system

Abstract

The system according to this embodiment aims to analyze the user's sleep data and provide a personalized sleep improvement plan. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a proposal unit, an audio provision unit, and a consultation promotion unit. The data collection unit collects data such as the user's heart rate, body temperature, breathing pattern, and frequency of turning over in sleep. The analysis unit analyzes the data collected by the data collection unit using deep learning. The proposal unit proposes an individualized sleep improvement plan based on the analysis results obtained by the analysis unit. The audio provision unit provides customized meditation audio for relaxation based on the plan proposed by the proposal unit. The consultation promotion unit prompts the user to consult a medical professional based on the sleep abnormalities detected by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that even if user sleep data is collected, the data has not been fully utilized effectively to provide an individualized sleep improvement plan.

[0005] The system according to the embodiment aims to analyze user sleep data and provide an individualized sleep improvement plan.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, an audio provision unit, and a consultation promotion unit. The data collection unit collects data such as the user's heart rate, body temperature, breathing pattern, and frequency of turning over in sleep. The analysis unit analyzes the data collected by the data collection unit using deep learning. The proposal unit proposes an individualized sleep improvement plan based on the analysis results obtained by the analysis unit. The audio provision unit provides customized meditation audio for relaxation based on the plan proposed by the proposal unit. The consultation promotion unit prompts the user to consult a medical professional based on the sleep abnormalities detected by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's sleep data and provide a personalized sleep improvement plan. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9]This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example of form 1) The sleep management system according to an embodiment of the present invention is a comprehensive sleep management system that thoroughly analyzes and optimizes the sleep of each individual user. This sleep management system works in conjunction with devices such as smartwatches and bed sensors to collect detailed data such as heart rate, body temperature, breathing patterns, and frequency of tossing and turning during sleep. AI analyzes this data using deep learning to reveal the correlation between the user's sleep cycle, sleep quality, and environmental factors (room temperature, humidity, noise level, etc.). Furthermore, it also takes into account the user's daytime activities, diet, and stress levels to identify factors that affect sleep quality. Based on these analysis results, the AI ​​proposes an individualized sleep improvement plan. For example, this may include setting optimal bedtime and wake-up times, adjusting the room temperature at night, and providing customized meditation audio for relaxation. The AI ​​also detects abnormalities during sleep (such as signs of sleep apnea) and prompts the user to consult a medical professional if necessary. The sleep management system goes beyond being a mere sleep tracker and functions as a 24-hour personal sleep concierge that supports the improvement of the user's overall health and well-being. For example, a sleep management system collects detailed data such as the user's heart rate, body temperature, breathing patterns, and frequency of tossing and turning using devices like smartwatches and bed sensors. This data is then analyzed by AI using deep learning. For instance, it might detect that the user's heart rate fluctuates in a consistent rhythm or that they toss and turn frequently. This reveals the user's sleep cycle and sleep quality. Next, based on the collected data, the AI ​​also considers the user's daytime activities, diet, and stress levels to identify factors that affect sleep quality. For example, it might find that if a user experiences a lot of stress during the day, their sleep quality will suffer. This allows the AI ​​to provide stress management advice to the user. Furthermore, based on these analysis results, the AI ​​proposes a personalized sleep improvement plan. For example, it might advise the user to set optimal bedtimes and wake times, or suggest adjusting the room temperature at night. It might also provide customized meditation audio for relaxation. This allows the user to get better sleep.Furthermore, the AI ​​detects abnormalities during sleep (such as signs of sleep apnea) and prompts users to consult a medical professional if necessary. For example, if an abnormality is detected in the user's breathing pattern, the AI ​​will advise the user to consult a medical professional. This allows the user to receive appropriate medical care early on. The sleep management system goes beyond being a simple sleep tracker; it functions as a 24 / 7 personal sleep concierge that supports the improvement of the user's overall health and well-being. By following the AI's advice, users can achieve better sleep and improve the quality of their daily lives. For example, by adjusting their bedtime and wake-up time according to the AI's advice, users can expect improved daytime performance and reduced stress. In this way, the sleep management system can thoroughly analyze and optimize the user's sleep.

[0029] The sleep management system according to this embodiment comprises a data collection unit, an analysis unit, a suggestion unit, a voice provision unit, and a consultation promotion unit. The data collection unit collects data such as the user's heart rate, body temperature, breathing pattern, and frequency of turning over in sleep. The data collection unit collects data in cooperation with devices such as a smartwatch or a bed sensor. For example, the data collection unit can measure the user's heart rate using a smartwatch. The data collection unit can also measure the frequency of the user's turning over in sleep using a bed sensor. Furthermore, the data collection unit can measure the user's body temperature using a body temperature sensor. For example, the data collection unit monitors the user's heart rate in real time using a smartwatch and collects the data. The data collection unit records the frequency of the user's turning over in sleep using a bed sensor and collects the data. The data collection unit periodically measures the user's body temperature using a body temperature sensor and collects the data. The analysis unit analyzes the data collected by the data collection unit using deep learning. The analysis unit analyzes the user's sleep cycle and sleep quality based on data such as the user's heart rate, body temperature, breathing patterns, and frequency of tossing and turning. For example, the analysis unit can analyze the user's heart rate fluctuation patterns using deep learning algorithms. It can also analyze the user's body temperature fluctuation patterns. Furthermore, it can analyze the user's breathing patterns. For instance, the analysis unit uses deep learning algorithms to analyze the user's heart rate fluctuation patterns and evaluate sleep quality. The analysis unit analyzes the user's body temperature fluctuation patterns and evaluates sleep quality. The analysis unit analyzes the user's breathing patterns and evaluates sleep quality. The suggestion unit proposes a personalized sleep improvement plan based on the analysis results obtained by the analysis unit. For example, the suggestion unit can advise the user to set optimal bedtime and wake-up times. For example, the suggestion unit can suggest adjusting the room temperature at night. The suggestion unit can also provide the user with customized meditation audio for relaxation. For example, the suggestion department advises users to set optimal bedtimes and wake-up times to improve sleep quality. The suggestion department also suggests users to adjust the room temperature at night to improve sleep quality.The suggestion unit provides users with customized meditation audio for relaxation and improves sleep quality. The audio provision unit provides customized meditation audio for relaxation based on the plan suggested by the suggestion unit. The audio provision unit can, for example, provide users with highly relaxing meditation audio. The audio provision unit can, for example, provide users with highly relaxing music. The audio provision unit can also provide users with highly relaxing nature sounds. For example, the audio provision unit provides users with highly relaxing meditation audio and improves sleep quality. The audio provision unit provides users with highly relaxing music and improves sleep quality. The audio provision unit provides users with highly relaxing nature sounds and improves sleep quality. The consultation promotion unit prompts consultation with a medical professional based on sleep abnormalities detected by the analysis unit. For example, if an abnormality is found in the user's breathing pattern, the consultation promotion unit can advise the user to consult a medical professional. For example, if an abnormality is found in the user's heart rate, the consultation promotion unit can advise the user to consult a medical professional. Furthermore, the consultation promotion unit can advise the user to consult a medical professional if an abnormality is detected in the user's body temperature. For example, if an abnormality is detected in the user's breathing pattern, the consultation promotion unit can advise the user to consult a medical professional, enabling them to receive appropriate medical care early. If an abnormality is detected in the user's heart rate, the consultation promotion unit can advise the user to consult a medical professional, enabling them to receive appropriate medical care early. If an abnormality is detected in the user's body temperature, the consultation promotion unit can advise the user to consult a medical professional, enabling them to receive appropriate medical care early. As a result, the sleep management system according to the embodiment can thoroughly analyze and optimize the user's sleep.

[0030] The data collection unit collects data such as the user's heart rate, body temperature, breathing patterns, and frequency of tossing and turning during sleep. The unit works in conjunction with devices such as smartwatches and bed sensors to collect data. Specifically, the smartwatch monitors the user's heart rate in real time and collects data. Heart rate variability is an important indicator of the user's stress level and relaxation state, and is essential for evaluating sleep quality. The bed sensor records and collects data on the user's frequency of tossing and turning. Frequency of tossing and turning is an indicator of the user's sleep depth and comfort, and is important for evaluating sleep quality. Furthermore, a body temperature sensor periodically measures the user's body temperature and collects data. Body temperature variability is an indicator of the user's sleep cycle and internal clock state, and is important for evaluating sleep quality. The data collection unit centrally manages this data and transmits it to a central database in real time. This allows the data collection unit to gain a detailed understanding of the user's sleep state, making it accessible to the analysis and recommendation units. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, if a user is in a particular health condition or environmental state, the data collection unit can increase the frequency of data collection, providing more detailed data. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes data collected by the data collection department using deep learning. Specifically, it analyzes the user's sleep cycle and sleep quality based on data such as the user's heart rate, body temperature, breathing patterns, and frequency of turning over in sleep. Using deep learning algorithms, it analyzes the user's heart rate fluctuation patterns to evaluate sleep quality. Heart rate fluctuations are an important indicator of the user's stress level and relaxation state, and are essential for evaluating sleep quality. It also analyzes the user's body temperature fluctuation patterns to evaluate sleep quality. Body temperature fluctuations are an indicator of the user's sleep cycle and the state of their internal clock, and are important for evaluating sleep quality. Furthermore, it analyzes the user's breathing patterns to evaluate sleep quality. Fluctuations in breathing patterns are an indicator of the user's relaxation state and health state, and are important for evaluating sleep quality. The analysis department comprehensively analyzes this data to evaluate the user's sleep quality. In addition, the analysis department can also utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past sleep data, it can predict fluctuations in risk at specific time periods or environmental conditions and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The Proposal Department proposes personalized sleep improvement plans based on the analysis results obtained by the Analysis Department. Specifically, it advises users on setting optimal bedtimes and wake-up times. For example, it proposes optimal bedtimes and wake-up times considering the user's sleep cycle and internal clock to improve sleep quality. The Proposal Department can also suggest adjusting the room temperature at night. For example, it proposes optimal room temperature based on the user's body temperature fluctuation patterns to improve sleep quality. Furthermore, the Proposal Department can provide users with customized meditation audio for relaxation. For example, it proposes optimal meditation audio considering the user's stress level and relaxation state to improve sleep quality. Through these suggestions, the Proposal Department provides concrete action plans to improve the user's sleep quality. In addition, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can revise its suggestions based on the results of users acting on the suggestions and provide more effective improvement plans. The Proposal Department can also provide personalized suggestions according to the user's lifestyle and health condition. This allows the Proposal Department to provide users with optimal sleep improvement plans and improve their sleep quality.

[0033] The audio provider unit provides customized meditation audio for relaxation based on the plan proposed by the proposal unit. Specifically, it can provide users with meditation audio that has a high relaxation effect. For example, it can select and provide the most suitable meditation audio considering the user's stress level and relaxation state. The audio provider unit can also provide users with music that has a high relaxation effect. For example, it can select and provide the most suitable music considering the user's preferences and relaxation state. Furthermore, the audio provider unit can provide users with nature sounds that have a high relaxation effect. For example, it can use natural sounds such as the sound of waves or birdsong to enhance relaxation. Through this audio content, the audio provider unit promotes the user's relaxation state and improves the quality of sleep. The audio provider unit can also collect user feedback and continuously improve the accuracy and effectiveness of the audio content. For example, it can revise the audio content based on feedback on whether the user was able to relax and provide more effective audio. In addition, the audio provider unit can provide individualized audio content according to the user's lifestyle and health condition. This allows the audio provider unit to provide users with optimal relaxation audio and improve the quality of sleep.

[0034] The Consultation Facilitation Department encourages users to consult with medical professionals based on sleep abnormalities detected by the Analysis Department. Specifically, if abnormalities are found in a user's breathing pattern, the department can advise the user to consult with a medical professional. For example, if a user's breathing pattern is different from normal, consulting a medical professional early can lead to receiving appropriate medical care. The Consultation Facilitation Department can also advise users to consult with medical professionals if abnormalities are found in their heart rate. For example, if a user's heart rate is different from normal, consulting a medical professional early can lead to receiving appropriate medical care. Furthermore, if abnormalities are found in a user's body temperature, the Consultation Facilitation Department can advise the user to consult with a medical professional. For example, if a user's body temperature is different from normal, consulting a medical professional early can lead to receiving appropriate medical care. Through this advice, the Consultation Facilitation Department supports users in receiving appropriate medical care early. In addition, the Consultation Facilitation Department can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, based on the results of users' consultations with medical professionals, the advice can be reviewed and more effective advice can be provided. Furthermore, the consultation promotion department can provide personalized advice tailored to the user's lifestyle and health condition. This allows the department to encourage users to seek optimal medical consultations and improve their health.

[0035] The data collection unit can collect data in conjunction with devices such as smartwatches and bed sensors. For example, the data collection unit can measure the user's heart rate using a smartwatch. For example, the data collection unit can measure the frequency of the user's tossing and turning using a bed sensor. For example, the data collection unit can measure the user's body temperature using a body temperature sensor. For example, the data collection unit can monitor the user's heart rate in real time using a smartwatch and collect data. The data collection unit can record the frequency of the user's tossing and turning using a bed sensor and collect data. The data collection unit can periodically measure the user's body temperature using a body temperature sensor and collect data. In this way, the data collection unit can obtain more detailed data by collecting data from a variety of devices. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from devices such as smartwatches and bed sensors into a generating AI and have the generating AI perform data analysis.

[0036] The analysis unit can analyze data while also taking into account the user's daytime activities, diet, stress level, etc. For example, the analysis unit can analyze the quality of a user's sleep based on the user's daytime activities. For example, the analysis unit can analyze the quality of a user's sleep based on the user's diet. For example, the analysis unit can analyze the quality of a user's sleep based on the user's stress level. For example, the analysis unit evaluates the quality of a user's sleep based on the user's daytime activities. The analysis unit evaluates the quality of a user's sleep based on the user's diet. The analysis unit evaluates the quality of a user's sleep based on the user's stress level. This enables the analysis unit to perform data analysis that takes into account the user's overall lifestyle. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data such as the user's daytime activities, diet, and stress level into a generating AI and have the generating AI perform the data analysis.

[0037] The suggestion unit can suggest things like setting optimal bedtime and wake-up times, adjusting the room temperature at night, and providing customized meditation audio for relaxation. For example, the suggestion unit can advise the user to set optimal bedtime and wake-up times. For example, the suggestion unit can suggest the user to adjust the room temperature at night. For example, the suggestion unit can provide the user with customized meditation audio for relaxation. For example, the suggestion unit can advise the user to set optimal bedtime and wake-up times to improve sleep quality. The suggestion unit can suggest the user to adjust the room temperature at night to improve sleep quality. The suggestion unit can provide the user with customized meditation audio for relaxation to improve sleep quality. In this way, the suggestion unit can propose a specific sleep improvement plan to the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input user data into a generating AI and have the generating AI execute a proposal for an optimal sleep improvement plan.

[0038] The audio provider can provide customized meditation audio for relaxation. For example, the audio provider can provide the user with highly relaxing meditation audio. For example, the audio provider can provide the user with highly relaxing music. For example, the audio provider can provide the user with highly relaxing nature sounds. For example, the audio provider can provide the user with highly relaxing meditation audio to improve sleep quality. The audio provider can provide the user with highly relaxing music to improve sleep quality. The audio provider can provide the user with highly relaxing nature sounds to improve sleep quality. Thus, the audio provider can provide the user with highly relaxing audio. Some or all of the above processing in the audio provider may be performed using AI, for example, or without AI. For example, the audio provider can input user data into a generating AI and have the generating AI provide the optimal meditation audio.

[0039] The consultation promotion unit can detect abnormalities during sleep, such as signs of sleep apnea syndrome, and encourage users to consult with medical professionals. For example, if the consultation promotion unit detects an abnormality in the user's breathing pattern, it can advise the user to consult with a medical professional. For example, if the consultation promotion unit detects an abnormality in the user's heart rate, it can advise the user to consult with a medical professional. For example, if the consultation promotion unit detects an abnormality in the user's body temperature, it can advise the user to consult with a medical professional. For example, if the consultation promotion unit detects an abnormality in the user's breathing pattern, it can advise the user to consult with a medical professional, allowing them to receive appropriate medical care early. If the consultation promotion unit detects an abnormality in the user's heart rate, it can advise the user to consult with a medical professional, allowing them to receive appropriate medical care early. If the consultation promotion unit detects an abnormality in the user's body temperature, it can advise the user to consult with a medical professional, allowing them to receive appropriate medical care early. In this way, the consultation promotion unit can enable users to receive appropriate medical care early. Some or all of the above-described processes in the consultation promotion department may be performed using AI, for example, or without AI. For example, the consultation promotion department can input user data into a generating AI and have the generating AI perform abnormality detection and facilitate consultation with medical professionals.

[0040] The data collection unit can analyze the user's past sleep data and select the optimal data collection method. For example, the data collection unit can identify the most stable sleep pattern from the user's past sleep data and collect data during that time period. For example, the data collection unit can confirm from the user's past data that a particular device provides the most accurate data and prioritize the use of that device. For example, the data collection unit can confirm from the user's past data that data collection is most effective under specific environmental conditions and reproduce those conditions. For example, the data collection unit can identify the most stable sleep pattern from the user's past sleep data and collect data during that time period. The data collection unit can confirm from the user's past data that a particular device provides the most accurate data and prioritize the use of that device. The data collection unit can confirm from the user's past data that data collection is most effective under specific environmental conditions and reproduce those conditions. This allows the data collection unit to select the optimal data collection method based on past data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past sleep data into a generating AI, which can then select the optimal data collection method.

[0041] The data collection unit can filter data based on the user's current health status and lifestyle habits during data collection. For example, if the user has a cold, the data collection unit can temporarily suspend normal data collection and wait until their health condition improves. For example, if the user starts a new exercise habit, the data collection unit can filter the data considering its effects. For example, if the user is taking a specific medication, the data collection unit can filter the data considering its effects. For example, if the user has a cold, the data collection unit can temporarily suspend normal data collection and wait until their health condition improves. If the user starts a new exercise habit, the data collection unit filters the data considering its effects. If the user is taking a specific medication, the data collection unit filters the data considering its effects. This enables the data collection unit to collect data according to the user's health status and lifestyle habits. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's health status and lifestyle habits into a generating AI and have the generating AI perform data filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data, taking into account the user's geographical location information during data collection. For example, if the user is at high altitude, the data collection unit can prioritize the collection of oxygen concentration and atmospheric pressure data. For example, if the user is in an urban area, the data collection unit can prioritize the collection of noise level and light data. For example, if the user is at the beach, the data collection unit can prioritize the collection of humidity and wind speed data. For example, if the user is at high altitude, the data collection unit prioritizes the collection of oxygen concentration and atmospheric pressure data to evaluate the effects of high altitude. If the user is in an urban area, the data collection unit prioritizes the collection of noise level and light data to evaluate the effects of the urban environment. If the user is at the beach, the data collection unit prioritizes the collection of humidity and wind speed data to evaluate the effects of the beach. This enables the data collection unit to collect data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into the generating AI, allowing the AI ​​to prioritize the collection of highly relevant data.

[0043] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user posts on social media indicating stress, the data collection unit can prioritize collecting heart rate and breathing pattern data. For example, if a user posts on social media indicating relaxation, the data collection unit can prioritize collecting frequency of tossing and turning and body temperature data. For example, if a user posts on social media indicating fatigue, the data collection unit can prioritize collecting sleep cycle data. For example, if a user posts on social media indicating stress, the data collection unit prioritizes collecting heart rate and breathing pattern data to mitigate the effects of stress. If a user posts on social media indicating relaxation, the data collection unit prioritizes collecting frequency of tossing and turning and body temperature data to enhance the effects of relaxation. If a user posts on social media indicating fatigue, the data collection unit prioritizes collecting sleep cycle data to promote recovery from fatigue. This enables the data collection unit to collect data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's social media activity into the generating AI, and have the generating AI collect related data.

[0044] The analysis unit can optimize the analysis algorithm by referring to the user's past data during analysis. For example, the analysis unit can select the optimal analysis algorithm based on the user's past sleep data. For example, the analysis unit can extract specific patterns from the user's past data and adjust the analysis algorithm. For example, the analysis unit can optimize the analysis algorithm by excluding outliers based on the user's past data. For example, the analysis unit selects the optimal analysis algorithm based on the user's past sleep data and evaluates sleep quality. The analysis unit extracts specific patterns from the user's past data and adjusts the analysis algorithm. The analysis unit optimizes the analysis algorithm by excluding outliers based on the user's past data. This allows the analysis unit to select the optimal analysis algorithm based on past data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0045] The analysis unit can weight data based on the user's lifestyle and health status during analysis. For example, if a user has healthy lifestyle habits, the analysis unit can give more weight to that data during analysis. For example, if a user has irregular lifestyle habits, the analysis unit can weight the data considering the impact of those habits. For example, if a user has a specific health condition (e.g., a chronic disease), the analysis unit can weight the data considering the impact of those conditions during analysis. For example, if a user has healthy lifestyle habits, the analysis unit will give more weight to that data during analysis and evaluate their health status. If a user has irregular lifestyle habits, the analysis unit will weight the data considering the impact of those habits and evaluate the impact of those habits. If a user has a specific health condition (e.g., a chronic disease), the analysis unit will weight the data considering the impact of those conditions and evaluate their health status. This enables the analysis unit to perform data analysis tailored to the user's lifestyle and health status. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis department can input data on users' lifestyles and health status into a generating AI and have the AI ​​perform data weighting.

[0046] The analysis unit can evaluate the relevance of data while considering the user's geographical location information during analysis. For example, if the user is at high altitude, the analysis unit can prioritize oxygen concentration and atmospheric pressure data in its analysis. For example, if the user is in an urban area, the analysis unit can prioritize noise level and light data in its analysis. For example, if the user is at the coast, the analysis unit can prioritize humidity and wind speed data in its analysis. For example, if the user is at high altitude, the analysis unit prioritizes oxygen concentration and atmospheric pressure data to evaluate the effects of high altitude. If the user is in an urban area, the analysis unit prioritizes noise level and light data to evaluate the effects of the urban environment. If the user is at the coast, the analysis unit prioritizes humidity and wind speed data to evaluate the effects of the coast. This enables the analysis unit to perform data analysis based on the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into a generating AI and have the generating AI perform the evaluation of data relevance.

[0047] The analysis unit can, during analysis, refer to users' social media activity to reveal correlations in the data. For example, if a user posts on social media expressing stress, the analysis unit can analyze the data while considering the impact of that. For example, if a user posts on social media expressing relaxation, the analysis unit can analyze the data while considering the impact of that. For example, if a user posts on social media expressing stress, the analysis unit can analyze the data while considering the impact of that and evaluate the impact of stress. If a user posts on social media expressing relaxation, the analysis unit can analyze the data while considering the impact of that and evaluate the effect of relaxation. If a user posts on social media expressing fatigue, the analysis unit can analyze the data while considering the impact of that and promote recovery from fatigue. This enables the analysis unit to perform data analysis based on users' social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on users' social media activity into a generating AI and have the generating AI clarify the correlations of the data.

[0048] The suggestion unit can select the most suitable suggestion by referring to the user's past behavioral data when making a suggestion. For example, the suggestion unit can suggest the optimal bedtime and wake-up time based on the user's past behavioral data. For example, the suggestion unit can suggest the optimal method of adjusting the room temperature based on the user's past behavioral data. For example, the suggestion unit can suggest the optimal relaxation method based on the user's past behavioral data. For example, the suggestion unit can suggest the optimal bedtime and wake-up time based on the user's past behavioral data to improve sleep quality. The suggestion unit can suggest the optimal method of adjusting the room temperature based on the user's past behavioral data to improve sleep quality. The suggestion unit can suggest the optimal relaxation method based on the user's past behavioral data to improve sleep quality. In this way, the suggestion unit can provide the most suitable suggestion based on past behavioral data. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past behavioral data into a generating AI and have the generating AI select the most suitable suggestion.

[0049] The suggestion unit can adjust the level of detail of its suggestions based on the user's lifestyle and health condition. For example, if the user has a healthy lifestyle, the suggestion unit can provide detailed suggestions. If the user has an irregular lifestyle, the suggestion unit can provide concise suggestions. If the user has a specific health condition (e.g., a chronic disease), the suggestion unit can adjust the level of detail of its suggestions to take its effects into consideration. For example, if the user has a healthy lifestyle, the suggestion unit can provide detailed suggestions to maintain that health. If the user has an irregular lifestyle, the suggestion unit can provide concise suggestions to encourage lifestyle improvement. If the user has a specific health condition (e.g., a chronic disease), the suggestion unit can adjust the level of detail of its suggestions to take its effects into consideration to support health management. This allows the suggestion unit to provide optimal suggestions tailored to the user's lifestyle and health condition. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data on the user's lifestyle and health condition into a generating AI and have the generating AI adjust the level of detail of its suggestions.

[0050] The suggestion unit can provide optimal suggestions by considering the user's geographical location information when making suggestions. For example, if the user is at high altitude, the suggestion unit can provide suggestions regarding oxygen concentration and atmospheric pressure. For example, if the user is in an urban area, the suggestion unit can provide suggestions regarding noise levels and light. For example, if the user is at the coast, the suggestion unit can provide suggestions regarding humidity and wind speed. For example, if the user is at high altitude, the suggestion unit can provide suggestions regarding oxygen concentration and atmospheric pressure to mitigate the effects of high altitude. If the user is in an urban area, the suggestion unit can provide suggestions regarding noise levels and light to mitigate the effects of the urban environment. If the user is at the coast, the suggestion unit can provide suggestions regarding humidity and wind speed to mitigate the effects of the coast. In this way, the suggestion unit can provide optimal suggestions based on the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing optimal suggestions.

[0051] The suggestion unit can analyze the user's social media activity and make relevant suggestions when making suggestions. For example, if the user posts on social media indicating they are feeling stressed, the suggestion unit can offer suggestions for stress management. For example, if the user posts on social media indicating they are relaxing, the suggestion unit can offer suggestions for relaxation methods. For example, if the user posts on social media indicating they are tired, the suggestion unit can offer suggestions for rest. For example, if the suggestion unit posts on social media indicating they are feeling stressed, it can offer suggestions for stress management to mitigate the effects of stress. If the user posts on social media indicating they are relaxing, it can offer suggestions for relaxation methods to enhance the effects of relaxation. If the user posts on social media indicating they are tired, it can offer suggestions for rest to promote recovery from fatigue. In this way, the suggestion unit can provide optimal suggestions based on the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data on the user's social media activity into a generating AI and have the generating AI perform the task of providing relevant suggestions.

[0052] The audio provider can select the optimal audio by referring to the user's past relaxation methods when providing audio. The audio provider can, for example, prioritize providing audio that the user has previously found relaxing. The audio provider can, for example, analyze the user's past relaxation methods and select the optimal audio. The audio provider can, for example, evaluate the effects of audio used by the user in the past and provide the optimal audio. For example, the audio provider can prioritize providing audio that the user has previously found relaxing to enhance the relaxation effect. The audio provider analyzes the user's past relaxation methods, selects the optimal audio, and enhances the relaxation effect. The audio provider evaluates the effects of audio used by the user in the past, provides the optimal audio, and enhances the relaxation effect. In this way, the audio provider can provide the optimal audio based on the user's past relaxation methods. Some or all of the above processing in the audio provider may be performed using AI, for example, or without AI. For example, the audio provider can input data on the user's past relaxation methods into a generating AI and have the generating AI select the optimal audio.

[0053] The voice provider can customize the type of audio based on the user's lifestyle and health condition when providing audio. For example, if the user has a healthy lifestyle, the voice provider can provide audio with a high relaxation effect. For example, if the user has an irregular lifestyle, the voice provider can provide audio that promotes restful sleep. For example, if the user has a specific health condition (e.g., a chronic disease), the voice provider can customize the audio considering its effects. For example, if the user has a healthy lifestyle, the voice provider can provide audio with a high relaxation effect to enhance relaxation. If the user has an irregular lifestyle, the voice provider can provide audio that promotes restful sleep to improve sleep quality. If the user has a specific health condition (e.g., a chronic disease), the voice provider can customize the audio considering its effects to support health management. This allows the voice provider to provide optimal audio tailored to the user's lifestyle and health condition. Some or all of the above processing in the voice provider may be performed using AI, for example, or without AI. For example, the voice provisioning unit can input data on the user's lifestyle and health status into the generating AI, and have the generating AI customize the type of voice it provides.

[0054] The voice provider unit can provide optimal audio by considering the user's geographical location information when providing audio. For example, if the user is at high altitude, the voice provider unit can provide audio regarding oxygen concentration and atmospheric pressure. For example, if the user is in an urban area, the voice provider unit can provide audio regarding noise levels and light. For example, if the user is at the beach, the voice provider unit can provide audio regarding humidity and wind speed. For example, if the user is at high altitude, the voice provider unit provides audio regarding oxygen concentration and atmospheric pressure to mitigate the effects of high altitude. If the user is in an urban area, the voice provider unit provides audio regarding noise levels and light to mitigate the effects of the urban environment. If the user is at the beach, the voice provider unit provides audio regarding humidity and wind speed to mitigate the effects of the beach. In this way, the voice provider unit can provide optimal audio based on the user's geographical location information. Some or all of the above processing in the voice provider unit may be performed using AI, for example, or without AI. For example, the voice provider unit can input the user's geographical location information into a generating AI and have the generating AI perform the provision of optimal audio.

[0055] The audio provider can analyze the user's social media activity and provide relevant audio when providing audio. For example, if the user posts on social media expressing stress, the audio provider can provide audio with a high relaxation effect. For example, if the user posts on social media expressing relaxation, the audio provider can provide audio for meditation or deep breathing. For example, if the user posts on social media expressing fatigue, the audio provider can provide audio to promote restful sleep. For example, if the user posts on social media expressing stress, the audio provider can provide audio with a high relaxation effect to reduce the impact of stress. If the user posts on social media expressing relaxation, the audio provider can provide audio for meditation or deep breathing to enhance the relaxation effect. If the user posts on social media expressing fatigue, the audio provider can provide audio to promote restful sleep and accelerate recovery from fatigue. In this way, the audio provider can provide optimal audio based on the user's social media activity. Some or all of the above processing in the audio provider may be performed using AI, for example, or without AI. For example, the audio provider can input data on the user's social media activity into a generating AI and have the generating AI perform the provision of relevant audio.

[0056] The consultation promotion unit can select the most appropriate consultation content by referring to the user's past health data when promoting consultations. For example, the consultation promotion unit can select the most appropriate consultation content based on the user's past health data. For example, the consultation promotion unit can prioritize consultations related to specific health problems based on the user's past health data. For example, the consultation promotion unit can select the most appropriate consultation content by excluding outliers based on the user's past health data. For example, the consultation promotion unit selects the most appropriate consultation content based on the user's past health data to support the management of their health status. The consultation promotion unit prioritizes consultations related to specific health problems based on the user's past health data to support the management of their health status. The consultation promotion unit selects the most appropriate consultation content by excluding outliers based on the user's past health data to support the management of their health status. As a result, the consultation promotion unit can provide the most appropriate consultation content based on past health data. Some or all of the above processing in the consultation promotion unit may be performed using AI, for example, or without AI. For example, the consultation promotion unit can input the user's past health data into a generating AI and have the generating AI select the most appropriate consultation content.

[0057] The consultation facilitator can adjust the level of detail of the consultation based on the user's lifestyle and health condition when facilitating a consultation. For example, if the user has a healthy lifestyle, the consultation facilitator can provide detailed consultation. For example, if the user has an irregular lifestyle, the consultation facilitator can provide concise consultation. For example, if the user has a specific health condition (e.g., a chronic disease), the consultation facilitator can adjust the level of detail of the consultation considering its impact. For example, if the user has a healthy lifestyle, the consultation facilitator can provide detailed consultation and support the management of their health condition. If the user has an irregular lifestyle, the consultation facilitator can provide concise consultation and encourage lifestyle improvements. If the user has a specific health condition (e.g., a chronic disease), the consultation facilitator can adjust the level of detail of the consultation considering its impact and support the management of their health condition. This allows the consultation facilitator to provide optimal consultation tailored to the user's lifestyle and health condition. Some or all of the above processing in the consultation facilitator may be performed using AI, for example, or without AI. For example, the consultation promotion department can input data on users' lifestyles and health status into a generating AI and have the AI ​​adjust the level of detail in the consultation.

[0058] The consultation facilitator can provide optimal consultation content by considering the user's geographical location information when facilitating consultations. For example, if the user is at high altitude, the consultation facilitator can provide consultation regarding oxygen concentration and atmospheric pressure. For example, if the user is in an urban area, the consultation facilitator can provide consultation regarding noise levels and light. For example, if the user is at the coast, the consultation facilitator can provide consultation regarding humidity and wind speed. For example, if the user is at high altitude, the consultation facilitator can provide consultation regarding oxygen concentration and atmospheric pressure to mitigate the effects of high altitude. If the user is in an urban area, the consultation facilitator can provide consultation regarding noise levels and light to mitigate the effects of the urban environment. If the user is at the coast, the consultation facilitator can provide consultation regarding humidity and wind speed to mitigate the effects of the coast. In this way, the consultation facilitator can provide optimal consultation content based on the user's geographical location information. Some or all of the above processing in the consultation facilitator may be performed using AI, for example, or without AI. For example, the consultation facilitator can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing optimal consultation content.

[0059] The consultation promotion department can analyze a user's social media activity and provide relevant consultation content when promoting consultations. For example, if a user posts on social media indicating they are feeling stressed, the consultation promotion department can provide stress management consultation. For example, if a user posts on social media indicating they are relaxed, the consultation promotion department can provide relaxation methods consultation. For example, if a user posts on social media indicating they are tired, the consultation promotion department can provide rest consultation. For example, if a user posts on social media indicating they are feeling stressed, the consultation promotion department can provide stress management consultation to mitigate the effects of stress. If a user posts on social media indicating they are relaxed, the consultation promotion department can provide relaxation methods consultation to enhance the effects of relaxation. If a user posts on social media indicating they are tired, the consultation promotion department can provide rest consultation to promote recovery from fatigue. In this way, the consultation promotion department can provide optimal consultation content based on the user's social media activity. Some or all of the above processing in the consultation promotion department may be performed using AI, for example, or without AI. For example, the consultation promotion department can input data on the user's social media activity into a generating AI and have the generating AI provide relevant consultation content.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The data collection unit can analyze the user's past sleep data and select the optimal data collection method. For example, it can identify the most stable sleep pattern from the user's past sleep data and collect data during that time period. It can also confirm that a specific device provides the most accurate data from the user's past data and prioritize the use of that device. Furthermore, it can confirm that data collection is most effective under specific environmental conditions from the user's past data and reproduce those conditions. In this way, the data collection unit can select the optimal data collection method based on past data.

[0062] The analysis department can analyze data while taking into account the user's daytime activities, diet, stress levels, and other factors. For example, it can analyze the quality of a user's sleep based on their daytime activities. It can also analyze the quality of a user's sleep based on their diet. Furthermore, it can analyze the quality of a user's sleep based on their stress levels. This allows the analysis department to perform data analysis that takes into account the user's overall lifestyle.

[0063] The suggestion department can propose things like setting optimal bedtime and wake-up times, adjusting room temperature at night, and providing customized meditation audio for relaxation. For example, it can advise users on setting optimal bedtime and wake-up times. It can also suggest users adjust room temperature at night. Furthermore, it can provide users with customized meditation audio for relaxation. In this way, the suggestion department can propose specific sleep improvement plans to users.

[0064] The audio provider can provide customized meditation audio for relaxation. For example, it can provide users with highly relaxing meditation audio. It can also provide users with highly relaxing music. Furthermore, it can provide users with highly relaxing nature sounds. In this way, the audio provider can provide users with highly relaxing audio.

[0065] The consultation promotion department can detect abnormalities during sleep, such as signs of sleep apnea, and encourage users to consult with medical professionals. For example, if an abnormality is detected in a user's breathing pattern, it can advise the user to consult with a medical professional. Similarly, if an abnormality is detected in a user's heart rate, it can advise the user to consult with a medical professional. Furthermore, if an abnormality is detected in a user's body temperature, it can advise the user to consult with a medical professional. This allows the consultation promotion department to ensure that users receive appropriate medical care early on.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit collects data such as the user's heart rate, body temperature, breathing pattern, and frequency of turning over in bed. The data collection unit collects data in conjunction with devices such as a smartwatch or bed sensor. The data collection unit monitors the user's heart rate in real time using a smartwatch and collects data. The data collection unit records the frequency of the user's turning over in bed using a bed sensor and collects data. The data collection unit periodically measures the user's body temperature using a body temperature sensor and collects data. Step 2: The analysis unit analyzes the data collected by the data collection unit using deep learning. The analysis unit analyzes the user's sleep cycle and sleep quality based on data such as the user's heart rate, body temperature, breathing patterns, and frequency of turning over in sleep. The analysis unit uses a deep learning algorithm to analyze the user's heart rate fluctuation patterns and evaluate sleep quality. The analysis unit analyzes the user's body temperature fluctuation patterns and evaluates sleep quality. The analysis unit analyzes the user's breathing patterns and evaluates sleep quality. Step 3: The Proposal Department proposes a personalized sleep improvement plan based on the analysis results obtained by the Analysis Department. The Proposal Department advises the user to set optimal bedtime and wake-up times to improve sleep quality. The Proposal Department suggests that the user adjust the room temperature at night to improve sleep quality. The Proposal Department provides the user with customized meditation audio for relaxation to improve sleep quality. Step 4: The audio provider provides customized meditation audio for relaxation based on the plan proposed by the suggestion provider. The audio provider provides users with highly relaxing meditation audio to improve sleep quality. The audio provider provides users with highly relaxing music to improve sleep quality. The audio provider provides users with highly relaxing nature sounds to improve sleep quality. Step 5: The Consultation Facilitation Department encourages users to consult with a medical professional based on sleep abnormalities detected by the Analysis Department. If abnormalities are found in the user's breathing pattern, the Consultation Facilitation Department advises the user to consult with a medical professional so that they can receive appropriate medical care early. If abnormalities are found in the user's heart rate, the Consultation Facilitation Department advises the user to consult with a medical professional so that they can receive appropriate medical care early. If abnormalities are found in the user's body temperature, the Consultation Facilitation Department advises the user to consult with a medical professional so that they can receive appropriate medical care early.

[0068] (Example of form 2) The sleep management system according to an embodiment of the present invention is a comprehensive sleep management system that thoroughly analyzes and optimizes the sleep of each individual user. This sleep management system works in conjunction with devices such as smartwatches and bed sensors to collect detailed data such as heart rate, body temperature, breathing patterns, and frequency of tossing and turning during sleep. AI analyzes this data using deep learning to reveal the correlation between the user's sleep cycle, sleep quality, and environmental factors (room temperature, humidity, noise level, etc.). Furthermore, it also takes into account the user's daytime activities, diet, and stress levels to identify factors that affect sleep quality. Based on these analysis results, the AI ​​proposes an individualized sleep improvement plan. For example, this may include setting optimal bedtime and wake-up times, adjusting the room temperature at night, and providing customized meditation audio for relaxation. The AI ​​also detects abnormalities during sleep (such as signs of sleep apnea) and prompts the user to consult a medical professional if necessary. The sleep management system goes beyond being a mere sleep tracker and functions as a 24-hour personal sleep concierge that supports the improvement of the user's overall health and well-being. For example, a sleep management system collects detailed data such as the user's heart rate, body temperature, breathing patterns, and frequency of tossing and turning using devices like smartwatches and bed sensors. This data is then analyzed by AI using deep learning. For instance, it might detect that the user's heart rate fluctuates in a consistent rhythm or that they toss and turn frequently. This reveals the user's sleep cycle and sleep quality. Next, based on the collected data, the AI ​​also considers the user's daytime activities, diet, and stress levels to identify factors that affect sleep quality. For example, it might find that if a user experiences a lot of stress during the day, their sleep quality will suffer. This allows the AI ​​to provide stress management advice to the user. Furthermore, based on these analysis results, the AI ​​proposes a personalized sleep improvement plan. For example, it might advise the user to set optimal bedtimes and wake times, or suggest adjusting the room temperature at night. It might also provide customized meditation audio for relaxation. This allows the user to get better sleep.Furthermore, the AI ​​detects abnormalities during sleep (such as signs of sleep apnea) and prompts users to consult a medical professional if necessary. For example, if an abnormality is detected in the user's breathing pattern, the AI ​​will advise the user to consult a medical professional. This allows the user to receive appropriate medical care early on. The sleep management system goes beyond being a simple sleep tracker; it functions as a 24 / 7 personal sleep concierge that supports the improvement of the user's overall health and well-being. By following the AI's advice, users can achieve better sleep and improve the quality of their daily lives. For example, by adjusting their bedtime and wake-up time according to the AI's advice, users can expect improved daytime performance and reduced stress. In this way, the sleep management system can thoroughly analyze and optimize the user's sleep.

[0069] The sleep management system according to this embodiment comprises a data collection unit, an analysis unit, a suggestion unit, a voice provision unit, and a consultation promotion unit. The data collection unit collects data such as the user's heart rate, body temperature, breathing pattern, and frequency of turning over in sleep. The data collection unit collects data in cooperation with devices such as a smartwatch or a bed sensor. For example, the data collection unit can measure the user's heart rate using a smartwatch. The data collection unit can also measure the frequency of the user's turning over in sleep using a bed sensor. Furthermore, the data collection unit can measure the user's body temperature using a body temperature sensor. For example, the data collection unit monitors the user's heart rate in real time using a smartwatch and collects the data. The data collection unit records the frequency of the user's turning over in sleep using a bed sensor and collects the data. The data collection unit periodically measures the user's body temperature using a body temperature sensor and collects the data. The analysis unit analyzes the data collected by the data collection unit using deep learning. The analysis unit analyzes the user's sleep cycle and sleep quality based on data such as the user's heart rate, body temperature, breathing patterns, and frequency of tossing and turning. For example, the analysis unit can analyze the user's heart rate fluctuation patterns using deep learning algorithms. It can also analyze the user's body temperature fluctuation patterns. Furthermore, it can analyze the user's breathing patterns. For instance, the analysis unit uses deep learning algorithms to analyze the user's heart rate fluctuation patterns and evaluate sleep quality. The analysis unit analyzes the user's body temperature fluctuation patterns and evaluates sleep quality. The analysis unit analyzes the user's breathing patterns and evaluates sleep quality. The suggestion unit proposes a personalized sleep improvement plan based on the analysis results obtained by the analysis unit. For example, the suggestion unit can advise the user to set optimal bedtime and wake-up times. For example, the suggestion unit can suggest adjusting the room temperature at night. The suggestion unit can also provide the user with customized meditation audio for relaxation. For example, the suggestion department advises users to set optimal bedtimes and wake-up times to improve sleep quality. The suggestion department also suggests users to adjust the room temperature at night to improve sleep quality.The suggestion unit provides users with customized meditation audio for relaxation and improves sleep quality. The audio provision unit provides customized meditation audio for relaxation based on the plan suggested by the suggestion unit. The audio provision unit can, for example, provide users with highly relaxing meditation audio. The audio provision unit can, for example, provide users with highly relaxing music. The audio provision unit can also provide users with highly relaxing nature sounds. For example, the audio provision unit provides users with highly relaxing meditation audio and improves sleep quality. The audio provision unit provides users with highly relaxing music and improves sleep quality. The audio provision unit provides users with highly relaxing nature sounds and improves sleep quality. The consultation promotion unit prompts consultation with a medical professional based on sleep abnormalities detected by the analysis unit. For example, if an abnormality is found in the user's breathing pattern, the consultation promotion unit can advise the user to consult a medical professional. For example, if an abnormality is found in the user's heart rate, the consultation promotion unit can advise the user to consult a medical professional. Furthermore, the consultation promotion unit can advise the user to consult a medical professional if an abnormality is detected in the user's body temperature. For example, if an abnormality is detected in the user's breathing pattern, the consultation promotion unit can advise the user to consult a medical professional, enabling them to receive appropriate medical care early. If an abnormality is detected in the user's heart rate, the consultation promotion unit can advise the user to consult a medical professional, enabling them to receive appropriate medical care early. If an abnormality is detected in the user's body temperature, the consultation promotion unit can advise the user to consult a medical professional, enabling them to receive appropriate medical care early. As a result, the sleep management system according to the embodiment can thoroughly analyze and optimize the user's sleep.

[0070] The data collection unit collects data such as the user's heart rate, body temperature, breathing patterns, and frequency of tossing and turning during sleep. The unit works in conjunction with devices such as smartwatches and bed sensors to collect data. Specifically, the smartwatch monitors the user's heart rate in real time and collects data. Heart rate variability is an important indicator of the user's stress level and relaxation state, and is essential for evaluating sleep quality. The bed sensor records and collects data on the user's frequency of tossing and turning. Frequency of tossing and turning is an indicator of the user's sleep depth and comfort, and is important for evaluating sleep quality. Furthermore, a body temperature sensor periodically measures the user's body temperature and collects data. Body temperature variability is an indicator of the user's sleep cycle and internal clock state, and is important for evaluating sleep quality. The data collection unit centrally manages this data and transmits it to a central database in real time. This allows the data collection unit to gain a detailed understanding of the user's sleep state, making it accessible to the analysis and recommendation units. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, if a user is in a particular health condition or environmental state, the data collection unit can increase the frequency of data collection, providing more detailed data. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0071] The analysis department analyzes data collected by the data collection department using deep learning. Specifically, it analyzes the user's sleep cycle and sleep quality based on data such as the user's heart rate, body temperature, breathing patterns, and frequency of turning over in sleep. Using deep learning algorithms, it analyzes the user's heart rate fluctuation patterns to evaluate sleep quality. Heart rate fluctuations are an important indicator of the user's stress level and relaxation state, and are essential for evaluating sleep quality. It also analyzes the user's body temperature fluctuation patterns to evaluate sleep quality. Body temperature fluctuations are an indicator of the user's sleep cycle and the state of their internal clock, and are important for evaluating sleep quality. Furthermore, it analyzes the user's breathing patterns to evaluate sleep quality. Fluctuations in breathing patterns are an indicator of the user's relaxation state and health state, and are important for evaluating sleep quality. The analysis department comprehensively analyzes this data to evaluate the user's sleep quality. In addition, the analysis department can also utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past sleep data, it can predict fluctuations in risk at specific time periods or environmental conditions and formulate future countermeasures. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0072] The Proposal Department proposes personalized sleep improvement plans based on the analysis results obtained by the Analysis Department. Specifically, it advises users on setting optimal bedtimes and wake-up times. For example, it proposes optimal bedtimes and wake-up times considering the user's sleep cycle and internal clock to improve sleep quality. The Proposal Department can also suggest adjusting the room temperature at night. For example, it proposes optimal room temperature based on the user's body temperature fluctuation patterns to improve sleep quality. Furthermore, the Proposal Department can provide users with customized meditation audio for relaxation. For example, it proposes optimal meditation audio considering the user's stress level and relaxation state to improve sleep quality. Through these suggestions, the Proposal Department provides concrete action plans to improve the user's sleep quality. In addition, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can revise its suggestions based on the results of users acting on the suggestions and provide more effective improvement plans. The Proposal Department can also provide personalized suggestions according to the user's lifestyle and health condition. This allows the Proposal Department to provide users with optimal sleep improvement plans and improve their sleep quality.

[0073] The audio provider unit provides customized meditation audio for relaxation based on the plan proposed by the proposal unit. Specifically, it can provide users with meditation audio that has a high relaxation effect. For example, it can select and provide the most suitable meditation audio considering the user's stress level and relaxation state. The audio provider unit can also provide users with music that has a high relaxation effect. For example, it can select and provide the most suitable music considering the user's preferences and relaxation state. Furthermore, the audio provider unit can provide users with nature sounds that have a high relaxation effect. For example, it can use natural sounds such as the sound of waves or birdsong to enhance relaxation. Through this audio content, the audio provider unit promotes the user's relaxation state and improves the quality of sleep. The audio provider unit can also collect user feedback and continuously improve the accuracy and effectiveness of the audio content. For example, it can revise the audio content based on feedback on whether the user was able to relax and provide more effective audio. In addition, the audio provider unit can provide individualized audio content according to the user's lifestyle and health condition. This allows the audio provider unit to provide users with optimal relaxation audio and improve the quality of sleep.

[0074] The Consultation Facilitation Department encourages users to consult with medical professionals based on sleep abnormalities detected by the Analysis Department. Specifically, if abnormalities are found in a user's breathing pattern, the department can advise the user to consult with a medical professional. For example, if a user's breathing pattern is different from normal, consulting a medical professional early can lead to receiving appropriate medical care. The Consultation Facilitation Department can also advise users to consult with medical professionals if abnormalities are found in their heart rate. For example, if a user's heart rate is different from normal, consulting a medical professional early can lead to receiving appropriate medical care. Furthermore, if abnormalities are found in a user's body temperature, the Consultation Facilitation Department can advise the user to consult with a medical professional. For example, if a user's body temperature is different from normal, consulting a medical professional early can lead to receiving appropriate medical care. Through this advice, the Consultation Facilitation Department supports users in receiving appropriate medical care early. In addition, the Consultation Facilitation Department can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, based on the results of users' consultations with medical professionals, the advice can be reviewed and more effective advice can be provided. Furthermore, the consultation promotion department can provide personalized advice tailored to the user's lifestyle and health condition. This allows the department to encourage users to seek optimal medical consultations and improve their health.

[0075] The data collection unit can collect data in conjunction with devices such as smartwatches and bed sensors. For example, the data collection unit can measure the user's heart rate using a smartwatch. For example, the data collection unit can measure the frequency of the user's tossing and turning using a bed sensor. For example, the data collection unit can measure the user's body temperature using a body temperature sensor. For example, the data collection unit can monitor the user's heart rate in real time using a smartwatch and collect data. The data collection unit can record the frequency of the user's tossing and turning using a bed sensor and collect data. The data collection unit can periodically measure the user's body temperature using a body temperature sensor and collect data. In this way, the data collection unit can obtain more detailed data by collecting data from a variety of devices. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from devices such as smartwatches and bed sensors into a generating AI and have the generating AI perform data analysis.

[0076] The analysis unit can analyze data while also taking into account the user's daytime activities, diet, stress level, etc. For example, the analysis unit can analyze the quality of a user's sleep based on the user's daytime activities. For example, the analysis unit can analyze the quality of a user's sleep based on the user's diet. For example, the analysis unit can analyze the quality of a user's sleep based on the user's stress level. For example, the analysis unit evaluates the quality of a user's sleep based on the user's daytime activities. The analysis unit evaluates the quality of a user's sleep based on the user's diet. The analysis unit evaluates the quality of a user's sleep based on the user's stress level. This enables the analysis unit to perform data analysis that takes into account the user's overall lifestyle. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data such as the user's daytime activities, diet, and stress level into a generating AI and have the generating AI perform the data analysis.

[0077] The suggestion unit can suggest things like setting optimal bedtime and wake-up times, adjusting the room temperature at night, and providing customized meditation audio for relaxation. For example, the suggestion unit can advise the user to set optimal bedtime and wake-up times. For example, the suggestion unit can suggest the user to adjust the room temperature at night. For example, the suggestion unit can provide the user with customized meditation audio for relaxation. For example, the suggestion unit can advise the user to set optimal bedtime and wake-up times to improve sleep quality. The suggestion unit can suggest the user to adjust the room temperature at night to improve sleep quality. The suggestion unit can provide the user with customized meditation audio for relaxation to improve sleep quality. In this way, the suggestion unit can propose a specific sleep improvement plan to the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input user data into a generating AI and have the generating AI execute a proposal for an optimal sleep improvement plan.

[0078] The audio provider can provide customized meditation audio for relaxation. For example, the audio provider can provide the user with highly relaxing meditation audio. For example, the audio provider can provide the user with highly relaxing music. For example, the audio provider can provide the user with highly relaxing nature sounds. For example, the audio provider can provide the user with highly relaxing meditation audio to improve sleep quality. The audio provider can provide the user with highly relaxing music to improve sleep quality. The audio provider can provide the user with highly relaxing nature sounds to improve sleep quality. Thus, the audio provider can provide the user with highly relaxing audio. Some or all of the above processing in the audio provider may be performed using AI, for example, or without AI. For example, the audio provider can input user data into a generating AI and have the generating AI provide the optimal meditation audio.

[0079] The consultation promotion unit can detect abnormalities during sleep, such as signs of sleep apnea syndrome, and encourage users to consult with medical professionals. For example, if the consultation promotion unit detects an abnormality in the user's breathing pattern, it can advise the user to consult with a medical professional. For example, if the consultation promotion unit detects an abnormality in the user's heart rate, it can advise the user to consult with a medical professional. For example, if the consultation promotion unit detects an abnormality in the user's body temperature, it can advise the user to consult with a medical professional. For example, if the consultation promotion unit detects an abnormality in the user's breathing pattern, it can advise the user to consult with a medical professional, allowing them to receive appropriate medical care early. If the consultation promotion unit detects an abnormality in the user's heart rate, it can advise the user to consult with a medical professional, allowing them to receive appropriate medical care early. If the consultation promotion unit detects an abnormality in the user's body temperature, it can advise the user to consult with a medical professional, allowing them to receive appropriate medical care early. In this way, the consultation promotion unit can enable users to receive appropriate medical care early. Some or all of the above-described processes in the consultation promotion department may be performed using AI, for example, or without AI. For example, the consultation promotion department can input user data into a generating AI and have the generating AI perform abnormality detection and facilitate consultation with medical professionals.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can collect data during times when the user is relaxed. For example, if the user is tired, the data collection unit can prioritize data collection during sleep and refrain from collecting data during the day. For example, if the user is relaxed, the data collection unit can collect detailed data, enabling more accurate analysis. For example, if the user is stressed, the data collection unit collects data during times when the user is relaxed to mitigate the effects of stress. If the user is tired, the data collection unit reduces the user's burden by prioritizing data collection during sleep and refraining from collecting data during the day. If the user is relaxed, the data collection unit can collect detailed data, enabling more accurate analysis. This allows the data collection unit to collect data at the optimal timing according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation and adjust the timing of data collection.

[0081] The data collection unit can analyze the user's past sleep data and select the optimal data collection method. For example, the data collection unit can identify the most stable sleep pattern from the user's past sleep data and collect data during that time period. For example, the data collection unit can confirm from the user's past data that a particular device provides the most accurate data and prioritize the use of that device. For example, the data collection unit can confirm from the user's past data that data collection is most effective under specific environmental conditions and reproduce those conditions. For example, the data collection unit can identify the most stable sleep pattern from the user's past sleep data and collect data during that time period. The data collection unit can confirm from the user's past data that a particular device provides the most accurate data and prioritize the use of that device. The data collection unit can confirm from the user's past data that data collection is most effective under specific environmental conditions and reproduce those conditions. This allows the data collection unit to select the optimal data collection method based on past data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past sleep data into a generating AI, which can then select the optimal data collection method.

[0082] The data collection unit can filter data based on the user's current health status and lifestyle habits during data collection. For example, if the user has a cold, the data collection unit can temporarily suspend normal data collection and wait until their health condition improves. For example, if the user starts a new exercise habit, the data collection unit can filter the data considering its effects. For example, if the user is taking a specific medication, the data collection unit can filter the data considering its effects. For example, if the user has a cold, the data collection unit can temporarily suspend normal data collection and wait until their health condition improves. If the user starts a new exercise habit, the data collection unit filters the data considering its effects. If the user is taking a specific medication, the data collection unit filters the data considering its effects. This enables the data collection unit to collect data according to the user's health status and lifestyle habits. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's health status and lifestyle habits into a generating AI and have the generating AI perform data filtering.

[0083] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting heart rate and breathing pattern data. For example, if the user is relaxed, the data collection unit can prioritize collecting frequency of turning over in sleep and body temperature data. For example, if the user is tired, the data collection unit can prioritize collecting sleep cycle data. For example, if the user is stressed, the data collection unit prioritizes collecting heart rate and breathing pattern data to mitigate the effects of stress. If the user is relaxed, the data collection unit prioritizes collecting frequency of turning over in sleep and body temperature data to enhance the effect of relaxation. If the user is tired, the data collection unit prioritizes collecting sleep cycle data to promote recovery from fatigue. In this way, the data collection unit can determine the priority of data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation and data prioritization.

[0084] The data collection unit can prioritize the collection of highly relevant data, taking into account the user's geographical location information during data collection. For example, if the user is at high altitude, the data collection unit can prioritize the collection of oxygen concentration and atmospheric pressure data. For example, if the user is in an urban area, the data collection unit can prioritize the collection of noise level and light data. For example, if the user is at the beach, the data collection unit can prioritize the collection of humidity and wind speed data. For example, if the user is at high altitude, the data collection unit prioritizes the collection of oxygen concentration and atmospheric pressure data to evaluate the effects of high altitude. If the user is in an urban area, the data collection unit prioritizes the collection of noise level and light data to evaluate the effects of the urban environment. If the user is at the beach, the data collection unit prioritizes the collection of humidity and wind speed data to evaluate the effects of the beach. This enables the data collection unit to collect data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into the generating AI, allowing the AI ​​to prioritize the collection of highly relevant data.

[0085] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, if a user posts on social media indicating stress, the data collection unit can prioritize collecting heart rate and breathing pattern data. For example, if a user posts on social media indicating relaxation, the data collection unit can prioritize collecting frequency of tossing and turning and body temperature data. For example, if a user posts on social media indicating fatigue, the data collection unit can prioritize collecting sleep cycle data. For example, if a user posts on social media indicating stress, the data collection unit prioritizes collecting heart rate and breathing pattern data to mitigate the effects of stress. If a user posts on social media indicating relaxation, the data collection unit prioritizes collecting frequency of tossing and turning and body temperature data to enhance the effects of relaxation. If a user posts on social media indicating fatigue, the data collection unit prioritizes collecting sleep cycle data to promote recovery from fatigue. This enables the data collection unit to collect data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's social media activity into the generating AI, and have the generating AI collect related data.

[0086] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit can focus on analyzing stress-related data. For example, if the user is relaxed, the analysis unit can analyze the overall quality of sleep. For example, if the user is tired, the analysis unit can focus on analyzing abnormalities in the sleep cycle. For example, if the user is stressed, the analysis unit will focus on analyzing stress-related data and assess the impact of stress. If the user is relaxed, the analysis unit will analyze the overall quality of sleep and assess the effect of relaxation. If the user is tired, the analysis unit will focus on analyzing abnormalities in the sleep cycle to promote recovery from fatigue. This enables the analysis unit to perform data analysis in accordance with the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation and adjust the data analysis methods.

[0087] The analysis unit can optimize the analysis algorithm by referring to the user's past data during analysis. For example, the analysis unit can select the optimal analysis algorithm based on the user's past sleep data. For example, the analysis unit can extract specific patterns from the user's past data and adjust the analysis algorithm. For example, the analysis unit can optimize the analysis algorithm by excluding outliers based on the user's past data. For example, the analysis unit selects the optimal analysis algorithm based on the user's past sleep data and evaluates sleep quality. The analysis unit extracts specific patterns from the user's past data and adjusts the analysis algorithm. The analysis unit optimizes the analysis algorithm by excluding outliers based on the user's past data. This allows the analysis unit to select the optimal analysis algorithm based on past data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0088] The analysis unit can weight data based on the user's lifestyle and health status during analysis. For example, if a user has healthy lifestyle habits, the analysis unit can give more weight to that data during analysis. For example, if a user has irregular lifestyle habits, the analysis unit can weight the data considering the impact of those habits. For example, if a user has a specific health condition (e.g., a chronic disease), the analysis unit can weight the data considering the impact of those conditions during analysis. For example, if a user has healthy lifestyle habits, the analysis unit will give more weight to that data during analysis and evaluate their health status. If a user has irregular lifestyle habits, the analysis unit will weight the data considering the impact of those habits and evaluate the impact of those habits. If a user has a specific health condition (e.g., a chronic disease), the analysis unit will weight the data considering the impact of those conditions and evaluate their health status. This enables the analysis unit to perform data analysis tailored to the user's lifestyle and health status. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis department can input data on users' lifestyles and health status into a generating AI and have the AI ​​perform data weighting.

[0089] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a concise display method. For example, if the user is stressed, the analysis unit provides a simple and highly visible display method to mitigate the effects of stress. If the user is relaxed, the analysis unit provides a display method that includes detailed information to enhance the effect of relaxation. If the user is in a hurry, the analysis unit provides a concise display method to provide information quickly. In this way, the analysis unit can provide the optimal display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI, which can then perform emotion estimation and adjust the display method.

[0090] The analysis unit can evaluate the relevance of data while considering the user's geographical location information during analysis. For example, if the user is at high altitude, the analysis unit can prioritize oxygen concentration and atmospheric pressure data in its analysis. For example, if the user is in an urban area, the analysis unit can prioritize noise level and light data in its analysis. For example, if the user is at the coast, the analysis unit can prioritize humidity and wind speed data in its analysis. For example, if the user is at high altitude, the analysis unit prioritizes oxygen concentration and atmospheric pressure data to evaluate the effects of high altitude. If the user is in an urban area, the analysis unit prioritizes noise level and light data to evaluate the effects of the urban environment. If the user is at the coast, the analysis unit prioritizes humidity and wind speed data to evaluate the effects of the coast. This enables the analysis unit to perform data analysis based on the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into a generating AI and have the generating AI perform the evaluation of data relevance.

[0091] The analysis unit can, during analysis, refer to users' social media activity to reveal correlations in the data. For example, if a user posts on social media expressing stress, the analysis unit can analyze the data while considering the impact of that. For example, if a user posts on social media expressing relaxation, the analysis unit can analyze the data while considering the impact of that. For example, if a user posts on social media expressing stress, the analysis unit can analyze the data while considering the impact of that and evaluate the impact of stress. If a user posts on social media expressing relaxation, the analysis unit can analyze the data while considering the impact of that and evaluate the effect of relaxation. If a user posts on social media expressing fatigue, the analysis unit can analyze the data while considering the impact of that and promote recovery from fatigue. This enables the analysis unit to perform data analysis based on users' social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on users' social media activity into a generating AI and have the generating AI clarify the correlations of the data.

[0092] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on the estimated emotions. For example, if the user is stressed, the suggestion unit can provide a simple and highly visible suggestion method. For example, if the user is relaxed, the suggestion unit can provide a suggestion method that includes detailed information. For example, if the user is in a hurry, the suggestion unit can provide a concise suggestion method. For example, if the user is stressed, the suggestion unit can provide a simple and highly visible suggestion method to mitigate the effects of stress. If the user is relaxed, the suggestion unit can provide a suggestion method that includes detailed information to enhance the relaxation effect. If the user is in a hurry, the suggestion unit can provide a concise suggestion method to deliver information quickly. In this way, the suggestion unit can provide the optimal suggestion method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the proposal unit can input user emotion data into a generation AI and have the generation AI perform emotion estimation and adjust the proposal method.

[0093] The suggestion unit can select the most suitable suggestion by referring to the user's past behavioral data when making a suggestion. For example, the suggestion unit can suggest the optimal bedtime and wake-up time based on the user's past behavioral data. For example, the suggestion unit can suggest the optimal method of adjusting the room temperature based on the user's past behavioral data. For example, the suggestion unit can suggest the optimal relaxation method based on the user's past behavioral data. For example, the suggestion unit can suggest the optimal bedtime and wake-up time based on the user's past behavioral data to improve sleep quality. The suggestion unit can suggest the optimal method of adjusting the room temperature based on the user's past behavioral data to improve sleep quality. The suggestion unit can suggest the optimal relaxation method based on the user's past behavioral data to improve sleep quality. In this way, the suggestion unit can provide the most suitable suggestion based on past behavioral data. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past behavioral data into a generating AI and have the generating AI select the most suitable suggestion.

[0094] The suggestion unit can adjust the level of detail of its suggestions based on the user's lifestyle and health condition. For example, if the user has a healthy lifestyle, the suggestion unit can provide detailed suggestions. If the user has an irregular lifestyle, the suggestion unit can provide concise suggestions. If the user has a specific health condition (e.g., a chronic disease), the suggestion unit can adjust the level of detail of its suggestions to take its effects into consideration. For example, if the user has a healthy lifestyle, the suggestion unit can provide detailed suggestions to maintain that health. If the user has an irregular lifestyle, the suggestion unit can provide concise suggestions to encourage lifestyle improvement. If the user has a specific health condition (e.g., a chronic disease), the suggestion unit can adjust the level of detail of its suggestions to take its effects into consideration to support health management. This allows the suggestion unit to provide optimal suggestions tailored to the user's lifestyle and health condition. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data on the user's lifestyle and health condition into a generating AI and have the generating AI adjust the level of detail of its suggestions.

[0095] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the user is feeling stressed, the suggestion unit can prioritize stress management suggestions. For example, if the user is relaxed, the suggestion unit can prioritize suggestions for improving sleep quality. For example, if the user is tired, the suggestion unit can prioritize suggestions for rest. For example, if the user is feeling stressed, the suggestion unit prioritizes stress management suggestions to mitigate the effects of stress. If the user is relaxed, the suggestion unit prioritizes suggestions for improving sleep quality to enhance the effects of relaxation. If the user is tired, the suggestion unit prioritizes suggestions for rest to promote recovery from fatigue. In this way, the suggestion unit can provide the optimal priority of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion department can input user emotion data into a generative AI, which can then perform emotion estimation and determine the priority of suggestions.

[0096] The suggestion unit can provide optimal suggestions by considering the user's geographical location information when making suggestions. For example, if the user is at high altitude, the suggestion unit can provide suggestions regarding oxygen concentration and atmospheric pressure. For example, if the user is in an urban area, the suggestion unit can provide suggestions regarding noise levels and light. For example, if the user is at the coast, the suggestion unit can provide suggestions regarding humidity and wind speed. For example, if the user is at high altitude, the suggestion unit can provide suggestions regarding oxygen concentration and atmospheric pressure to mitigate the effects of high altitude. If the user is in an urban area, the suggestion unit can provide suggestions regarding noise levels and light to mitigate the effects of the urban environment. If the user is at the coast, the suggestion unit can provide suggestions regarding humidity and wind speed to mitigate the effects of the coast. In this way, the suggestion unit can provide optimal suggestions based on the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing optimal suggestions.

[0097] The suggestion unit can analyze the user's social media activity and make relevant suggestions when making suggestions. For example, if the user posts on social media indicating they are feeling stressed, the suggestion unit can offer suggestions for stress management. For example, if the user posts on social media indicating they are relaxing, the suggestion unit can offer suggestions for relaxation methods. For example, if the user posts on social media indicating they are tired, the suggestion unit can offer suggestions for rest. For example, if the suggestion unit posts on social media indicating they are feeling stressed, it can offer suggestions for stress management to mitigate the effects of stress. If the user posts on social media indicating they are relaxing, it can offer suggestions for relaxation methods to enhance the effects of relaxation. If the user posts on social media indicating they are tired, it can offer suggestions for rest to promote recovery from fatigue. In this way, the suggestion unit can provide optimal suggestions based on the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data on the user's social media activity into a generating AI and have the generating AI perform the task of providing relevant suggestions.

[0098] The voice provider can estimate the user's emotions and adjust the content of the audio based on the estimated emotions. For example, if the user is stressed, the voice provider can provide highly relaxing audio. For example, if the user is relaxed, the voice provider can provide meditation or deep breathing audio. For example, if the user is tired, the voice provider can provide audio that promotes restful sleep. For example, if the user is stressed, the voice provider can provide highly relaxing audio to reduce the effects of stress. If the user is relaxed, the voice provider can provide meditation or deep breathing audio to enhance the relaxation effect. If the user is tired, the voice provider can provide audio that promotes restful sleep to facilitate recovery from fatigue. In this way, the voice provider can provide optimal audio content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the voice provider may be performed using AI, for example, or without AI. For example, the voice provision unit can input user emotion data into a generating AI, which can then perform emotion estimation and adjust the voice content.

[0099] The audio provider can select the optimal audio by referring to the user's past relaxation methods when providing audio. The audio provider can, for example, prioritize providing audio that the user has previously found relaxing. The audio provider can, for example, analyze the user's past relaxation methods and select the optimal audio. The audio provider can, for example, evaluate the effects of audio used by the user in the past and provide the optimal audio. For example, the audio provider can prioritize providing audio that the user has previously found relaxing to enhance the relaxation effect. The audio provider analyzes the user's past relaxation methods, selects the optimal audio, and enhances the relaxation effect. The audio provider evaluates the effects of audio used by the user in the past, provides the optimal audio, and enhances the relaxation effect. In this way, the audio provider can provide the optimal audio based on the user's past relaxation methods. Some or all of the above processing in the audio provider may be performed using AI, for example, or without AI. For example, the audio provider can input data on the user's past relaxation methods into a generating AI and have the generating AI select the optimal audio.

[0100] The voice provider can customize the type of audio based on the user's lifestyle and health condition when providing audio. For example, if the user has a healthy lifestyle, the voice provider can provide audio with a high relaxation effect. For example, if the user has an irregular lifestyle, the voice provider can provide audio that promotes restful sleep. For example, if the user has a specific health condition (e.g., a chronic disease), the voice provider can customize the audio considering its effects. For example, if the user has a healthy lifestyle, the voice provider can provide audio with a high relaxation effect to enhance relaxation. If the user has an irregular lifestyle, the voice provider can provide audio that promotes restful sleep to improve sleep quality. If the user has a specific health condition (e.g., a chronic disease), the voice provider can customize the audio considering its effects to support health management. This allows the voice provider to provide optimal audio tailored to the user's lifestyle and health condition. Some or all of the above processing in the voice provider may be performed using AI, for example, or without AI. For example, the voice provisioning unit can input data on the user's lifestyle and health status into the generating AI, and have the generating AI customize the type of voice it provides.

[0101] The audio provider can estimate the user's emotions and adjust the playback order of audio based on the estimated emotions. For example, if the user is stressed, the audio provider can play relaxing audio first. For example, if the user is relaxed, the audio provider can play meditation or deep breathing audio first. For example, if the user is tired, the audio provider can play sleep-inducing audio first. For example, if the audio provider is stressed, it can play relaxing audio first to reduce the effects of stress. If the user is relaxed, it can play meditation or deep breathing audio first to enhance the relaxation effect. If the user is tired, it can play sleep-inducing audio first to promote recovery from fatigue. In this way, the audio provider can provide an optimal audio playback order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the voice provisioning unit may be performed using AI, for example, or without AI. For example, the voice provisioning unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation and adjustment of the audio playback order.

[0102] The voice provider unit can provide optimal audio by considering the user's geographical location information when providing audio. For example, if the user is at high altitude, the voice provider unit can provide audio regarding oxygen concentration and atmospheric pressure. For example, if the user is in an urban area, the voice provider unit can provide audio regarding noise levels and light. For example, if the user is at the beach, the voice provider unit can provide audio regarding humidity and wind speed. For example, if the user is at high altitude, the voice provider unit provides audio regarding oxygen concentration and atmospheric pressure to mitigate the effects of high altitude. If the user is in an urban area, the voice provider unit provides audio regarding noise levels and light to mitigate the effects of the urban environment. If the user is at the beach, the voice provider unit provides audio regarding humidity and wind speed to mitigate the effects of the beach. In this way, the voice provider unit can provide optimal audio based on the user's geographical location information. Some or all of the above processing in the voice provider unit may be performed using AI, for example, or without AI. For example, the voice provider unit can input the user's geographical location information into a generating AI and have the generating AI perform the provision of optimal audio.

[0103] The audio provider can analyze the user's social media activity and provide relevant audio when providing audio. For example, if the user posts on social media expressing stress, the audio provider can provide audio with a high relaxation effect. For example, if the user posts on social media expressing relaxation, the audio provider can provide audio for meditation or deep breathing. For example, if the user posts on social media expressing fatigue, the audio provider can provide audio to promote restful sleep. For example, if the user posts on social media expressing stress, the audio provider can provide audio with a high relaxation effect to reduce the impact of stress. If the user posts on social media expressing relaxation, the audio provider can provide audio for meditation or deep breathing to enhance the relaxation effect. If the user posts on social media expressing fatigue, the audio provider can provide audio to promote restful sleep and accelerate recovery from fatigue. In this way, the audio provider can provide optimal audio based on the user's social media activity. Some or all of the above processing in the audio provider may be performed using AI, for example, or without AI. For example, the audio provider can input data on the user's social media activity into a generating AI and have the generating AI perform the provision of relevant audio.

[0104] The consultation facilitator can estimate the user's emotions and adjust the timing of consultations based on the estimated emotions. For example, if the user is feeling stressed, the consultation facilitator can encourage consultation during a time when the user is relaxed. For example, if the user is tired, the consultation facilitator can encourage consultation after rest. For example, if the user is relaxed, the consultation facilitator can encourage detailed consultations. For example, if the user is feeling stressed, the consultation facilitator can encourage consultation during a time when the user is relaxed to mitigate the effects of stress. If the user is tired, the consultation facilitator can encourage consultation after rest to promote recovery from fatigue. If the user is relaxed, the consultation facilitator can encourage detailed consultations to enhance the effects of relaxation. In this way, the consultation facilitator can encourage consultations at the optimal timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the consultation facilitator may be performed using AI, for example, or without AI. For example, the consultation promotion department can input user emotion data into a generating AI, which can then perform emotion estimation and adjust the timing of consultations.

[0105] The consultation promotion unit can select the most appropriate consultation content by referring to the user's past health data when promoting consultations. For example, the consultation promotion unit can select the most appropriate consultation content based on the user's past health data. For example, the consultation promotion unit can prioritize consultations related to specific health problems based on the user's past health data. For example, the consultation promotion unit can select the most appropriate consultation content by excluding outliers based on the user's past health data. For example, the consultation promotion unit selects the most appropriate consultation content based on the user's past health data to support the management of their health status. The consultation promotion unit prioritizes consultations related to specific health problems based on the user's past health data to support the management of their health status. The consultation promotion unit selects the most appropriate consultation content by excluding outliers based on the user's past health data to support the management of their health status. As a result, the consultation promotion unit can provide the most appropriate consultation content based on past health data. Some or all of the above processing in the consultation promotion unit may be performed using AI, for example, or without AI. For example, the consultation promotion unit can input the user's past health data into a generating AI and have the generating AI select the most appropriate consultation content.

[0106] The consultation facilitator can adjust the level of detail of the consultation based on the user's lifestyle and health condition when facilitating a consultation. For example, if the user has a healthy lifestyle, the consultation facilitator can provide detailed consultation. For example, if the user has an irregular lifestyle, the consultation facilitator can provide concise consultation. For example, if the user has a specific health condition (e.g., a chronic disease), the consultation facilitator can adjust the level of detail of the consultation considering its impact. For example, if the user has a healthy lifestyle, the consultation facilitator can provide detailed consultation and support the management of their health condition. If the user has an irregular lifestyle, the consultation facilitator can provide concise consultation and encourage lifestyle improvements. If the user has a specific health condition (e.g., a chronic disease), the consultation facilitator can adjust the level of detail of the consultation considering its impact and support the management of their health condition. This allows the consultation facilitator to provide optimal consultation tailored to the user's lifestyle and health condition. Some or all of the above processing in the consultation facilitator may be performed using AI, for example, or without AI. For example, the consultation promotion department can input data on users' lifestyles and health status into a generating AI and have the AI ​​adjust the level of detail in the consultation.

[0107] The consultation facilitator can estimate the user's emotions and determine the priority of consultations based on the estimated emotions. For example, if the user is feeling stressed, the consultation facilitator can prioritize stress management consultations. For example, if the user is relaxed, the consultation facilitator can prioritize sleep quality improvement consultations. For example, if the user is tired, the consultation facilitator can prioritize rest consultations. For example, if the user is feeling stressed, the consultation facilitator can prioritize stress management consultations to mitigate the effects of stress. If the user is relaxed, the consultation facilitator can prioritize sleep quality improvement consultations to enhance the effects of relaxation. If the user is tired, the consultation facilitator can prioritize rest consultations to promote recovery from fatigue. In this way, the consultation facilitator can provide the optimal priority of consultations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the consultation promotion department may be performed using AI, for example, or without AI. For example, the consultation promotion department can input user emotion data into a generating AI and have the generating AI perform emotion estimation and determine the priority of consultations.

[0108] The consultation facilitator can provide optimal consultation content by considering the user's geographical location information when facilitating consultations. For example, if the user is at high altitude, the consultation facilitator can provide consultation regarding oxygen concentration and atmospheric pressure. For example, if the user is in an urban area, the consultation facilitator can provide consultation regarding noise levels and light. For example, if the user is at the coast, the consultation facilitator can provide consultation regarding humidity and wind speed. For example, if the user is at high altitude, the consultation facilitator can provide consultation regarding oxygen concentration and atmospheric pressure to mitigate the effects of high altitude. If the user is in an urban area, the consultation facilitator can provide consultation regarding noise levels and light to mitigate the effects of the urban environment. If the user is at the coast, the consultation facilitator can provide consultation regarding humidity and wind speed to mitigate the effects of the coast. In this way, the consultation facilitator can provide optimal consultation content based on the user's geographical location information. Some or all of the above processing in the consultation facilitator may be performed using AI, for example, or without AI. For example, the consultation facilitator can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing optimal consultation content.

[0109] The consultation promotion department can analyze a user's social media activity and provide relevant consultation content when promoting consultations. For example, if a user posts on social media indicating they are feeling stressed, the consultation promotion department can provide stress management consultation. For example, if a user posts on social media indicating they are relaxed, the consultation promotion department can provide relaxation methods consultation. For example, if a user posts on social media indicating they are tired, the consultation promotion department can provide rest consultation. For example, if a user posts on social media indicating they are feeling stressed, the consultation promotion department can provide stress management consultation to mitigate the effects of stress. If a user posts on social media indicating they are relaxed, the consultation promotion department can provide relaxation methods consultation to enhance the effects of relaxation. If a user posts on social media indicating they are tired, the consultation promotion department can provide rest consultation to promote recovery from fatigue. In this way, the consultation promotion department can provide optimal consultation content based on the user's social media activity. Some or all of the above processing in the consultation promotion department may be performed using AI, for example, or without AI. For example, the consultation promotion department can input data on the user's social media activity into a generating AI and have the generating AI provide relevant consultation content.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if the user is stressed, data collection can be performed during times when they are relaxed. If the user is tired, data collection during sleep can be prioritized, and data collection during the day can be reduced. Furthermore, if the user is relaxed, detailed data collection can be performed, enabling more accurate analysis. In this way, the data collection unit can collect data at the optimal timing according to the user's emotions.

[0112] The data collection unit can analyze the user's past sleep data and select the optimal data collection method. For example, it can identify the most stable sleep pattern from the user's past sleep data and collect data during that time period. It can also confirm that a specific device provides the most accurate data from the user's past data and prioritize the use of that device. Furthermore, it can confirm that data collection is most effective under specific environmental conditions from the user's past data and reproduce those conditions. In this way, the data collection unit can select the optimal data collection method based on past data.

[0113] The analysis department can analyze data while taking into account the user's daytime activities, diet, stress levels, and other factors. For example, it can analyze the quality of a user's sleep based on their daytime activities. It can also analyze the quality of a user's sleep based on their diet. Furthermore, it can analyze the quality of a user's sleep based on their stress levels. This allows the analysis department to perform data analysis that takes into account the user's overall lifestyle.

[0114] The suggestion department can propose things like setting optimal bedtime and wake-up times, adjusting room temperature at night, and providing customized meditation audio for relaxation. For example, it can advise users on setting optimal bedtime and wake-up times. It can also suggest users adjust room temperature at night. Furthermore, it can provide users with customized meditation audio for relaxation. In this way, the suggestion department can propose specific sleep improvement plans to users.

[0115] The audio provider can provide customized meditation audio for relaxation. For example, it can provide users with highly relaxing meditation audio. It can also provide users with highly relaxing music. Furthermore, it can provide users with highly relaxing nature sounds. In this way, the audio provider can provide users with highly relaxing audio.

[0116] The consultation promotion department can detect abnormalities during sleep, such as signs of sleep apnea, and encourage users to consult with medical professionals. For example, if an abnormality is detected in a user's breathing pattern, it can advise the user to consult with a medical professional. Similarly, if an abnormality is detected in a user's heart rate, it can advise the user to consult with a medical professional. Furthermore, if an abnormality is detected in a user's body temperature, it can advise the user to consult with a medical professional. This allows the consultation promotion department to ensure that users receive appropriate medical care early on.

[0117] The analysis unit can estimate the user's emotions and adjust the data analysis method based on those estimated emotions. For example, if the user is stressed, it can focus on analyzing stress-related data. If the user is relaxed, it can analyze the overall quality of sleep. Furthermore, if the user is tired, it can focus on analyzing abnormalities in the sleep cycle. This allows the analysis unit to perform data analysis tailored to the user's emotions.

[0118] The suggestion function can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is stressed, it can provide a simple and highly visible suggestion. If the user is relaxed, it can provide a suggestion that includes detailed information. Furthermore, if the user is in a hurry, it can provide a suggestion that gets straight to the point. In this way, the suggestion function can provide the most appropriate suggestion to match the user's emotions.

[0119] The voice provider can estimate the user's emotions and adjust the audio content based on those emotions. For example, if the user is stressed, it can provide relaxing audio. If the user is relaxed, it can provide meditation or deep breathing exercises. Furthermore, if the user is tired, it can provide audio to promote restful sleep. In this way, the voice provider can deliver optimal audio content tailored to the user's emotions.

[0120] The consultation promotion unit can estimate the user's emotions and adjust the timing of consultations based on those estimates. For example, if a user is feeling stressed, it can encourage consultation during a time when they are relaxed. If a user is tired, it can encourage consultation after they have rested. Furthermore, if a user is relaxed, it can encourage a more detailed consultation. In this way, the consultation promotion unit can encourage consultations at the optimal time according to the user's emotions.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The data collection unit collects data such as the user's heart rate, body temperature, breathing pattern, and frequency of turning over in bed. The data collection unit collects data in conjunction with devices such as a smartwatch or bed sensor. The data collection unit monitors the user's heart rate in real time using a smartwatch and collects data. The data collection unit records the frequency of the user's turning over in bed using a bed sensor and collects data. The data collection unit periodically measures the user's body temperature using a body temperature sensor and collects data. Step 2: The analysis unit analyzes the data collected by the data collection unit using deep learning. The analysis unit analyzes the user's sleep cycle and sleep quality based on data such as the user's heart rate, body temperature, breathing patterns, and frequency of turning over in sleep. The analysis unit uses a deep learning algorithm to analyze the user's heart rate fluctuation patterns and evaluate sleep quality. The analysis unit analyzes the user's body temperature fluctuation patterns and evaluates sleep quality. The analysis unit analyzes the user's breathing patterns and evaluates sleep quality. Step 3: The Proposal Department proposes a personalized sleep improvement plan based on the analysis results obtained by the Analysis Department. The Proposal Department advises the user to set optimal bedtime and wake-up times to improve sleep quality. The Proposal Department suggests that the user adjust the room temperature at night to improve sleep quality. The Proposal Department provides the user with customized meditation audio for relaxation to improve sleep quality. Step 4: The audio provider provides customized meditation audio for relaxation based on the plan proposed by the suggestion provider. The audio provider provides users with highly relaxing meditation audio to improve sleep quality. The audio provider provides users with highly relaxing music to improve sleep quality. The audio provider provides users with highly relaxing nature sounds to improve sleep quality. Step 5: The Consultation Facilitation Department encourages users to consult with a medical professional based on sleep abnormalities detected by the Analysis Department. If abnormalities are found in the user's breathing pattern, the Consultation Facilitation Department advises the user to consult with a medical professional so that they can receive appropriate medical care early. If abnormalities are found in the user's heart rate, the Consultation Facilitation Department advises the user to consult with a medical professional so that they can receive appropriate medical care early. If abnormalities are found in the user's body temperature, the Consultation Facilitation Department advises the user to consult with a medical professional so that they can receive appropriate medical care early.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, voice provision unit, and consultation facilitation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data such as the user's heart rate and frequency of turning over in sleep using a smartwatch or bed sensor. The analysis unit analyzes the collected data using deep learning by the identification processing unit 290 of the data processing unit 12. The proposal unit proposes an individualized sleep improvement plan based on the analysis results. The voice provision unit provides customized meditation audio for relaxation based on the proposed plan. The consultation facilitation unit detects abnormalities during sleep and prompts consultation with a medical professional if necessary. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, voice provision unit, and consultation facilitation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data such as the user's heart rate and frequency of turning over in sleep using a smartwatch or bed sensor. The analysis unit analyzes the collected data using deep learning by the identification processing unit 290 of the data processing unit 12. The proposal unit proposes an individualized sleep improvement plan based on the analysis results. The voice provision unit provides customized meditation audio for relaxation based on the proposed plan. The consultation facilitation unit detects abnormalities during sleep and prompts consultation with a medical professional if necessary. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, voice provision unit, and consultation facilitation unit, is implemented in at least one of the following: a headset terminal 314 and a data processing unit 12. For example, the data collection unit collects data such as the user's heart rate and frequency of turning over in sleep using a smartwatch or bed sensor. The analysis unit analyzes the collected data using deep learning by the identification processing unit 290 of the data processing unit 12. The proposal unit proposes an individualized sleep improvement plan based on the analysis results. The voice provision unit provides customized meditation audio for relaxation based on the proposed plan. The consultation facilitation unit detects abnormalities during sleep and prompts consultation with a medical professional if necessary. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, voice provision unit, and consultation facilitation unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects data such as the user's heart rate and frequency of turning over in sleep using a smartwatch or bed sensor. The analysis unit analyzes the collected data using deep learning by the identification processing unit 290 of the data processing unit 12. The proposal unit proposes an individualized sleep improvement plan based on the analysis results. The voice provision unit provides customized meditation audio for relaxation based on the proposed plan. The consultation facilitation unit detects abnormalities during sleep and prompts consultation with a medical professional if necessary. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0176] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0186] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) A data collection unit that collects data such as the user's heart rate, body temperature, breathing pattern, and frequency of turning over in bed, The analysis unit analyzes the data collected by the aforementioned collection unit using deep learning, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes an individualized sleep improvement plan. An audio provider unit provides customized meditation audio for relaxation based on the plan proposed by the aforementioned proposal unit, The system includes a consultation promotion unit that prompts consultation with a medical professional based on sleep abnormalities detected by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is It collects data in conjunction with devices such as smartwatches and bed sensors. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is We analyze data while also taking into account the user's daily activities, diet, stress levels, and other factors. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We suggest setting optimal bedtimes and wake-up times, adjusting room temperature at night, and providing customized meditation audio for relaxation. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned voice provisioning unit is Provides customized meditation audio for relaxation. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned consultation promotion department, It detects sleep abnormalities such as signs of sleep apnea and encourages consultation with a medical professional. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past sleep data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, filtering is performed based on the user's current health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is We estimate user sentiment and adjust the data analysis method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, the analysis algorithm is optimized by referencing the user's past data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, data is weighted based on the user's lifestyle and health status. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, the relevance of data is evaluated by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, we refer to users' social media activity to reveal data correlations. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, the system selects the most suitable proposal by referring to the user's past behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the user's lifestyle and health condition. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, we will provide the most suitable proposal content by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and make relevant suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned voice provisioning unit is It estimates the user's emotions and adjusts the audio content based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned voice provisioning unit is When providing audio, the system selects the most suitable audio by referring to the user's past relaxation methods. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned voice provisioning unit is When providing audio, the type of audio is customized based on the user's lifestyle and health condition. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned voice provisioning unit is It estimates the user's emotions and adjusts the audio playback order based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned voice provisioning unit is When providing audio, the system takes the user's geographical location into consideration to provide the most suitable audio. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned voice provisioning unit is When providing audio, the system analyzes the user's social media activity and provides relevant audio. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned consultation promotion department, The system estimates the user's emotions and adjusts the timing of consultations based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned consultation promotion department, When facilitating consultations, the system selects the most appropriate consultation topic by referring to the user's past health data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned consultation promotion department, When facilitating consultations, the level of detail in the consultation is adjusted based on the user's lifestyle and health condition. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned consultation promotion department, It estimates the user's emotions and determines the priority of consultations based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned consultation promotion department, When facilitating consultations, we provide the most suitable consultation content by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned consultation promotion department, When facilitating consultations, the system analyzes the user's social media activity and provides relevant consultation content. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data such as the user's heart rate, body temperature, breathing pattern, and frequency of turning over in bed, An analysis unit analyzes the data collected by the aforementioned collection unit using deep learning, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes an individualized sleep improvement plan. An audio provider unit provides customized meditation audio for relaxation based on the plan proposed by the aforementioned proposal unit, The system includes a consultation promotion unit that prompts consultation with a medical professional based on sleep abnormalities detected by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is It collects data in conjunction with devices such as smartwatches and bed sensors. The system according to feature 1.

3. The aforementioned analysis unit is We analyze data while also taking into account the user's daily activities, diet, stress levels, and other factors. The system according to feature 1.

4. The aforementioned proposal section is, We suggest setting optimal bedtime and wake-up times, adjusting room temperature at night, and providing customized meditation audio for relaxation. The system according to feature 1.

5. The aforementioned voice provisioning unit is Provides customized meditation audio for relaxation. The system according to feature 1.

6. The aforementioned consultation promotion department, It detects sleep abnormalities such as signs of sleep apnea and encourages consultation with a medical professional. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past sleep data and select the optimal data collection method. The system according to feature 1.

Images