system

Abstract

The system according to this embodiment aims to calculate the optimal time for falling asleep for each individual and to provide personalized advice. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, an advice provision unit, and a feedback unit. The data collection unit collects detailed sleep data. The analysis unit analyzes the data collected by the data collection unit and calculates the optimal time for falling asleep for each individual. The advice provision unit provides personalized advice based on the analysis results obtained by the analysis unit, according to the depth of sleep and fatigue level. The feedback unit visually presents the advice provided by the advice provision 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 conventional technology, there is a problem that it is difficult to manage the quality and duration of sleep and to find an individual optimal bedtime.

[0005] The system according to the embodiment aims to calculate an individual optimal bedtime and provide personalized advice.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, an advice provision unit, and a feedback unit. The data collection unit collects detailed sleep data. The analysis unit analyzes the data collected by the data collection unit and calculates the optimal time for falling asleep for each individual. The advice provision unit provides personalized advice based on the analysis results obtained by the analysis unit, according to the depth of sleep and fatigue level. The feedback unit visually presents the advice provided by the advice provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can calculate the optimal time for falling asleep for each individual and provide personalized advice. [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 system that proposes an individual's optimal time to fall asleep by collecting detailed sleep data using a smart ring and analyzing it using AI. This sleep management system improves the quality of the user's sleep by collecting detailed sleep data and providing personalized advice based on the analysis results. For example, the sleep management system collects detailed sleep data such as heart rate, body temperature, and movement using a smart ring. The smart ring is worn on the user's finger and records biometric information during sleep in real time. For example, fluctuations in heart rate, changes in body temperature, and the number of times the user turns over in their sleep are recorded. This allows for a detailed understanding of the user's sleep state. Next, the collected data is analyzed by AI. Based on the collected data, the AI ​​analyzes the user's sleep pattern and calculates an individual's optimal time to fall asleep. For example, the optimal time to fall asleep can be identified from fluctuations in the user's heart rate and body temperature. This allows the user to know their optimal time to fall asleep. Furthermore, based on the results of the AI ​​analysis, personalized advice is provided according to the depth of sleep and fatigue level. For example, if the user's sleep is light, advice on how to relax is provided, and if fatigue is accumulating, advice to fall asleep earlier is provided. This allows users to receive appropriate advice tailored to their sleep patterns. Finally, it provides graphical feedback that visually displays sleep patterns and sleep onset times. For example, it displays the user's sleep patterns as a graph, visually showing sleep onset times and sleep depth. This allows users to intuitively understand their sleep patterns. This mechanism enables users to know their optimal sleep onset time and improve the quality of their sleep. It also supports the formation of healthy sleep habits and makes it easier to manage changes in environment and sleep. For example, even when traveling or in a new environment, knowing the optimal sleep onset time allows for comfortable sleep. In this way, the sleep management system can improve the quality of the user's sleep.

[0029] The sleep management system according to this embodiment comprises a data collection unit, an analysis unit, an advice provision unit, and a feedback unit. The data collection unit collects detailed sleep data. The data collection unit collects detailed sleep data such as heart rate, body temperature, and movement using, for example, a smart ring. The smart ring is worn on the user's finger and records biometric information during sleep in real time. For example, fluctuations in heart rate, changes in body temperature, and the number of times the user turns over in their sleep are recorded. This allows for a detailed understanding of the user's sleep state. The analysis unit analyzes the data collected by the data collection unit and calculates the individual's optimal time to fall asleep. For example, the analysis unit analyzes the user's sleep pattern based on the collected data and calculates the individual's optimal time to fall asleep. For example, the optimal time to fall asleep can be identified from fluctuations in the user's heart rate and body temperature. This allows the user to know their optimal time to fall asleep. The advice provision unit provides personalized advice based on the analysis results obtained by the analysis unit, according to the depth of sleep and fatigue level. The advice-providing unit, for example, provides advice on how to relax if the user's sleep is light, and advises them to go to sleep earlier if they are experiencing fatigue. This allows the user to receive appropriate advice tailored to their sleep state. The feedback unit visually presents the advice provided by the advice-providing unit. For example, the feedback unit displays the user's sleep waveform in a graph, visually indicating the time it takes to fall asleep and the depth of sleep. This allows the user to intuitively understand their sleep state. As a result, the sleep management system according to this embodiment can improve the quality of the user's sleep.

[0030] The data collection unit collects detailed sleep data. For example, it uses a smart ring to collect detailed sleep data such as heart rate, body temperature, and movement. The smart ring is worn on the user's finger and records biometric information during sleep in real time. Specifically, the smart ring has high-precision sensors built in; the heart rate sensor records fluctuations in the user's heart rate in milliseconds, and the body temperature sensor continuously measures the temperature of the skin surface. In addition, an accelerometer detects the user's movement and records the number of times they turn over in their sleep and the intensity of their movements. This data is transmitted to a smartphone or cloud server via Bluetooth® or Wi-Fi and collected in real time. Furthermore, the data collection unit can also collect data about the user's sleep environment. For example, it uses a microphone built into the smart ring to measure the ambient noise level and a light sensor to record the brightness of the room. This allows the unit to understand environmental factors that affect the user's sleep. The collected data is encrypted using a secure protocol to protect privacy. This allows the data collection unit to safely and efficiently collect detailed sleep data from users and provide it to the analysis unit and the advice provision unit.

[0031] The analysis unit analyzes the data collected by the data collection unit and calculates the optimal time for each individual to fall asleep. For example, the analysis unit analyzes the user's sleep patterns based on the collected data and calculates the optimal time for each individual to fall asleep. Specifically, it uses an AI algorithm to analyze data such as heart rate, body temperature fluctuations, and the number of times a user turns over in their sleep to identify the user's sleep cycle. The AI ​​learns the user's sleep quality and patterns by comparing them with past data, enabling more accurate analysis. For example, it identifies the timing when a user enters deep sleep from heart rate fluctuations and predicts the optimal time to fall asleep from changes in body temperature. The analysis unit also takes into account the user's lifestyle rhythm and daily activity level. For example, it analyzes data from days when the user exercises or experiences stress and evaluates the impact of these on sleep. This allows it to suggest the optimal time to fall asleep that suits the user's lifestyle. Furthermore, the analysis unit monitors the user's sleep data over the long term and detects changes in trends and patterns. This allows it to evaluate whether the user's sleep quality is improving and adjust advice as needed. In this way, the analysis unit can provide the optimal time to fall asleep that meets the user's individual sleep needs and improve their sleep quality.

[0032] The advice department provides personalized advice based on the analysis results obtained by the analysis department, tailored to the depth of sleep and fatigue level. For example, if a user's sleep is light, the advice department will provide advice on how to relax, and if fatigue is accumulating, it will advise them to go to sleep earlier. Specifically, based on the user's sleep data, it will suggest breathing techniques and stretching methods for relaxation. It will also advise on appropriate sleep duration and pre-sleep routines according to the user's fatigue level. For example, if a user is feeling stressed, it will introduce relaxing music or meditation apps and advise setting aside time to relax before going to sleep. Furthermore, the advice department also provides advice tailored to the user's lifestyle and daily activity level. For example, on days when a user has exercised, it will suggest appropriate cool-down methods and pre-sleep stretches to support post-exercise recovery. If a user is tired from work, it can also recommend aromatherapy or warm drinks to help them relax. In this way, the advice department can provide personalized advice tailored to the individual needs of each user, improving the quality of their sleep.

[0033] The feedback unit visually presents the advice provided by the advice unit. For example, the feedback unit displays the user's sleep waveform in a graph, visually showing the time it takes to fall asleep and the depth of sleep. Specifically, it displays the user's sleep data in graphs and charts through a smartphone or tablet application. This allows the user to intuitively understand their sleep state. For example, in a sleep waveform graph, each stage of deep sleep, light sleep, and REM sleep is displayed in different colors, allowing the user to grasp their sleep pattern at a glance. The feedback unit can also aggregate the user's sleep data on a daily, weekly, and monthly basis, showing long-term trends. This allows the user to see how the quality of their sleep is changing and to feel the effects of improvement. Furthermore, the feedback unit not only visually presents advice from the advice unit but also has a function to collect user feedback. For example, by inputting the results of following the advice, the effectiveness of the advice can be evaluated and reflected in future advice. In this way, the feedback unit not only helps users intuitively understand their sleep state but also continuously improves the effectiveness of the advice.

[0034] The data collection unit can collect detailed sleep data such as heart rate, body temperature, and movement using a smart ring. The data collection unit collects detailed sleep data such as heart rate, body temperature, and movement using a smart ring. The smart ring is worn on the user's finger and records biometric information during sleep in real time. For example, fluctuations in heart rate, changes in body temperature, and the number of times the user turns over in their sleep are recorded. This allows for a detailed understanding of the user's sleep state. As a result, detailed sleep data can be collected using a smart ring. Some or all of the above-described 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 data acquired by the smart ring into a generating AI and have the generating AI perform data analysis.

[0035] The analysis unit can analyze the user's sleep pattern based on the collected data and calculate the individual's optimal time to fall asleep. For example, the analysis unit can analyze the user's sleep pattern based on the collected data and calculate the individual's optimal time to fall asleep. For example, it can identify the optimal time to fall asleep from fluctuations in the user's heart rate and body temperature. This allows the user to know their optimal time to fall asleep. By analyzing the user's sleep pattern and calculating the optimal time to fall asleep, the quality of the user's sleep is improved. 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 collected data into a generating AI and have the generating AI perform the sleep pattern analysis.

[0036] The advice-providing unit can provide personalized advice based on the analysis results, tailored to the depth of sleep and fatigue level. For example, if the user's sleep is light, the advice-providing unit will provide advice on how to relax, and if fatigue is accumulating, it will advise the user to go to sleep earlier. This allows the user to receive appropriate advice according to their sleep state. By providing personalized advice based on the analysis results, the quality of the user's sleep is improved. Some or all of the above-described processes in the advice-providing unit may be performed using AI, for example, or without AI. For example, the advice-providing unit can input the analysis results into a generating AI and have the generating AI generate personalized advice.

[0037] The feedback unit can visually present sleep patterns and sleep onset times. For example, the feedback unit can display the user's sleep patterns as a graph, visually indicating sleep onset times and sleep depth. This allows the user to intuitively understand their sleep state. By visually presenting sleep patterns and sleep onset times, the user can intuitively understand their sleep state. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can use generative AI to display the user's sleep patterns as a graph.

[0038] 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. The data collection unit can also adjust the frequency of data collection based on the user's past sleep data. Furthermore, the data collection unit can analyze the user's past sleep data and collect data based on specific events (e.g., after exercise, after meals). This allows the optimal data collection method to be selected by analyzing past sleep 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 past sleep data into a generating AI and have the generating AI select the optimal data collection method.

[0039] The data collection unit can filter sleep data based on the user's current health status and lifestyle. For example, the data collection unit can monitor the user's current health status (e.g., heart rate, body temperature) in real time and interrupt data collection if an abnormality is detected. The data collection unit can also adjust the timing of data collection considering the user's lifestyle (e.g., exercise, diet, alcohol consumption). Furthermore, the data collection unit can select the types of data to collect based on the user's health status and lifestyle. This allows for the collection of more accurate data by filtering the data based on the user's health status and lifestyle. 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 health status and lifestyle data into a generating AI and have the generating AI perform data filtering.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting sleep data. For example, if the user is traveling, the data collection unit will prioritize the collection of sleep data from different environments. The data collection unit can also collect normal sleep data if the user is at home. Furthermore, if the user is in a new location, the data collection unit can prioritize the collection of sleep data from that location. This allows for the priority collection of highly relevant data by considering the user's geographical location. 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 geographical location information into a generating AI and have the generating AI prioritize the collection of highly relevant data.

[0041] The data collection unit can analyze the user's social media activity and collect relevant data when collecting sleep data. For example, if the user is experiencing stress from social media, the data collection unit can consider the impact of that stress when collecting data. Similarly, if the user is relaxing from social media, the data collection unit can also consider the impact of that stress when collecting data. Furthermore, the data collection unit can identify factors influencing sleep from the user's social media activity and collect data accordingly. This allows for the collection of relevant data by analyzing 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 social media activity data into a generating AI and have the generating AI collect relevant data.

[0042] The analysis unit can adjust the level of detail in its analysis based on the importance of the sleep data. For example, the analysis unit will perform a detailed analysis of important data (e.g., heart rate, body temperature). It can also simplify the analysis of less important data (e.g., number of times the sleeper tosses and turns). Furthermore, the analysis unit can adjust the frequency of analysis according to importance. This allows for efficient analysis by adjusting the level of detail based on the importance of the sleep data. 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 high-importance data into a generating AI and have the generating AI perform a detailed analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of sleep data during analysis. For example, the analysis unit can apply a heart rate variability analysis algorithm to heart rate data. It can also apply a body temperature variability analysis algorithm to body temperature data. Furthermore, it can apply a movement pattern analysis algorithm to movement data. By applying different analysis algorithms depending on the category of sleep data, more accurate analysis results can be obtained. 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 from different categories into a generating AI and have the generating AI perform analysis according to the category.

[0044] The analysis unit can determine the priority of analysis based on the timing of sleep data collection during the analysis process. For example, the analysis unit may prioritize the analysis of recently collected data. It can also prioritize the analysis of data collected based on specific events (e.g., after exercise, after meals). Furthermore, the analysis unit may prioritize the analysis of data that may influence the user's sleep patterns. This enables efficient analysis by prioritizing the analysis based on the timing of sleep data collection. 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 based on collection timing into a generating AI and have the generating AI perform the priority determination.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the sleep data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the sleep data. 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 highly relevant data into a generating AI and have the generating AI execute the analysis in the correct order.

[0046] The advice-providing unit can adjust the level of detail in the advice based on the importance of the user's sleep data when providing advice. For example, the advice-providing unit can provide detailed advice based on important data (e.g., heart rate, body temperature). It can also provide simplified advice based on less important data (e.g., number of times the user turns over in their sleep). Furthermore, the advice-providing unit can adjust the frequency of advice according to its importance. This allows for efficient advice by adjusting the level of detail based on the importance of the sleep data. Some or all of the above processing in the advice-providing unit may be performed using AI, for example, or without AI. For example, the advice-providing unit can input high-importance data into a generating AI and have the generating AI generate detailed advice.

[0047] The advice-providing unit can apply different advice algorithms depending on the category of the user's sleep data when providing advice. For example, the advice-providing unit can provide advice based on heart rate variability for heart rate data. It can also provide advice based on body temperature variability for body temperature data. Furthermore, it can provide advice based on movement patterns for movement data. By applying different advice algorithms depending on the category of sleep data, more accurate advice can be provided. Some or all of the above processing in the advice-providing unit may be performed using AI, for example, or without AI. For example, the advice-providing unit can input data from different categories into a generating AI and have the generating AI generate advice according to the category.

[0048] The advice-providing unit can prioritize advice based on when the user's sleep data is collected. For example, the advice-providing unit can provide advice based on recently collected data. It can also provide advice based on data collected based on specific events (e.g., after exercise, after a meal). Furthermore, it can provide advice based on data that may influence the user's sleep patterns. This enables efficient advice by prioritizing advice based on when sleep data is collected. Some or all of the above processing in the advice-providing unit may be performed using AI, for example, or without AI. For example, the advice-providing unit can input data based on collection timing into a generating AI and have the generating AI determine the priority of advice.

[0049] The advice-providing unit can adjust the order of advice based on the relevance of the user's sleep data when providing advice. For example, the advice-providing unit can provide advice based on highly relevant data. It can also postpone advice based on less relevant data. Furthermore, the advice-providing unit can adjust the level of detail of the advice according to its relevance. This allows for efficient advice by adjusting the order of advice based on the relevance of sleep data. Some or all of the above processing in the advice-providing unit may be performed using AI, for example, or without AI. For example, the advice-providing unit can input highly relevant data into a generating AI and have the generating AI execute the order of advice.

[0050] The feedback unit can select the optimal display method by referring to the user's past sleep data when displaying feedback. For example, the feedback unit can identify the most effective display method from the user's past sleep data and apply it. The feedback unit can also adjust the level of detail of the feedback based on the user's past sleep data. Furthermore, the feedback unit can select a display method based on specific events (e.g., after exercise, after a meal) by referring to the user's past sleep data. This allows the optimal display method to be selected by referring to past sleep data. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input past sleep data into a generating AI and have the generating AI select the optimal display method.

[0051] The feedback unit can select the optimal display method when displaying feedback, taking into account the user's device information. For example, if the user is using a smartphone, the feedback unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the feedback unit can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the feedback unit can provide a concise and highly visible display method. This allows the optimal display method to be selected by considering the user's device information. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input device information into a generating AI and have the generating AI select the optimal display method.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The data collection unit can monitor the user's sleep environment and adjust the timing of data collection based on environmental data. For example, if the room temperature or humidity is not appropriate, data collection can be temporarily suspended. The data collection unit can also delay data collection if the noise level is high. Furthermore, the data collection unit can adjust data collection if the lighting brightness is not appropriate. By adjusting the timing of data collection based on the user's sleep environment, more accurate data can be collected.

[0054] The data collection unit can monitor the user's sleep data in real time and issue alerts if an abnormality is detected. For example, if the heart rate is abnormally high, the data collection unit will alert the user. It can also alert the user if the body temperature is abnormally low. Furthermore, it can alert the user if there is an abnormal lack of movement. This allows for rapid response by detecting abnormalities in real time and issuing alerts to the user.

[0055] The data collection unit can adjust the frequency of data collection based on the user's activity level when collecting sleep data. For example, the frequency of data collection can be increased after the user has exercised. Conversely, the frequency can be decreased when the user is engaged in sedentary activities. Furthermore, the frequency can be adjusted if the user has been sitting for a long period of time. By adjusting the frequency of data collection based on the user's activity level, more accurate data can be collected.

[0056] The data collection unit can adjust the timing of data collection when collecting user sleep data, taking into account the user's meal information. For example, data collection can be temporarily suspended immediately after the user has eaten. It can also adjust the timing of data collection before the user eats. Furthermore, if the user has eaten a specific meal, the timing of data collection can be adjusted to take that into account. By adjusting the timing of data collection based on the user's meal information, more accurate data can be collected.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The data collection unit collects detailed sleep data. For example, it uses a smart ring to collect detailed sleep data such as heart rate, body temperature, and movement. The smart ring is worn on the user's finger and records biometric information during sleep in real time. This allows for a detailed understanding of the user's sleep state. Step 2: The analysis unit analyzes the data collected by the data collection unit and calculates the optimal time for each individual to fall asleep. For example, based on the collected data, it analyzes the user's sleep pattern and identifies the optimal time to fall asleep from fluctuations in heart rate and body temperature. This allows the user to know the optimal time to fall asleep for themselves. Step 3: The advice-providing unit provides personalized advice based on the analysis results obtained by the analysis unit, tailored to the depth of sleep and fatigue level. For example, if the user's sleep is light, it provides advice on how to relax; if fatigue is accumulating, it advises them to go to sleep earlier. This allows the user to receive appropriate advice based on their sleep condition. Step 4: The feedback unit visually presents the advice provided by the advice unit. For example, it displays the user's sleep patterns as a graph, visually showing the time it takes to fall asleep and the depth of sleep. This allows the user to intuitively understand their own sleep patterns.

[0059] (Example of form 2) The sleep management system according to an embodiment of the present invention is a system that proposes an individual's optimal time to fall asleep by collecting detailed sleep data using a smart ring and analyzing it using AI. This sleep management system improves the quality of the user's sleep by collecting detailed sleep data and providing personalized advice based on the analysis results. For example, the sleep management system collects detailed sleep data such as heart rate, body temperature, and movement using a smart ring. The smart ring is worn on the user's finger and records biometric information during sleep in real time. For example, fluctuations in heart rate, changes in body temperature, and the number of times the user turns over in their sleep are recorded. This allows for a detailed understanding of the user's sleep state. Next, the collected data is analyzed by AI. Based on the collected data, the AI ​​analyzes the user's sleep pattern and calculates an individual's optimal time to fall asleep. For example, the optimal time to fall asleep can be identified from fluctuations in the user's heart rate and body temperature. This allows the user to know their optimal time to fall asleep. Furthermore, based on the results of the AI ​​analysis, personalized advice is provided according to the depth of sleep and fatigue level. For example, if the user's sleep is light, advice on how to relax is provided, and if fatigue is accumulating, advice to fall asleep earlier is provided. This allows users to receive appropriate advice tailored to their sleep patterns. Finally, it provides graphical feedback that visually displays sleep patterns and sleep onset times. For example, it displays the user's sleep patterns as a graph, visually showing sleep onset times and sleep depth. This allows users to intuitively understand their sleep patterns. This mechanism enables users to know their optimal sleep onset time and improve the quality of their sleep. It also supports the formation of healthy sleep habits and makes it easier to manage changes in environment and sleep. For example, even when traveling or in a new environment, knowing the optimal sleep onset time allows for comfortable sleep. In this way, the sleep management system can improve the quality of the user's sleep.

[0060] The sleep management system according to this embodiment comprises a data collection unit, an analysis unit, an advice provision unit, and a feedback unit. The data collection unit collects detailed sleep data. The data collection unit collects detailed sleep data such as heart rate, body temperature, and movement using, for example, a smart ring. The smart ring is worn on the user's finger and records biometric information during sleep in real time. For example, fluctuations in heart rate, changes in body temperature, and the number of times the user turns over in their sleep are recorded. This allows for a detailed understanding of the user's sleep state. The analysis unit analyzes the data collected by the data collection unit and calculates the individual's optimal time to fall asleep. For example, the analysis unit analyzes the user's sleep pattern based on the collected data and calculates the individual's optimal time to fall asleep. For example, the optimal time to fall asleep can be identified from fluctuations in the user's heart rate and body temperature. This allows the user to know their optimal time to fall asleep. The advice provision unit provides personalized advice based on the analysis results obtained by the analysis unit, according to the depth of sleep and fatigue level. The advice-providing unit, for example, provides advice on how to relax if the user's sleep is light, and advises them to go to sleep earlier if they are experiencing fatigue. This allows the user to receive appropriate advice tailored to their sleep state. The feedback unit visually presents the advice provided by the advice-providing unit. For example, the feedback unit displays the user's sleep waveform in a graph, visually indicating the time it takes to fall asleep and the depth of sleep. This allows the user to intuitively understand their sleep state. As a result, the sleep management system according to this embodiment can improve the quality of the user's sleep.

[0061] The data collection unit collects detailed sleep data. For example, it uses a smart ring to collect detailed sleep data such as heart rate, body temperature, and movement. The smart ring is worn on the user's finger and records biometric information during sleep in real time. Specifically, the smart ring has high-precision sensors built in; the heart rate sensor records the user's heart rate fluctuations in milliseconds, and the body temperature sensor continuously measures the temperature of the skin surface. In addition, an accelerometer detects the user's movements and records the number of times the user turns over and the intensity of their movements. This data is transmitted to a smartphone or cloud server via Bluetooth or Wi-Fi and collected in real time. Furthermore, the data collection unit can also collect data about the user's sleep environment. For example, it uses a microphone built into the smart ring to measure the ambient noise level and a light sensor to record the brightness of the room. This allows the unit to understand environmental factors that affect the user's sleep. The collected data is encrypted using a secure protocol to protect privacy. This allows the data collection unit to safely and efficiently collect detailed sleep data from users and provide it to the analysis unit and the advice provision unit.

[0062] The analysis unit analyzes the data collected by the data collection unit and calculates the optimal time for each individual to fall asleep. For example, the analysis unit analyzes the user's sleep patterns based on the collected data and calculates the optimal time for each individual to fall asleep. Specifically, it uses an AI algorithm to analyze data such as heart rate, body temperature fluctuations, and the number of times a user turns over in their sleep to identify the user's sleep cycle. The AI ​​learns the user's sleep quality and patterns by comparing them with past data, enabling more accurate analysis. For example, it identifies the timing when a user enters deep sleep from heart rate fluctuations and predicts the optimal time to fall asleep from changes in body temperature. The analysis unit also takes into account the user's lifestyle rhythm and daily activity level. For example, it analyzes data from days when the user exercises or experiences stress and evaluates the impact of these on sleep. This allows it to suggest the optimal time to fall asleep that suits the user's lifestyle. Furthermore, the analysis unit monitors the user's sleep data over the long term and detects changes in trends and patterns. This allows it to evaluate whether the user's sleep quality is improving and adjust advice as needed. In this way, the analysis unit can provide the optimal time to fall asleep that meets the user's individual sleep needs and improve their sleep quality.

[0063] The advice department provides personalized advice based on the analysis results obtained by the analysis department, tailored to the depth of sleep and fatigue level. For example, if a user's sleep is light, the advice department will provide advice on how to relax, and if fatigue is accumulating, it will advise them to go to sleep earlier. Specifically, based on the user's sleep data, it will suggest breathing techniques and stretching methods for relaxation. It will also advise on appropriate sleep duration and pre-sleep routines according to the user's fatigue level. For example, if a user is feeling stressed, it will introduce relaxing music or meditation apps and advise setting aside time to relax before going to sleep. Furthermore, the advice department also provides advice tailored to the user's lifestyle and daily activity level. For example, on days when a user has exercised, it will suggest appropriate cool-down methods and pre-sleep stretches to support post-exercise recovery. If a user is tired from work, it can also recommend aromatherapy or warm drinks to help them relax. In this way, the advice department can provide personalized advice tailored to the individual needs of each user, improving the quality of their sleep.

[0064] The feedback unit visually presents the advice provided by the advice unit. For example, the feedback unit displays the user's sleep waveform in a graph, visually showing the time it takes to fall asleep and the depth of sleep. Specifically, it displays the user's sleep data in graphs and charts through a smartphone or tablet application. This allows the user to intuitively understand their sleep state. For example, in a sleep waveform graph, each stage of deep sleep, light sleep, and REM sleep is displayed in different colors, allowing the user to grasp their sleep pattern at a glance. The feedback unit can also aggregate the user's sleep data on a daily, weekly, and monthly basis, showing long-term trends. This allows the user to see how the quality of their sleep is changing and to feel the effects of improvement. Furthermore, the feedback unit not only visually presents advice from the advice unit but also has a function to collect user feedback. For example, by inputting the results of following the advice, the effectiveness of the advice can be evaluated and reflected in future advice. In this way, the feedback unit not only helps users intuitively understand their sleep state but also continuously improves the effectiveness of the advice.

[0065] The data collection unit can collect detailed sleep data such as heart rate, body temperature, and movement using a smart ring. The data collection unit collects detailed sleep data such as heart rate, body temperature, and movement using a smart ring. The smart ring is worn on the user's finger and records biometric information during sleep in real time. For example, fluctuations in heart rate, changes in body temperature, and the number of times the user turns over in their sleep are recorded. This allows for a detailed understanding of the user's sleep state. As a result, detailed sleep data can be collected using a smart ring. Some or all of the above-described 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 data acquired by the smart ring into a generating AI and have the generating AI perform data analysis.

[0066] The analysis unit can analyze the user's sleep pattern based on the collected data and calculate the individual's optimal time to fall asleep. For example, the analysis unit can analyze the user's sleep pattern based on the collected data and calculate the individual's optimal time to fall asleep. For example, it can identify the optimal time to fall asleep from fluctuations in the user's heart rate and body temperature. This allows the user to know their optimal time to fall asleep. By analyzing the user's sleep pattern and calculating the optimal time to fall asleep, the quality of the user's sleep is improved. 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 collected data into a generating AI and have the generating AI perform the sleep pattern analysis.

[0067] The advice-providing unit can provide personalized advice based on the analysis results, tailored to the depth of sleep and fatigue level. For example, if the user's sleep is light, the advice-providing unit will provide advice on how to relax, and if fatigue is accumulating, it will advise the user to go to sleep earlier. This allows the user to receive appropriate advice according to their sleep state. By providing personalized advice based on the analysis results, the quality of the user's sleep is improved. Some or all of the above-described processes in the advice-providing unit may be performed using AI, for example, or without AI. For example, the advice-providing unit can input the analysis results into a generating AI and have the generating AI generate personalized advice.

[0068] The feedback unit can visually present sleep patterns and sleep onset times. For example, the feedback unit can display the user's sleep patterns as a graph, visually indicating sleep onset times and sleep depth. This allows the user to intuitively understand their sleep state. By visually presenting sleep patterns and sleep onset times, the user can intuitively understand their sleep state. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can use generative AI to display the user's sleep patterns as a graph.

[0069] The data collection unit can estimate the user's emotions and adjust the timing of sleep data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can start collecting data while considering time for relaxation. If the user is relaxed, the data collection unit can also collect data in a way that does not interfere with natural sleep onset. Furthermore, if the user is agitated, the data collection unit can wait for them to calm down before starting data collection. This allows for data collection at a more appropriate time by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0070] 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. The data collection unit can also adjust the frequency of data collection based on the user's past sleep data. Furthermore, the data collection unit can analyze the user's past sleep data and collect data based on specific events (e.g., after exercise, after meals). This allows the optimal data collection method to be selected by analyzing past sleep 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 past sleep data into a generating AI and have the generating AI select the optimal data collection method.

[0071] The data collection unit can filter sleep data based on the user's current health status and lifestyle. For example, the data collection unit can monitor the user's current health status (e.g., heart rate, body temperature) in real time and interrupt data collection if an abnormality is detected. The data collection unit can also adjust the timing of data collection considering the user's lifestyle (e.g., exercise, diet, alcohol consumption). Furthermore, the data collection unit can select the types of data to collect based on the user's health status and lifestyle. This allows for the collection of more accurate data by filtering the data based on the user's health status and lifestyle. 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 health status and lifestyle data into a generating AI and have the generating AI perform data filtering.

[0072] 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 may prioritize collecting heart rate and body temperature data. It may also prioritize collecting sleep depth and movement data if the user is relaxed. Furthermore, if the user is excited, it may prioritize collecting fluctuations in heart rate and body temperature. This allows for the priority collection of important data by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the data prioritization.

[0073] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting sleep data. For example, if the user is traveling, the data collection unit will prioritize the collection of sleep data from different environments. The data collection unit can also collect normal sleep data if the user is at home. Furthermore, if the user is in a new location, the data collection unit can prioritize the collection of sleep data from that location. This allows for the priority collection of highly relevant data by considering the user's geographical location. 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 geographical location information into a generating AI and have the generating AI prioritize the collection of highly relevant data.

[0074] The data collection unit can analyze the user's social media activity and collect relevant data when collecting sleep data. For example, if the user is experiencing stress from social media, the data collection unit can consider the impact of that stress when collecting data. Similarly, if the user is relaxing from social media, the data collection unit can also consider the impact of that stress when collecting data. Furthermore, the data collection unit can identify factors influencing sleep from the user's social media activity and collect data accordingly. This allows for the collection of relevant data by analyzing 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 social media activity data into a generating AI and have the generating AI collect relevant data.

[0075] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit can apply an analysis algorithm that focuses on stress reduction. If the user is relaxed, the analysis unit can also apply an analysis algorithm to maintain that relaxed state. Furthermore, if the user is agitated, the analysis unit can apply an analysis algorithm to calm them down. By adjusting the analysis algorithm based on the user's emotions, more appropriate analysis results can be obtained. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the analysis algorithm.

[0076] The analysis unit can adjust the level of detail in its analysis based on the importance of the sleep data. For example, the analysis unit will perform a detailed analysis of important data (e.g., heart rate, body temperature). It can also simplify the analysis of less important data (e.g., number of times the sleeper tosses and turns). Furthermore, the analysis unit can adjust the frequency of analysis according to importance. This allows for efficient analysis by adjusting the level of detail based on the importance of the sleep data. 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 high-importance data into a generating AI and have the generating AI perform a detailed analysis.

[0077] The analysis unit can apply different analysis algorithms depending on the category of sleep data during analysis. For example, the analysis unit can apply a heart rate variability analysis algorithm to heart rate data. It can also apply a body temperature variability analysis algorithm to body temperature data. Furthermore, it can apply a movement pattern analysis algorithm to movement data. By applying different analysis algorithms depending on the category of sleep data, more accurate analysis results can be obtained. 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 from different categories into a generating AI and have the generating AI perform analysis according to the category.

[0078] 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. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating display method. By adjusting the display method of the analysis results based on the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. 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 generative AI and have the generative AI perform the adjustment of the display method.

[0079] The analysis unit can determine the priority of analysis based on the timing of sleep data collection during the analysis process. For example, the analysis unit may prioritize the analysis of recently collected data. It can also prioritize the analysis of data collected based on specific events (e.g., after exercise, after meals). Furthermore, the analysis unit may prioritize the analysis of data that may influence the user's sleep patterns. This enables efficient analysis by prioritizing the analysis based on the timing of sleep data collection. 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 based on collection timing into a generating AI and have the generating AI perform the priority determination.

[0080] The analysis unit can adjust the order of analysis based on the relevance of the sleep data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can adjust the level of detail of the analysis according to the relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the sleep data. 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 highly relevant data into a generating AI and have the generating AI execute the analysis in the correct order.

[0081] The advice-providing unit can estimate the user's emotions and adjust the way advice is expressed based on the estimated emotions. For example, if the user is feeling stressed, the advice-providing unit can provide advice to help them relax. If the user is relaxed, the advice-providing unit can also provide advice to help them maintain that relaxed state. Furthermore, if the user is agitated, the advice-providing unit can provide advice to help them calm down. By adjusting the way advice is expressed based on the user's emotions, more effective advice can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the advice-providing unit may be performed using AI, for example, or not using AI. For example, the advice-providing unit can input user emotion data into a generative AI and have the generative AI adjust the way advice is expressed.

[0082] The advice-providing unit can estimate the user's emotions and adjust the way advice is expressed based on the estimated emotions. For example, if the user is feeling stressed, the advice-providing unit can provide advice to help them relax. If the user is relaxed, the advice-providing unit can also provide advice to help them maintain that relaxed state. Furthermore, if the user is agitated, the advice-providing unit can provide advice to help them calm down. By adjusting the way advice is expressed based on the user's emotions, more effective advice can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the advice-providing unit may be performed using AI, for example, or not using AI. For example, the advice-providing unit can input user emotion data into a generative AI and have the generative AI adjust the way advice is expressed.

[0083] The advice-providing unit can adjust the level of detail in the advice based on the importance of the user's sleep data when providing advice. For example, the advice-providing unit can provide detailed advice based on important data (e.g., heart rate, body temperature). It can also provide simplified advice based on less important data (e.g., number of times the user turns over in their sleep). Furthermore, the advice-providing unit can adjust the frequency of advice according to its importance. This allows for efficient advice by adjusting the level of detail based on the importance of the sleep data. Some or all of the above processing in the advice-providing unit may be performed using AI, for example, or without AI. For example, the advice-providing unit can input high-importance data into a generating AI and have the generating AI generate detailed advice.

[0084] The advice-providing unit can apply different advice algorithms depending on the category of the user's sleep data when providing advice. For example, the advice-providing unit can provide advice based on heart rate variability for heart rate data. It can also provide advice based on body temperature variability for body temperature data. Furthermore, it can provide advice based on movement patterns for movement data. By applying different advice algorithms depending on the category of sleep data, more accurate advice can be provided. Some or all of the above processing in the advice-providing unit may be performed using AI, for example, or without AI. For example, the advice-providing unit can input data from different categories into a generating AI and have the generating AI generate advice according to the category.

[0085] The advice-providing unit can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is stressed, the advice-providing unit can provide short, concise advice. If the user is relaxed, the advice-providing unit can also provide detailed advice. Furthermore, if the user is excited, the advice-providing unit can provide concise, visually stimulating advice. By adjusting the length of the advice based on the user's emotions, more effective advice can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice-providing unit may be performed using AI, for example, or not using AI. For example, the advice-providing unit can input user emotion data into the generative AI and have the generative AI adjust the length of the advice.

[0086] The advice-providing unit can prioritize advice based on when the user's sleep data is collected. For example, the advice-providing unit can provide advice based on recently collected data. It can also provide advice based on data collected based on specific events (e.g., after exercise, after a meal). Furthermore, it can provide advice based on data that may influence the user's sleep patterns. This enables efficient advice by prioritizing advice based on when sleep data is collected. Some or all of the above processing in the advice-providing unit may be performed using AI, for example, or without AI. For example, the advice-providing unit can input data based on collection timing into a generating AI and have the generating AI determine the priority of advice.

[0087] The advice-providing unit can adjust the order of advice based on the relevance of the user's sleep data when providing advice. For example, the advice-providing unit can provide advice based on highly relevant data. It can also postpone advice based on less relevant data. Furthermore, the advice-providing unit can adjust the level of detail of the advice according to its relevance. This allows for efficient advice by adjusting the order of advice based on the relevance of sleep data. Some or all of the above processing in the advice-providing unit may be performed using AI, for example, or without AI. For example, the advice-providing unit can input highly relevant data into a generating AI and have the generating AI execute the order of advice.

[0088] The feedback unit can estimate the user's emotions and adjust the way feedback is displayed based on the estimated emotions. For example, if the user is stressed, the feedback unit can provide a simple and highly visible display. If the user is relaxed, the feedback unit can also provide a display that includes detailed information. Furthermore, if the user is excited, the feedback unit can provide a visually stimulating display. By adjusting the way feedback is displayed based on the user's emotions, it becomes possible to provide a display that is easy for the user to understand. 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 feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0089] The feedback unit can select the optimal display method by referring to the user's past sleep data when displaying feedback. For example, the feedback unit can identify the most effective display method from the user's past sleep data and apply it. The feedback unit can also adjust the level of detail of the feedback based on the user's past sleep data. Furthermore, the feedback unit can select a display method based on specific events (e.g., after exercise, after a meal) by referring to the user's past sleep data. This allows the optimal display method to be selected by referring to past sleep data. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input past sleep data into a generating AI and have the generating AI select the optimal display method.

[0090] The feedback unit can estimate the user's emotions and adjust the feedback operation procedure based on the estimated user emotions. For example, if the user is stressed, the feedback unit can simplify the operation procedure to make it intuitive to use. If the user is relaxed, the feedback unit can also provide detailed operation procedures and suggest customizable operation methods. Furthermore, if the user is excited, the feedback unit can provide operation procedures in a visually stimulating way. This makes it possible to provide user-friendly feedback by adjusting the operation procedure based on 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 feedback unit may be performed using AI or not using AI. For example, the feedback unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the operation procedure.

[0091] The feedback unit can select the optimal display method when displaying feedback, taking into account the user's device information. For example, if the user is using a smartphone, the feedback unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the feedback unit can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the feedback unit can provide a concise and highly visible display method. This allows the optimal display method to be selected by considering the user's device information. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input device information into a generating AI and have the generating AI select the optimal display method.

[0092] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0093] The analysis unit can estimate the user's emotions and evaluate the reliability of the analysis results based on the estimated emotions. For example, if the user is stressed, the reliability of the analysis results may decrease, so the analysis unit will rate their reliability low. Conversely, if the user is relaxed, the reliability of the analysis results will increase, so the analysis unit can rate their reliability high. Furthermore, if the user is excited, the reliability of the analysis results may fluctuate, so the analysis unit can dynamically evaluate their reliability. By evaluating the reliability of the analysis results based on the user's emotions, more accurate analysis results can be provided.

[0094] The data collection unit can monitor the user's sleep environment and adjust the timing of data collection based on environmental data. For example, if the room temperature or humidity is not appropriate, data collection can be temporarily suspended. The data collection unit can also delay data collection if the noise level is high. Furthermore, the data collection unit can adjust data collection if the lighting brightness is not appropriate. By adjusting the timing of data collection based on the user's sleep environment, more accurate data can be collected.

[0095] The advice-providing unit can estimate the user's emotions and adjust the content of the advice based on those emotions. For example, if the user is feeling stressed, it can provide specific advice to reduce stress. If the user is relaxed, it can also provide advice to maintain that relaxed state. Furthermore, if the user is agitated, it can provide advice to calm them down. By adjusting the content of the advice based on the user's emotions, it can provide more effective advice.

[0096] The feedback unit can estimate the user's emotions and adjust the timing of feedback based on those emotions. For example, if the user is stressed, the feedback timing can be delayed to allow the user time to relax. Conversely, if the user is relaxed, the feedback timing can be advanced to help the user maintain their relaxed state. Furthermore, if the user is agitated, the feedback timing can be adjusted to allow the user time to calm down. In this way, by adjusting the timing of feedback based on the user's emotions, more effective feedback can be provided.

[0097] The data collection unit can monitor the user's sleep data in real time and issue alerts if an abnormality is detected. For example, if the heart rate is abnormally high, the data collection unit will alert the user. It can also alert the user if the body temperature is abnormally low. Furthermore, it can alert the user if there is an abnormal lack of movement. This allows for rapid response by detecting abnormalities in real time and issuing alerts to the user.

[0098] The analysis unit can estimate the user's emotions and evaluate the importance of the analysis results based on the estimated emotions. For example, if the user is stressed, the importance of analysis results related to stress will be highly evaluated. Similarly, if the user is relaxed, the importance of analysis results related to relaxation will also be highly evaluated. Furthermore, if the user is excited, the importance of analysis results related to excitement will also be highly evaluated. By evaluating the importance of analysis results based on the user's emotions, more appropriate analysis results can be provided.

[0099] The data collection unit can adjust the frequency of data collection based on the user's activity level when collecting sleep data. For example, the frequency of data collection can be increased after the user has exercised. Conversely, the frequency can be decreased when the user is engaged in sedentary activities. Furthermore, the frequency can be adjusted if the user has been sitting for a long period of time. By adjusting the frequency of data collection based on the user's activity level, more accurate data can be collected.

[0100] The advice-providing unit can estimate the user's emotions and prioritize advice based on those emotions. For example, if the user is stressed, it will prioritize advice to reduce stress. If the user is relaxed, it can prioritize advice to maintain that relaxed state. Furthermore, if the user is agitated, it can prioritize advice to calm them down. By prioritizing advice based on the user's emotions, it can provide more effective advice.

[0101] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on those emotions. For example, if the user is stressed, it can provide feedback to reduce stress. If the user is relaxed, it can provide feedback to maintain that relaxed state. Furthermore, if the user is agitated, it can provide feedback to calm them down. By adjusting the content of the feedback based on the user's emotions, it is possible to provide more effective feedback.

[0102] The data collection unit can adjust the timing of data collection when collecting user sleep data, taking into account the user's meal information. For example, data collection can be temporarily suspended immediately after the user has eaten. It can also adjust the timing of data collection before the user eats. Furthermore, if the user has eaten a specific meal, the timing of data collection can be adjusted to take that into account. By adjusting the timing of data collection based on the user's meal information, more accurate data can be collected.

[0103] The following briefly describes the processing flow for example form 2.

[0104] Step 1: The data collection unit collects detailed sleep data. For example, it uses a smart ring to collect detailed sleep data such as heart rate, body temperature, and movement. The smart ring is worn on the user's finger and records biometric information during sleep in real time. This allows for a detailed understanding of the user's sleep state. Step 2: The analysis unit analyzes the data collected by the data collection unit and calculates the optimal time for each individual to fall asleep. For example, based on the collected data, it analyzes the user's sleep pattern and identifies the optimal time to fall asleep from fluctuations in heart rate and body temperature. This allows the user to know the optimal time to fall asleep for themselves. Step 3: The advice-providing unit provides personalized advice based on the analysis results obtained by the analysis unit, tailored to the depth of sleep and fatigue level. For example, if the user's sleep is light, it provides advice on how to relax; if fatigue is accumulating, it advises them to go to sleep earlier. This allows the user to receive appropriate advice based on their sleep condition. Step 4: The feedback unit visually presents the advice provided by the advice unit. For example, it displays the user's sleep patterns as a graph, visually showing the time it takes to fall asleep and the depth of sleep. This allows the user to intuitively understand their own sleep patterns.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] Each of the multiple elements described above, including the data collection unit, analysis unit, advice provision unit, and feedback 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 detailed sleep data such as heart rate, body temperature, and movement using the smart ring of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, which analyzes the user's sleep pattern based on the collected data and calculates the individual optimal time to fall asleep. The advice provision unit is implemented in the specific processing unit 290 of the data processing unit 12, which provides personalized advice based on the analysis results. The feedback unit is implemented in the control unit 46A of the smart device 14, which displays the user's sleep waveform in a graph, visually indicating the time to fall asleep and the depth of sleep. 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.

[0109] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the data collection unit, analysis unit, advice provision unit, and feedback 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 detailed sleep data such as heart rate, body temperature, and movement using the smart ring of the smart glasses 214. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, which analyzes the user's sleep pattern based on the collected data and calculates the individual optimal time to fall asleep. The advice provision unit is implemented in the specific processing unit 290 of the data processing unit 12, which provides personalized advice based on the analysis results. The feedback unit is implemented in the control unit 46A of the smart glasses 214, which displays the user's sleep waveform in a graph, visually indicating the time to fall asleep and the depth of sleep. 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.

[0125] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the data collection unit, analysis unit, advice provision unit, and feedback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects detailed sleep data such as heart rate, body temperature, and movement using the smart ring of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, which analyzes the user's sleep pattern based on the collected data and calculates the individual optimal time to fall asleep. The advice provision unit is implemented in the specific processing unit 290 of the data processing unit 12, which provides personalized advice based on the analysis results. The feedback unit is implemented in the control unit 46A of the headset terminal 314, which displays the user's sleep waveform in a graph, visually indicating the time to fall asleep and the depth of sleep. 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.

[0141] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the data collection unit, analysis unit, advice provision unit, and feedback unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects detailed sleep data such as heart rate, body temperature, and movement using the smart ring of the robot 414. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, which analyzes the user's sleep pattern based on the collected data and calculates the individual optimal time to fall asleep. The advice provision unit is implemented in the specific processing unit 290 of the data processing unit 12, which provides personalized advice based on the analysis results. The feedback unit is implemented in the control unit 46A of the robot 414, which displays the user's sleep waveform in a graph, visually indicating the time to fall asleep and the depth of sleep. 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] (Note 1) A data collection unit that collects detailed sleep data, An analysis unit analyzes the data collected by the aforementioned collection unit and calculates the optimal time for falling asleep for each individual, Based on the analysis results obtained by the aforementioned analysis unit, an advice provision unit provides personalized advice according to the depth of sleep and fatigue level. The system includes a feedback unit that visually presents the advice provided by the advice-providing unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is A smart ring is used to collect detailed sleep data such as heart rate, body temperature, and movement. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected data, the system analyzes the user's sleep patterns and calculates the optimal time for each individual to fall asleep. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned advice-providing unit, Based on the analysis results, personalized advice is provided tailored to the depth of sleep and fatigue level. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned feedback unit is Visually displays sleep patterns and sleep onset times. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of sleep data collection based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) 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 8) The aforementioned collection unit is When collecting sleep data, 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 9) 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 10) The aforementioned collection unit is When collecting sleep data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting sleep data, the system analyzes the user's social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the sleep data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of sleep data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of analysis is determined based on when the sleep data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the sleep data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned advice-providing unit, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned advice-providing unit, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned advice-providing unit, When providing advice, the level of detail in the advice is adjusted based on the importance of the user's sleep data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned advice-providing unit, When providing advice, different advice algorithms are applied depending on the category of the user's sleep data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned advice-providing unit, It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned advice-providing unit, When providing advice, we prioritize the advice based on when the user's sleep data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned advice-providing unit, When providing advice, the order of advice is adjusted based on the relevance of the user's sleep data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is It estimates the user's emotions and adjusts how feedback is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback unit is When displaying feedback, the system selects the optimal display method by referring to the user's past sleep data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is It estimates the user's emotions and adjusts the feedback process based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is When displaying feedback, the system selects the optimal display method considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0177] 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 detailed sleep data, An analysis unit analyzes the data collected by the aforementioned collection unit and calculates the optimal time for falling asleep for each individual, Based on the analysis results obtained by the aforementioned analysis unit, an advice provision unit provides personalized advice according to the depth of sleep and fatigue level. The system includes a feedback unit that visually presents the advice provided by the advice-providing unit. A system characterized by the following features.

2. The aforementioned collection unit is A smart ring is used to collect detailed sleep data such as heart rate, body temperature, and movement. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected data, the system analyzes the user's sleep patterns and calculates the optimal time for each individual to fall asleep. The system according to feature 1.

4. The aforementioned advice-providing unit, Based on the analysis results, personalized advice is provided tailored to the depth of sleep and fatigue level. The system according to feature 1.

5. The aforementioned feedback unit is Visually displays sleep patterns and sleep onset times. The system according to feature 1.

6. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of sleep data collection based on those emotions. The system according to feature 1.

7. 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.

8. The aforementioned collection unit is When collecting sleep data, filtering is performed based on the user's current health status and lifestyle. The system according to feature 1.

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