of device, and the like, but is not limited to such examples. For example, when the user is using a smartphone, the providing unit provides sound content optimized for the smartphone. When the user is using a speaker, the providing unit can also provide sound content optimized for the speaker. The providing means of the sound content can also be customized based on the device status of the user. For example, when the user is using a smartphone, the providing unit provides sound content optimized for the smartphone. When the user is using a speaker, the providing unit can also provide sound content optimized for the speaker. The providing means of the sound content can also be customized based on the device status of the user. As a result, by customizing the providing means based on the device status of the user, more appropriate sound can be provided. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the device status data of the user to the generating AI and cause the generating AI to execute customization of the providing means.

[0054] The providing unit can select the optimal providing method in consideration of the geographical location information of the user when providing sound content. The geographical location information includes, for example, GPS data, Wi-Fi location information, and the like, but is not limited to such examples. For example, when the user is at home, the providing unit provides sound content with a high relaxing effect. When the user is traveling, the providing unit can also provide sound content with a high stress reduction effect. The providing method of the sound content can also be adjusted based on the geographical location information of the user. For example, when the user is at home, the providing unit provides sound content with a high relaxing effect. When the user is traveling, the providing unit can also provide sound content with a high stress reduction effect. The providing method of the sound content can also be adjusted based on the geographical location information of the user. As a result, by selecting the optimal providing method based on the geographical location information of the user, more appropriate sound can be provided. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to the generating AI and cause the generating AI to select the providing method.

[0055] The providing unit can adjust the providing means by analyzing the social media activity of the user when providing sound content. Social media activities include, for example, the content of posts, the number of likes, comments, and the like, but are not limited to such examples. For example, when the user is relaxing on social media, the providing unit provides sound content with a high relaxing effect. When the user is feeling stressed on social media, the providing unit can also provide sound content with a high stress reduction effect. The providing means of the sound content can also be adjusted based on the social media activity of the user. For example, when the user is relaxing on social media, the providing unit provides sound content with a high relaxing effect. When the user is feeling stressed on social media, the providing unit can also provide sound content with a high stress reduction effect. The providing means of the sound content can also be adjusted based on the social media activity of the user. As a result, by adjusting the providing means based on the social media activity of the user, more appropriate sound can be provided. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the social media activity data of the user to the generating AI and cause the generating AI to adjust the providing means.

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

[0057] The collection unit can collect the user's dietary data, and the analysis unit can generate sound content based on the data. For example, if the user consumes caffeine, a sound with a high relaxing effect can be generated. If the user consumes alcohol, a sound with a stable rhythm can be generated. This makes it possible to promote sleep more effectively by adjusting the sound content based on the user's dietary content.

[0058] The collection unit can collect the user's exercise data, and the analysis unit can generate sound content based on the data. For example, if the user exercises, a sound with a high relaxing effect can be generated. If the user does not exercise, a sound that replenishes energy can be generated. This makes it possible to promote sleep more effectively by adjusting the sound content based on the user's exercise status.

[0059] The generator may generate sound content taking into consideration the user's preferred music genre. For example, if the user likes classical music, the generator may generate relaxing sounds based on classical music. If the user likes jazz, the generator may generate relaxing sounds based on jazz. This allows for more effective sleep promotion by adjusting the sound content based on the user's musical preferences.

[0060] The collection unit collects changes in the user's body temperature while sleeping, and the analysis unit can generate sound content based on the data. For example, if the user's body temperature rises, a sound with a cooling effect can be generated. If the user's body temperature drops, a warm sound can be generated. This makes it possible to promote sleep more effectively by adjusting the sound content based on changes in the user's body temperature.

[0061] The collection unit collects voice data of the user while he or she is sleeping, and the analysis unit can generate sound content based on that data. For example, if the user is snoring, a sound that has a snoring reduction effect can be generated. If the user is sleeping quietly, a sound that has a high relaxing effect can be generated. This makes it possible to promote sleep more effectively by adjusting the sound content based on the user's voice data.

[0062] The process flow of the first embodiment will be briefly described below.

[0063] Step 1: The collection unit collects environmental data and health data. The environmental data includes room temperature, humidity, noise level, etc., and the health data includes heart rate, respiratory rate, body movement, etc. The collection unit collects these data using a temperature sensor, a humidity sensor, a microphone, etc. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis of the data and machine learning algorithms. For example, statistical analysis of temperature data, analysis of humidity data using machine learning algorithms, statistical analysis of noise level data, etc. Step 3: The generator generates sound content based on the analysis result obtained by the analyzer, which may include relaxing music, natural sounds, white noise, etc. Step 4: The providing unit provides the sound content generated by the generating unit to the user via a smartphone app, a speaker, headphones, etc.

[0064] (Example 2) A sleep promotion system according to an embodiment of the present invention is a system that generates sound content that promotes sleep optimized according to the user's environment and health condition. This system collects the user's environmental data (room temperature, humidity, noise level, etc.) and health data (heart rate, breathing rate, body movement, etc.), and generates optimal sound content by analyzing it with AI. The generated sound content is provided to the user, and the user sleeps while listening to the sound. Data during sleep is also collected and analyzed by AI. The analysis results are fed back, and further personalized sound content is generated the next day. This allows the user to get a high-quality sleep while listening to optimal sound content every night. In addition, if the user dislikes wearable devices, it is also possible to develop a compatible pillow in collaboration with a bedding manufacturer. This pillow has a built-in sensor that collects the user's health data. This makes it possible to collect the user's health data and provide optimal sound content without using a wearable device. For example, the temperature, humidity, and noise level of the user's room are measured by a sensor, and health data such as heart rate, breathing rate, and body movement are collected. AI analyzes these data and generates optimal sound content for the user. The generated sound content is provided to the user through a smartphone or speaker. The user sleeps while listening to the sound. Data during sleep is also collected and analyzed by AI. The analysis results are fed back, and further personalized sound content is generated the next day. This allows the user to get a high-quality sleep while listening to the optimal sound content every night. In addition, if the user dislikes wearable devices, it is also possible to collaborate with bedding manufacturers to develop a compatible pillow. This pillow has a built-in sensor that collects the user's health data. This makes it possible to collect the user's health data and provide the optimal sound content without using a wearable device. This allows the sleep promotion system to provide the optimal sound content according to the user's environment and health condition, promoting high-quality sleep.

[0065] The sleep promotion system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects environmental data and health data. The environmental data includes, for example, room temperature, humidity, noise level, etc., but is not limited to such examples. The health data includes, for example, heart rate, respiratory rate, body movement, etc., but is not limited to such examples. The collection unit, for example, measures the temperature of the room with a sensor and collects data. The collection unit can also measure the humidity of the room using a humidity sensor and collect data. Furthermore, the collection unit can measure the noise level with a microphone and collect data. For example, the collection unit measures the temperature of the room with a temperature sensor and collects data. The humidity of the room can also be measured using a humidity sensor and collected data. The noise level can also be measured with a microphone and collected data. The analysis unit analyzes the data collected by the collection unit. The analysis is performed, for example, using statistical analysis of data or a machine learning algorithm, but is not limited to such examples. For example, the analysis unit performs statistical analysis on the collected temperature data and analyzes the fluctuation of the temperature in the room. The collected humidity data can be analyzed using a machine learning algorithm to analyze the humidity fluctuation in the room. The collected noise level data can be statistically analyzed to analyze the noise level fluctuation in the room. The generation unit generates sound content based on the analysis result obtained by the analysis unit. The sound content includes, for example, relaxing music, natural sounds, white noise, etc., but is not limited to such examples. For example, the generation unit generates relaxing music based on the analysis result. Natural sounds can also be generated based on the analysis result. White noise can also be generated based on the analysis result. The provision unit provides the sound content generated by the generation unit to the user. The provision is performed, for example, through a smartphone app, a speaker, headphones, etc., but is not limited to such examples. For example, the provision unit provides the generated sound content to the user through a smartphone app. The sound content can also be provided through a speaker. The sound content can also be provided through headphones.As a result, the sleep promotion system according to the embodiment can provide optimal sound content according to the user's environment and health condition, and promote high-quality sleep.

[0066] The collection unit collects environmental data and health data. The environmental data includes, for example, room temperature, humidity, noise level, etc., but is not limited to such examples. Specifically, the collection unit measures the temperature of the room with a temperature sensor and collects data. The temperature sensor is installed in multiple locations in the room, and can grasp the uniformity and fluctuation of the temperature in detail. In addition, the humidity sensor can be used to measure the humidity of the room and collect data. The humidity sensor monitors the air quality of the room and grasps the dryness and humidity state in real time. Furthermore, the collection unit can measure the noise level with a microphone and collect data. The microphone identifies the noise source in the room and records the fluctuation of the noise level. In this way, the collection unit can collect environmental data such as the room temperature, humidity, and noise level in detail and accurately grasp the user's sleep environment. The health data includes, for example, heart rate, respiratory rate, body movement, etc., but is not limited to such examples. The collection unit measures the user's heart rate with a heart rate sensor and collects data. The heart rate sensor monitors the fluctuation of the user's heart rate in real time and grasps the stress and relaxation state. The respiration rate can also be measured by a respiration sensor to collect data. The respiration sensor records the user's breathing pattern in detail and analyzes the depth and rhythm of breathing. Furthermore, the body movement can also be measured by an acceleration sensor to collect data. The acceleration sensor detects the user's body movement and records the movement and posture changes during sleep. This allows the collection unit to collect detailed health data such as the user's heart rate, respiration rate, and body movement, and accurately grasp the user's health condition. The collected environmental data and health data are transmitted to a central database so that the analysis unit can access them. This allows the collection unit to efficiently and effectively collect data and improve the performance of the entire system.

[0067] The analysis unit analyzes the data collected by the collection unit. The analysis is performed, for example, using statistical analysis of data or a machine learning algorithm, but is not limited to such examples. Specifically, the analysis unit statistically analyzes the collected temperature data and analyzes the temperature fluctuations in the room. For the analysis of the temperature data, methods such as time series analysis and histogram analysis are used to detect the fluctuation pattern of the room temperature and abnormal values. In addition, the analysis unit can analyze the humidity fluctuations in the room by analyzing the collected humidity data with a machine learning algorithm. The machine learning algorithm learns past data and builds a model that predicts future humidity fluctuations. Furthermore, the analysis unit can statistically analyze the collected noise level data and analyze the noise level fluctuations in the room. For the analysis of the noise level, methods such as frequency analysis and spectrum analysis are used to identify the noise source and grasp the noise level fluctuation pattern. Based on these analysis results, the analysis unit can evaluate the state of the user's sleep environment and identify areas for improvement. Furthermore, the analysis unit analyzes the collected health data and evaluates the health state of the user. For example, the analysis unit statistically analyzes the heart rate data and analyzes the user's heart rate fluctuations. The heart rate is analyzed using techniques such as time series analysis and Fourier transform to detect the rhythm of the heart rate and abnormal values. The breathing rate data can also be analyzed using a machine learning algorithm to analyze the user's breathing pattern. The machine learning algorithm learns from past data and builds a model to predict future breathing patterns. Furthermore, the body movement data can be statistically analyzed to analyze changes in the user's body movement and posture. The body movement is analyzed using techniques such as time series analysis and clustering to grasp the change patterns of movement and posture during sleep. This allows the analysis unit to quickly and accurately analyze the collected data and grasp the user's sleeping environment and health condition in real time.

[0068] The generating unit generates sound content based on the analysis result obtained by the analyzing unit. The sound content includes, for example, relaxing music, natural sounds, white noise, and the like, but is not limited to such examples. Specifically, the generating unit generates relaxing music based on the analysis result. The relaxing music adjusts the tempo and rhythm according to the user's heart rate and breathing rate to maximize the relaxing effect. In addition, natural sounds can be generated based on the analysis result. The natural sounds include, for example, the sound of waves, the sound of wind, and the chirping of birds, and are selected according to the user's preferences and environment. Furthermore, white noise can be generated based on the analysis result. White noise includes sounds in a certain frequency band, blocks out external noise, and supports the user's sleep. The generating unit can generate these sound contents in real time and provide the optimal sound for the user's sleep environment. Furthermore, the generating unit can adjust the sound content based on the user's feedback to maximize the effect. For example, if the user likes relaxing music, the generating unit increases the variety of relaxing music and provides it according to the user's preferences. In addition, if the user likes natural sounds, the generating unit adjusts the type and playback pattern of the natural sounds to enhance the user's relaxing effect. This enables the generation unit to generate optimal sound content based on the analysis results, thereby promoting sleep for the user.

[0069] The providing unit provides the sound content generated by the generating unit to the user. The provision is performed, for example, through a smartphone app, a speaker, headphones, etc., but is not limited to such examples. Specifically, the providing unit provides the generated sound content to the user through a smartphone app. The smartphone app has a function of selecting and playing sound content according to the user's preferences and environment. The sound content can also be provided through a speaker. The speaker spreads sound throughout the room to improve the user's sleep environment. Furthermore, the sound content can also be provided through headphones. The headphones block external noise and provide the user with a relaxing environment in which he or she can concentrate. The providing unit can provide the user with optimal sound content through these devices and promote high-quality sleep. Furthermore, the providing unit can collect user feedback and continuously improve the accuracy and effect of the provided content. For example, if the user likes a particular sound content, the providing unit increases the playback frequency of that content to increase the user's satisfaction. The providing unit can also reliably transmit information using multiple communication means. For example, important information can be reliably delivered by using not only smartphone notifications but also voice calls, SMS, emails, etc. This enables the providing unit to quickly and reliably provide sound content to the user, thereby promoting high-quality sleep.

[0070] The collection unit can collect environmental data such as the temperature, humidity, and noise level of the room. The environmental data includes, for example, temperature, humidity, and noise level, but is not limited to such examples. For example, the collection unit measures the temperature of the room with a sensor and collects data. It is also possible to measure the humidity of the room using a humidity sensor and collect data. It is also possible to measure the noise level with a microphone and collect data. For example, the collection unit measures the temperature of the room with a temperature sensor and collects data. It is also possible to measure the humidity of the room using a humidity sensor and collect data. It is also possible to measure the noise level with a microphone and collect data. In this way, the user's sleep environment can be optimized by collecting environmental data. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit inputs temperature data acquired by a temperature sensor to the generation AI, and causes the generation AI to execute analysis of the temperature data.

[0071] The collection unit can collect health data such as heart rate, respiratory rate, and body movement. The health data includes, for example, heart rate, respiratory rate, and body movement, but is not limited to such examples. For example, the collection unit measures the heart rate with a sensor and collects data. The respiratory rate can also be measured with a sensor and collected data. The body movement can also be measured with a sensor and collected data. For example, the collection unit measures the heart rate with a heart rate sensor and collects data. The respiratory rate can also be measured with a respiratory sensor and collected data. The body movement can also be measured with a body movement sensor and collected data. As a result, by collecting health data, sound content based on the user's health condition can be provided. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit inputs heart rate data acquired by a heart rate sensor to the generation AI, and causes the generation AI to execute analysis of the heart rate data.

[0072] The analysis unit can evaluate the quality of the user's sleep based on the collected data. The quality of sleep includes, for example, a sleep score, an analysis of sleep stages, and the like, but is not limited to such examples. For example, the analysis unit statistically analyzes the collected data to evaluate the quality of the user's sleep. The collected data can also be analyzed using a machine learning algorithm to evaluate the quality of the user's sleep. For example, the analysis unit statistically analyzes the collected heart rate data to evaluate the quality of the user's sleep. The collected respiratory rate data can also be analyzed using a machine learning algorithm to evaluate the quality of the user's sleep. The collected body movement data can also be statistically analyzed to evaluate the quality of the user's sleep. As a result, more appropriate sound content can be provided by evaluating the quality of the user's sleep. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to the generation AI and cause the generation AI to execute the evaluation of the quality of sleep.

[0073] The generation unit can generate sound content based on the analysis result. Sound content includes, for example, relaxing music, natural sounds, white noise, etc., but is not limited to such examples. The generation unit, for example, generates relaxing music based on the analysis result. Natural sounds can also be generated based on the analysis result. White noise can also be generated based on the analysis result. For example, the generation unit generates relaxing music based on the analysis result. Natural sounds can also be generated based on the analysis result. White noise can also be generated based on the analysis result. Thereby, by generating sound content based on the analysis result, it is possible to provide an optimal sound to the user. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the analysis result to the generation AI and causes the generation AI to generate the sound content.

[0074] The providing unit can provide sound content through a smartphone or speaker of the user. The provision is performed, for example, through a smartphone app, a speaker, headphones, etc., but is not limited to such examples. The providing unit, for example, provides the generated sound content to the user through a smartphone app. The sound content can also be provided through a speaker. The sound content can also be provided through headphones. For example, the providing unit provides the generated sound content to the user through a smartphone app. The sound content can also be provided through a speaker. The sound content can also be provided through headphones. As a result, convenience is improved by providing the sound content through the user's smartphone or speaker. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit inputs user data acquired through a smartphone app to the generating AI, and causes the generating AI to execute a method for providing the sound content.

[0075] The collection unit can collect data using a sensor built into the pillow. The sensor includes, for example, a pressure sensor, a temperature sensor, a position sensor, and the like, but is not limited to such examples. The collection unit, for example, collects data using a pressure sensor built into the pillow. The temperature sensor can also be used to collect temperature data of the pillow. The position sensor can also be used to collect position data of the pillow. For example, the collection unit collects data using a pressure sensor built into the pillow. The temperature sensor can also be used to collect temperature data of the pillow. The position sensor can also be used to collect position data of the pillow. As a result, by using the sensor built into the pillow, data can be collected without using a wearable device. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data acquired by a sensor built into the pillow to the generation AI and have the generation AI execute analysis of the data.

[0076] The collection unit can estimate the user's emotion and adjust the timing of data collection based on the estimated user's emotion. For example, when the user is relaxed, the collection unit frequently collects data to obtain detailed data. When the user is stressed, the collection unit can also reduce the frequency of data collection to reduce the burden on the user. The data collection unit can also collect data just before the user falls asleep to obtain the most accurate data. For example, when the user is relaxed, the collection unit frequently collects data to obtain detailed data. When the user is stressed, the collection unit can also reduce the frequency of data collection to reduce the burden on the user. The data collection unit can also collect data just before the user falls asleep to obtain the most accurate data. This allows more accurate data to be collected by adjusting the timing of data collection based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input a user's emotion data to the generation AI and cause the generation AI to adjust the timing of data collection.

[0077] The collection unit can analyze the user's past sleep data and select the optimal data collection method. The past sleep data includes, for example, past sleep scores, records of sleep stages, and the like, but is not limited to such examples. The collection unit, for example, identifies the most effective data collection time period from the user's past sleep data. The frequency of data collection can also be adjusted based on the user's past sleep data. The user's past sleep data can be analyzed and a data collection method can be selected based on a specific pattern. For example, the collection unit identifies the most effective data collection time period from the user's past sleep data. The frequency of data collection can also be adjusted based on the user's past sleep data. The user's past sleep data can also be analyzed and a data collection method can be selected based on a specific pattern. In this way, the optimal data collection method can be selected by analyzing the past sleep data. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the past sleep data to the generation AI and cause the generation AI to select the data collection method.

[0078] The collection unit can perform filtering based on the user's current living situation and stress level when collecting data. Living situations include, for example, daily activity levels, eating patterns, stress levels, etc., but are not limited to such examples. For example, the collection unit reduces the frequency of data collection when the user is stressed at work. When the user is relaxed, detailed data can also be collected. The timing of data collection can also be adjusted according to the user's living situation. For example, the collection unit reduces the frequency of data collection when the user is stressed at work. When the user is relaxed, detailed data can also be collected. The timing of data collection can also be adjusted according to the user's living situation. As a result, more appropriate data can be collected by filtering data based on the user's living situation and stress level. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's living situation data to the generation AI and cause the generation AI to filter the data.

[0079] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. For example, when the user is relaxed, the collection unit preferentially collects detailed health data such as heart rate and respiratory rate. When the user is stressed, the collection unit can also preferentially collect environmental data. The type of data to be collected can also be adjusted based on the user's emotion. For example, when the user is relaxed, the collection unit preferentially collects detailed health data such as heart rate and respiratory rate. When the user is stressed, the collection unit can also preferentially collect environmental data. The type of data to be collected can also be adjusted based on the user's emotion. In this way, important data can be preferentially collected by determining the priority of data based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input a user's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0080] The collection unit can prioritize collecting highly relevant data in consideration of the geographical location information of the user when collecting data. The geographical location information includes, for example, GPS data, Wi-Fi location information, and the like, but is not limited to such examples. For example, when the user is at home, the collection unit prioritizes collecting environmental data such as room temperature and humidity. When the user is traveling, the collection unit can also prioritize collecting health data such as heart rate and respiratory rate. The type of data to be collected can also be adjusted based on the geographical location information of the user. For example, when the user is at home, the collection unit prioritizes collecting environmental data such as room temperature and humidity. When the user is traveling, the collection unit can also prioritize collecting health data such as heart rate and respiratory rate. The type of data to be collected can also be adjusted based on the geographical location information of the user. As a result, more appropriate data can be collected by preferentially collecting highly relevant data based on the geographical location information of the user. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to the generation AI and cause the generation AI to execute the determination of the priority order of data.

[0081] The collection unit can analyze the user's social media activity at the time of data collection and collect related data. Social media activity includes, for example, the content of posts, the number of likes, comments, etc., but is not limited to such examples. For example, when the user feels stressed on social media, the collection unit preferentially collects health data such as heart rate and respiratory rate. When the user is relaxing on social media, the collection unit can also preferentially collect environmental data. The type of data to be collected can also be adjusted based on the user's social media activity. For example, when the user feels stressed on social media, the collection unit preferentially collects health data such as heart rate and respiratory rate. When the user is relaxing on social media, the collection unit can also preferentially collect environmental data. The type of data to be collected can also be adjusted based on the user's social media activity. In this way, more appropriate data can be collected by collecting related data based on the user's social media activity. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data to the generation AI and cause the generation AI to execute the data collection method.

[0082] The analysis unit can estimate the user's emotion and adjust the analysis algorithm based on the estimated user's emotion. For example, when the user is relaxed, the analysis unit performs a detailed analysis to obtain a highly accurate result. When the user is stressed, the analysis unit can also perform a simplified analysis to obtain a result quickly. The parameters of the analysis algorithm can also be adjusted based on the user's emotion. For example, when the user is relaxed, the analysis unit performs a detailed analysis to obtain a highly accurate result. When the user is stressed, the analysis unit can also perform a simplified analysis to obtain a result quickly. The parameters of the analysis algorithm can also be adjusted based on the user's emotion. As a result, a more accurate analysis result can be obtained by adjusting the analysis algorithm based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the analysis algorithm.

[0083] The analysis unit can improve the accuracy of the analysis by referring to past analysis data during the analysis. The past analysis data includes, for example, past analysis results, data sets, etc., but is not limited to such examples. The analysis unit corrects the current analysis result, for example, based on the user's past analysis data. The parameters of the analysis algorithm can also be optimized by referring to the past analysis data. The accuracy of the analysis can also be improved by using the past analysis data. For example, the analysis unit corrects the current analysis result based on the user's past analysis data. The parameters of the analysis algorithm can also be optimized by referring to the past analysis data. The accuracy of the analysis can also be improved by using the past analysis data. As a result, the accuracy of the analysis can be improved by referring to the past analysis data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the past analysis data to the generation AI and cause the generation AI to execute the improvement of the accuracy of the analysis.

[0084] The analysis unit can customize the analysis method based on the health condition and lifestyle of the user during analysis. The health condition includes, for example, heart rate, blood pressure, body temperature, etc., but is not limited to such examples. The lifestyle includes, for example, eating patterns, exercise habits, sleep patterns, etc., but is not limited to such examples. The analysis unit adjusts the analysis method, for example, based on the health condition of the user. The analysis method can also be customized based on the lifestyle of the user. The analysis method can also be optimized based on the health data of the user. For example, the analysis unit adjusts the analysis method based on the health condition of the user. The analysis method can also be customized based on the lifestyle of the user. The analysis method can also be optimized based on the health data of the user. As a result, by customizing the analysis method based on the health condition and lifestyle of the user, more appropriate analysis results can be obtained. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit inputs the health data of the user to the generation AI and causes the generation AI to execute customization of the analysis method.

[0085] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. For example, when the user is relaxed, the analysis unit displays a detailed analysis result. When the user is stressed, the analysis unit can also display a simplified analysis result. The display method of the analysis result can also be adjusted based on the user's emotion. For example, when the user is relaxed, the analysis unit displays a detailed analysis result. When the user is stressed, the analysis unit can also display a simplified analysis result. The display method of the analysis result can also be adjusted based on the user's emotion. In this way, by adjusting the display method of the analysis result based on the user's emotion, it is possible to provide a result that is easy for the user to understand. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method of the analysis result.

[0086] The analysis unit can perform the analysis while considering the geographical location information of the user. The geographical location information includes, for example, GPS data, Wi-Fi location information, and the like, but is not limited to such examples. For example, when the user is at home, the analysis unit performs the analysis with emphasis on environmental data. When the user is traveling, the analysis unit can also perform the analysis with emphasis on health data. The analysis method can also be adjusted based on the geographical location information of the user. For example, when the user is at home, the analysis unit performs the analysis with emphasis on environmental data. When the user is traveling, the analysis unit can also perform the analysis with emphasis on health data. The analysis method can also be adjusted based on the geographical location information of the user. As a result, a more appropriate analysis result can be obtained by performing the analysis based on the geographical location information of the user. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the geographical location information of the user to the generation AI and cause the generation AI to adjust the analysis method.

[0087] The analysis unit can improve the accuracy of the analysis by referring to the related literature of the user during the analysis. The related literature includes, for example, academic papers, technical reports, patent documents, etc., but is not limited to such examples. The analysis unit can improve the accuracy of the analysis by referring to, for example, related literature on the health condition of the user. The analysis method can also be optimized by referring to related literature on the lifestyle habits of the user. The analysis result can also be corrected by referring to related literature on the environmental data of the user. For example, the analysis unit can improve the accuracy of the analysis by referring to related literature on the health condition of the user. The analysis method can also be optimized by referring to related literature on the lifestyle habits of the user. The analysis result can also be corrected by referring to related literature on the environmental data of the user. As a result, the accuracy of the analysis can be improved by referring to the related literature of the user. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the related literature to the generation AI and cause the generation AI to execute the improvement of the accuracy of the analysis.

[0088] The generation unit can estimate the user's emotion and adjust the generation method of the sound content based on the estimated user's emotion. For example, when the user is relaxed, the generation unit generates a relaxed sound. When the user is stressed, the generation unit can also generate a sound with a high relaxing effect. The characteristics of the sound content can also be adjusted based on the user's emotion. For example, when the user is relaxed, the generation unit generates a relaxed sound. When the user is stressed, the generation unit can also generate a sound with a high relaxing effect. The characteristics of the sound content can also be adjusted based on the user's emotion. In this way, by adjusting the generation method of the sound content based on the user's emotion, a more appropriate sound can be provided. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the generation method of the sound content.

[0089] The generation unit can generate an optimal sound by referring to the user's past sound history when generating sound content. The past sound history includes, for example, a playback history, an evaluation history, and a frequency of use, but is not limited to such examples. The generation unit generates an optimal sound, for example, based on the user's past sound history. The generation unit can also adjust the characteristics of the sound content by referring to the user's past sound history. The generation unit can also generate the most effective sound by analyzing the user's past sound history. For example, the generation unit generates an optimal sound based on the user's past sound history. The generation unit can also adjust the characteristics of the sound content by referring to the user's past sound history. The generation unit can also generate the most effective sound by analyzing the user's past sound history. In this way, the generation unit can generate the optimal sound by referring to the user's past sound history. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the past sound history to the generation AI and cause the generation AI to generate the sound content.

[0090] The generation unit can customize the characteristics of the sound based on the current health condition of the user when generating the sound content. The health condition includes, for example, heart rate, blood pressure, body temperature, etc., but is not limited to such examples. The generation unit adjusts the rhythm of the sound based on, for example, the heart rate and breathing rate of the user. The frequency of the sound can also be adjusted based on the health condition of the user. The characteristics of the sound content can also be customized based on the health data of the user. For example, the generation unit adjusts the rhythm of the sound based on the heart rate and breathing rate of the user. The frequency of the sound can also be adjusted based on the health condition of the user. The characteristics of the sound content can also be customized based on the health data of the user. As a result, more effective sound can be provided by customizing the characteristics of the sound based on the health condition of the user. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input the health data of the user to the generation AI and cause the generation AI to customize the characteristics of the sound content.

[0091] The generation unit can estimate the user's emotion and determine the priority of sound content based on the estimated user's emotion. For example, when the user is relaxed, the generation unit preferentially generates a sound with a high relaxing effect. When the user is stressed, the generation unit can also preferentially generate a sound with a high stress reduction effect. The priority of sound content can also be adjusted based on the user's emotion. For example, when the user is relaxed, the generation unit preferentially generates a sound with a high relaxing effect. When the user is stressed, the generation unit can also preferentially generate a sound with a high stress reduction effect. The priority of sound content can also be adjusted based on the user's emotion. In this way, by determining the priority of sound content based on the user's emotion, more appropriate sound can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input user emotion data to the generation AI and have the generation AI determine the priority of sound content.

[0092] The generation unit can generate optimal sounds in consideration of the geographical location information of the user when generating sound content. The geographical location information includes, but is not limited to, for example, GPS data, Wi-Fi location information, and the like. For example, when the user is at home, the generation unit generates a sound with a high relaxing effect. When the user is traveling, the generation unit can also generate a sound with a high stress reduction effect. The characteristics of the sound content can also be adjusted based on the geographical location information of the user. For example, when the user is at home, the generation unit generates a sound with a high relaxing effect. When the user is traveling, the generation unit can also generate a sound with a high stress reduction effect. The characteristics of the sound content can also be adjusted based on the geographical location information of the user. As a result, a more effective sound can be provided by generating optimal sounds based on the geographical location information of the user. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit inputs the geographical location information of the user to the generation AI and causes the generation AI to generate the sound content.

[0093] The generation unit can adjust the characteristics of the sound by analyzing the social media activity of the user when generating the sound content. The social media activity includes, for example, the content of the post, the number of likes, and comments, but is not limited to such examples. For example, when the user is relaxing on social media, the generation unit generates a sound with a high relaxing effect. When the user is feeling stressed on social media, the generation unit can also generate a sound with a high stress reduction effect. The characteristics of the sound content can also be adjusted based on the social media activity of the user. For example, when the user is relaxing on social media, the generation unit generates a sound with a high relaxing effect. When the user is feeling stressed on social media, the generation unit can also generate a sound with a high stress reduction effect. The characteristics of the sound content can also be adjusted based on the social media activity of the user. As a result, a more appropriate sound can be provided by adjusting the characteristics of the sound based on the social media activity of the user. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit inputs the social media activity data of the user to the generation AI and causes the generation AI to adjust the characteristics of the sound content.

[0094] The providing unit can estimate the user's emotion and adjust the method of providing the sound content based on the estimated user's emotion. For example, when the user is relaxed, the providing unit provides the sound content at a leisurely pace. When the user is stressed, the providing unit can also quickly provide sound content with a high relaxing effect. The method of providing the sound content can also be adjusted based on the user's emotion. For example, when the user is relaxed, the providing unit provides the sound content at a leisurely pace. When the user is stressed, the providing unit can also quickly provide sound content with a high relaxing effect. The method of providing the sound content can also be adjusted based on the user's emotion. As a result, by adjusting the method of providing the sound content based on the user's emotion, a more appropriate sound can be provided. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the method of providing the sound content.

[0095] The providing unit can select the optimal providing method by referring to the user's past usage history when providing sound content. The past usage history includes, for example, playback history, evaluation history, and usage frequency, but is not limited to such examples. The providing unit selects the optimal providing method, for example, based on the user's past usage history. The timing of providing the sound content can also be adjusted by referring to the user's past usage history. The most effective providing method can also be selected by analyzing the user's past usage history. For example, the providing unit selects the optimal providing method based on the user's past usage history. The timing of providing the sound content can also be adjusted by referring to the user's past usage history. The most effective providing method can also be selected by analyzing the user's past usage history. In this way, the optimal providing method can be selected by referring to the user's past usage history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the past usage history to the generating AI and cause the generating AI to select the providing method.

[0096] The providing unit can customize the providing means based on the current device status of the user when providing sound content. The device status includes, for example, a remaining battery level, a connection status, a type of device, and the like, but is not limited to such examples. For example, when the user is using a smartphone, the providing unit provides sound content optimized for the smartphone. When the user is using a speaker, the providing unit can also provide sound content optimized for the speaker. The providing means of the sound content can also be customized based on the device status of the user. For example, when the user is using a smartphone, the providing unit provides sound content optimized for the smartphone. When the user is using a speaker, the providing unit can also provide sound content optimized for the speaker. The providing means of the sound content can also be customized based on the device status of the user. As a result, by customizing the providing means based on the device status of the user, more appropriate sound can be provided. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the device status data of the user to the generating AI and cause the generating AI to execute customization of the providing means.

[0097] The providing unit can estimate the user's emotion and determine the order of providing sound content based on the estimated user's emotion. For example, when the user is relaxed, the providing unit preferentially provides sound content with a high relaxing effect. When the user is stressed, the providing unit can also preferentially provide sound content with a high stress reduction effect. The providing order of sound content can also be adjusted based on the user's emotion. For example, when the user is relaxed, the providing unit preferentially provides sound content with a high relaxing effect. When the user is stressed, the providing unit can also preferentially provide sound content with a high stress reduction effect. The providing order of sound content can also be adjusted based on the user's emotion. In this way, by determining the providing order of sound content based on the user's emotion, more appropriate sound can be provided. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input user emotion data to the generating AI and cause the generating AI to determine the order in which sound content is provided.

[0098] The providing unit can select the optimal providing method in consideration of the geographical location information of the user when providing sound content. The geographical location information includes, for example, GPS data, Wi-Fi location information, and the like, but is not limited to such examples. For example, when the user is at home, the providing unit provides sound content with a high relaxing effect. When the user is traveling, the providing unit can also provide sound content with a high stress reduction effect. The providing method of the sound content can also be adjusted based on the geographical location information of the user. For example, when the user is at home, the providing unit provides sound content with a high relaxing effect. When the user is traveling, the providing unit can also provide sound content with a high stress reduction effect. The providing method of the sound content can also be adjusted based on the geographical location information of the user. As a result, by selecting the optimal providing method based on the geographical location information of the user, more appropriate sound can be provided. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to the generating AI and cause the generating AI to select the providing method.

[0099] The providing unit can adjust the providing means by analyzing the social media activity of the user when providing sound content. Social media activities include, for example, the content of posts, the number of likes, comments, and the like, but are not limited to such examples. For example, when the user is relaxing on social media, the providing unit provides sound content with a high relaxing effect. When the user is feeling stressed on social media, the providing unit can also provide sound content with a high stress reduction effect. The providing means of the sound content can also be adjusted based on the social media activity of the user. For example, when the user is relaxing on social media, the providing unit provides sound content with a high relaxing effect. When the user is feeling stressed on social media, the providing unit can also provide sound content with a high stress reduction effect. The providing means of the sound content can also be adjusted based on the social media activity of the user. As a result, by adjusting the providing means based on the social media activity of the user, more appropriate sound can be provided. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the social media activity data of the user to the generating AI and cause the generating AI to adjust the providing means.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The analysis unit can also analyze the content of the user's dreams while sleeping and generate sound content based on the analysis results. For example, if the user is having a relaxing dream, a sound with a high relaxing effect can be generated. If the user is having a stressful dream, a sound with a high stress reducing effect can be generated. In this way, by adjusting the sound content based on the content of the user's dream, it is possible to promote sleep more effectively.

[0102] The collection unit can collect the user's dietary data, and the analysis unit can generate sound content based on the data. For example, if the user consumes caffeine, a sound with a high relaxing effect can be generated. If the user consumes alcohol, a sound with a stable rhythm can be generated. This makes it possible to promote sleep more effectively by adjusting the sound content based on the user's dietary content.

[0103] The collection unit can collect the user's exercise data, and the analysis unit can generate sound content based on the data. For example, if the user exercises, a sound with a high relaxing effect can be generated. If the user does not exercise, a sound that replenishes energy can be generated. This makes it possible to promote sleep more effectively by adjusting the sound content based on the user's exercise status.

[0104] The analysis unit can also analyze the user's stress level and generate sound content based on the analysis result. For example, if the user is in a high stress state, a sound with a high stress reduction effect can be generated. If the user is in a low stress state, a sound with a high relaxation effect can be generated. This makes it possible to promote sleep more effectively by adjusting the sound content based on the user's stress level.

[0105] The generator may generate sound content taking into consideration the user's preferred music genre. For example, if the user likes classical music, the generator may generate relaxing sounds based on classical music. If the user likes jazz, the generator may generate relaxing sounds based on jazz. This allows for more effective sleep promotion by adjusting the sound content based on the user's musical preferences.

[0106] The providing unit can also estimate the user's emotion and adjust the timing of providing the sound content based on the estimated user's emotion. For example, when the user is relaxed, the sound content can be provided slowly. When the user is stressed, the sound content can be provided quickly. This makes it possible to promote sleep more effectively by adjusting the timing of providing the sound content based on the user's emotion.

[0107] The collection unit collects changes in the user's body temperature while sleeping, and the analysis unit can generate sound content based on the data. For example, if the user's body temperature rises, a sound with a cooling effect can be generated. If the user's body temperature drops, a warm sound can be generated. This makes it possible to promote sleep more effectively by adjusting the sound content based on changes in the user's body temperature.

[0108] The analysis unit can also estimate the user's emotions and adjust the feedback method of the analysis results based on the estimated user's emotions. For example, if the user is relaxed, detailed feedback can be provided. If the user is stressed, simplified feedback can be provided. This makes it possible to promote sleep more effectively by adjusting the feedback method based on the user's emotions.

[0109] The collection unit collects voice data of the user while he or she is sleeping, and the analysis unit can generate sound content based on that data. For example, if the user is snoring, a sound that has a snoring reduction effect can be generated. If the user is sleeping quietly, a sound that has a high relaxing effect can be generated. This makes it possible to promote sleep more effectively by adjusting the sound content based on the user's voice data.

[0110] The providing unit can also estimate the user's emotion and select a device for providing sound content based on the estimated user's emotion. For example, when the user is relaxed, the sound content can be provided through a speaker. When the user is stressed, the sound content can be provided through headphones. In this way, by selecting a providing device based on the user's emotion, more effective sleep promotion is possible.

[0111] The process flow of the second embodiment will be briefly described below.

[0112] Step 1: The collection unit collects environmental data and health data. The environmental data includes room temperature, humidity, noise level, etc., and the health data includes heart rate, respiratory rate, body movement, etc. The collection unit collects these data using a temperature sensor, a humidity sensor, a microphone, etc. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed using statistical analysis of the data and machine learning algorithms. For example, statistical analysis of temperature data, analysis of humidity data using machine learning algorithms, statistical analysis of noise level data, etc. Step 3: The generator generates sound content based on the analysis result obtained by the analyzer, which may include relaxing music, natural sounds, white noise, etc. Step 4: The providing unit provides the sound content generated by the generating unit to the user via a smartphone app, a speaker, headphones, etc.

[0113] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0115] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects environmental data and health data using a sensor or a microphone of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates sound content based on the analysis result. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides the generated sound content to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0120] 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.

[0121] 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 the voice according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0123] 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 performed in a secure state.

[0124] Fig. 4 shows an example of 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0129] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0130] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0131] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects environmental data and health data using a sensor or a microphone of the smart glasses 214. The analysis unit is realized, for example, by a specific processing unit 290 of the data processing device 12, and analyzes the collected data. The generation unit is realized, for example, by a specific processing unit 290 of the data processing device 12, and generates sound content based on the analysis result. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the generated sound content to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 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.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0136] 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.

[0137] 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 the voice according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0139] 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 performed in a secure state.

[0140] 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.

[0141] 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.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0143] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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 identification processing unit 290 using these models.

[0144] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0145] 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 voice indicating a user input for 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.

[0146] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0147] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects environmental data and health data using a sensor or a microphone of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates sound content based on the analysis result. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides the generated sound content to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 7, a 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.

[0151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0152] 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.

[0153] 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 the voice according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0155] 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 performed in a secure state.

[0156] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0157] 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.

[0158] 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.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0160] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 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.

[0161] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0162] 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 a voice indicating a user input for 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.

[0163] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0164] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0165] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects environmental data and health data using a sensor or a microphone of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates sound content based on the analysis result. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the generated sound content to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0166] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0167] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0168] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0169] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0170] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0171] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0173] 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.

[0174] 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 Universal Serial Bus (USB) 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.

[0175] In addition, 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 upon request from the data processing device 12.

[0176] 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.

[0177] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0178] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0179] As an example of a configuration using one 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 configuration in which a processor is used that realizes the functions of the 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.

[0180] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0181] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0182] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0183] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0184] (Appendix 1) A collection unit for collecting environmental data and health data; an analysis unit that analyzes the data collected by the collection unit; a generation unit that generates sound content based on the analysis result obtained by the analysis unit; a providing unit for providing a user with the sound content generated by the generating unit; Equipped A system characterized by: (Appendix 2) The collecting unit includes: Collect environmental data on room temperature, humidity, and noise levels 2. The system of claim 1, (Appendix 3) The collecting unit includes: Collect health data such as heart rate, breathing rate, and body movement 2. The system of claim 1, (Appendix 4) The analysis unit is Evaluate the user's sleep quality based on collected data 2. The system of claim 1, (Appendix 5) The generation unit is Generate sound content based on analysis results 2. The system of claim 1, (Appendix 6) The providing unit is Providing sound content through the user's smartphone or speaker 2. The system of claim 1, (Appendix 7) The collecting unit includes: Data is collected using sensors built into the pillow 2. The system of claim 1, (Appendix 8) The collecting unit includes: Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1, (Appendix 9) The collecting unit includes: Analyze the user's past sleep data and select the optimal data collection method 2. The system of claim 1, (Appendix 10) The collecting unit includes: Filter data collection based on the user's current life situation and stress level 2. The system of claim 1, (Appendix 11) The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting data, prioritize collecting data that is more relevant by taking into account the user's geographic location. 2. The system of claim 1, (Appendix 13) The collecting unit includes: When collecting data, analyze your social media activity and collect relevant data 2. The system of claim 1, (Appendix 14) The analysis unit is Inferring user emotions and adjusting analysis algorithms based on the inferred user emotions 2. The system of claim 1, (Appendix 15) The analysis unit is When analyzing, refer to past analysis data to improve the accuracy of the analysis. 2. The system of claim 1, (Appendix 16) The analysis unit is Customize analysis methods based on the user's health condition and lifestyle habits during analysis 2. The system of claim 1, (Appendix 17) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 18) The analysis unit is When analyzing, the geographic location of the user is taken into account. 2. The system of claim 1, (Appendix 19) The analysis unit is During analysis, the accuracy of the analysis is improved by referring to the user's related literature. 2. The system of claim 1, (Appendix 20) The generation unit is To estimate a user's emotion and adjust a method of generating sound content based on the estimated user's emotion 2. The system of claim 1, (Appendix 21) The generation unit is When generating sound content, the system generates optimal sounds by referring to the user's past sound history. 2. The system of claim 1, (Appendix 22) The generation unit is Customize sound characteristics based on the user's current health status when generating sound content 2. The system of claim 1, (Appendix 23) The generation unit is Estimating a user's emotion and prioritizing sound content based on the estimated user's emotion 2. The system of claim 1, (Appendix 24) The generation unit is When generating sound content, the optimal sound is generated taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 25) The generation unit is When generating sound content, the characteristics of the sound are adjusted by analyzing the user's social media activity. 2. The system of claim 1, (Appendix 26) The providing unit is To estimate a user's emotion and adjust a method of providing sound content based on the estimated user's emotion 2. The system of claim 1, (Appendix 27) The providing unit is When providing sound content, the optimal method of providing it is selected by referring to the user's past usage history. 2. The system of claim 1, (Appendix 28) The providing unit is When providing sound content, customize the means of providing it based on the user's current device status 2. The system of claim 1, (Appendix 29) The providing unit is A user's emotion is estimated, and a sequence of providing sound content is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 30) The providing unit is When providing sound content, the optimal method of providing the content is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 31) The providing unit is When providing sound content, the means of providing it is adjusted by analyzing the user's social media activity. 2. The system of claim 1, [Explanation of symbols]

[0185] 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 collection unit for collecting environmental data and health data; an analysis unit that analyzes the data collected by the collection unit; a generation unit that generates sound content based on the analysis result obtained by the analysis unit; a providing unit for providing a user with the sound content generated by the generating unit; Equipped A system characterized by:

2. The collecting unit includes: Collect environmental data on room temperature, humidity, and noise levels 2. The system of claim 1 .

3. The collecting unit includes: Collect health data such as heart rate, breathing rate, and body movement 2. The system of claim 1 .

4. The analysis unit is Evaluating the quality of sleep of the user based on the data collected by the collection unit 2. The system of claim 1 .

5. The generation unit is Generate sound content based on analysis results 2. The system of claim 1 .

6. The providing unit is Providing sound content through the user's smartphone or speaker 2. The system of claim 1 .

7. The collecting unit includes: Data is collected using sensors built into the pillow 2. The system of claim 1 .

8. The collecting unit includes: Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1 .

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