system

Abstract

The system according to this embodiment aims to enable business people to effectively utilize their morning time. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a suggestion unit, an alarm unit, and a feedback unit. The data collection unit collects sleep data. The analysis unit analyzes the data collected by the data collection unit. The suggestion unit suggests an ideal wake-up time based on the analysis results obtained by the analysis unit. The alarm unit sets an alarm optimized for the wake-up time suggested by the suggestion unit. The feedback unit provides daily feedback.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that many business people had difficulty waking up in the morning and could not effectively utilize the morning time.

[0005] The system according to the embodiment aims to enable business people to effectively utilize the morning time.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a suggestion unit, an alarm unit, and a feedback unit. The data collection unit collects sleep data. The analysis unit analyzes the data collected by the data collection unit. The suggestion unit suggests an ideal wake-up time based on the analysis results obtained by the analysis unit. The alarm unit sets an alarm optimized for the wake-up time suggested by the suggestion unit. The feedback unit provides daily feedback. [Effects of the Invention]

[0007] The system according to this embodiment can enable business people to make effective use of their morning time. [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, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". 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 system according to an embodiment of the present invention is a system that uses an AI agent to support business people in waking up early and improve productivity by making effective use of their morning time. This system is a mechanism that supports business people in waking up early and improves productivity by making effective use of their morning time. Specifically, it consists of the following steps. First, it collects sleep data in conjunction with a wearable device and generates AI to acquire this data. Next, based on the acquired data, the generating AI analyzes individual sleep patterns and proposes an ideal wake-up time. An alarm optimized for the proposed wake-up time is set, and continuous improvement is promoted through daily feedback. This mechanism makes it possible to engage in morning activities and improves overall productivity. It also enables the continuation of a healthy lifestyle and improves employee job satisfaction. The target is business people who waste their morning time, and it will be provided as a smartphone app, as well as customized solutions for companies. As the next step, partnership building and market deployment are planned, aiming for further service evolution using sleep data. For example, the system analyzes individual sleep patterns and proposes an ideal wake-up time so that business people can make effective use of their morning time. For example, the system works with wearable devices to collect sleep data, and a generative AI retrieves that data. For example, based on the retrieved data, the generative AI analyzes individual sleep patterns and suggests an ideal wake-up time. For example, the system sets an alarm optimized for the suggested wake-up time and promotes continuous improvement through daily feedback. This enables the system to facilitate morning activities and improve overall productivity. In this way, the system can support business people in waking up early and improve productivity by making effective use of morning time.

[0029] The system according to the embodiment comprises a data collection unit, an analysis unit, a suggestion unit, an alarm unit, and a feedback unit. The data collection unit collects sleep data. The data collection unit collects sleep data in cooperation with, for example, a wearable device. The data collection unit can collect user sleep data using, for example, a wearable device such as a smartwatch or a fitness tracker. The data collection unit can collect data such as heart rate, respiratory rate, sleep duration, and sleep quality. The data collection unit can input data acquired from a wearable device into a generating AI, for example, and the generating AI can analyze that data. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the collected data using, for example, a generating AI. The analysis unit suggests, for example, an ideal wake-up time, based on the individual sleep patterns analyzed by the generating AI. The analysis unit can calculate the ideal wake-up time, for example, by considering the sleep cycle and the user's lifestyle rhythm. The analysis unit can analyze individual sleep patterns using, for example, a data analysis algorithm or a machine learning model, based on the generating AI. The suggestion unit proposes an ideal wake-up time based on the analysis results obtained by the analysis unit. The suggestion unit proposes an ideal wake-up time, for example, using a generative AI. The suggestion unit can propose the optimal wake-up time for the user based on the analysis results using a generative AI. The suggestion unit can propose an ideal wake-up time, for example, by considering the user's lifestyle and sleep cycle using a generative AI. The alarm unit sets an alarm optimized for the wake-up time proposed by the suggestion unit. The alarm unit can optimize the alarm volume, tone, and vibration pattern based on the proposed wake-up time, for example. The alarm unit can estimate the user's emotions and adjust the alarm settings based on the estimated emotions. The alarm unit can set an alarm with a gentle tone if the user is stressed, for example. The alarm unit can set an alarm at a normal volume if the user is relaxed, for example. The alarm unit can set an alarm with a soft tone if the user is tired, for example. The feedback unit provides daily feedback.The feedback unit can, for example, provide an evaluation of sleep quality and suggestions for improvement based on the user's sleep data. The feedback unit can, for example, estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. The feedback unit can, for example, provide relaxation advice if the user is feeling stressed. The feedback unit can, for example, provide normal feedback if the user is relaxed. The feedback unit can, for example, provide advice prioritizing rest if the user is tired. Thus, the system according to the embodiment can provide effective early rising support through collecting, analyzing, suggesting, setting alarms, and providing feedback on the user's sleep data.

[0030] The data collection unit collects sleep data. For example, the data collection unit collects sleep data in conjunction with wearable devices. Specifically, it can use wearable devices such as smartwatches and fitness trackers to collect data such as the user's heart rate, respiratory rate, sleep duration, and sleep quality. These devices are worn on the user's wrist or chest and continuously record data during nighttime sleep. The collected data is transmitted to a smartphone or cloud server via Bluetooth® or Wi-Fi. The data collection unit centrally manages this data and inputs it into the generating AI as needed. The generating AI analyzes the collected data and evaluates the user's sleep patterns and health status. For example, it can determine whether the user is in deep sleep based on heart rate variability and changes in respiratory rate. Furthermore, by evaluating sleep duration and quality, it can identify the efficiency and areas for improvement of the user's sleep. The data collection unit can improve the overall accuracy and effectiveness of the system by collecting this data in real time and providing it to the analysis and suggestion units. In addition, the data collection unit can encrypt and anonymize data to protect user privacy. This allows the data collection unit to safely and efficiently collect data, contributing to improving the user's sleep.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses generative AI to analyze the collected data. Specifically, the generative AI analyzes individual sleep patterns and proposes an ideal wake-up time. Using data analysis algorithms and machine learning models, the generative AI can calculate the optimal wake-up time, taking into account the user's sleep cycle and lifestyle rhythm. For example, by analyzing fluctuations in the user's heart rate and respiratory rate, it can identify the timing of the transition from deep to light sleep, thereby promoting a natural awakening. Furthermore, based on the user's past sleep data, it can analyze long-term sleep patterns and trends to provide personalized sleep improvement suggestions. The analysis unit provides these analysis results to the proposal unit, providing the foundational data for proposing the optimal wake-up time to the user. In addition, the analysis unit can use anomaly detection algorithms to detect unusual sleep patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term sleep management and anomaly detection, improving the overall reliability and safety of the system.

[0032] The suggestion unit proposes an ideal wake-up time based on the analysis results obtained by the analysis unit. For example, the suggestion unit uses generative AI to propose the ideal wake-up time. Specifically, the generative AI can propose the optimal wake-up time to the user based on the analysis results. The generative AI calculates the optimal wake-up time considering the user's lifestyle and sleep cycle. For example, if the user has an important appointment early in the morning, it can propose an optimal wake-up time to match that appointment. Furthermore, it can analyze long-term sleep patterns and trends based on the user's past sleep data and provide personalized sleep improvement suggestions. The suggestion unit notifies the user of these suggestions and supports them in waking up at the ideal time. In addition, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can evaluate whether the user is satisfied with the proposed wake-up time and adjust the suggestions as needed. This allows the suggestion unit to provide users with the optimal wake-up time and effectively support early rising.

[0033] The alarm unit sets an alarm optimized for the wake-up time suggested by the suggestion unit. For example, the alarm unit can optimize the alarm volume, tone, and vibration pattern based on the suggested wake-up time. Specifically, it estimates the user's emotions and adjusts the alarm settings based on the estimated emotions. For example, if the user is stressed, the alarm can be set to a gentle tone. If the user is relaxed, the alarm can be set to a normal volume. If the user is tired, the alarm can be set to a soft tone. The alarm unit automatically adjusts these settings to help the user wake up comfortably. Furthermore, the alarm unit can collect user feedback and continuously improve the accuracy and effectiveness of the alarm settings. For example, it can evaluate how the user reacted to the alarm sound and adjust the alarm settings as needed. This allows the alarm unit to provide the user with the optimal alarm settings and effectively support early rising.

[0034] The feedback unit provides daily feedback. For example, based on the user's sleep data, the feedback unit can provide an assessment of sleep quality and suggestions for improvement. Specifically, it estimates the user's emotions and adjusts the content of the feedback based on the estimated emotions. For example, if the user is feeling stressed, it can provide advice to help them relax. If the user is relaxed, it can provide normal feedback. If the user is tired, it can provide advice to prioritize rest. The feedback unit notifies the user of this feedback and provides specific advice to help the user improve their sleep quality. Furthermore, the feedback unit can collect user feedback and continuously improve the accuracy and effectiveness of the feedback content. For example, it can evaluate how the user reacted to the feedback provided and adjust the feedback content as needed. In this way, the feedback unit can provide the user with optimal feedback and achieve effective sleep improvement.

[0035] The health promotion department promotes the continuation of a healthy lifestyle. For example, the health promotion department can provide advice to promote the continuation of a healthy lifestyle based on the user's sleep data. For example, the health promotion department can estimate the user's emotions and adjust health promotion methods based on the estimated emotions. For example, if the user is feeling stressed, the health promotion department can suggest relaxing health promotion methods. For example, if the user is relaxed, the health promotion department can suggest normal health promotion methods. For example, if the user is tired, the health promotion department can suggest health promotion methods that prioritize rest. In this way, the health promotion department can improve the user's overall health by promoting the continuation of a healthy lifestyle. Some or all of the above processing in the health promotion department may be performed using AI, for example, or not using AI. For example, the health promotion department can input the user's emotional data into a generating AI and have the generating AI perform the adjustment of health promotion methods.

[0036] The Satisfaction Improvement Department aims to improve employee job satisfaction. For example, the Satisfaction Improvement Department can provide advice to improve user job satisfaction. For example, the Satisfaction Improvement Department can estimate a user's emotions and adjust satisfaction improvement methods based on the estimated emotions. For example, if a user is feeling stressed, the Satisfaction Improvement Department can suggest relaxation-oriented satisfaction improvement methods. For example, if a user is relaxed, the Satisfaction Improvement Department can suggest normal satisfaction improvement methods. For example, if a user is tired, the Satisfaction Improvement Department can suggest satisfaction improvement methods that prioritize rest. In this way, the Satisfaction Improvement Department can improve the work environment by improving employee job satisfaction. Some or all of the above processes in the Satisfaction Improvement Department may be performed using AI, for example, or without AI. For example, the Satisfaction Improvement Department can input user emotion data into a generating AI and have the generating AI adjust the satisfaction improvement methods.

[0037] The customization department provides customized solutions for businesses. For example, the customization department can add features or change settings to meet the needs of a business. For example, the customization department can select the optimal customization method by referring to a business's past data. For example, the customization department can select the optimal customization method based on customization methods that have previously yielded high satisfaction ratings for the business. For example, the customization department can identify the most effective customization method from a business's past data and propose it. For example, the customization department can analyze a business's past data, identify the cause of any unusual patterns, and adjust the customization method accordingly. In this way, the customization department can provide customized solutions to businesses, thereby offering services tailored to their needs. Some or all of the above-described processes in the customization department may be performed using AI, or not. For example, the customization department can input a business's past data into a generating AI and have the generating AI select a customization method.

[0038] The data collection unit can generate sleep data using an AI in conjunction with a wearable device. The data collection unit can collect user sleep data using a wearable device such as a smartwatch or fitness tracker. The data collection unit can collect data such as heart rate, respiratory rate, sleep duration, and sleep quality. The data collection unit can input the data acquired from the wearable device into the generation AI, which can then analyze the data. This allows the data collection unit to acquire more accurate sleep data by working in conjunction with a wearable device. Some or all of the above-described processes in the data collection unit may be performed using an AI, or they may not. For example, the data collection unit can input the data acquired from the wearable device into the generation AI and have the generation AI perform the data acquisition.

[0039] The analysis unit allows the generating AI to analyze individual sleep patterns based on the acquired data. The analysis unit, for example, uses the generating AI to analyze the collected data. The analysis unit, for example, uses the generating AI to analyze individual sleep patterns and propose an ideal wake-up time. The analysis unit, for example, uses the generating AI to calculate an ideal wake-up time considering the sleep cycle and the user's lifestyle rhythm. The analysis unit, for example, uses the generating AI to analyze individual sleep patterns using data analysis algorithms and machine learning models. This allows the analysis unit to accurately analyze individual sleep patterns by using the generating AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the acquired data into the generating AI and have the generating AI perform the sleep pattern analysis.

[0040] The data collection unit can analyze the user's past sleep data and select the optimal data collection method. For example, the data collection unit can set the timing of data collection based on the time periods in the user's past sleep when they had good sleep. For example, the data collection unit can identify the most stable sleep pattern from the user's past sleep data and collect data based on that pattern. For example, the data collection unit can analyze the user's past sleep data, and if an abnormal pattern is found, it can identify the cause and adjust the data collection method. In this way, the data collection unit can select the optimal data collection method by analyzing past data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past sleep data into a generating AI and have the generating AI select the data collection method.

[0041] 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 adjust the timing of data collection considering problems identified in the user's health checkup. For example, the data collection unit can optimize the data collection method based on the user's lifestyle (e.g., exercise habits and eating patterns). For example, the data collection unit can adjust the frequency and timing of data collection considering the user's current health status (e.g., if the user has a cold). This allows the data collection unit to collect 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 data on the user's health status and lifestyle into a generating AI and have the generating AI perform the filtering.

[0042] 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. For example, if the user is at home, the data collection unit will prioritize the collection of sleep data from their usual environment. For example, if the user is on a business trip, the data collection unit will prioritize the collection of sleep data from different environments such as a hotel. In this way, the data collection unit can prioritize the collection of highly relevant data by considering 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 the user's geographical location information into a generating AI and have the generating AI determine the priority of the data.

[0043] 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 uses social media late at night, the data collection unit will consider the impact and collect data accordingly. For example, if the user feels stressed on social media, the data collection unit will consider the impact and collect data accordingly. For example, if the user feels relaxed on social media, the data collection unit will consider the impact and collect data accordingly. In this way, the data collection unit can collect relevant data by analyzing 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 the user's social media activity data into a generating AI and have the generating AI perform the data collection.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the sleep data during the analysis. For example, the analysis unit performs a detailed analysis on important data (e.g., data from deep sleep). For example, the analysis unit performs a simplified analysis on general data (e.g., data from light sleep). For example, the analysis unit performs a special analysis on abnormal data (e.g., abnormal movements during sleep). In this way, the analysis unit can perform a detailed analysis on important data by adjusting the level of detail of the analysis based on the importance of the 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 the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of sleep data during analysis. For example, the analysis unit applies a specific algorithm to deep sleep data. For example, the analysis unit applies a different algorithm to light sleep data. For example, the analysis unit applies a special algorithm to abnormal movements during sleep. This allows the analysis unit to perform more accurate analysis by applying different analysis algorithms depending on the data category. 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 data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0046] 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. For example, the analysis unit may analyze current data while referring to past data. For example, the analysis unit may analyze data for a specific period (e.g., one week) all at once. This allows the analysis unit to prioritize the analysis of the latest data by determining the priority of analysis based on the collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input the data collection timing to a generating AI and have the generating AI determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the sleep data during analysis. For example, the analysis unit may prioritize the analysis of important data (e.g., deep sleep data). For example, the analysis unit may postpone the analysis of general data (e.g., light sleep data). For example, the analysis unit may analyze abnormal data (e.g., abnormal movements during sleep) first. In this way, the analysis unit can prioritize the analysis of important data by adjusting the order of analysis based on the relevance of the 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 may input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0048] The suggestion unit can adjust the level of detail of its suggestions based on the importance of the wake-up time. For example, the suggestion unit will provide detailed suggestions for important wake-up times (e.g., before a meeting). For example, the suggestion unit will provide simple suggestions for general wake-up times. For example, the suggestion unit will provide special suggestions for special wake-up times (e.g., before a trip). In this way, the suggestion unit can provide more appropriate suggestions by adjusting the level of detail of its suggestions based on the importance of the wake-up time. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the importance of the wake-up time into a generating AI and have the generating AI perform the adjustment of the level of detail of the suggestions.

[0049] The suggestion unit can apply different suggestion algorithms depending on the user's lifestyle when making suggestions. For example, if the user is a night owl, the suggestion unit will suggest staying active late into the night. For example, if the user is an early riser, the suggestion unit will suggest waking up early. For example, the suggestion unit will apply the optimal suggestion algorithm according to the user's lifestyle. This allows the suggestion unit to make optimal suggestions according to the user's lifestyle. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's lifestyle data into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0050] The suggestion unit can determine the priority of suggestions based on when wake times are collected. For example, the suggestion unit may prioritize recently collected wake times. For example, the suggestion unit may suggest current wake times while referring to past wake times. For example, the suggestion unit may suggest wake times for a specific period (e.g., one week) as a group. In this way, the suggestion unit can prioritize the latest data by determining the priority of suggestions based on the collection period. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit may input the wake time collection period into a generating AI and have the generating AI perform the determination of the suggestion priority.

[0051] The suggestion unit can adjust the order of suggestions based on the relevance of wake-up times. For example, the suggestion unit may prioritize suggesting important wake-up times (e.g., before a meeting). For example, it may postpone suggesting general wake-up times. For example, it may suggest suggesting special wake-up times (e.g., before a trip) first. In this way, the suggestion unit can prioritize important suggestions by adjusting the order of suggestions based on the relevance of wake-up times. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the relevance of wake-up times into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0052] The alarm unit can select the optimal alarm time by referring to the user's past wake-up history when setting an alarm. For example, the alarm unit sets the alarm time based on the time of day when the user has had good awakenings in the past. For example, the alarm unit identifies the most stable wake-up time from the user's past wake-up history and sets the alarm at that time. For example, the alarm unit analyzes the user's past wake-up history, identifies the cause if an abnormal pattern is found, and adjusts the alarm time accordingly. In this way, the alarm unit can set the optimal alarm time by referring to past wake-up history. Some or all of the above processing in the alarm unit may be performed using AI, for example, or without AI. For example, the alarm unit can input the user's past wake-up history into a generating AI and have the generating AI select the alarm time.

[0053] The alarm unit can customize alarm settings based on the user's current lifestyle when setting an alarm. For example, if the user is on a business trip, the alarm unit will set an alarm to match the schedule at the destination. For example, if the user is on holiday, the alarm unit will set an alarm later than usual. For example, if the user has a special event (e.g., an early morning meeting), the alarm unit will set an alarm to match that event. In this way, the alarm unit can set more appropriate alarms by customizing them based on the user's current lifestyle. Some or all of the above processing in the alarm unit may be performed using AI, for example, or without AI. For example, the alarm unit can input user lifestyle data into a generating AI and have the generating AI perform the customization of alarm settings.

[0054] The alarm unit can select the optimal alarm time by considering the user's geographical location when setting an alarm. For example, if the user is traveling, the alarm unit will set the alarm according to local time. For example, if the user is at home, the alarm unit will set the normal alarm time. For example, if the user is on a business trip, the alarm unit will set the alarm according to the schedule at the business trip destination. In this way, the alarm unit can set the optimal alarm time by considering geographical location information. Some or all of the above processing in the alarm unit may be performed using AI, for example, or without AI. For example, the alarm unit can input the user's geographical location information into a generating AI and have the generating AI perform the selection of the alarm time.

[0055] The alarm unit can analyze the user's social media activity when setting an alarm and suggest appropriate alarm settings. For example, if the user is using social media late into the night, the alarm unit will consider the impact and set the alarm time accordingly. For example, if the user is experiencing stress from social media, the alarm unit will consider the impact and set the alarm time accordingly. For example, if the user is relaxing from social media, the alarm unit will consider the impact and set the alarm time accordingly. In this way, the alarm unit can suggest more appropriate alarm settings by analyzing social media activity. Some or all of the above processing in the alarm unit may be performed using AI, for example, or without AI. For example, the alarm unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of suggesting alarm settings.

[0056] The feedback unit can provide optimal feedback by referring to the user's past sleep data when providing feedback. For example, the feedback unit can provide feedback based on data showing that the user has had good sleep in the past. For example, the feedback unit can identify the most stable sleep pattern from the user's past sleep data and provide feedback based on that pattern. For example, the feedback unit can analyze the user's past sleep data, and if an abnormal pattern is found, it can identify the cause and provide feedback. In this way, the feedback unit can provide optimal feedback 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 the user's past sleep data into a generating AI and have the generating AI perform the task of providing feedback.

[0057] The feedback unit can customize the content of feedback based on the user's current living situation when providing feedback. For example, if the user is on a business trip, the feedback unit will provide feedback tailored to the user's schedule at their destination. For example, if the user is on holiday, the feedback unit will provide relaxing feedback. For example, if the user has a special event (e.g., an early morning meeting), the feedback unit will provide feedback tailored to that event. In this way, the feedback unit can provide more appropriate feedback by customizing it based on the user's current living situation. 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 living situation data into a generating AI and have the generating AI perform the customization of the feedback.

[0058] The feedback unit can provide optimal feedback by considering the user's geographical location information when providing feedback. For example, if the user is traveling, the feedback unit will provide feedback tailored to the local environment. For example, if the user is at home, the feedback unit will provide standard feedback. For example, if the user is on a business trip, the feedback unit will provide feedback tailored to the schedule at the business trip destination. In this way, the feedback unit can provide optimal feedback by considering geographical location 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 the user's geographical location information into a generating AI and have the generating AI perform the task of providing feedback.

[0059] The feedback unit can analyze the user's social media activity and suggest content for the feedback when providing it. For example, if the user uses social media late at night, the feedback unit will consider the impact of that when providing feedback. For example, if the user is experiencing stress on social media, the feedback unit will consider the impact of that when providing feedback. For example, if the user is relaxing on social media, the feedback unit will consider the impact of that when providing feedback. In this way, the feedback unit can provide more appropriate feedback by analyzing social media activity. 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 the user's social media activity data into a generating AI and have the generating AI suggest content for the feedback.

[0060] The health promotion unit can select the optimal health promotion method by referring to the user's past health data during health promotion. For example, the health promotion unit selects a health promotion method based on data showing that the user has previously achieved good health. For example, the health promotion unit identifies the most effective health promotion method from the user's past health data and proposes that method. For example, the health promotion unit analyzes the user's past health data, identifies the cause if an abnormal pattern is found, and adjusts the health promotion method accordingly. In this way, the health promotion unit can select the optimal health promotion method by referring to past health data. Some or all of the above processes in the health promotion unit may be performed using AI, for example, or without AI. For example, the health promotion unit can input the user's past health data into a generating AI and have the generating AI select a health promotion method.

[0061] The health promotion unit can select the optimal health promotion method when promoting health, taking into account the user's geographical location information. For example, if the user is traveling, the health promotion unit will suggest a health promotion method suited to the local environment. For example, if the user is at home, the health promotion unit will suggest a standard health promotion method. For example, if the user is on a business trip, the health promotion unit will suggest a health promotion method suited to the environment of the destination. In this way, the health promotion unit can select the optimal health promotion method by taking geographical location information into consideration. Some or all of the above processing in the health promotion unit may be performed using AI, for example, or without AI. For example, the health promotion unit can input the user's geographical location information into a generating AI and have the generating AI select a health promotion method.

[0062] The satisfaction improvement unit can select the optimal satisfaction improvement method by referring to the user's past workplace satisfaction data when improving satisfaction. For example, the satisfaction improvement unit selects a satisfaction improvement method based on data showing that the user has previously achieved high workplace satisfaction. For example, the satisfaction improvement unit identifies the most effective satisfaction improvement method from the user's past workplace satisfaction data and proposes that method. For example, the satisfaction improvement unit analyzes the user's past workplace satisfaction data, and if an abnormal pattern is found, it identifies the cause and adjusts the satisfaction improvement method. In this way, the satisfaction improvement unit can select the optimal satisfaction improvement method by referring to past workplace satisfaction data. Some or all of the above processing in the satisfaction improvement unit may be performed using AI, for example, or without AI. For example, the satisfaction improvement unit can input the user's past workplace satisfaction data into a generating AI and have the generating AI perform the selection of a satisfaction improvement method.

[0063] The satisfaction improvement unit can select the optimal satisfaction improvement method by considering the user's geographical location information when improving satisfaction. For example, if the user is traveling, the satisfaction improvement unit will suggest a satisfaction improvement method tailored to the local environment. For example, if the user is at home, the satisfaction improvement unit will suggest a standard satisfaction improvement method. For example, if the user is on a business trip, the satisfaction improvement unit will suggest a satisfaction improvement method tailored to the environment of the business trip destination. In this way, the satisfaction improvement unit can select the optimal satisfaction improvement method by considering geographical location information. Some or all of the above processing in the satisfaction improvement unit may be performed using AI, for example, or without using AI. For example, the satisfaction improvement unit can input the user's geographical location information into a generating AI and have the generating AI perform the selection of a satisfaction improvement method.

[0064] The customization unit can select the optimal customization method by referring to the company's past data during the customization process. For example, the customization unit can select the optimal customization method based on customization methods that have resulted in high customer satisfaction in the past. For example, the customization unit can identify the most effective customization method from the company's past data and propose that method. For example, the customization unit can analyze the company's past data, identify the cause if an abnormal pattern is found, and adjust the customization method accordingly. In this way, the customization unit can select the optimal customization method by referring to past data. Some or all of the above processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the company's past data into a generating AI and have the generating AI perform the selection of a customization method.

[0065] The customization unit can select the optimal customization method by considering the geographical location information of the company during the customization process. For example, the customization unit can select the optimal customization method based on customization methods that have yielded high satisfaction rates for the company in a particular region. For example, the customization unit can identify the most effective customization method based on the geographical location information of the company and propose that method. For example, the customization unit can analyze the geographical location information of the company, and if an abnormal pattern is found, it can identify the cause and adjust the customization method. In this way, the customization unit can select the optimal customization method by considering geographical location information. Some or all of the above processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the geographical location information of the company into a generating AI and have the generating AI perform the selection of a customization method.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The data collection unit can analyze the user's past sleep data and select the optimal data collection method. For example, it can set the timing of data collection based on the time periods when the user has had good sleep in the past. It can identify the most stable sleep pattern from the user's past sleep data and collect data based on that pattern. If an abnormal pattern is found in the user's past sleep data, it can identify the cause and adjust the data collection method. In this way, the data collection unit can select the optimal data collection method by analyzing past data. 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 past sleep data into a generating AI and have the generating AI select the data collection method.

[0068] The analysis unit can adjust the level of detail of the analysis based on the importance of the sleep data during the analysis. For example, detailed analysis can be performed on important data (e.g., data from deep sleep). Simplified analysis can be performed on general data (e.g., data from light sleep). Special analysis can be performed on abnormal data (e.g., abnormal movements during sleep). In this way, the analysis unit can perform detailed analysis on important data by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0069] The suggestion unit can adjust the level of detail of its suggestions based on the importance of the wake-up time. For example, it can provide detailed suggestions for important wake-up times (e.g., before a meeting), simplified suggestions for general wake-up times, and special suggestions for special wake-up times (e.g., before a trip). This allows the suggestion unit to provide more appropriate suggestions by adjusting the level of detail based on the importance of the wake-up time. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the importance of the wake-up time into a generating AI and have the generating AI adjust the level of detail of the suggestions.

[0070] The alarm unit can select the optimal alarm time by referring to the user's past wake-up history when setting an alarm. For example, it can set the alarm time based on the time of day when the user has had good awakenings in the past. It can identify the most stable wake-up time from the user's past wake-up history and set the alarm at that time. By analyzing the user's past wake-up history, if an abnormal pattern is found, it can identify the cause and adjust the alarm time. In this way, the alarm unit can set the optimal alarm time by referring to past wake-up history. Some or all of the above processing in the alarm unit may be performed using AI or not. For example, the alarm unit can input the user's past wake-up history into a generating AI and have the generating AI perform the selection of the alarm time.

[0071] The feedback unit can provide optimal feedback by referring to the user's past sleep data when providing feedback. For example, it can provide feedback based on data showing that the user has had good sleep in the past. It can identify the most stable sleep pattern from the user's past sleep data and provide feedback based on that pattern. It can analyze the user's past sleep data, and if an abnormal pattern is found, it can identify the cause and provide feedback. In this way, the feedback unit can provide optimal feedback by referring to past sleep data. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's past sleep data into a generating AI and have the generating AI perform the task of providing feedback.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The data collection unit collects sleep data. The data collection unit collects sleep data in conjunction with, for example, a wearable device. The data collection unit can collect user sleep data using, for example, a wearable device such as a smartwatch or fitness tracker. The data collection unit can collect data such as heart rate, respiratory rate, sleep duration, and sleep quality. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the collected data, for example, using a generative AI. The analysis unit, for example, uses a generative AI to analyze individual sleep patterns and propose an ideal wake-up time. The analysis unit, for example, uses a generative AI to calculate an ideal wake-up time considering sleep cycles and the user's lifestyle rhythm. The analysis unit, for example, uses a generative AI to analyze individual sleep patterns using data analysis algorithms and machine learning models. Step 3: The suggestion unit proposes an ideal wake-up time based on the analysis results obtained by the analysis unit. The suggestion unit proposes an ideal wake-up time, for example, using a generative AI. The suggestion unit can propose an optimal wake-up time for the user based on the analysis results using a generative AI. The suggestion unit can propose an ideal wake-up time, for example, by considering the user's lifestyle and sleep cycle using a generative AI. Step 4: The alarm unit sets an alarm optimized for the wake-up time suggested by the suggestion unit. The alarm unit can, for example, optimize the alarm volume, tone, and vibration pattern based on the suggested wake-up time. The alarm unit can, for example, estimate the user's emotions and adjust the alarm settings based on the estimated emotions. For example, if the user is feeling stressed, the alarm unit can set the alarm with a gentle tone. For example, if the user is relaxed, the alarm unit can set the alarm at a normal volume. For example, if the user is tired, the alarm unit can set the alarm with a soft tone. Step 5: The feedback unit provides daily feedback. For example, the feedback unit can provide sleep quality assessments and improvement suggestions based on the user's sleep data. For example, the feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the user is feeling stressed, the feedback unit can provide advice to help them relax. For example, if the user is relaxed, the feedback unit can provide normal feedback. For example, if the user is tired, the feedback unit can advise them to prioritize rest.

[0074] (Example of form 2) The system according to an embodiment of the present invention is a system that uses an AI agent to support business people in waking up early and improve productivity by making effective use of their morning time. This system is a mechanism that supports business people in waking up early and improves productivity by making effective use of their morning time. Specifically, it consists of the following steps. First, it collects sleep data in conjunction with a wearable device and generates AI to acquire this data. Next, based on the acquired data, the generating AI analyzes individual sleep patterns and proposes an ideal wake-up time. An alarm optimized for the proposed wake-up time is set, and continuous improvement is promoted through daily feedback. This mechanism makes it possible to engage in morning activities and improves overall productivity. It also enables the continuation of a healthy lifestyle and improves employee job satisfaction. The target is business people who waste their morning time, and it will be provided as a smartphone app, as well as customized solutions for companies. As the next step, partnership building and market deployment are planned, aiming for further service evolution using sleep data. For example, the system analyzes individual sleep patterns and proposes an ideal wake-up time so that business people can make effective use of their morning time. For example, the system works with wearable devices to collect sleep data, and a generative AI retrieves that data. For example, based on the retrieved data, the generative AI analyzes individual sleep patterns and suggests an ideal wake-up time. For example, the system sets an alarm optimized for the suggested wake-up time and promotes continuous improvement through daily feedback. This enables the system to facilitate morning activities and improve overall productivity. In this way, the system can support business people in waking up early and improve productivity by making effective use of morning time.

[0075] The system according to the embodiment comprises a data collection unit, an analysis unit, a suggestion unit, an alarm unit, and a feedback unit. The data collection unit collects sleep data. The data collection unit collects sleep data in cooperation with, for example, a wearable device. The data collection unit can collect user sleep data using, for example, a wearable device such as a smartwatch or a fitness tracker. The data collection unit can collect data such as heart rate, respiratory rate, sleep duration, and sleep quality. The data collection unit can input data acquired from a wearable device into a generating AI, for example, and the generating AI can analyze that data. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the collected data using, for example, a generating AI. The analysis unit suggests, for example, an ideal wake-up time, based on the individual sleep patterns analyzed by the generating AI. The analysis unit can calculate the ideal wake-up time, for example, by considering the sleep cycle and the user's lifestyle rhythm. The analysis unit can analyze individual sleep patterns using, for example, a data analysis algorithm or a machine learning model, based on the generating AI. The suggestion unit proposes an ideal wake-up time based on the analysis results obtained by the analysis unit. The suggestion unit proposes an ideal wake-up time, for example, using a generative AI. The suggestion unit can propose the optimal wake-up time for the user based on the analysis results using a generative AI. The suggestion unit can propose an ideal wake-up time, for example, by considering the user's lifestyle and sleep cycle using a generative AI. The alarm unit sets an alarm optimized for the wake-up time proposed by the suggestion unit. The alarm unit can optimize the alarm volume, tone, and vibration pattern based on the proposed wake-up time, for example. The alarm unit can estimate the user's emotions and adjust the alarm settings based on the estimated emotions. The alarm unit can set an alarm with a gentle tone if the user is stressed, for example. The alarm unit can set an alarm at a normal volume if the user is relaxed, for example. The alarm unit can set an alarm with a soft tone if the user is tired, for example. The feedback unit provides daily feedback.The feedback unit can, for example, provide an evaluation of sleep quality and suggestions for improvement based on the user's sleep data. The feedback unit can, for example, estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. The feedback unit can, for example, provide relaxation advice if the user is feeling stressed. The feedback unit can, for example, provide normal feedback if the user is relaxed. The feedback unit can, for example, provide advice prioritizing rest if the user is tired. Thus, the system according to the embodiment can provide effective early rising support through collecting, analyzing, suggesting, setting alarms, and providing feedback on the user's sleep data.

[0076] The data collection unit collects sleep data. For example, the data collection unit collects sleep data in conjunction with wearable devices. Specifically, it can use wearable devices such as smartwatches and fitness trackers to collect data such as the user's heart rate, respiratory rate, sleep duration, and sleep quality. These devices are worn on the user's wrist or chest and continuously record data during nighttime sleep. The collected data is transmitted to a smartphone or cloud server via Bluetooth or Wi-Fi. The data collection unit centrally manages this data and inputs it into the generating AI as needed. The generating AI analyzes the collected data to evaluate the user's sleep patterns and health status. For example, it can determine whether the user is in deep sleep based on heart rate variability and changes in respiratory rate. Furthermore, by evaluating sleep duration and quality, it can identify the efficiency and areas for improvement of the user's sleep. The data collection unit can improve the overall accuracy and effectiveness of the system by collecting this data in real time and providing it to the analysis and suggestion units. In addition, the data collection unit can encrypt and anonymize data to protect user privacy. This allows the data collection unit to safely and efficiently collect data, contributing to improving the user's sleep.

[0077] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses generative AI to analyze the collected data. Specifically, the generative AI analyzes individual sleep patterns and proposes an ideal wake-up time. Using data analysis algorithms and machine learning models, the generative AI can calculate the optimal wake-up time, taking into account the user's sleep cycle and lifestyle rhythm. For example, by analyzing fluctuations in the user's heart rate and respiratory rate, it can identify the timing of the transition from deep to light sleep, thereby promoting a natural awakening. Furthermore, based on the user's past sleep data, it can analyze long-term sleep patterns and trends to provide personalized sleep improvement suggestions. The analysis unit provides these analysis results to the proposal unit, providing the foundational data for proposing the optimal wake-up time to the user. In addition, the analysis unit can use anomaly detection algorithms to detect unusual sleep patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term sleep management and anomaly detection, improving the overall reliability and safety of the system.

[0078] The suggestion unit proposes an ideal wake-up time based on the analysis results obtained by the analysis unit. For example, the suggestion unit uses generative AI to propose the ideal wake-up time. Specifically, the generative AI can propose the optimal wake-up time to the user based on the analysis results. The generative AI calculates the optimal wake-up time considering the user's lifestyle and sleep cycle. For example, if the user has an important appointment early in the morning, it can propose an optimal wake-up time to match that appointment. Furthermore, it can analyze long-term sleep patterns and trends based on the user's past sleep data and provide personalized sleep improvement suggestions. The suggestion unit notifies the user of these suggestions and supports them in waking up at the ideal time. In addition, the suggestion unit can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, it can evaluate whether the user is satisfied with the proposed wake-up time and adjust the suggestions as needed. This allows the suggestion unit to provide users with the optimal wake-up time and effectively support early rising.

[0079] The alarm unit sets an alarm optimized for the wake-up time suggested by the suggestion unit. For example, the alarm unit can optimize the alarm volume, tone, and vibration pattern based on the suggested wake-up time. Specifically, it estimates the user's emotions and adjusts the alarm settings based on the estimated emotions. For example, if the user is stressed, the alarm can be set to a gentle tone. If the user is relaxed, the alarm can be set to a normal volume. If the user is tired, the alarm can be set to a soft tone. The alarm unit automatically adjusts these settings to help the user wake up comfortably. Furthermore, the alarm unit can collect user feedback and continuously improve the accuracy and effectiveness of the alarm settings. For example, it can evaluate how the user reacted to the alarm sound and adjust the alarm settings as needed. This allows the alarm unit to provide the user with the optimal alarm settings and effectively support early rising.

[0080] The feedback unit provides daily feedback. For example, based on the user's sleep data, the feedback unit can provide an assessment of sleep quality and suggestions for improvement. Specifically, it estimates the user's emotions and adjusts the content of the feedback based on the estimated emotions. For example, if the user is feeling stressed, it can provide advice to help them relax. If the user is relaxed, it can provide normal feedback. If the user is tired, it can provide advice to prioritize rest. The feedback unit notifies the user of this feedback and provides specific advice to help the user improve their sleep quality. Furthermore, the feedback unit can collect user feedback and continuously improve the accuracy and effectiveness of the feedback content. For example, it can evaluate how the user reacted to the feedback provided and adjust the feedback content as needed. In this way, the feedback unit can provide the user with optimal feedback and achieve effective sleep improvement.

[0081] The health promotion department promotes the continuation of a healthy lifestyle. For example, the health promotion department can provide advice to promote the continuation of a healthy lifestyle based on the user's sleep data. For example, the health promotion department can estimate the user's emotions and adjust health promotion methods based on the estimated emotions. For example, if the user is feeling stressed, the health promotion department can suggest relaxing health promotion methods. For example, if the user is relaxed, the health promotion department can suggest normal health promotion methods. For example, if the user is tired, the health promotion department can suggest health promotion methods that prioritize rest. In this way, the health promotion department can improve the user's overall health by promoting the continuation of a healthy lifestyle. Some or all of the above processing in the health promotion department may be performed using AI, for example, or not using AI. For example, the health promotion department can input the user's emotional data into a generating AI and have the generating AI perform the adjustment of health promotion methods.

[0082] The Satisfaction Improvement Department aims to improve employee job satisfaction. For example, the Satisfaction Improvement Department can provide advice to improve user job satisfaction. For example, the Satisfaction Improvement Department can estimate a user's emotions and adjust satisfaction improvement methods based on the estimated emotions. For example, if a user is feeling stressed, the Satisfaction Improvement Department can suggest relaxation-oriented satisfaction improvement methods. For example, if a user is relaxed, the Satisfaction Improvement Department can suggest normal satisfaction improvement methods. For example, if a user is tired, the Satisfaction Improvement Department can suggest satisfaction improvement methods that prioritize rest. In this way, the Satisfaction Improvement Department can improve the work environment by improving employee job satisfaction. Some or all of the above processes in the Satisfaction Improvement Department may be performed using AI, for example, or without AI. For example, the Satisfaction Improvement Department can input user emotion data into a generating AI and have the generating AI adjust the satisfaction improvement methods.

[0083] The customization department provides customized solutions for businesses. For example, the customization department can add features or change settings to meet the needs of a business. For example, the customization department can select the optimal customization method by referring to a business's past data. For example, the customization department can select the optimal customization method based on customization methods that have previously yielded high satisfaction ratings for the business. For example, the customization department can identify the most effective customization method from a business's past data and propose it. For example, the customization department can analyze a business's past data, identify the cause of any unusual patterns, and adjust the customization method accordingly. In this way, the customization department can provide customized solutions to businesses, thereby offering services tailored to their needs. Some or all of the above-described processes in the customization department may be performed using AI, or not. For example, the customization department can input a business's past data into a generating AI and have the generating AI select a customization method.

[0084] The data collection unit can generate sleep data using an AI in conjunction with a wearable device. The data collection unit can collect user sleep data using a wearable device such as a smartwatch or fitness tracker. The data collection unit can collect data such as heart rate, respiratory rate, sleep duration, and sleep quality. The data collection unit can input the data acquired from the wearable device into the generation AI, which can then analyze the data. This allows the data collection unit to acquire more accurate sleep data by working in conjunction with a wearable device. Some or all of the above-described processes in the data collection unit may be performed using an AI, or they may not. For example, the data collection unit can input the data acquired from the wearable device into the generation AI and have the generation AI perform the data acquisition.

[0085] The analysis unit allows the generating AI to analyze individual sleep patterns based on the acquired data. The analysis unit, for example, uses the generating AI to analyze the collected data. The analysis unit, for example, uses the generating AI to analyze individual sleep patterns and propose an ideal wake-up time. The analysis unit, for example, uses the generating AI to calculate an ideal wake-up time considering the sleep cycle and the user's lifestyle rhythm. The analysis unit, for example, uses the generating AI to analyze individual sleep patterns using data analysis algorithms and machine learning models. This allows the analysis unit to accurately analyze individual sleep patterns by using the generating AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the acquired data into the generating AI and have the generating AI perform the sleep pattern analysis.

[0086] 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 will collect sleep data during times when the user is relaxed. For example, if the user is relaxed, the data collection unit will collect data based on the user's normal sleep cycle. For example, if the user is tired, the data collection unit will collect data earlier and prioritize rest. This allows the data collection unit to collect more appropriate data by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the user's emotion data into the generative AI and have the generative AI adjust the collection timing.

[0087] 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 set the timing of data collection based on the time periods in the user's past sleep when they had good sleep. For example, the data collection unit can identify the most stable sleep pattern from the user's past sleep data and collect data based on that pattern. For example, the data collection unit can analyze the user's past sleep data, and if an abnormal pattern is found, it can identify the cause and adjust the data collection method. In this way, the data collection unit can select the optimal data collection method by analyzing past data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past sleep data into a generating AI and have the generating AI select the data collection method.

[0088] 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 adjust the timing of data collection considering problems identified in the user's health checkup. For example, the data collection unit can optimize the data collection method based on the user's lifestyle (e.g., exercise habits and eating patterns). For example, the data collection unit can adjust the frequency and timing of data collection considering the user's current health status (e.g., if the user has a cold). This allows the data collection unit to collect 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 data on the user's health status and lifestyle into a generating AI and have the generating AI perform the filtering.

[0089] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit will prioritize collecting stress-related data. For example, if the user is relaxed, the data collection unit will prioritize collecting normal sleep data. For example, if the user is tired, the data collection unit will prioritize collecting data related to fatigue recovery. In this way, the data collection unit can prioritize the collection of important data by determining the priority of data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the determination of data priority.

[0090] 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. For example, if the user is at home, the data collection unit will prioritize the collection of sleep data from their usual environment. For example, if the user is on a business trip, the data collection unit will prioritize the collection of sleep data from different environments such as a hotel. In this way, the data collection unit can prioritize the collection of highly relevant data by considering 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 the user's geographical location information into a generating AI and have the generating AI determine the priority of the data.

[0091] 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 uses social media late at night, the data collection unit will consider the impact and collect data accordingly. For example, if the user feels stressed on social media, the data collection unit will consider the impact and collect data accordingly. For example, if the user feels relaxed on social media, the data collection unit will consider the impact and collect data accordingly. In this way, the data collection unit can collect relevant data by analyzing 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 the user's social media activity data into a generating AI and have the generating AI perform the data collection.

[0092] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit provides a simple and easy-to-understand analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. For example, if the user is tired, the analysis unit provides a concise analysis result. In this way, the analysis unit can provide more appropriate analysis results by adjusting the presentation of the analysis according to the user's emotions. 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 the generative AI and have the generative AI adjust the presentation of the analysis.

[0093] The analysis unit can adjust the level of detail of the analysis based on the importance of the sleep data during the analysis. For example, the analysis unit performs a detailed analysis on important data (e.g., data from deep sleep). For example, the analysis unit performs a simplified analysis on general data (e.g., data from light sleep). For example, the analysis unit performs a special analysis on abnormal data (e.g., abnormal movements during sleep). In this way, the analysis unit can perform a detailed analysis on important data by adjusting the level of detail of the analysis based on the importance of the 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 the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0094] The analysis unit can apply different analysis algorithms depending on the category of sleep data during analysis. For example, the analysis unit applies a specific algorithm to deep sleep data. For example, the analysis unit applies a different algorithm to light sleep data. For example, the analysis unit applies a special algorithm to abnormal movements during sleep. This allows the analysis unit to perform more accurate analysis by applying different analysis algorithms depending on the data category. 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 data category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0095] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. For example, if the user is tired, the analysis unit provides a brief analysis result. In this way, the analysis unit can provide more appropriate analysis results by adjusting the length of the analysis according to the user's emotions. 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0096] 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. For example, the analysis unit may analyze current data while referring to past data. For example, the analysis unit may analyze data for a specific period (e.g., one week) all at once. This allows the analysis unit to prioritize the analysis of the latest data by determining the priority of analysis based on the collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input the data collection timing to a generating AI and have the generating AI determine the priority of analysis.

[0097] The analysis unit can adjust the order of analysis based on the relevance of the sleep data during analysis. For example, the analysis unit may prioritize the analysis of important data (e.g., deep sleep data). For example, the analysis unit may postpone the analysis of general data (e.g., light sleep data). For example, the analysis unit may analyze abnormal data (e.g., abnormal movements during sleep) first. In this way, the analysis unit can prioritize the analysis of important data by adjusting the order of analysis based on the relevance of the 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 may input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0098] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will present simple and easy-to-understand suggestions. If the user is relaxed, the suggestion unit will present detailed suggestions. If the user is tired, the suggestion unit will present concise suggestions. In this way, the suggestion unit can provide more appropriate suggestions by adjusting the way it presents its suggestions according to 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-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way it presents its suggestions.

[0099] The suggestion unit can adjust the level of detail of its suggestions based on the importance of the wake-up time. For example, the suggestion unit will provide detailed suggestions for important wake-up times (e.g., before a meeting). For example, the suggestion unit will provide simple suggestions for general wake-up times. For example, the suggestion unit will provide special suggestions for special wake-up times (e.g., before a trip). In this way, the suggestion unit can provide more appropriate suggestions by adjusting the level of detail of its suggestions based on the importance of the wake-up time. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the importance of the wake-up time into a generating AI and have the generating AI perform the adjustment of the level of detail of the suggestions.

[0100] The suggestion unit can apply different suggestion algorithms depending on the user's lifestyle when making suggestions. For example, if the user is a night owl, the suggestion unit will suggest staying active late into the night. For example, if the user is an early riser, the suggestion unit will suggest waking up early. For example, the suggestion unit will apply the optimal suggestion algorithm according to the user's lifestyle. This allows the suggestion unit to make optimal suggestions according to the user's lifestyle. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's lifestyle data into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0101] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit will provide short, concise suggestions. If the user is relaxed, the suggestion unit will provide detailed suggestions. If the user is tired, the suggestion unit will provide brief suggestions. In this way, the suggestion unit can provide more appropriate suggestions by adjusting the length of the suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of the suggestions.

[0102] The suggestion unit can determine the priority of suggestions based on when wake times are collected. For example, the suggestion unit may prioritize recently collected wake times. For example, the suggestion unit may suggest current wake times while referring to past wake times. For example, the suggestion unit may suggest wake times for a specific period (e.g., one week) as a group. In this way, the suggestion unit can prioritize the latest data by determining the priority of suggestions based on the collection period. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit may input the wake time collection period into a generating AI and have the generating AI perform the determination of the suggestion priority.

[0103] The suggestion unit can adjust the order of suggestions based on the relevance of wake-up times. For example, the suggestion unit may prioritize suggesting important wake-up times (e.g., before a meeting). For example, it may postpone suggesting general wake-up times. For example, it may suggest suggesting special wake-up times (e.g., before a trip) first. In this way, the suggestion unit can prioritize important suggestions by adjusting the order of suggestions based on the relevance of wake-up times. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the relevance of wake-up times into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0104] The alarm unit can estimate the user's emotions and adjust the alarm volume and tone based on the estimated emotions. For example, if the user is stressed, the alarm unit will set the alarm to a gentle tone. If the user is relaxed, the alarm unit will set the alarm to a normal volume. If the user is tired, the alarm unit will set the alarm to a soft tone. In this way, the alarm unit can provide a more comfortable awakening by adjusting the alarm volume and tone according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 alarm unit may be performed using AI, for example, or without AI. For example, the alarm unit can input user emotion data into the generative AI and have the generative AI adjust the alarm volume and tone.

[0105] The alarm unit can select the optimal alarm time by referring to the user's past wake-up history when setting an alarm. For example, the alarm unit sets the alarm time based on the time of day when the user has had good awakenings in the past. For example, the alarm unit identifies the most stable wake-up time from the user's past wake-up history and sets the alarm at that time. For example, the alarm unit analyzes the user's past wake-up history, identifies the cause if an abnormal pattern is found, and adjusts the alarm time accordingly. In this way, the alarm unit can set the optimal alarm time by referring to past wake-up history. Some or all of the above processing in the alarm unit may be performed using AI, for example, or without AI. For example, the alarm unit can input the user's past wake-up history into a generating AI and have the generating AI select the alarm time.

[0106] The alarm unit can customize alarm settings based on the user's current lifestyle when setting an alarm. For example, if the user is on a business trip, the alarm unit will set an alarm to match the schedule at the destination. For example, if the user is on holiday, the alarm unit will set an alarm later than usual. For example, if the user has a special event (e.g., an early morning meeting), the alarm unit will set an alarm to match that event. In this way, the alarm unit can set more appropriate alarms by customizing them based on the user's current lifestyle. Some or all of the above processing in the alarm unit may be performed using AI, for example, or without AI. For example, the alarm unit can input user lifestyle data into a generating AI and have the generating AI perform the customization of alarm settings.

[0107] The alarm unit can estimate the user's emotions and determine alarm priorities based on the estimated emotions. For example, if the user is stressed, the alarm unit will prioritize a calm-sounding alarm. If the user is relaxed, the alarm unit will prioritize a normal alarm. If the user is tired, the alarm unit will prioritize a gentle-sounding alarm. In this way, the alarm unit can provide more appropriate alarms by determining alarm priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the alarm unit may be performed using AI, or not using AI. For example, the alarm unit can input user emotion data into a generative AI and have the generative AI determine the alarm priorities.

[0108] The alarm unit can select the optimal alarm time by considering the user's geographical location when setting an alarm. For example, if the user is traveling, the alarm unit will set the alarm according to local time. For example, if the user is at home, the alarm unit will set the normal alarm time. For example, if the user is on a business trip, the alarm unit will set the alarm according to the schedule at the business trip destination. In this way, the alarm unit can set the optimal alarm time by considering geographical location information. Some or all of the above processing in the alarm unit may be performed using AI, for example, or without AI. For example, the alarm unit can input the user's geographical location information into a generating AI and have the generating AI perform the selection of the alarm time.

[0109] The alarm unit can analyze the user's social media activity when setting an alarm and suggest appropriate alarm settings. For example, if the user is using social media late into the night, the alarm unit will consider the impact and set the alarm time accordingly. For example, if the user is experiencing stress from social media, the alarm unit will consider the impact and set the alarm time accordingly. For example, if the user is relaxing from social media, the alarm unit will consider the impact and set the alarm time accordingly. In this way, the alarm unit can suggest more appropriate alarm settings by analyzing social media activity. Some or all of the above processing in the alarm unit may be performed using AI, for example, or without AI. For example, the alarm unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of suggesting alarm settings.

[0110] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the user is stressed, the feedback unit can provide advice to help them relax. For example, if the user is relaxed, the feedback unit can provide normal feedback. For example, if the user is tired, the feedback unit can provide advice to prioritize rest. In this way, the feedback unit can provide more appropriate feedback by adjusting the content of the feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input user emotion data into the generative AI and have the generative AI adjust the content of the feedback.

[0111] The feedback unit can provide optimal feedback by referring to the user's past sleep data when providing feedback. For example, the feedback unit can provide feedback based on data showing that the user has had good sleep in the past. For example, the feedback unit can identify the most stable sleep pattern from the user's past sleep data and provide feedback based on that pattern. For example, the feedback unit can analyze the user's past sleep data, and if an abnormal pattern is found, it can identify the cause and provide feedback. In this way, the feedback unit can provide optimal feedback 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 the user's past sleep data into a generating AI and have the generating AI perform the task of providing feedback.

[0112] The feedback unit can customize the content of feedback based on the user's current living situation when providing feedback. For example, if the user is on a business trip, the feedback unit will provide feedback tailored to the user's schedule at their destination. For example, if the user is on holiday, the feedback unit will provide relaxing feedback. For example, if the user has a special event (e.g., an early morning meeting), the feedback unit will provide feedback tailored to that event. In this way, the feedback unit can provide more appropriate feedback by customizing it based on the user's current living situation. 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 living situation data into a generating AI and have the generating AI perform the customization of the feedback.

[0113] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, if the user is stressed, the feedback unit will prioritize providing relaxing feedback. For example, if the user is relaxed, the feedback unit will prioritize providing normal feedback. For example, if the user is tired, the feedback unit will prioritize providing restful feedback. In this way, the feedback unit can provide more appropriate feedback by determining the priority of feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the 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 determine the priority of feedback.

[0114] The feedback unit can provide optimal feedback by considering the user's geographical location information when providing feedback. For example, if the user is traveling, the feedback unit will provide feedback tailored to the local environment. For example, if the user is at home, the feedback unit will provide standard feedback. For example, if the user is on a business trip, the feedback unit will provide feedback tailored to the schedule at the business trip destination. In this way, the feedback unit can provide optimal feedback by considering geographical location 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 the user's geographical location information into a generating AI and have the generating AI perform the task of providing feedback.

[0115] The feedback unit can analyze the user's social media activity and suggest content for the feedback when providing it. For example, if the user uses social media late at night, the feedback unit will consider the impact of that when providing feedback. For example, if the user is experiencing stress on social media, the feedback unit will consider the impact of that when providing feedback. For example, if the user is relaxing on social media, the feedback unit will consider the impact of that when providing feedback. In this way, the feedback unit can provide more appropriate feedback by analyzing social media activity. 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 the user's social media activity data into a generating AI and have the generating AI suggest content for the feedback.

[0116] The health promotion unit can estimate the user's emotions and adjust health promotion methods based on the estimated emotions. For example, if the user is stressed, the health promotion unit will suggest a relaxing health promotion method. For example, if the user is relaxed, the health promotion unit will suggest a normal health promotion method. For example, if the user is tired, the health promotion unit will suggest a health promotion method that prioritizes rest. In this way, the health promotion unit can provide more appropriate health promotion by adjusting health promotion methods according to the user's emotions. 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 health promotion unit may be performed using AI, for example, or without AI. For example, the health promotion unit can input user emotion data into a generative AI and have the generative AI adjust the health promotion methods.

[0117] The health promotion unit can select the optimal health promotion method by referring to the user's past health data during health promotion. For example, the health promotion unit selects a health promotion method based on data showing that the user has previously achieved good health. For example, the health promotion unit identifies the most effective health promotion method from the user's past health data and proposes that method. For example, the health promotion unit analyzes the user's past health data, identifies the cause if an abnormal pattern is found, and adjusts the health promotion method accordingly. In this way, the health promotion unit can select the optimal health promotion method by referring to past health data. Some or all of the above processes in the health promotion unit may be performed using AI, for example, or without AI. For example, the health promotion unit can input the user's past health data into a generating AI and have the generating AI select a health promotion method.

[0118] The health promotion unit can select the optimal health promotion method when promoting health, taking into account the user's geographical location information. For example, if the user is traveling, the health promotion unit will suggest a health promotion method suited to the local environment. For example, if the user is at home, the health promotion unit will suggest a standard health promotion method. For example, if the user is on a business trip, the health promotion unit will suggest a health promotion method suited to the environment of the destination. In this way, the health promotion unit can select the optimal health promotion method by taking geographical location information into consideration. Some or all of the above processing in the health promotion unit may be performed using AI, for example, or without AI. For example, the health promotion unit can input the user's geographical location information into a generating AI and have the generating AI select a health promotion method.

[0119] The satisfaction improvement unit can estimate the user's emotions and adjust the satisfaction improvement method based on the estimated user emotions. For example, if the user is stressed, the satisfaction improvement unit will suggest a relaxation method to improve satisfaction. For example, if the user is relaxed, the satisfaction improvement unit will suggest a normal satisfaction improvement method. For example, if the user is tired, the satisfaction improvement unit will suggest a satisfaction improvement method that prioritizes rest. In this way, the satisfaction improvement unit can improve satisfaction more appropriately by adjusting the satisfaction improvement method according to the user's emotions. 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 satisfaction improvement unit may be performed using AI, for example, or without using AI. For example, the satisfaction improvement unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the satisfaction improvement method.

[0120] The satisfaction improvement unit can select the optimal satisfaction improvement method by referring to the user's past workplace satisfaction data when improving satisfaction. For example, the satisfaction improvement unit selects a satisfaction improvement method based on data showing that the user has previously achieved high workplace satisfaction. For example, the satisfaction improvement unit identifies the most effective satisfaction improvement method from the user's past workplace satisfaction data and proposes that method. For example, the satisfaction improvement unit analyzes the user's past workplace satisfaction data, and if an abnormal pattern is found, it identifies the cause and adjusts the satisfaction improvement method. In this way, the satisfaction improvement unit can select the optimal satisfaction improvement method by referring to past workplace satisfaction data. Some or all of the above processing in the satisfaction improvement unit may be performed using AI, for example, or without AI. For example, the satisfaction improvement unit can input the user's past workplace satisfaction data into a generating AI and have the generating AI perform the selection of a satisfaction improvement method.

[0121] The satisfaction improvement unit can select the optimal satisfaction improvement method by considering the user's geographical location information when improving satisfaction. For example, if the user is traveling, the satisfaction improvement unit will suggest a satisfaction improvement method tailored to the local environment. For example, if the user is at home, the satisfaction improvement unit will suggest a standard satisfaction improvement method. For example, if the user is on a business trip, the satisfaction improvement unit will suggest a satisfaction improvement method tailored to the environment of the business trip destination. In this way, the satisfaction improvement unit can select the optimal satisfaction improvement method by considering geographical location information. Some or all of the above processing in the satisfaction improvement unit may be performed using AI, for example, or without using AI. For example, the satisfaction improvement unit can input the user's geographical location information into a generating AI and have the generating AI perform the selection of a satisfaction improvement method.

[0122] The customization unit can estimate the user's emotions and adjust the customization content based on the estimated emotions. For example, if the user is stressed, the customization unit will suggest relaxing customization content. For example, if the user is relaxed, the customization unit will suggest normal customization content. For example, if the user is tired, the customization unit will suggest customization content that prioritizes rest. In this way, the customization unit can provide more appropriate customization by adjusting the customization content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the customization content.

[0123] The customization unit can select the optimal customization method by referring to the company's past data during the customization process. For example, the customization unit can select the optimal customization method based on customization methods that have resulted in high customer satisfaction in the past. For example, the customization unit can identify the most effective customization method from the company's past data and propose that method. For example, the customization unit can analyze the company's past data, identify the cause if an abnormal pattern is found, and adjust the customization method accordingly. In this way, the customization unit can select the optimal customization method by referring to past data. Some or all of the above processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the company's past data into a generating AI and have the generating AI perform the selection of a customization method.

[0124] The customization unit can select the optimal customization method by considering the geographical location information of the company during the customization process. For example, the customization unit can select the optimal customization method based on customization methods that have yielded high satisfaction rates for the company in a particular region. For example, the customization unit can identify the most effective customization method based on the geographical location information of the company and propose that method. For example, the customization unit can analyze the geographical location information of the company, and if an abnormal pattern is found, it can identify the cause and adjust the customization method. In this way, the customization unit can select the optimal customization method by considering geographical location information. Some or all of the above processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the geographical location information of the company into a generating AI and have the generating AI perform the selection of a customization method.

[0125] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0126] The suggestion unit can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is stressed, it can suggest relaxing activities or rest. If the user is relaxed, it can suggest normal activities or new challenges. If the user is tired, it can suggest rest or light exercise. This allows the suggestion unit to make optimal suggestions according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text-generating AI or multimodal-generating AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the content of its suggestions.

[0127] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis based on the estimated emotions. For example, if the user is stressed, it can provide a simple and easy-to-understand analysis result. If the user is relaxed, it can provide a detailed analysis result. If the user is tired, it can provide a concise analysis result. In this way, the analysis unit can provide more appropriate analysis results by adjusting the level of detail of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not using 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 level of detail of the analysis.

[0128] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the user is stressed, it can provide advice to help them relax. If the user is relaxed, it can provide normal feedback. If the user is tired, it can provide advice to prioritize rest. In this way, the feedback unit can provide optimal feedback according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input user emotion data into a generative AI and have the generative AI adjust the content of the feedback.

[0129] The alarm unit can estimate the user's emotions and adjust the alarm volume and tone based on the estimated emotions. For example, if the user is stressed, the alarm can be set to a gentle tone. If the user is relaxed, the alarm can be set to a normal volume. If the user is tired, the alarm can be set to a soft tone. In this way, the alarm unit can make optimal alarm settings according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the alarm unit may be performed using AI or not. For example, the alarm unit can input user emotion data into a generative AI and have the generative AI adjust the alarm volume and tone.

[0130] The health promotion unit can estimate the user's emotions and adjust health promotion methods based on the estimated emotions. For example, if the user is stressed, it can suggest relaxing health promotion methods. If the user is relaxed, it can suggest normal health promotion methods. If the user is tired, it can suggest health promotion methods that prioritize rest. In this way, the health promotion unit can provide the optimal health promotion method according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the health promotion unit may be performed using AI or not. For example, the health promotion unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of health promotion methods.

[0131] The data collection unit can analyze the user's past sleep data and select the optimal data collection method. For example, it can set the timing of data collection based on the time periods when the user has had good sleep in the past. It can identify the most stable sleep pattern from the user's past sleep data and collect data based on that pattern. If an abnormal pattern is found in the user's past sleep data, it can identify the cause and adjust the data collection method. In this way, the data collection unit can select the optimal data collection method by analyzing past data. 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 past sleep data into a generating AI and have the generating AI select the data collection method.

[0132] The analysis unit can adjust the level of detail of the analysis based on the importance of the sleep data during the analysis. For example, detailed analysis can be performed on important data (e.g., data from deep sleep). Simplified analysis can be performed on general data (e.g., data from light sleep). Special analysis can be performed on abnormal data (e.g., abnormal movements during sleep). In this way, the analysis unit can perform detailed analysis on important data by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0133] The suggestion unit can adjust the level of detail of its suggestions based on the importance of the wake-up time. For example, it can provide detailed suggestions for important wake-up times (e.g., before a meeting), simplified suggestions for general wake-up times, and special suggestions for special wake-up times (e.g., before a trip). This allows the suggestion unit to provide more appropriate suggestions by adjusting the level of detail based on the importance of the wake-up time. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the importance of the wake-up time into a generating AI and have the generating AI adjust the level of detail of the suggestions.

[0134] The alarm unit can select the optimal alarm time by referring to the user's past wake-up history when setting an alarm. For example, it can set the alarm time based on the time of day when the user has had good awakenings in the past. It can identify the most stable wake-up time from the user's past wake-up history and set the alarm at that time. By analyzing the user's past wake-up history, if an abnormal pattern is found, it can identify the cause and adjust the alarm time. In this way, the alarm unit can set the optimal alarm time by referring to past wake-up history. Some or all of the above processing in the alarm unit may be performed using AI or not. For example, the alarm unit can input the user's past wake-up history into a generating AI and have the generating AI perform the selection of the alarm time.

[0135] The feedback unit can provide optimal feedback by referring to the user's past sleep data when providing feedback. For example, it can provide feedback based on data showing that the user has had good sleep in the past. It can identify the most stable sleep pattern from the user's past sleep data and provide feedback based on that pattern. It can analyze the user's past sleep data, and if an abnormal pattern is found, it can identify the cause and provide feedback. In this way, the feedback unit can provide optimal feedback by referring to past sleep data. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's past sleep data into a generating AI and have the generating AI perform the task of providing feedback.

[0136] The following briefly describes the processing flow for example form 2.

[0137] Step 1: The data collection unit collects sleep data. The data collection unit collects sleep data in conjunction with, for example, a wearable device. The data collection unit can collect user sleep data using, for example, a wearable device such as a smartwatch or fitness tracker. The data collection unit can collect data such as heart rate, respiratory rate, sleep duration, and sleep quality. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the collected data, for example, using a generative AI. The analysis unit, for example, uses a generative AI to analyze individual sleep patterns and propose an ideal wake-up time. The analysis unit, for example, uses a generative AI to calculate an ideal wake-up time considering sleep cycles and the user's lifestyle rhythm. The analysis unit, for example, uses a generative AI to analyze individual sleep patterns using data analysis algorithms and machine learning models. Step 3: The suggestion unit proposes an ideal wake-up time based on the analysis results obtained by the analysis unit. The suggestion unit proposes an ideal wake-up time, for example, using a generative AI. The suggestion unit can propose an optimal wake-up time for the user based on the analysis results using a generative AI. The suggestion unit can propose an ideal wake-up time, for example, by considering the user's lifestyle and sleep cycle using a generative AI. Step 4: The alarm unit sets an alarm optimized for the wake-up time suggested by the suggestion unit. The alarm unit can, for example, optimize the alarm volume, tone, and vibration pattern based on the suggested wake-up time. The alarm unit can, for example, estimate the user's emotions and adjust the alarm settings based on the estimated emotions. For example, if the user is feeling stressed, the alarm unit can set the alarm with a gentle tone. For example, if the user is relaxed, the alarm unit can set the alarm at a normal volume. For example, if the user is tired, the alarm unit can set the alarm with a soft tone. Step 5: The feedback unit provides daily feedback. For example, the feedback unit can provide sleep quality assessments and improvement suggestions based on the user's sleep data. For example, the feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the user is feeling stressed, the feedback unit can provide advice to help them relax. For example, if the user is relaxed, the feedback unit can provide normal feedback. For example, if the user is tired, the feedback unit can advise them to prioritize rest.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the data collection unit, analysis unit, suggestion unit, alarm unit, feedback unit, health promotion unit, satisfaction improvement unit, and customization unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the smart device 14 and collects data from the wearable device. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and suggests an ideal wake-up time based on the analysis results. The alarm unit is implemented, for example, by the control unit 46A of the smart device 14 and sets an alarm optimized for the suggested wake-up time. The feedback unit is implemented, for example, by the control unit 46A of the smart device 14 and provides daily feedback. The health promotion unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and promotes the continuation of a healthy lifestyle. The satisfaction improvement unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and improves employee job satisfaction. The customization section is implemented, for example, by the specific processing unit 290 of the data processing device 12, providing customized solutions for businesses. The correspondence between each section and the device or control unit is not limited to the example described above, and various modifications are possible.

[0142] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] 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).

[0148] 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.

[0149] 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.

[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 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.

[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 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.

[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 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.

[0157] Each of the multiple elements described above, including the data collection unit, analysis unit, suggestion unit, alarm unit, feedback unit, health promotion unit, satisfaction improvement unit, and customization unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the smart glasses 214 and collects data from the wearable device. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests an ideal wake-up time based on the analysis results. The alarm unit is implemented by the control unit 46A of the smart glasses 214 and sets an alarm optimized for the suggested wake-up time. The feedback unit is implemented by the control unit 46A of the smart glasses 214 and provides daily feedback. The health promotion unit is implemented by the specific processing unit 290 of the data processing unit 12 and promotes the continuation of a healthy lifestyle. The satisfaction improvement unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and improves employee job satisfaction. The customization unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides customized solutions for companies. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0158] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Each of the multiple elements described above, including the data collection unit, analysis unit, suggestion unit, alarm unit, feedback unit, health promotion unit, satisfaction improvement unit, and customization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the headset terminal 314 and collects data from the wearable device. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests an ideal wake-up time based on the analysis results. The alarm unit is implemented by the control unit 46A of the headset terminal 314 and sets an alarm optimized for the suggested wake-up time. The feedback unit is implemented by the control unit 46A of the headset terminal 314 and provides daily feedback. The health promotion unit is implemented by the specific processing unit 290 of the data processing unit 12 and promotes the continuation of a healthy lifestyle. The satisfaction improvement unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and improves employee job satisfaction. The customization unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and provides customized solutions for companies. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0174] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0179] 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).

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.).

[0187] 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.

[0188] 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.

[0189] 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.

[0190] Each of the multiple elements described above, including the data collection unit, analysis unit, suggestion unit, alarm unit, feedback unit, health promotion unit, satisfaction improvement unit, and customization unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the robot 414 and collects data from the wearable device. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The suggestion unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and suggests an ideal wake-up time based on the analysis results. The alarm unit is implemented by, for example, the control unit 46A of the robot 414 and sets an alarm optimized for the suggested wake-up time. The feedback unit is implemented by, for example, the control unit 46A of the robot 414 and provides daily feedback. The health promotion unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and promotes the continuation of a healthy lifestyle. The satisfaction improvement unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and improves employee job satisfaction. The customization section is implemented, for example, by the specific processing unit 290 of the data processing device 12, providing customized solutions for businesses. The correspondence between each section and the device or control unit is not limited to the example described above, and various modifications are possible.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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."

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] (Note 1) A data collection unit that collects sleep data, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the analysis unit, the proposal unit proposes an ideal wake-up time. An alarm unit sets an alarm optimized for the wake-up time proposed by the aforementioned proposal unit, It includes a feedback section that provides daily feedback. A system characterized by the following features. (Note 2) It has a health promotion department that encourages the continuation of a healthy lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 3) We have a Satisfaction Improvement Department to enhance employee job satisfaction. The system described in Appendix 1, characterized by the features described herein. (Note 4) We have a customization department that provides customized solutions for businesses. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is AI generates and acquires sleep data in conjunction with wearable devices. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, Based on the acquired data, the generating AI analyzes individual sleep patterns. The system described in Appendix 1, characterized by the features described herein. (Note 7) 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 8) The aforementioned collection unit is Analyze the user's past sleep data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is 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 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting 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 12) 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 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) 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 15) 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 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) 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 18) 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 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of wake-up time. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the user's daily routine. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making proposals, prioritize them based on when wake-up time data is collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on their relevance to wake-up times. The system described in Appendix 1, characterized by the features described herein. (Note 25) The alarm unit is, It estimates the user's emotions and adjusts the alarm volume and tone based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The alarm unit is, When setting an alarm, the system selects the optimal alarm time by referring to the user's past wake-up history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The alarm unit is, When setting an alarm, the alarm settings are customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 28) The alarm unit is, It estimates the user's emotions and determines the priority of alarms based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The alarm unit is, When setting an alarm, the system selects the optimal alarm time by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The alarm unit is, When setting an alarm, the system analyzes the user's social media activity and suggests appropriate alarm settings. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned feedback unit is It estimates the user's emotions and adjusts the content of the feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned feedback unit is When providing feedback, we refer to the user's past sleep data to provide the most optimal feedback. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned feedback unit is When providing feedback, customize the content of the feedback based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned feedback unit is When providing feedback, we take the user's geographical location into consideration to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned feedback unit is When providing feedback, we analyze the user's social media activity and suggest content for the feedback. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned health promotion department, It estimates the user's emotions and adjusts health promotion methods based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned health promotion department, When promoting health, the system selects the most suitable health promotion method by referring to the user's past health data. The system described in Appendix 2, characterized by the features described herein. (Note 39) The aforementioned health promotion department, When promoting health, the optimal health promotion method is selected considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 40) The aforementioned customer satisfaction improvement unit, It estimates user emotions and adjusts methods for improving satisfaction based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned customer satisfaction improvement unit, When improving employee satisfaction, the system selects the most suitable method for improving satisfaction by referring to the user's past workplace satisfaction data. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned customer satisfaction improvement unit, When improving user satisfaction, the optimal method for improving satisfaction is selected by considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 43) The aforementioned customization unit is It estimates the user's emotions and adjusts the customization based on those emotions. The system described in Appendix 4, characterized by the features described herein. (Note 44) The aforementioned customization unit is During customization, the optimal customization method is selected by referring to the company's past data. The system described in Appendix 4, characterized by the features described herein. (Note 45) The aforementioned customization unit is During customization, the optimal customization method is selected by considering the company's geographical location. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0210] 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 sleep data, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the analysis unit, the proposal unit proposes an ideal wake-up time. An alarm unit sets an alarm optimized for the wake-up time proposed by the aforementioned proposal unit, It includes a feedback section that provides daily feedback. A system characterized by the following features.

2. It has a health promotion department that encourages the continuation of a healthy lifestyle. The system according to feature 1.

3. We have a Satisfaction Improvement Department to enhance employee job satisfaction. The system according to feature 1.

4. We have a customization department that provides customized solutions for businesses. The system according to feature 1.

5. The aforementioned collection unit is AI generates and acquires sleep data in conjunction with wearable devices. The system according to feature 1.

6. The aforementioned analysis unit, Based on the acquired data, the generating AI analyzes individual sleep patterns. The system according to feature 1.

7. 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.

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

9. 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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