System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting lifestyle data and sleep pattern data provided by a user; means for collecting environment data of a bedroom using a sensor for collecting environment data; means for transmitting the lifestyle data, the sleep pattern data, and the environment data to a server; means for analyzing the collected data and determining a cause of a sleep disorder of the user; means for generating personalized improvement advice based on the cause of the sleep disorder and transmitting the personalized improvement advice to a terminal of the user; and means for notifying the user of the improvement advice.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Many people today have unstable sleep patterns due to stress and lifestyle disruptions, resulting in problems such as poor daytime performance, lack of concentration, and depressed mood. Conventional methods have struggled to provide sleep improvement measures tailored to individual users, and have primarily provided general advice, often resulting in ineffective improvement. Therefore, there is a need for a system that provides personalized sleep improvement programs based on individual lifestyles and environmental conditions to solve individual problems. [Means for solving the problem]

[0005] To solve the above problems, the present invention proposes a system including: means for collecting lifestyle habit data and sleep pattern data provided by a user; means for collecting bedroom environmental data using a sensor for collecting environmental data; means for transmitting the lifestyle habit data, sleep pattern data, and environmental data to a server; means for the server to analyze the collected data and identify the cause of the user's sleep disorder; means for the server to generate personalized improvement advice based on the cause of the sleep disorder and transmit the advice to the user's device; and means for the device to notify the user of the improvement advice. This system allows users to receive specific sleep improvement measures tailored to their individual needs, which is expected to improve the quality of their sleep.

[0006] "User" refers to an individual who uses this system to improve their sleep.

[0007] "Lifestyle data" refers to data that indicates a user's daily behavioral patterns, and specifically includes bedtime, wake-up time, meal times, exercise time, caffeine and alcohol intake, etc.

[0008] "Sleep pattern data" refers to data related to the user's sleep, and specifically includes the time when sleep started, the time when sleep ended, the depth of sleep, the number of awakenings, and the like.

[0009] "Environmental data" refers to data about the user's bedroom environment, specifically including temperature, humidity, light intensity, noise level, etc.

[0010] A "sensor" is a device or equipment used to collect environmental data, and specifically refers to a temperature sensor, humidity sensor, illuminance sensor, microphone, etc.

[0011] "Server" refers to a computer system in this system that stores data, analyzes data, and generates advice.

[0012] "Terminal" means a device used by a user to input data and receive advice, and specifically includes a smartphone, tablet, or PC.

[0013] "Means for collecting" refers to the methods and devices for obtaining data from users and transmitting it to the server.

[0014] "Means for sending" refers to the communication means for sending collected data to the server.

[0015] "Means for analyzing" refers to a method and device for analyzing a user's sleep patterns and lifestyle habits based on the collected data and identifying the cause of sleep disorders.

[0016] "Means for identifying" refers to methods and devices for finding problems and causes from the analyzed data.

[0017] "Means for generating" refers to a method and apparatus for generating specific improvement advice for a user based on the identified problems and causes.

[0018] The "notifying means" refers to a method and device for displaying or notifying the generated advice on the user's terminal. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5]FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0020] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0021] First, the terms used in the following description will be explained.

[0022] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0023] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0024] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0025] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0026] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0030] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0031] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0032] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0033] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0034] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0037] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0038] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0039] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0040] The system of the present invention is a personalized service that utilizes AI to improve sleep, and collects and analyzes the user's lifestyle data, sleep pattern data, and environmental data to provide individualized advice on how to improve sleep. A specific embodiment of this system is described below.

[0041] User Data Collection

[0042] Users enter their daily lifestyle and sleep pattern data via devices such as smartphones or tablets. This data includes bedtime, wake-up time, meal times, exercise time, and caffeine and alcohol intake. Additionally, sensors installed in the devices are used to collect bedroom environmental data, including temperature, humidity, light intensity, and noise level.

[0043] Data transmission and storage

[0044] The device sends the collected lifestyle, sleep pattern, and environmental data to a server via the Internet. After receiving the data, the server stores it in a database. The data is sent periodically, updated daily, for example.

[0045] Data analysis

[0046] The server analyzes the user's sleep patterns based on the stored data, using time series analysis, cluster analysis, and machine learning algorithms to identify sleep disorders, such as difficulty falling asleep, frequent nighttime awakenings, or early morning awakenings.

[0047] Identifying sleep disorders

[0048] The server uses the analysis results to identify the cause of the user's sleep disorder, such as consuming caffeine before bedtime causing difficulty falling asleep, or having a bedroom that is too hot causing frequent awakenings.

[0049] Generate and provide improvement advice

[0050] The server generates specific improvement advice for the user based on the identified causes. The advice is personalized for each user and provides specific guidelines for action. For example, it may include refraining from caffeine intake four hours before going to bed and setting the bedroom temperature to 20°C. The generated improvement advice is sent from the server to the device, which notifies the user. The notification is displayed as a push notification in the app or as a message on the home screen.

[0051] Specific examples

[0052] Suppose a user enters lifestyle data stating that they go to bed at 11pm every night, but have difficulty falling asleep and are unable to fall asleep for 30 minutes. The user also has a habit of drinking coffee in the evening, and their bedroom temperature is 25°C. The device sends this data to a server, which analyzes it and determines that the cause of their difficulty falling asleep is caffeine intake and a high bedroom temperature. The server generates specific advice for improvement, such as "avoid caffeine intake four hours before bedtime" and "set the bedroom temperature to 20°C." The device receives this advice and notifies the user. In this way, the user can receive specific improvement measures tailored to their lifestyle and environment.

[0053] This system allows users to obtain sleep improvement measures tailored to their individual needs, effectively improving the quality of their sleep.

[0054] The processing flow will be explained below.

[0055] Step 1:

[0056] Using a smartphone app, users input their lifestyle and sleep pattern data, including bedtime, wake-up time, meal times, exercise time, and caffeine and alcohol intake.

[0057] Step 2:

[0058] The device uses built-in sensors to collect bedroom environmental data, including temperature, humidity, light intensity, and noise levels.

[0059] Step 3:

[0060] The terminal temporarily stores the lifestyle habit data input by the user and the collected environmental data.

[0061] Step 4:

[0062] The terminal periodically (for example, once a day) transmits the collected lifestyle habit data, sleep pattern data, and environmental data to the server.

[0063] Step 5:

[0064] The server receives the data sent from the terminal and stores it in a database.

[0065] Step 6:

[0066] The server then analyzes the user's sleep patterns based on the stored data, using time series analysis, cluster analysis, and machine learning algorithms.

[0067] Step 7:

[0068] The server uses the analysis results to identify the cause of the user's sleep disorder, such as difficulty falling asleep due to consuming caffeine before bedtime, or frequent awakenings due to a bedroom temperature that is too high.

[0069] Step 8:

[0070] The server generates personalized improvement advice based on the identified causes, including how to improve lifestyle habits and adjust the bedroom environment.

[0071] Step 9:

[0072] The server transmits the generated improvement advice to the terminal.

[0073] Step 10:

[0074] The device will notify the user of the improvement advice it has received. The advice will be displayed as a push notification in the app or as a message on the home screen.

[0075] Through this series of steps, users can receive specific sleep improvement measures tailored to their individual needs, improving the quality of their sleep by helping them fall asleep more easily and preventing them from waking up during the night.

[0076] Example 1

[0077] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0078] Previous sleep improvement services only offered general advice and were unable to provide specific advice based on individual users' lifestyles and environmental factors. Furthermore, they lacked the technological means to properly analyze collected data and identify the causes of sleep disorders. This made it difficult to effectively improve sleep.

[0079] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0080] In this invention, the server includes means for collecting daily living data and sleep data provided by the user, means for collecting bedroom environmental data using a detector for collecting environmental data, means for transmitting the daily living data, sleep data, and environmental data to the server, means for the server to analyze the collected data and identify the cause of the user's sleep disorder, means for the server to generate personalized improvement advice based on the cause of the sleep disorder and transmit the advice to the user's terminal, and means for the terminal to notify the user of the improvement advice. This enables effective sleep improvement based on the user's individual lifestyle habits and environmental factors.

[0081] "Daily Life Data" refers to data provided by users, including information such as bedtime, wake-up time, meal times, exercise time, and caffeine and alcohol intake.

[0082] "Sleep data" refers to data that includes information about a user's sleep patterns, particularly data such as the time they go to bed, the time they wake up, and the number of times they wake up during their sleep.

[0083] "Environmental data" refers to data including the temperature, humidity, light intensity, noise level, etc. of the bedroom.

[0084] A "detector" refers to a device that collects environmental data using multiple sensors, including temperature sensors, humidity sensors, light sensors, microphones, etc.

[0085] A "server" is a computer that receives, stores, analyzes, and notifies users of data.

[0086] A "terminal" is a device used by a user to input and receive data, including devices such as smartphones and tablets.

[0087] A "learning algorithm" is a computational method or algorithm used to identify patterns based on collected data and automatically learn from new information.

[0088] "Individualized Improvement Advice" refers to specific, personalized advice provided based on a user's individual lifestyle and environmental data.

[0089] The system of the present invention is a personalized service that utilizes AI to improve sleep, and collects and analyzes the user's daily life data, sleep data, and environmental data to provide individualized advice on how to improve sleep. A specific embodiment of this system is described below.

[0090] First, the user program collects daily life and sleep data. The user inputs information about their daily life through a device such as a smartphone or tablet. This information includes bedtime, wake-up time, meal times, exercise time, and caffeine and alcohol intake. Bedroom environmental data is also automatically collected using the device's built-in temperature, humidity, light, and microphone sensors. This provides information such as the bedroom's temperature, humidity, light intensity, and noise level.

[0091] The collected data is then sent over the Internet to a server, which stores the data in a database. Data is sent periodically, for example, once a day.

[0092] The server program analyzes the collected data using time series analysis, cluster analysis, and machine learning algorithms (such as the Python libraries TensorFlow and Scikit-learn) to identify the user's sleep patterns and identify any sleep disorders they may be experiencing.

[0093] Once the cause of the sleep disorder is identified, the server generates personalized recommendations based on the user's lifestyle and environmental factors. These recommendations are specific and actionable, and are based on the user's lifestyle and environmental factors. Examples include "avoid caffeine intake four hours before bedtime" and "set the bedroom temperature to 20°C."

[0094] The generated improvement advice is sent from the server to the device, and the device notifies the user via the app's push notification function or a message on the home screen.

[0095] As a concrete example, suppose a user enters daily life data stating that they go to bed at 11pm every night, but have difficulty falling asleep and are unable to fall asleep for 30 minutes. The user has the habit of drinking coffee in the evening, and their bedroom temperature is 25°C. The user's sleep pattern data and environmental data are also collected. The device sends this data to a server, which analyzes it and determines that the cause of their difficulty falling asleep is caffeine intake and a high bedroom temperature. The server generates specific improvement advice, such as "avoid caffeine intake four hours before bedtime" and "set the bedroom temperature to 20°C." The device receives this advice and notifies the user. In this way, the user can receive specific improvement measures tailored to their lifestyle and environment.

[0096] An example of a prompt sentence to input to the generative AI model is as follows:

[0097] "Generate specific advice for improving sleep based on the user's sleep data and lifestyle data. For example, if a user goes to bed at 11pm every night but has trouble falling asleep, identify the cause and provide specific guidelines for improvement. Data includes bedtime, wake-up time, caffeine and alcohol intake, bedroom temperature, etc."

[0098] The above is a detailed description of the embodiment of the present invention.

[0099] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0100] Step 1:

[0101] Users input daily life and sleep data via devices such as smartphones or tablets. Input data includes bedtime, wake-up time, meal times, exercise time, and caffeine and alcohol intake. Bedroom environmental data (temperature, humidity, light intensity, noise level) is automatically collected using detectors such as temperature, humidity, light sensors, and microphones built into the device. The input and output are as follows:

[0102] Input: User's daily life data, sleep data, environmental data

[0103] Output: Data stored on the device

[0104] Step 2:

[0105] The device sends the collected daily life data, sleep data, and environmental data to a server via the Internet. The HTTPS protocol is used for transmission, and data is sent periodically. The server stores the received data in a database.

[0106] Input: Data stored on the device

[0107] Output: Data sent to the server, data stored in the server's database

[0108] Step 3:

[0109] The server analyzes the stored data using time series analysis, cluster analysis, and machine learning algorithms (such as the Python libraries TensorFlow and Scikit-learn) to identify the user's sleep patterns and identify any sleep disorders the user may be experiencing.

[0110] Input: User data stored in a database

[0111] Output: Analysis results (identification of user's sleep patterns and sleep disorders)

[0112] Step 4:

[0113] The server then uses the analysis results to identify the cause of the user's sleep disorder. For example, it may identify that the cause is caffeine intake before bedtime or an inappropriate bedroom temperature. Specific actions are then taken based on the analysis results.

[0114] Input: Analysis results

[0115] Output: Causes of sleep disorders

[0116] Step 5:

[0117] The server generates personalized improvement advice based on the identified causes, including specific guidelines tailored to the user's lifestyle and environmental factors, such as "avoid caffeine intake four hours before bedtime" or "set the bedroom temperature to 20°C."

[0118] Input: Causes of sleep disorders

[0119] Output: Improvement advice

[0120] Step 6:

[0121] The server sends the generated improvement advice to the device, which then notifies the user of the received advice. The notification is displayed via the app's push notification function or as a message on the home screen, allowing the user to receive specific improvement measures that can be applied to their daily lives.

[0122] Input: Improvement advice

[0123] Output: User notification

[0124] (Application example 1)

[0125] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0126] Poor sleep quality and sleep disorders are serious problems faced by many people in modern society. Conventional solutions to this problem have been limited to general advice, making it difficult to provide effective improvement measures tailored to the specific conditions of each individual user. Furthermore, there has been a lack of systems that effectively utilize lifestyle and environmental data provided by users to generate detailed improvement advice. Therefore, there is a need for a new system that provides detailed and effective improvement advice based on users' personalized lifestyle and sleep pattern data.

[0127] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0128] In this invention, the server includes means for analyzing user data and identifying sleep patterns using a clustering algorithm, means for sending generated improvement advice to the user by email, and means for providing the generated improvement advice in a virtual store, thereby enabling the user to easily receive personalized, detailed sleep improvement advice.

[0129] "User-provided lifestyle data" refers to information about the user's daily activities and habits, including, for example, bedtime, wake-up time, meal times, exercise time, and caffeine and alcohol intake.

[0130] "Sleep pattern data" is detailed information about the user's sleep, specifically including the amount of time they sleep, how well they fall asleep, how many times they wake up during the night, and the time they wake up.

[0131] "Environmental data" refers to data about the user's bedroom environment, specifically including temperature, humidity, light intensity, noise level, etc.

[0132] A "sensor" is a device used to collect environmental data, including temperature sensors, humidity sensors, light sensors, noise sensors, etc.

[0133] "Server" means a central computer system that receives, stores, and analyzes data sent by users over the Internet.

[0134] A "clustering algorithm" is a computational technique for grouping (clustering) similar data, and is used in this invention to identify a user's sleep patterns.

[0135] "Email" means an electronic messaging service for notifying and communicating information to users.

[0136] A "virtual store" is a virtual sales and service space that exists on the Internet and can be accessed online by users.

[0137] "Personalized improvement advice" refers to specific improvement measures that are individually created based on collected lifestyle, sleep pattern, and environmental data.

[0138] The system of the present invention is a personalized service that utilizes AI to improve sleep, and collects and analyzes a user's lifestyle data, sleep pattern data, and environmental data to provide individualized advice on how to improve sleep. A specific embodiment of this system is described below.

[0139] First, the user uses a device such as a smartphone or tablet to input daily lifestyle and sleep pattern data. This data includes bedtime, wake-up time, meal times, exercise time, caffeine and alcohol intake, etc. Additionally, sensors installed on the device are used to collect bedroom environmental data, including temperature, humidity, light intensity, and noise level.

[0140] The device then transmits the collected lifestyle, sleep, and environmental data to a server via the Internet. The server receives the data and stores it in a database. The data is transmitted periodically, updated daily, for example.

[0141] The server analyzes the user's sleep patterns based on the stored data. The analysis uses machine learning algorithms, including clustering algorithms, to identify the user's sleep disorders. The server then identifies the cause of the user's sleep disorder based on the analysis results. For example, specific causes may be revealed, such as consuming caffeine before bedtime causing difficulty falling asleep, or the bedroom temperature being too high causing frequent awakenings.

[0142] The server then generates specific improvement advice for the user based on the identified causes. The advice is personalized for each user and provides specific guidelines for action. For example, it may include refraining from caffeine intake four hours before going to bed and setting the bedroom temperature to 20°C. The generated improvement advice is sent from the server to the device, which then notifies the user.

[0143] Furthermore, advice generated based on the lifestyle and environmental data entered by the user can be sent to the user via email. It can also be provided online in the form of a virtual store, where users can not only receive advice but also purchase related information and products.

[0144] As a specific example, suppose a user enters lifestyle habit data stating that they go to bed at 11pm every night, but have difficulty falling asleep and are unable to fall asleep for 30 minutes. The user has a habit of drinking coffee in the evening, and their bedroom temperature is 25°C. The device sends this data to the server, which analyzes it and determines that the cause of their difficulty falling asleep is caffeine intake and a high bedroom temperature. The server generates specific improvement advice, such as "avoid caffeine intake four hours before bedtime" and "set the bedroom temperature to 20°C." The device receives this advice and notifies the user, allowing them to receive specific improvement measures tailored to their lifestyle and environment.

[0145] An example prompt might have the following format:

[0146] Your sleep data:

[0147] Bedtime: 11:00 PM

[0148] Wake up time: 07:00

[0149] Meal times: 08:00, 12:00, 18:00

[0150] Exercise time: 07:30

[0151] Caffeine intake: 5:00 PM

[0152] Alcohol intake: 9:00 p.m.

[0153] Environmental data: Temperature: 25, Humidity: 50, Light intensity: 300, Noise level: 30

[0154] Analyze the above data and generate advice for improving your sleep.

[0155] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0156] Step 1:

[0157] The device inputs the user's lifestyle and sleep pattern data, including bedtime, wake-up time, meal times, exercise time, caffeine and alcohol intake, etc. By inputting this data, the device records in detail the factors that affect the user's sleep quality.

[0158] Step 2:

[0159] The device uses sensors to collect bedroom environmental data, such as temperature, humidity, light intensity, and noise level, to monitor the user's sleeping environment in detail.

[0160] Step 3:

[0161] The device sends the collected lifestyle, sleep pattern, and environmental data to a server via the Internet. The sent data is stored in a database on the server and organized for each user. Since large amounts of data are often processed at this step, efficient data transmission and storage management are important.

[0162] Step 4:

[0163] The server uses a clustering algorithm to analyze the user's sleep patterns based on the stored data. Specifically, it uses a machine learning algorithm to classify and analyze the data and identify the cause of the user's sleep disorder. Here, data with similar patterns are grouped (clustered) to make the analysis more efficient.

[0164] Step 5:

[0165] The server uses the analysis results to identify the cause of the user's sleep disorder. Based on the identified cause, the server generates personalized improvement advice that is specific and actionable, such as limiting caffeine intake for certain periods of time or setting an appropriate bedroom temperature.

[0166] Step 6:

[0167] The server generates improvement advice and sends it to the user as an email. In this process, the advice is sent in text format to the email address specified by the user, allowing the user to receive the advice on their smartphone or tablet.

[0168] Step 7:

[0169] The device will notify the user of the improvement advice received from the server. Notification methods can include push notifications, in-app messages, and even messages displayed on the home screen. This notification makes it easier for users to implement improvement measures in their daily lives.

[0170] Step 8:

[0171] In the virtual store, users can view improvement advice online and purchase related information and products. The virtual store helps users find and purchase products based on the advice, enabling them to obtain more practical solutions.

[0172] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0173] The system of the present invention is a personalized service that utilizes AI to improve sleep. It collects and analyzes the user's lifestyle data, sleep pattern data, and environmental data, and combines it with an emotion engine to provide improvement advice that takes the user's emotions into consideration. A specific embodiment of this system is described below.

[0174] User Data Collection

[0175] Users enter their daily lifestyle and sleep pattern data via devices such as smartphones or tablets. This data includes bedtime, wake-up time, meal times, exercise time, and caffeine and alcohol intake. Additionally, sensors installed on the devices are used to collect bedroom environmental data, including temperature, humidity, light intensity, and noise levels.

[0176] Data transmission and storage

[0177] The device transmits the collected lifestyle data, sleep pattern data, and environmental data to a server via the Internet. The server receives the data and stores it in a database. Data transmission is performed periodically.

[0178] Collecting Emotional Data

[0179] When a user inputs text into the device or uses the device's microphone to input voice, the emotion engine can analyze the content. The emotion engine analyzes the user's text input, tone of voice, and facial expression to identify the user's emotion. For example, it can recognize the user's emotional state, such as whether they are stressed or relaxed.

[0180] Data analysis

[0181] The server analyzes the user's sleep patterns based on the stored lifestyle, sleep pattern, environmental, and emotional data. The analysis uses time series analysis, cluster analysis, and machine learning algorithms to identify the cause of the user's sleep disorder.

[0182] Identifying sleep disorders and generating improvement advice

[0183] The server uses the analysis results to identify the cause of the user's sleep disorder. For example, consuming caffeine before bedtime can cause difficulty falling asleep, the bedroom temperature can be too high and cause frequent awakenings, or the user's stress can negatively affect sleep. Based on the identified causes, the server generates personalized improvement advice. The advice can include suggestions for improving lifestyle habits, adjusting the bedroom environment, and emotional support.

[0184] Providing advice

[0185] The server sends the generated improvement advice to the device. The device then notifies the user of the received improvement advice. The notification is displayed as a push notification in the app or a message on the home screen. For example, specific advice is provided, such as "limit caffeine intake at night," "lower the temperature in your bedroom," and "try relaxation techniques to reduce stress."

[0186] Specific examples

[0187] A user enters data that they go to bed at 11 PM every night, but have difficulty falling asleep and are unable to fall asleep for 30 minutes. They also have a habit of drinking coffee in the evening and their bedroom temperature is 25°C. The emotion engine then recognizes that the user is feeling stressed. The device sends this data to the server, which analyzes it and determines that the causes of their difficulty falling asleep are caffeine intake, high bedroom temperature, and stress. The server generates specific improvement advice, such as "avoid caffeine intake four hours before bedtime," "set the bedroom temperature to 20°C," and "try deep breathing exercises to reduce stress." The device receives this advice and notifies the user. In this way, the user can receive specific improvement measures based on their lifestyle, environment, and emotions.

[0188] This system allows users to receive specific sleep improvement measures tailored to their individual needs, improving the quality of their sleep by helping them fall asleep more easily and preventing them from waking up during the night.

[0189] The processing flow will be explained below.

[0190] Step 1:

[0191] Using a smartphone app, users input their lifestyle and sleep pattern data, including bedtime, wake-up time, meal times, exercise time, and caffeine and alcohol intake.

[0192] Step 2:

[0193] The device uses built-in sensors (such as a temperature sensor, light sensor, and microphone) to collect real-time environmental data about the bedroom, including temperature, humidity, light intensity, and noise level.

[0194] Step 3:

[0195] The terminal temporarily stores the lifestyle habit data input by the user and the collected environmental data.

[0196] Step 4:

[0197] The device sends user input data and environmental data to the emotion engine, which then analyzes the user's tone of voice and facial expressions to generate emotion data, including the user's emotional state, such as whether they are stressed or relaxed.

[0198] Step 5:

[0199] The terminal transmits the collected lifestyle habit data, sleep pattern data, environmental data, and emotional data to a server via the Internet.

[0200] Step 6:

[0201] The server receives the data sent from the terminal and stores it in a database.

[0202] Step 7:

[0203] The server analyzes the user's sleep patterns based on the stored lifestyle, sleep pattern, environmental, and emotional data, using time series analysis, cluster analysis, and machine learning algorithms.

[0204] Step 8:

[0205] The server uses the analysis results to identify the cause of the user's sleep disorder, such as difficulty falling asleep due to consuming caffeine before bedtime, frequent awakenings due to a bedroom temperature that is too high, or stress.

[0206] Step 9:

[0207] Based on the identified causes, the server generates personalized improvement advice, including suggestions for lifestyle improvements, bedroom environment adjustments, and emotional support.

[0208] Step 10:

[0209] The server transmits the generated improvement advice to the terminal.

[0210] Step 11:

[0211] The device will then notify the user of the improvement advice it has received. The notification will appear as a push notification in the app or a message on the home screen. Specific advice provided includes "limiting caffeine intake at night," "setting the bedroom temperature to 20°C," and "trying deep breathing exercises to reduce stress."

[0212] Through this series of steps, users can receive specific sleep improvement measures tailored to their individual needs, improving the quality of their sleep by helping them fall asleep more easily and preventing them from waking up during the night.

[0213] Example 2

[0214] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0215] Conventional sleep improvement systems not only collect users' lifestyle and environmental data, but also have difficulty providing personalized advice that takes into account the user's emotional state. As a result, they have been unable to identify the root causes of sleep disorders and propose effective solutions. Furthermore, it has been challenging to identify the causes of complex sleep disorders that cannot be identified through simple data analysis.

[0216] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0217] In this invention, the server includes means incorporating an emotion engine for analyzing user emotion data, means for identifying the cause of the user's sleep disorder using time series analysis, cluster analysis, and machine learning algorithms, and means for generating personalized improvement advice and sending it to the user's terminal, thereby enabling highly accurate identification of the cause of the sleep disorder that also takes the user's emotional state into consideration, and providing personalized improvement advice.

[0218] "Lifestyle data" refers to information about a user's daily activities, including bedtime, wake-up time, meal times, exercise time, caffeine and alcohol intake, etc.

[0219] "Sleep pattern data" refers to information about the user's sleep behavior and state, including the time it takes to fall asleep and the frequency of waking up during the night.

[0220] "Environmental data" refers to information about the user's bedroom environment, including temperature, humidity, light intensity, noise level, etc.

[0221] "Emotion data" is information about the user's emotional state, including stress or relaxation levels analyzed by the emotion engine from text input or voice input.

[0222] An "emotion engine" is a system that analyzes a user's text input, voice input, and facial expressions to identify the user's emotional state.

[0223] "Server" means a computer system that stores and analyzes collected data and provides specific services and information to users.

[0224] "Time series analysis" and "cluster analysis" are statistical methods used by servers to analyze collected data, analyzing fluctuations in data over time and identifying groups of similar data.

[0225] A "machine learning algorithm" is an algorithm for artificial intelligence used by servers when analyzing data. It is a technology that learns patterns based on past data and makes future predictions and classifications.

[0226] "Personalized improvement advice" is specific advice that suggests the best improvement measures for each user based on their individual lifestyle, sleep pattern, environmental, and emotional data.

[0227] "Terminal" means an electronic device used by a user to input data or receive advice from a server, including a smartphone or tablet.

[0228] The system of the present invention collects and analyzes a user's lifestyle data, sleep pattern data, environmental data, and emotional data, and combines them with an emotional engine to provide the user with personalized improvement advice. A detailed embodiment of this system is described below.

[0229] Users use devices such as smartphones or tablets to input data on their daily lifestyle habits and sleep patterns, such as bedtime, wake-up time, meal times, and exercise time. Furthermore, sensors installed on the devices are used to collect environmental data, such as temperature, humidity, light intensity, and noise levels. Along with this data, an emotion engine installed on the devices analyzes text and voice input to collect the user's emotion data. The emotion engine analyzes the text content, tone of voice, and facial expressions to identify the user's emotional state.

[0230] The device periodically transmits this lifestyle data, sleep pattern data, environmental data, and emotional data to a server via the Internet. The server receives this data and stores it in a database. The stored data is analyzed using time series analysis, cluster analysis, and machine learning algorithms. In particular, the machine learning algorithm is used to identify the cause of the user's sleep disorder based on past data.

[0231] The server uses the analysis results to identify the cause of the user's sleep disorder, such as whether consuming caffeine before bedtime is causing difficulty falling asleep, whether the bedroom temperature is too high causing frequent awakenings, or whether stress is negatively affecting sleep.

[0232] Based on the identified causes, the server uses a generative AI model to generate personalized improvement advice, such as "avoid caffeine intake four hours before bedtime," "set the bedroom temperature to 20°C," and "perform deep breathing exercises."

[0233] The server sends the generated improvement advice to the device, which then notifies the user of the received advice. This advice is displayed as a push notification or an in-app message. For example, it might say, "Reduce caffeine intake at night," "Lower the temperature in your bedroom," or "Try deep breathing exercises to reduce stress."

[0234] As a concrete example, suppose a user enters data that indicates that they go to bed at 11pm every night but are unable to fall asleep for 30 minutes. This user has the habit of drinking coffee in the evening, and their bedroom temperature is 25°C. The emotion engine also recognizes that the user is feeling stressed. The device sends this data to the server, which analyzes it and determines that the causes of their difficulty falling asleep are caffeine intake, a high bedroom temperature, and stress. The server generates specific improvement advice, such as "avoid caffeine intake four hours before bedtime," "set the bedroom temperature to 20°C," and "try deep breathing exercises," and the device notifies the user of this advice.

[0235] It is also possible to create specific advice by inputting a prompt into the generative AI model. An example of a prompt is, "I can't fall asleep for 30 minutes even though I go to bed at 11pm. I have a habit of drinking coffee in the evening, my bedroom temperature is 25°C, and I'm feeling stressed. Please provide advice to improve this situation." Based on this prompt, the generative AI model will propose the most appropriate advice.

[0236] This system allows users to receive specific sleep improvement measures tailored to their individual needs, helping them fall asleep faster, prevent waking up during the night, and improve the quality of their sleep.

[0237] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0238] Step 1:

[0239] Users input their daily lifestyle and sleep pattern data via devices such as smartphones or tablets. For example, data such as "I went to bed at 10 p.m. last night" and "I woke up at 7 a.m." In addition, sensors installed in the device are used to collect environmental data such as temperature, humidity, light intensity, and noise level. This allows lifestyle data, sleep pattern data, and environmental data to be obtained. Input data: lifestyle data, sleep pattern data, environmental data.

[0240] Step 2:

[0241] The device periodically sends collected lifestyle data, sleep pattern data, and environmental data to a server via the Internet. For example, it can be set to send data every night at 11pm. The server stores the received data in a database. Checksums and data verification protocols are used to ensure that the data being sent has been received properly. Input data: lifestyle data, sleep pattern data, environmental data. Output data: data stored in the database.

[0242] Step 3:

[0243] When a user inputs text or voice into a device, the emotion engine analyzes the content. The emotion engine analyzes the user's text content, tone of voice, and facial expressions to identify emotional states such as stress or relaxation. For example, if a user says "I'm tired today" through voice input, the emotion engine analyzes the user's emotions from the tone of voice and choice of words. Input data: text data, voice data. Output data: emotion data.

[0244] Step 4:

[0245] The device sends the analyzed emotional data to a server via the Internet, and the server stores the received emotional data in a database. This adds the user's emotional data to the database. Input data: Emotional data. Output data: Emotional data stored in the database.

[0246] Step 5:

[0247] The server analyzes the user's sleep patterns based on the lifestyle, sleep pattern, environmental, and emotional data stored in the database. This analysis uses time series analysis, cluster analysis, and machine learning algorithms. For example, the server analyzes the lifestyle data of a user who has trouble falling asleep to identify the cause. Input data: lifestyle data, sleep pattern data, environmental data, emotional data. Output data: analysis results.

[0248] Step 6:

[0249] The server identifies the cause of the user's sleep disorder based on the analysis results. For example, caffeine intake, bedroom temperature, stress, etc. are identified as possible causes. Input data: Analysis results. Output data: Cause of sleep disorder.

[0250] Step 7:

[0251] Based on the identified causes, the server uses a generative AI model to generate personalized improvement advice. For example, specific advice may be generated such as "avoid caffeine intake four hours before bedtime," "set the bedroom temperature to 20°C," or "try deep breathing exercises to relieve stress." Input data: Causes of sleep disorders. Output data: Personalized improvement advice.

[0252] Step 8:

[0253] The server sends the generated improvement advice to the device, and the device notifies the user of the received advice. Notification methods include push notifications and in-app messages. For example, messages such as "Reduce caffeine intake at night," "Lower the temperature in your bedroom," and "Try deep breathing exercises to reduce stress" are displayed. Input data: personalized improvement advice. Output data: notification to the user.

[0254] (Application example 2)

[0255] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0256] While conventional sleep improvement systems utilize users' lifestyle, sleep pattern, and environmental data, they lack real-time feedback and emotional data analysis, making it difficult to provide immediate and effective advice. Furthermore, they lack a means to comprehensively analyze users' emotional data and real-time environmental data to provide specific improvement advice. This makes it difficult for users to effectively adjust their lifestyles.

[0257] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0258] In this invention, the server includes means for collecting lifestyle habit data and sleep pattern data provided by the user, means for collecting bedroom environment data using a sensor for collecting environmental data, and means for collecting environmental data and emotion data in real time through smart glasses, thereby enabling real-time and personalized improvement advice to be provided to the user.

[0259] "User-provided lifestyle data" refers to information provided by a user regarding their daily lifestyle, including bedtime, wake-up time, meal times, exercise time, caffeine and alcohol intake, etc.

[0260] "Sleep pattern data" refers to data related to the user's sleep, such as the amount of sleep, the quality of sleep, the time it takes to fall asleep and wake up, etc.

[0261] "Environmental data" refers to data about the environment in which the user sleeps, including temperature, humidity, light intensity, noise level, etc.

[0262] "Real-time collection means" refers to a method, device, or system that collects data immediately and transmits it to a server without delay.

[0263] "Emotional data" refers to data relating to the user's emotional state, including stress levels and relaxation levels obtained by analyzing voice input and facial expressions.

[0264] A "machine learning algorithm" is a type of algorithm that learns patterns and regularities from large amounts of data and uses them to predict or classify future data.

[0265] "Personalized improvement advice" refers to specific improvement suggestions customized based on each user's individual lifestyle, sleep pattern, environmental, and emotional data.

[0266] The present invention is a system that utilizes data on a user's lifestyle habits, sleep patterns, environment, and emotions to provide personalized advice on improving sleep in real time at a brick-and-mortar store. This system is implemented using smart glasses, a server, and devices such as smartphones.

[0267] Hardware Configuration

[0268] Smart glasses: Equipped with sensors that collect real-time environmental data such as temperature, humidity, light intensity, and noise levels, as well as emotional data through sensors that analyze voice input and facial expressions.

[0269] Server: Analyzes collected data and generates personalized improvement advice using machine learning algorithms. Uses cloud computing services (e.g., AWS, Google Cloud) for large-scale data analysis.

[0270] Smartphone or tablet: Used as an interface for users to input lifestyle and sleep pattern data.

[0271] Software Configuration

[0272] Data collection module: Collects user lifestyle data, sleep pattern data, environmental data, and emotional data via smart glasses or smartphone.

[0273] Data analysis module: Runs on the server and analyzes the collected data using machine learning algorithms. Specifically, it performs time series analysis and cluster analysis to identify the causes of sleep disorders.

[0274] Emotion engine: Uses APIs (e.g., Microsoft Azure Emotion API) that analyze emotion data from user voice input and facial expressions.

[0275] Notification module: displays personalized advice in real time on the smart glasses, providing visual feedback through the smart glasses display.

[0276] Processing flow

[0277] 1. Data collection: Users input lifestyle and sleep pattern data using a smartphone or tablet, and environmental and emotional data is collected in real time through sensors installed in the smart glasses.

[0278] 2. Data transmission: The collected data is transmitted to a server via the Internet.

[0279] 3. Data analysis: The server analyzes lifestyle data, sleep pattern data, environmental data, and emotional data, and uses machine learning algorithms to identify the cause of the user's sleep disorder.

[0280] 4. Advice generation: Generate personalized improvement advice based on the cause of sleep disorders.

[0281] 5. Providing advice: Providing advice to users in real time via smart glasses.

[0282] Specific examples

[0283] For example, if a user enters data that they go to bed at 11pm every night and have trouble falling asleep, the smart glasses' sensors will determine that the room temperature is 25°C, the light intensity is 150 lux, and face recognition and voice input will confirm a high stress level.The server will analyze this data and display advice such as "limit your caffeine intake," "set the bedroom temperature to 20°C," and "do deep breathing exercises to reduce stress" on the smart glasses in real time.

[0284] Prompt Sentence Examples

[0285] "Develop an AI system that generates personalized advice for improving sleep based on a user's lifestyle data (e.g., bedtime, wake-up time, meal times), environmental data (e.g., temperature, humidity, light intensity, noise level), and emotional data (e.g., stress level). Integrate this into an application that displays advice in real time on smart glasses."

[0286] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0287] Step 1:

[0288] Users use their smartphones or tablets to input lifestyle and sleep pattern data, including bedtime, wake-up time, meal times, exercise time, and timing of caffeine and alcohol intake. The device temporarily stores this input data and prepares it to be sent to a server via the Internet.

[0289] Step 2:

[0290] The smart glasses collect environmental data in real time using built-in sensors (temperature, humidity, light, and noise sensors). In addition, the glasses use a built-in camera and microphone to collect the user's voice and facial expressions and analyze their emotional data. This data is temporarily stored inside the glasses and periodically sent to a server.

[0291] Step 3:

[0292] The server receives lifestyle data, sleep pattern data, environmental data, and emotion data sent from the device and smart glasses. The server stores this data in a database and simultaneously performs data preprocessing, such as filling in missing values ​​and removing outliers.

[0293] Step 4:

[0294] The server's analysis module uses machine learning algorithms to analyze the received data. Specifically, it performs time-series analysis to extract sleep patterns, cluster analysis to group lifestyle habits, and evaluate emotional states. Through this analysis, the server identifies the cause of the user's sleep disorder.

[0295] Step 5:

[0296] The server generates personalized improvement advice based on the analysis results. Examples of generated advice include "limit caffeine intake," "adjust the temperature in your bedroom," and "perform emotional regulation exercises if you are under high stress." The server generates this advice individually for each user and temporarily stores it.

[0297] Step 6:

[0298] The server sends the generated advice in real time to the smart glasses, which display the advice in the form of a visual notification, for example, a message pop-up on the glasses' display.

[0299] Step 7:

[0300] The user checks the notifications on the smart glasses and adjusts their lifestyle and environment accordingly. The user then changes their behavior in accordance with the advice provided, and this is reflected in future data inputs and environmental data collection by sensors.

[0301] These steps provide real-time, personalized advice on how to improve your sleep.

[0302] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0303] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0304] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0305] [Second embodiment]

[0306] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0307] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0308] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0309] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0310] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0311] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0312] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0313] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0314] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0315] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0316] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0317] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0318] The system of the present invention is a personalized service that utilizes AI to improve sleep, and collects and analyzes the user's lifestyle data, sleep pattern data, and environmental data to provide individualized advice on how to improve sleep. A specific embodiment of this system is described below.

[0319] User Data Collection

[0320] Users enter their daily lifestyle and sleep pattern data via devices such as smartphones or tablets. This data includes bedtime, wake-up time, meal times, exercise time, and caffeine and alcohol intake. Additionally, sensors installed in the devices are used to collect bedroom environmental data, including temperature, humidity, light intensity, and noise level.

[0321] Data transmission and storage

[0322] The device sends the collected lifestyle, sleep pattern, and environmental data to a server via the Internet. After receiving the data, the server stores it in a database. The data is sent periodically, updated daily, for example.

[0323] Data analysis

[0324] The server analyzes the user's sleep patterns based on the stored data, using time series analysis, cluster analysis, and machine learning algorithms to identify sleep disorders, such as difficulty falling asleep, frequent nighttime awakenings, or early morning awakenings.

[0325] Identifying sleep disorders

[0326] The server uses the analysis results to identify the cause of the user's sleep disorder, such as consuming caffeine before bedtime causing difficulty falling asleep, or having a bedroom that is too hot causing frequent awakenings.

[0327] Generate and provide improvement advice

[0328] The server generates specific improvement advice for the user based on the identified causes. The advice is personalized for each user and provides specific guidelines for action. For example, it may include refraining from caffeine intake four hours before going to bed and setting the bedroom temperature to 20°C. The generated improvement advice is sent from the server to the device, which notifies the user. The notification is displayed as a push notification in the app or as a message on the home screen.

[0329] Specific examples

[0330] Suppose a user enters lifestyle data stating that they go to bed at 11pm every night, but have difficulty falling asleep and are unable to fall asleep for 30 minutes. The user also has a habit of drinking coffee in the evening, and their bedroom temperature is 25°C. The device sends this data to a server, which analyzes it and determines that the cause of their difficulty falling asleep is caffeine intake and a high bedroom temperature. The server generates specific advice for improvement, such as "avoid caffeine intake four hours before bedtime" and "set the bedroom temperature to 20°C." The device receives this advice and notifies the user. In this way, the user can receive specific improvement measures tailored to their lifestyle and environment.

[0331] This system allows users to obtain sleep improvement measures tailored to their individual needs, effectively improving the quality of their sleep.

[0332] The processing flow will be explained below.

[0333] Step 1:

[0334] Using a smartphone app, users input their lifestyle and sleep pattern data, including bedtime, wake-up time, meal times, exercise time, and caffeine and alcohol intake.

[0335] Step 2:

[0336] The device uses built-in sensors to collect bedroom environmental data, including temperature, humidity, light intensity, and noise levels.

[0337] Step 3:

[0338] The terminal temporarily stores the lifestyle habit data input by the user and the collected environmental data.

[0339] Step 4:

[0340] The terminal periodically (for example, once a day) transmits the collected lifestyle habit data, sleep pattern data, and environmental data to the server.

[0341] Step 5:

[0342] The server receives the data sent from the terminal and stores it in a database.

[0343] Step 6:

[0344] The server then analyzes the user's sleep patterns based on the stored data, using time series analysis, cluster analysis, and machine learning algorithms.

[0345] Step 7:

[0346] The server uses the analysis results to identify the cause of the user's sleep disorder, such as difficulty falling asleep due to consuming caffeine before bedtime, or frequent awakenings due to a bedroom temperature that is too high.

[0347] Step 8:

[0348] The server generates personalized improvement advice based on the identified causes, including how to improve lifestyle habits and adjust the bedroom environment.

[0349] Step 9:

[0350] The server transmits the generated improvement advice to the terminal.

[0351] Step 10:

[0352] The device will notify the user of the improvement advice it has received. The advice will be displayed as a push notification in the app or as a message on the home screen.

[0353] Through this series of steps, users can receive specific sleep improvement measures tailored to their individual needs, improving the quality of their sleep by helping them fall asleep more easily and preventing them from waking up during the night.

[0354] Example 1

[0355] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0356] Previous sleep improvement services only offered general advice and were unable to provide specific advice based on individual users' lifestyles and environmental factors. Furthermore, they lacked the technological means to properly analyze collected data and identify the causes of sleep disorders. This made it difficult to effectively improve sleep.

[0357] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0358] In this invention, the server includes means for collecting daily living data and sleep data provided by the user, means for collecting bedroom environmental data using a detector for collecting environmental data, means for transmitting the daily living data, sleep data, and environmental data to the server, means for the server to analyze the collected data and identify the cause of the user's sleep disorder, means for the server to generate personalized improvement advice based on the cause of the sleep disorder and transmit the advice to the user's terminal, and means for the terminal to notify the user of the improvement advice. This enables effective sleep improvement based on the user's individual lifestyle habits and environmental factors.

[0359] "Daily Life Data" refers to data provided by users, including information such as bedtime, wake-up time, meal times, exercise time, and caffeine and alcohol intake.

[0360] "Sleep data" refers to data that includes information about a user's sleep patterns, particularly data such as the time they go to bed, the time they wake up, and the number of times they wake up during their sleep.

[0361] "Environmental data" refers to data including the temperature, humidity, light intensity, noise level, etc. of the bedroom.

[0362] A "detector" refers to a device that collects environmental data using multiple sensors, including temperature sensors, humidity sensors, light sensors, microphones, etc.

[0363] A "server" is a computer that receives, stores, analyzes, and notifies users of data.

[0364] A "terminal" is a device used by a user to input and receive data, including devices such as smartphones and tablets.

[0365] A "learning algorithm" is a computational method or algorithm used to identify patterns based on collected data and automatically learn from new information.

[0366] "Individualized Improvement Advice" refers to specific, personalized advice provided based on a user's individual lifestyle and environmental data.

[0367] The system of the present invention is a personalized service that utilizes AI to improve sleep, and collects and analyzes the user's daily life data, sleep data, and environmental data to provide individualized advice on how to improve sleep. A specific embodiment of this system is described below.

[0368] First, the user program collects daily life and sleep data. The user inputs information about their daily life through a device such as a smartphone or tablet. This information includes bedtime, wake-up time, meal times, exercise time, and caffeine and alcohol intake. Bedroom environmental data is also automatically collected using the device's built-in temperature, humidity, light, and microphone sensors. This provides information such as the bedroom's temperature, humidity, light intensity, and noise level.

[0369] The collected data is then sent over the Internet to a server, which stores the data in a database. Data is sent periodically, for example, once a day.

[0370] The server program analyzes the collected data using time series analysis, cluster analysis, and machine learning algorithms (such as the Python libraries TensorFlow and Scikit-learn) to identify the user's sleep patterns and identify any sleep disorders they may be experiencing.

[0371] Once the cause of the sleep disorder is identified, the server generates personalized recommendations based on the user's lifestyle and environmental factors. These recommendations are specific and actionable, and are based on the user's lifestyle and environmental factors. Examples include "avoid caffeine intake four hours before bedtime" and "set the bedroom temperature to 20°C."

[0372] The generated improvement advice is sent from the server to the device, and the device notifies the user via the app's push notification function or a message on the home screen.

[0373] As a concrete example, suppose a user enters daily life data stating that they go to bed at 11pm every night, but have difficulty falling asleep and are unable to fall asleep for 30 minutes. The user has the habit of drinking coffee in the evening, and their bedroom temperature is 25°C. The user's sleep pattern data and environmental data are also collected. The device sends this data to a server, which analyzes it and determines that the cause of their difficulty falling asleep is caffeine intake and a high bedroom temperature. The server generates specific improvement advice, such as "avoid caffeine intake four hours before bedtime" and "set the bedroom temperature to 20°C." The device receives this advice and notifies the user. In this way, the user can receive specific improvement measures tailored to their lifestyle and environment.

[0374] An example of a prompt sentence to input to the generative AI model is as follows:

[0375] "Generate specific advice for improving sleep based on the user's sleep data and lifestyle data. For example, if a user goes to bed at 11pm every night but has trouble falling asleep, identify the cause and provide specific guidelines for improvement. Data includes bedtime, wake-up time, caffeine and alcohol intake, bedroom temperature, etc."

[0376] The above is a detailed description of the embodiment of the present invention.

[0377] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0378] Step 1:

[0379] Users input daily life and sleep data via devices such as smartphones or tablets. Input data includes bedtime, wake-up time, meal times, exercise time, and caffeine and alcohol intake. Bedroom environmental data (temperature, humidity, light intensity, noise level) is automatically collected using detectors such as temperature, humidity, light sensors, and microphones built into the device. The input and output are as follows:

[0380] Input: User's daily life data, sleep data, environmental data

[0381] Output: Data stored on the device

[0382] Step 2:

[0383] The device sends the collected daily life data, sleep data, and environmental data to a server via the Internet. The HTTPS protocol is used for transmission, and data is sent periodically. The server stores the received data in a database.

[0384] Input: Data stored on the device

[0385] Output: Data sent to the server, data stored in the server's database

[0386] Step 3:

[0387] The server analyzes the stored data using time series analysis, cluster analysis, and machine learning algorithms (such as the Python libraries TensorFlow and Scikit-learn) to identify the user's sleep patterns and identify any sleep disorders the user may be experiencing.

[0388] Input: User data stored in a database

[0389] Output: Analysis results (identification of user's sleep patterns and sleep disorders)

[0390] Step 4:

[0391] The server then uses the analysis results to identify the cause of the user's sleep disorder. For example, it may identify that the cause is caffeine intake before bedtime or an inappropriate bedroom temperature. Specific actions are then taken based on the analysis results.

[0392] Input: Analysis results

[0393] Output: Causes of sleep disorders

[0394] Step 5:

[0395] The server generates personalized improvement advice based on the identified causes, including specific guidelines tailored to the user's lifestyle and environmental factors, such as "avoid caffeine intake four hours before bedtime" or "set the bedroom temperature to 20°C."

[0396] Input: Causes of sleep disorders

[0397] Output: Improvement advice

[0398] Step 6:

[0399] The server sends the generated improvement advice to the device, which then notifies the user of the received advice. The notification is displayed via the app's push notification function or as a message on the home screen, allowing the user to receive specific improvement measures that can be applied to their daily lives.

[0400] Input: Improvement advice

[0401] Output: User notification

[0402] (Application example 1)

[0403] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0404] Poor sleep quality and sleep disorders are serious problems faced by many people in modern society. Conventional solutions to this problem have been limited to general advice, making it difficult to provide effective improvement measures tailored to the specific conditions of each individual user. Furthermore, there has been a lack of systems that effectively utilize lifestyle and environmental data provided by users to generate detailed improvement advice. Therefore, there is a need for a new system that provides detailed and effective improvement advice based on users' personalized lifestyle and sleep pattern data.

[0405] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0406] In this invention, the server includes means for analyzing user data and identifying sleep patterns using a clustering algorithm, means for sending generated improvement advice to the user by email, and means for providing the generated improvement advice in a virtual store, thereby enabling the user to easily receive personalized, detailed sleep improvement advice.

[0407] "User-provided lifestyle data" refers to information about the user's daily activities and habits, including, for example, bedtime, wake-up time, meal times, exercise time, and caffeine and alcohol intake.

[0408] "Sleep pattern data" is detailed information about the user's sleep, specifically including the amount of time they sleep, how well they fall asleep, how many times they wake up during the night, and the time they wake up.

[0409] "Environmental data" refers to data about the user's bedroom environment, specifically including temperature, humidity, light intensity, noise level, etc.

[0410] A "sensor" is a device used to collect environmental data, including temperature sensors, humidity sensors, light sensors, noise sensors, etc.

[0411] "Server" means a central computer system that receives, stores, and analyzes data sent by users over the Internet.

[0412] A "clustering algorithm" is a computational technique for grouping (clustering) similar data, and is used in this invention to identify a user's sleep patterns.

[0413] "Email" means an electronic messaging service for notifying and communicating information to users.

[0414] A "virtual store" is a virtual sales and service space that exists on the Internet and can be accessed online by users.

[0415] "Personalized improvement advice" refers to specific improvement measures that are individually created based on collected lifestyle, sleep pattern, and environmental data.

[0416] The system of the present invention is a personalized service that utilizes AI to improve sleep, and collects and analyzes a user's lifestyle data, sleep pattern data, and environmental data to provide individualized advice on how to improve sleep. A specific embodiment of this system is described below.

[0417] First, the user uses a device such as a smartphone or tablet to input daily lifestyle and sleep pattern data. This data includes bedtime, wake-up time, meal times, exercise time, caffeine and alcohol intake, etc. Additionally, sensors installed on the device are used to collect bedroom environmental data, including temperature, humidity, light intensity, and noise level.

[0418] The device then transmits the collected lifestyle, sleep, and environmental data to a server via the Internet. The server receives the data and stores it in a database. The data is transmitted periodically, updated daily, for example.

[0419] The server analyzes the user's sleep patterns based on the stored data. The analysis uses machine learning algorithms, including clustering algorithms, to identify the user's sleep disorders. The server then identifies the cause of the user's sleep disorder based on the analysis results. For example, specific causes may be revealed, such as consuming caffeine before bedtime causing difficulty falling asleep, or the bedroom temperature being too high causing frequent awakenings.

[0420] The server then generates specific improvement advice for the user based on the identified causes. The advice is personalized for each user and provides specific guidelines for action. For example, it may include refraining from caffeine intake four hours before going to bed and setting the bedroom temperature to 20°C. The generated improvement advice is sent from the server to the device, which then notifies the user.

[0421] Furthermore, advice generated based on the lifestyle and environmental data entered by the user can be sent to the user via email. It can also be provided online in the form of a virtual store, where users can not only receive advice but also purchase related information and products.

[0422] As a specific example, suppose a user enters lifestyle habit data stating that they go to bed at 11pm every night, but have difficulty falling asleep and are unable to fall asleep for 30 minutes. The user has a habit of drinking coffee in the evening, and their bedroom temperature is 25°C. The device sends this data to the server, which analyzes it and determines that the cause of their difficulty falling asleep is caffeine intake and a high bedroom temperature. The server generates specific improvement advice, such as "avoid caffeine intake four hours before bedtime" and "set the bedroom temperature to 20°C." The device receives this advice and notifies the user, allowing them to receive specific improvement measures tailored to their lifestyle and environment.

[0423] An example prompt might have the following format:

[0424] Your sleep data:

[0425] Bedtime: 11:00 PM

[0426] Wake up time: 07:00

[0427] Meal times: 08:00, 12:00, 18:00

[0428] Exercise time: 07:30

[0429] Caffeine intake: 5:00 PM

[0430] Alcohol intake: 9:00 p.m.

[0431] Environmental data: Temperature: 25, Humidity: 50, Light intensity: 300, Noise level: 30

[0432] Analyze the above data and generate advice for improving your sleep.

[0433] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0434] Step 1:

[0435] The device inputs the user's lifestyle and sleep pattern data, including bedtime, wake-up time, meal times, exercise time, caffeine and alcohol intake, etc. By inputting this data, the device records in detail the factors that affect the user's sleep quality.

[0436] Step 2:

[0437] The device uses sensors to collect bedroom environmental data, such as temperature, humidity, light intensity, and noise level, to monitor the user's sleeping environment in detail.

[0438] Step 3:

[0439] The device sends the collected lifestyle, sleep pattern, and environmental data to a server via the Internet. The sent data is stored in a database on the server and organized for each user. Since large amounts of data are often processed at this step, efficient data transmission and storage management are important.

[0440] Step 4:

[0441] The server uses a clustering algorithm to analyze the user's sleep patterns based on the stored data. Specifically, it uses a machine learning algorithm to classify and analyze the data and identify the cause of the user's sleep disorder. Here, data with similar patterns are grouped (clustered) to make the analysis more efficient.

[0442] Step 5:

[0443] The server uses the analysis results to identify the cause of the user's sleep disorder. Based on the identified cause, the server generates personalized improvement advice that is specific and actionable, such as limiting caffeine intake for certain periods of time or setting an appropriate bedroom temperature.

[0444] Step 6:

[0445] The server generates improvement advice and sends it to the user as an email. In this process, the advice is sent in text format to the email address specified by the user, allowing the user to receive the advice on their smartphone or tablet.

[0446] Step 7:

[0447] The device will notify the user of the improvement advice received from the server. Notification methods can include push notifications, in-app messages, and even messages displayed on the home screen. This notification makes it easier for users to implement improvement measures in their daily lives.

[0448] Step 8:

[0449] In the virtual store, users can view improvement advice online and purchase related information and products. The virtual store helps users find and purchase products based on the advice, enabling them to obtain more practical solutions.

[0450] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0451] The system of the present invention is a personalized service that utilizes AI to improve sleep. It collects and analyzes the user's lifestyle data, sleep pattern data, and environmental data, and combines it with an emotion engine to provide improvement advice that takes the user's emotions into consideration. A specific embodiment of this system is described below.

[0452] User Data Collection

[0453] Users enter their daily lifestyle and sleep pattern data via devices such as smartphones or tablets. This data includes bedtime, wake-up time, meal times, exercise time, and caffeine and alcohol intake. Additionally, sensors installed on the devices are used to collect bedroom environmental data, including temperature, humidity, light intensity, and noise levels.

[0454] Data transmission and storage

[0455] The device transmits the collected lifestyle data, sleep pattern data, and environmental data to a server via the Internet. The server receives the data and stores it in a database. Data transmission is performed periodically.

[0456] Collecting Emotional Data

[0457] When a user inputs text into the device or uses the device's microphone to input voice, the emotion engine can analyze the content. The emotion engine analyzes the user's text input, tone of voice, and facial expression to identify the user's emotion. For example, it can recognize the user's emotional state, such as whether they are stressed or relaxed.

[0458] Data analysis

[0459] The server analyzes the user's sleep patterns based on the stored lifestyle, sleep pattern, environmental, and emotional data. The analysis uses time series analysis, cluster analysis, and machine learning algorithms to identify the cause of the user's sleep disorder.

[0460] Identifying sleep disorders and generating improvement advice

[0461] The server uses the analysis results to identify the cause of the user's sleep disorder. For example, consuming caffeine before bedtime can cause difficulty falling asleep, the bedroom temperature can be too high and cause frequent awakenings, or the user's stress can negatively affect sleep. Based on the identified causes, the server generates personalized improvement advice. The advice can include suggestions for improving lifestyle habits, adjusting the bedroom environment, and emotional support.

[0462] Providing advice

[0463] The server sends the generated improvement advice to the device. The device then notifies the user of the received improvement advice. The notification is displayed as a push notification in the app or a message on the home screen. For example, specific advice is provided, such as "limit caffeine intake at night," "lower the temperature in your bedroom," and "try relaxation techniques to reduce stress."

[0464] Specific examples

[0465] A user enters data that they go to bed at 11 PM every night, but have difficulty falling asleep and are unable to fall asleep for 30 minutes. They also have a habit of drinking coffee in the evening and their bedroom temperature is 25°C. The emotion engine then recognizes that the user is feeling stressed. The device sends this data to the server, which analyzes it and determines that the causes of their difficulty falling asleep are caffeine intake, high bedroom temperature, and stress. The server generates specific improvement advice, such as "avoid caffeine intake four hours before bedtime," "set the bedroom temperature to 20°C," and "try deep breathing exercises to reduce stress." The device receives this advice and notifies the user. In this way, the user can receive specific improvement measures based on their lifestyle, environment, and emotions.

[0466] This system allows users to receive specific sleep improvement measures tailored to their individual needs, improving the quality of their sleep by helping them fall asleep more easily and preventing them from waking up during the night.

[0467] The processing flow will be explained below.

[0468] Step 1:

[0469] Using a smartphone app, users input their lifestyle and sleep pattern data, including bedtime, wake-up time, meal times, exercise time, and caffeine and alcohol intake.

[0470] Step 2:

[0471] The device uses built-in sensors (such as a temperature sensor, light sensor, and microphone) to collect real-time environmental data about the bedroom, including temperature, humidity, light intensity, and noise level.

[0472] Step 3:

[0473] The terminal temporarily stores the lifestyle habit data input by the user and the collected environmental data.

[0474] Step 4:

[0475] The device sends user input data and environmental data to the emotion engine, which then analyzes the user's tone of voice and facial expressions to generate emotion data, including the user's emotional state, such as whether they are stressed or relaxed.

[0476] Step 5:

[0477] The terminal transmits the collected lifestyle habit data, sleep pattern data, environmental data, and emotional data to a server via the Internet.

[0478] Step 6:

[0479] The server receives the data sent from the terminal and stores it in a database.

[0480] Step 7:

[0481] The server analyzes the user's sleep patterns based on the stored lifestyle, sleep pattern, environmental, and emotional data, using time series analysis, cluster analysis, and machine learning algorithms.

[0482] Step 8:

[0483] The server uses the analysis results to identify the cause of the user's sleep disorder, such as difficulty falling asleep due to consuming caffeine before bedtime, frequent awakenings due to a bedroom temperature that is too high, or stress.

[0484] Step 9:

[0485] Based on the identified causes, the server generates personalized improvement advice, including suggestions for lifestyle improvements, bedroom environment adjustments, and emotional support.

[0486] Step 10:

[0487] The server transmits the generated improvement advice to the terminal.

[0488] Step 11:

[0489] The device will then notify the user of the improvement advice it has received. The notification will appear as a push notification in the app or a message on the home screen. Specific advice provided includes "limiting caffeine intake at night," "setting the bedroom temperature to 20°C," and "trying deep breathing exercises to reduce stress."

[0490] Through this series of steps, users can receive specific sleep improvement measures tailored to their individual needs, improving the quality of their sleep by helping them fall asleep more easily and preventing them from waking up during the night.

[0491] Example 2

[0492] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0493] Conventional sleep improvement systems not only collect users' lifestyle and environmental data, but also have difficulty providing personalized advice that takes into account the user's emotional state. As a result, they have been unable to identify the root causes of sleep disorders and propose effective solutions. Furthermore, it has been challenging to identify the causes of complex sleep disorders that cannot be identified through simple data analysis.

[0494] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0495] In this invention, the server includes means incorporating an emotion engine for analyzing user emotion data, means for identifying the cause of the user's sleep disorder using time series analysis, cluster analysis, and machine learning algorithms, and means for generating personalized improvement advice and sending it to the user's terminal, thereby enabling highly accurate identification of the cause of the sleep disorder that also takes the user's emotional state into consideration, and providing personalized improvement advice.

[0496] "Lifestyle data" refers to information about a user's daily activities, including bedtime, wake-up time, meal times, exercise time, caffeine and alcohol intake, etc.

[0497] "Sleep pattern data" refers to information about the user's sleep behavior and state, including the time it takes to fall asleep and the frequency of waking up during the night.

[0498] "Environmental data" refers to information about the user's bedroom environment, including temperature, humidity, light intensity, noise level, etc.

[0499] "Emotion data" is information about the user's emotional state, including stress or relaxation levels analyzed by the emotion engine from text input or voice input.

[0500] An "emotion engine" is a system that analyzes a user's text input, voice input, and facial expressions to identify the user's emotional state.

[0501] "Server" means a computer system that stores and analyzes collected data and provides specific services and information to users.

[0502] "Time series analysis" and "cluster analysis" are statistical methods used by servers to analyze collected data, analyzing fluctuations in data over time and identifying groups of similar data.

[0503] A "machine learning algorithm" is an algorithm for artificial intelligence used by servers when analyzing data. It is a technology that learns patterns based on past data and makes future predictions and classifications.

[0504] "Personalized improvement advice" is specific advice that suggests the best improvement measures for each user based on their individual lifestyle, sleep pattern, environmental, and emotional data.

[0505] "Terminal" means an electronic device used by a user to input data or receive advice from a server, including a smartphone or tablet.

[0506] The system of the present invention collects and analyzes a user's lifestyle data, sleep pattern data, environmental data, and emotional data, and combines them with an emotional engine to provide the user with personalized improvement advice. A detailed embodiment of this system is described below.

[0507] Users use devices such as smartphones or tablets to input data on their daily lifestyle habits and sleep patterns, such as bedtime, wake-up time, meal times, and exercise time. Furthermore, sensors installed on the devices are used to collect environmental data, such as temperature, humidity, light intensity, and noise levels. Along with this data, an emotion engine installed on the devices analyzes text and voice input to collect the user's emotion data. The emotion engine analyzes the text content, tone of voice, and facial expressions to identify the user's emotional state.

[0508] The device periodically transmits this lifestyle data, sleep pattern data, environmental data, and emotional data to a server via the Internet. The server receives this data and stores it in a database. The stored data is analyzed using time series analysis, cluster analysis, and machine learning algorithms. In particular, the machine learning algorithm is used to identify the cause of the user's sleep disorder based on past data.

[0509] The server uses the analysis results to identify the cause of the user's sleep disorder, such as whether consuming caffeine before bedtime is causing difficulty falling asleep, whether the bedroom temperature is too high causing frequent awakenings, or whether stress is negatively affecting sleep.

[0510] Based on the identified causes, the server uses a generative AI model to generate personalized improvement advice, such as "avoid caffeine intake four hours before bedtime," "set the bedroom temperature to 20°C," and "perform deep breathing exercises."

[0511] The server sends the generated improvement advice to the device, which then notifies the user of the received advice. This advice is displayed as a push notification or an in-app message. For example, it might say, "Reduce caffeine intake at night," "Lower the temperature in your bedroom," or "Try deep breathing exercises to reduce stress."

[0512] As a concrete example, suppose a user enters data that indicates that they go to bed at 11pm every night but are unable to fall asleep for 30 minutes. This user has the habit of drinking coffee in the evening, and their bedroom temperature is 25°C. The emotion engine also recognizes that the user is feeling stressed. The device sends this data to the server, which analyzes it and determines that the causes of their difficulty falling asleep are caffeine intake, a high bedroom temperature, and stress. The server generates specific improvement advice, such as "avoid caffeine intake four hours before bedtime," "set the bedroom temperature to 20°C," and "try deep breathing exercises," and the device notifies the user of this advice.

[0513] It is also possible to create specific advice by inputting a prompt into the generative AI model. An example of a prompt is, "I can't fall asleep for 30 minutes even though I go to bed at 11pm. I have a habit of drinking coffee in the evening, my bedroom temperature is 25°C, and I'm feeling stressed. Please provide advice to improve this situation." Based on this prompt, the generative AI model will propose the most appropriate advice.

[0514] This system allows users to receive specific sleep improvement measures tailored to their individual needs, helping them fall asleep faster, prevent waking up during the night, and improve the quality of their sleep.

[0515] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0516] Step 1:

[0517] Users input their daily lifestyle and sleep pattern data via devices such as smartphones or tablets. For example, data such as "I went to bed at 10 p.m. last night" and "I woke up at 7 a.m." In addition, sensors installed in the device are used to collect environmental data such as temperature, humidity, light intensity, and noise level. This allows lifestyle data, sleep pattern data, and environmental data to be obtained. Input data: lifestyle data, sleep pattern data, environmental data.

[0518] Step 2:

[0519] The device periodically sends collected lifestyle data, sleep pattern data, and environmental data to a server via the Internet. For example, it can be set to send data every night at 11pm. The server stores the received data in a database. Checksums and data verification protocols are used to ensure that the data being sent has been received properly. Input data: lifestyle data, sleep pattern data, environmental data. Output data: data stored in the database.

[0520] Step 3:

[0521] When a user inputs text or voice into a device, the emotion engine analyzes the content. The emotion engine analyzes the user's text content, tone of voice, and facial expressions to identify emotional states such as stress or relaxation. For example, if a user says "I'm tired today" through voice input, the emotion engine analyzes the user's emotions from the tone of voice and choice of words. Input data: text data, voice data. Output data: emotion data.

[0522] Step 4:

[0523] The device sends the analyzed emotional data to a server via the Internet, and the server stores the received emotional data in a database. This adds the user's emotional data to the database. Input data: Emotional data. Output data: Emotional data stored in the database.

[0524] Step 5:

[0525] The server analyzes the user's sleep patterns based on the lifestyle, sleep pattern, environmental, and emotional data stored in the database. This analysis uses time series analysis, cluster analysis, and machine learning algorithms. For example, the server analyzes the lifestyle data of a user who has trouble falling asleep to identify the cause. Input data: lifestyle data, sleep pattern data, environmental data, emotional data. Output data: analysis results.

[0526] Step 6:

[0527] The server identifies the cause of the user's sleep disorder based on the analysis results. For example, caffeine intake, bedroom temperature, stress, etc. are identified as possible causes. Input data: Analysis results. Output data: Cause of sleep disorder.

[0528] Step 7:

[0529] Based on the identified causes, the server uses a generative AI model to generate personalized improvement advice. For example, specific advice may be generated such as "avoid caffeine intake four hours before bedtime," "set the bedroom temperature to 20°C," or "try deep breathing exercises to relieve stress." Input data: Causes of sleep disorders. Output data: Personalized improvement advice.

[0530] Step 8:

[0531] The server sends the generated improvement advice to the device, and the device notifies the user of the received advice. Notification methods include push notifications and in-app messages. For example, messages such as "Reduce caffeine intake at night," "Lower the temperature in your bedroom," and "Try deep breathing exercises to reduce stress" are displayed. Input data: personalized improvement advice. Output data: notification to the user.

[0532] (Application example 2)

[0533] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0534] While conventional sleep improvement systems utilize users' lifestyle, sleep pattern, and environmental data, they lack real-time feedback and emotional data analysis, making it difficult to provide immediate and effective advice. Furthermore, they lack a means to comprehensively analyze users' emotional data and real-time environmental data to provide specific improvement advice. This makes it difficult for users to effectively adjust their lifestyles.

[0535] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0536] In this invention, the server includes means for collecting lifestyle habit data and sleep pattern data provided by the user, means for collecting bedroom environment data using a sensor for collecting environmental data, and means for collecting environmental data and emotion data in real time through smart glasses, thereby enabling real-time and personalized improvement advice to be provided to the user.

[0537] "User-provided lifestyle data" refers to information provided by a user regarding their daily lifestyle, including bedtime, wake-up time, meal times, exercise time, caffeine and alcohol intake, etc.

[0538] "Sleep pattern data" refers to data related to the user's sleep, such as the amount of sleep, the quality of sleep, the time it takes to fall asleep and wake up, etc.

[0539] "Environmental data" refers to data about the environment in which the user sleeps, including temperature, humidity, light intensity, noise level, etc.

[0540] "Real-time collection means" refers to a method, device, or system that collects data immediately and transmits it to a server without delay.

[0541] "Emotional data" refers to data relating to the user's emotional state, including stress levels and relaxation levels obtained by analyzing voice input and facial expressions.

[0542] A "machine learning algorithm" is a type of algorithm that learns patterns and regularities from large amounts of data and uses them to predict or classify future data.

[0543] "Personalized improvement advice" refers to specific improvement suggestions customized based on each user's individual lifestyle, sleep pattern, environmental, and emotional data.

[0544] The present invention is a system that utilizes data on a user's lifestyle habits, sleep patterns, environment, and emotions to provide personalized advice on improving sleep in real time at a brick-and-mortar store. This system is implemented using smart glasses, a server, and devices such as smartphones.

[0545] Hardware Configuration

[0546] Smart glasses: Equipped with sensors that collect real-time environmental data such as temperature, humidity, light intensity, and noise levels, as well as emotional data through sensors that analyze voice input and facial expressions.

[0547] Server: Analyzes collected data and generates personalized improvement advice using machine learning algorithms. Uses cloud computing services (e.g., AWS, Google Cloud) for large-scale data analysis.

[0548] Smartphone or tablet: Used as an interface for users to input lifestyle and sleep pattern data.

[0549] Software Configuration

[0550] Data collection module: Collects user lifestyle data, sleep pattern data, environmental data, and emotional data via smart glasses or smartphone.

[0551] Data analysis module: Runs on the server and analyzes the collected data using machine learning algorithms. Specifically, it performs time series analysis and cluster analysis to identify the causes of sleep disorders.

[0552] Emotion engine: Uses APIs (e.g., Microsoft Azure Emotion API) that analyze emotion data from user voice input and facial expressions.

[0553] Notification module: displays personalized advice in real time on the smart glasses, providing visual feedback through the smart glasses display.

[0554] Processing flow

[0555] 1. Data collection: Users input lifestyle and sleep pattern data using a smartphone or tablet, and environmental and emotional data is collected in real time through sensors installed in the smart glasses.

[0556] 2. Data transmission: The collected data is transmitted to a server via the Internet.

[0557] 3. Data analysis: The server analyzes lifestyle data, sleep pattern data, environmental data, and emotional data, and uses machine learning algorithms to identify the cause of the user's sleep disorder.

[0558] 4. Advice generation: Generate personalized improvement advice based on the cause of sleep disorders.

[0559] 5. Providing advice: Providing advice to users in real time via smart glasses.

[0560] Specific examples

[0561] For example, if a user enters data that they go to bed at 11pm every night and have trouble falling asleep, the smart glasses' sensors will determine that the room temperature is 25°C, the light intensity is 150 lux, and face recognition and voice input will confirm a high stress level.The server will analyze this data and display advice such as "limit your caffeine intake," "set the bedroom temperature to 20°C," and "do deep breathing exercises to reduce stress" on the smart glasses in real time.

[0562] Prompt Sentence Examples

[0563] "Develop an AI system that generates personalized advice for improving sleep based on a user's lifestyle data (e.g., bedtime, wake-up time, meal times), environmental data (e.g., temperature, humidity, light intensity, noise level), and emotional data (e.g., stress level). Integrate this into an application that displays advice in real time on smart glasses."

[0564] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0565] Step 1:

[0566] Users use their smartphones or tablets to input lifestyle and sleep pattern data, including bedtime, wake-up time, meal times, exercise time, and timing of caffeine and alcohol intake. The device temporarily stores this input data and prepares it to be sent to a server via the Internet.

[0567] Step 2:

[0568] The smart glasses collect environmental data in real time using built-in sensors (temperature, humidity, light, and noise sensors). In addition, the glasses use a built-in camera and microphone to collect the user's voice and facial expressions and analyze their emotional data. This data is temporarily stored inside the glasses and periodically sent to a server.

[0569] Step 3:

[0570] The server receives lifestyle data, sleep pattern data, environmental data, and emotion data sent from the device and smart glasses. The server stores this data in a database and simultaneously performs data preprocessing, such as filling in missing values ​​and removing outliers.

[0571] Step 4:

[0572] The server's analysis module uses machine learning algorithms to analyze the received data. Specifically, it performs time-series analysis to extract sleep patterns, cluster analysis to group lifestyle habits, and evaluate emotional states. Through this analysis, the server identifies the cause of the user's sleep disorder.

[0573] Step 5:

[0574] The server generates personalized improvement advice based on the analysis results. Examples of generated advice include "limit caffeine intake," "adjust the temperature in your bedroom," and "perform emotional regulation exercises if you are under high stress." The server generates this advice individually for each user and temporarily stores it.

[0575] Step 6:

[0576] The server sends the generated advice in real time to the smart glasses, which display the advice in the form of a visual notification, for example, a message pop-up on the glasses' display.

[0577] Step 7:

[0578] The user checks the notifications on the smart glasses and adjusts their lifestyle and environment accordingly. The user then changes their behavior in accordance with the advice provided, and this is reflected in future data inputs and environmental data collection by sensors.

[0579] These steps provide real-time, personalized advice on how to improve your sleep.

[0580] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0581] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0582] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0583] [Third embodiment]

[0584] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0585] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0586] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0587] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0588] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0589] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0590] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0591] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0592] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0593] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0594] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0595] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0596] The system of the present invention is a personalized service that utilizes AI to improve sleep, and collects and analyzes the user's lifestyle data, sleep pattern data, and environmental data to provide individualized advice on how to improve sleep. A specific embodiment of this system is described below.

[0597] User Data Collection

[0598] Users enter their daily lifestyle and sleep pattern data via devices such as smartphones or tablets. This data includes bedtime, wake-up time, meal times, exercise time, and caffeine and alcohol intake. Additionally, sensors installed in the devices are used to collect bedroom environmental data, including temperature, humidity, light intensity, and noise level.

[0599] Data transmission and storage

[0600] The device sends the collected lifestyle, sleep pattern, and environmental data to a server via the Internet. After receiving the data, the server stores it in a database. The data is sent periodically, updated daily, for example.

[0601] Data analysis

[0602] The server analyzes the user's sleep patterns based on the stored data, using time series analysis, cluster analysis, and machine learning algorithms to identify sleep disorders, such as difficulty falling asleep, frequent nighttime awakenings, or early morning awakenings.

[0603] Identifying sleep disorders

[0604] The server uses the analysis results to identify the cause of the user's sleep disorder, such as consuming caffeine before bedtime causing difficulty falling asleep, or having a bedroom that is too hot causing frequent awakenings.

[0605] Generate and provide improvement advice

[0606] The server generates specific improvement advice for the user based on the identified causes. The advice is personalized for each user and provides specific guidelines for action. For example, it may include refraining from caffeine intake four hours before going to bed and setting the bedroom temperature to 20°C. The generated improvement advice is sent from the server to the device, which notifies the user. The notification is displayed as a push notification in the app or as a message on the home screen.

[0607] Specific examples

[0608] Suppose a user enters lifestyle data stating that they go to bed at 11pm every night, but have difficulty falling asleep and are unable to fall asleep for 30 minutes. The user also has a habit of drinking coffee in the evening, and their bedroom temperature is 25°C. The device sends this data to a server, which analyzes it and determines that the cause of their difficulty falling asleep is caffeine intake and a high bedroom temperature. The server generates specific advice for improvement, such as "avoid caffeine intake four hours before bedtime" and "set the bedroom temperature to 20°C." The device receives this advice and notifies the user. In this way, the user can receive specific improvement measures tailored to their lifestyle and environment.

[0609] This system allows users to obtain sleep improvement measures tailored to their individual needs, effectively improving the quality of their sleep.

[0610] The processing flow will be explained below.

[0611] Step 1:

[0612] Using a smartphone app, users input their lifestyle and sleep pattern data, including bedtime, wake-up time, meal times, exercise time, and caffeine and alcohol intake.

[0613] Step 2:

[0614] The device uses built-in sensors to collect bedroom environmental data, including temperature, humidity, light intensity, and noise levels.

[0615] Step 3:

[0616] The terminal temporarily stores the lifestyle habit data input by the user and the collected environmental data.

[0617] Step 4:

[0618] The terminal periodically (for example, once a day) transmits the collected lifestyle habit data, sleep pattern data, and environmental data to the server.

[0619] Step 5:

[0620] The server receives the data sent from the terminal and stores it in a database.

[0621] Step 6:

[0622] The server then analyzes the user's sleep patterns based on the stored data, using time series analysis, cluster analysis, and machine learning algorithms.

[0623] Step 7:

[0624] The server uses the analysis results to identify the cause of the user's sleep disorder, such as difficulty falling asleep due to consuming caffeine before bedtime, or frequent awakenings due to a bedroom temperature that is too high.

[0625] Step 8:

[0626] The server generates personalized improvement advice based on the identified causes, including how to improve lifestyle habits and adjust the bedroom environment.

[0627] Step 9:

[0628] The server transmits the generated improvement advice to the terminal.

[0629] Step 10:

[0630] The device will notify the user of the improvement advice it has received. The advice will be displayed as a push notification in the app or as a message on the home screen.

[0631] Through this series of steps, users can receive specific sleep improvement measures tailored to their individual needs, improving the quality of their sleep by helping them fall asleep more easily and preventing them from waking up during the night.

[0632] Example 1

[0633] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0634] Previous sleep improvement services only offered general advice and were unable to provide specific advice based on individual users' lifestyles and environmental factors. Furthermore, they lacked the technological means to properly analyze collected data and identify the causes of sleep disorders. This made it difficult to effectively improve sleep.

[0635] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0636] In this invention, the server includes means for collecting daily living data and sleep data provided by the user, means for collecting bedroom environmental data using a detector for collecting environmental data, means for transmitting the daily living data, sleep data, and environmental data to the server, means for the server to analyze the collected data and identify the cause of the user's sleep disorder, means for the server to generate personalized improvement advice based on the cause of the sleep disorder and transmit the advice to the user's terminal, and means for the terminal to notify the user of the improvement advice. This enables effective sleep improvement based on the user's individual lifestyle habits and environmental factors.

[0637] "Daily Life Data" refers to data provided by users, including information such as bedtime, wake-up time, meal times, exercise time, and caffeine and alcohol intake.

[0638] "Sleep data" refers to data that includes information about a user's sleep patterns, particularly data such as the time they go to bed, the time they wake up, and the number of times they wake up during their sleep.

[0639] "Environmental data" refers to data including the temperature, humidity, light intensity, noise level, etc. of the bedroom.

[0640] A "detector" refers to a device that collects environmental data using multiple sensors, including temperature sensors, humidity sensors, light sensors, microphones, etc.

[0641] A "server" is a computer that receives, stores, analyzes, and notifies users of data.

[0642] A "terminal" is a device used by a user to input and receive data, including devices such as smartphones and tablets.

[0643] A "learning algorithm" is a computational method or algorithm used to identify patterns based on collected data and automatically learn from new information.

[0644] "Individualized Improvement Advice" refers to specific, personalized advice provided based on a user's individual lifestyle and environmental data.

[0645] The system of the present invention is a personalized service that utilizes AI to improve sleep, and collects and analyzes the user's daily life data, sleep data, and environmental data to provide individualized advice on how to improve sleep. A specific embodiment of this system is described below.

[0646] First, the user program collects daily life and sleep data. The user inputs information about their daily life through a device such as a smartphone or tablet. This information includes bedtime, wake-up time, meal times, exercise time, and caffeine and alcohol intake. Bedroom environmental data is also automatically collected using the device's built-in temperature, humidity, light, and microphone sensors. This provides information such as the bedroom's temperature, humidity, light intensity, and noise level.

[0647] The collected data is then sent over the Internet to a server, which stores the data in a database. Data is sent periodically, for example, once a day.

[0648] The server program analyzes the collected data using time series analysis, cluster analysis, and machine learning algorithms (such as the Python libraries TensorFlow and Scikit-learn) to identify the user's sleep patterns and identify any sleep disorders they may be experiencing.

[0649] Once the cause of the sleep disorder is identified, the server generates personalized recommendations based on the user's lifestyle and environmental factors. These recommendations are specific and actionable, and are based on the user's lifestyle and environmental factors. Examples include "avoid caffeine intake four hours before bedtime" and "set the bedroom temperature to 20°C."

[0650] The generated improvement advice is sent from the server to the device, and the device notifies the user via the app's push notification function or a message on the home screen.

[0651] As a concrete example, suppose a user enters daily life data stating that they go to bed at 11pm every night, but have difficulty falling asleep and are unable to fall asleep for 30 minutes. The user has the habit of drinking coffee in the evening, and their bedroom temperature is 25°C. The user's sleep pattern data and environmental data are also collected. The device sends this data to a server, which analyzes it and determines that the cause of their difficulty falling asleep is caffeine intake and a high bedroom temperature. The server generates specific improvement advice, such as "avoid caffeine intake four hours before bedtime" and "set the bedroom temperature to 20°C." The device receives this advice and notifies the user. In this way, the user can receive specific improvement measures tailored to their lifestyle and environment.

[0652] An example of a prompt sentence to input to the generative AI model is as follows:

[0653] "Generate specific advice for improving sleep based on the user's sleep data and lifestyle data. For example, if a user goes to bed at 11pm every night but has trouble falling asleep, identify the cause and provide specific guidelines for improvement. Data includes bedtime, wake-up time, caffeine and alcohol intake, bedroom temperature, etc."

[0654] The above is a detailed description of the embodiment of the present invention.

[0655] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0656] Step 1:

[0657] Users input daily life and sleep data via devices such as smartphones or tablets. Input data includes bedtime, wake-up time, meal times, exercise time, and caffeine and alcohol intake. Bedroom environmental data (temperature, humidity, light intensity, noise level) is automatically collected using detectors such as temperature, humidity, light sensors, and microphones built into the device. The input and output are as follows:

[0658] Input: User's daily life data, sleep data, environmental data

[0659] Output: Data stored on the device

[0660] Step 2:

[0661] The device sends the collected daily life data, sleep data, and environmental data to a server via the Internet. The HTTPS protocol is used for transmission, and data is sent periodically. The server stores the received data in a database.

[0662] Input: Data stored on the device

[0663] Output: Data sent to the server, data stored in the server's database

[0664] Step 3:

[0665] The server analyzes the stored data using time series analysis, cluster analysis, and machine learning algorithms (such as the Python libraries TensorFlow and Scikit-learn) to identify the user's sleep patterns and identify any sleep disorders the user may be experiencing.

[0666] Input: User data stored in a database

[0667] Output: Analysis results (identification of user's sleep patterns and sleep disorders)

[0668] Step 4:

[0669] The server then uses the analysis results to identify the cause of the user's sleep disorder. For example, it may identify that the cause is caffeine intake before bedtime or an inappropriate bedroom temperature. Specific actions are then taken based on the analysis results.

[0670] Input: Analysis results

[0671] Output: Causes of sleep disorders

[0672] Step 5:

[0673] The server generates personalized improvement advice based on the identified causes, including specific guidelines tailored to the user's lifestyle and environmental factors, such as "avoid caffeine intake four hours before bedtime" or "set the bedroom temperature to 20°C."

[0674] Input: Causes of sleep disorders

[0675] Output: Improvement advice

[0676] Step 6:

[0677] The server sends the generated improvement advice to the device, which then notifies the user of the received advice. The notification is displayed via the app's push notification function or as a message on the home screen, allowing the user to receive specific improvement measures that can be applied to their daily lives.

[0678] Input: Improvement advice

[0679] Output: User notification

[0680] (Application example 1)

[0681] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0682] Poor sleep quality and sleep disorders are serious problems faced by many people in modern society. Conventional solutions to this problem have been limited to general advice, making it difficult to provide effective improvement measures tailored to the specific conditions of each individual user. Furthermore, there has been a lack of systems that effectively utilize lifestyle and environmental data provided by users to generate detailed improvement advice. Therefore, there is a need for a new system that provides detailed and effective improvement advice based on users' personalized lifestyle and sleep pattern data.

[0683] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0684] In this invention, the server includes means for analyzing user data and identifying sleep patterns using a clustering algorithm, means for sending generated improvement advice to the user by email, and means for providing the generated improvement advice in a virtual store, thereby enabling the user to easily receive personalized, detailed sleep improvement advice.

[0685] "User-provided lifestyle data" refers to information about the user's daily activities and habits, including, for example, bedtime, wake-up time, meal times, exercise time, and caffeine and alcohol intake.

[0686] "Sleep pattern data" is detailed information about the user's sleep, specifically including the amount of time they sleep, how well they fall asleep, how many times they wake up during the night, and the time they wake up.

[0687] "Environmental data" refers to data about the user's bedroom environment, specifically including temperature, humidity, light intensity, noise level, etc.

[0688] A "sensor" is a device used to collect environmental data, including temperature sensors, humidity sensors, light sensors, noise sensors, etc.

[0689] "Server" means a central computer system that receives, stores, and analyzes data sent by users over the Internet.

[0690] A "clustering algorithm" is a computational technique for grouping (clustering) similar data, and is used in this invention to identify a user's sleep patterns.

[0691] "Email" means an electronic messaging service for notifying and communicating information to users.

[0692] A "virtual store" is a virtual sales and service space that exists on the Internet and can be accessed online by users.

[0693] "Personalized improvement advice" refers to specific improvement measures that are individually created based on collected lifestyle, sleep pattern, and environmental data.

[0694] The system of the present invention is a personalized service that utilizes AI to improve sleep, and collects and analyzes a user's lifestyle data, sleep pattern data, and environmental data to provide individualized advice on how to improve sleep. A specific embodiment of this system is described below.

[0695] First, the user uses a device such as a smartphone or tablet to input daily lifestyle and sleep pattern data. This data includes bedtime, wake-up time, meal times, exercise time, caffeine and alcohol intake, etc. Additionally, sensors installed on the device are used to collect bedroom environmental data, including temperature, humidity, light intensity, and noise level.

[0696] The device then transmits the collected lifestyle, sleep, and environmental data to a server via the Internet. The server receives the data and stores it in a database. The data is transmitted periodically, updated daily, for example.

[0697] The server analyzes the user's sleep patterns based on the stored data. The analysis uses machine learning algorithms, including clustering algorithms, to identify the user's sleep disorders. The server then identifies the cause of the user's sleep disorder based on the analysis results. For example, specific causes may be revealed, such as consuming caffeine before bedtime causing difficulty falling asleep, or the bedroom temperature being too high causing frequent awakenings.

[0698] The server then generates specific improvement advice for the user based on the identified causes. The advice is personalized for each user and provides specific guidelines for action. For example, it may include refraining from caffeine intake four hours before going to bed and setting the bedroom temperature to 20°C. The generated improvement advice is sent from the server to the device, which then notifies the user.

[0699] Furthermore, advice generated based on the lifestyle and environmental data entered by the user can be sent to the user via email. It can also be provided online in the form of a virtual store, where users can not only receive advice but also purchase related information and products.

[0700] As a specific example, suppose a user enters lifestyle habit data stating that they go to bed at 11pm every night, but have difficulty falling asleep and are unable to fall asleep for 30 minutes. The user has a habit of drinking coffee in the evening, and their bedroom temperature is 25°C. The device sends this data to the server, which analyzes it and determines that the cause of their difficulty falling asleep is caffeine intake and a high bedroom temperature. The server generates specific improvement advice, such as "avoid caffeine intake four hours before bedtime" and "set the bedroom temperature to 20°C." The device receives this advice and notifies the user, allowing them to receive specific improvement measures tailored to their lifestyle and environment.

[0701] An example prompt might have the following format:

[0702] Your sleep data:

[0703] Bedtime: 11:00 PM

[0704] Wake up time: 07:00

[0705] Meal times: 08:00, 12:00, 18:00

[0706] Exercise time: 07:30

[0707] Caffeine intake: 5:00 PM

[0708] Alcohol intake: 9:00 p.m.

[0709] Environmental data: Temperature: 25, Humidity: 50, Light intensity: 300, Noise level: 30

[0710] Analyze the above data and generate advice for improving your sleep.

[0711] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0712] Step 1:

[0713] The device inputs the user's lifestyle and sleep pattern data, including bedtime, wake-up time, meal times, exercise time, caffeine and alcohol intake, etc. By inputting this data, the device records in detail the factors that affect the user's sleep quality.

[0714] Step 2:

[0715] The device uses sensors to collect bedroom environmental data, such as temperature, humidity, light intensity, and noise level, to monitor the user's sleeping environment in detail.

[0716] Step 3:

[0717] The device sends the collected lifestyle, sleep pattern, and environmental data to a server via the Internet. The sent data is stored in a database on the server and organized for each user. Since large amounts of data are often processed at this step, efficient data transmission and storage management are important.

[0718] Step 4:

[0719] The server uses a clustering algorithm to analyze the user's sleep patterns based on the stored data. Specifically, it uses a machine learning algorithm to classify and analyze the data and identify the cause of the user's sleep disorder. Here, data with similar patterns are grouped (clustered) to make the analysis more efficient.

[0720] Step 5:

[0721] The server uses the analysis results to identify the cause of the user's sleep disorder. Based on the identified cause, the server generates personalized improvement advice that is specific and actionable, such as limiting caffeine intake for certain periods of time or setting an appropriate bedroom temperature.

[0722] Step 6:

[0723] The server generates improvement advice and sends it to the user as an email. In this process, the advice is sent in text format to the email address specified by the user, allowing the user to receive the advice on their smartphone or tablet.

[0724] Step 7:

[0725] The device will notify the user of the improvement advice received from the server. Notification methods can include push notifications, in-app messages, and even messages displayed on the home screen. This notification makes it easier for users to implement improvement measures in their daily lives.

[0726] Step 8:

[0727] In the virtual store, users can view improvement advice online and purchase related information and products. The virtual store helps users find and purchase products based on the advice, enabling them to obtain more practical solutions.

[0728] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0729] The system of the present invention is a personalized service that utilizes AI to improve sleep. It collects and analyzes the user's lifestyle data, sleep pattern data, and environmental data, and combines it with an emotion engine to provide improvement advice that takes the user's emotions into consideration. A specific embodiment of this system is described below.

[0730] User Data Collection

[0731] Users enter their daily lifestyle and sleep pattern data via devices such as smartphones or tablets. This data includes bedtime, wake-up time, meal times, exercise time, and caffeine and alcohol intake. Additionally, sensors installed on the devices are used to collect bedroom environmental data, including temperature, humidity, light intensity, and noise levels.

[0732] Data transmission and storage

[0733] The device transmits the collected lifestyle data, sleep pattern data, and environmental data to a server via the Internet. The server receives the data and stores it in a database. Data transmission is performed periodically.

[0734] Collecting Emotional Data

[0735] When a user inputs text into the device or uses the device's microphone to input voice, the emotion engine can analyze the content. The emotion engine analyzes the user's text input, tone of voice, and facial expression to identify the user's emotion. For example, it can recognize the user's emotional state, such as whether they are stressed or relaxed.

[0736] Data analysis

[0737] The server analyzes the user's sleep patterns based on the stored lifestyle, sleep pattern, environmental, and emotional data. The analysis uses time series analysis, cluster analysis, and machine learning algorithms to identify the cause of the user's sleep disorder.

[0738] Identifying sleep disorders and generating improvement advice

[0739] The server uses the analysis results to identify the cause of the user's sleep disorder. For example, consuming caffeine before bedtime can cause difficulty falling asleep, the bedroom temperature can be too high and cause frequent awakenings, or the user's stress can negatively affect sleep. Based on the identified causes, the server generates personalized improvement advice. The advice can include suggestions for improving lifestyle habits, adjusting the bedroom environment, and emotional support.

[0740] Providing advice

[0741] The server sends the generated improvement advice to the device. The device then notifies the user of the received improvement advice. The notification is displayed as a push notification in the app or a message on the home screen. For example, specific advice is provided, such as "limit caffeine intake at night," "lower the temperature in your bedroom," and "try relaxation techniques to reduce stress."

[0742] Specific examples

[0743] A user enters data that they go to bed at 11 PM every night, but have difficulty falling asleep and are unable to fall asleep for 30 minutes. They also have a habit of drinking coffee in the evening and their bedroom temperature is 25°C. The emotion engine then recognizes that the user is feeling stressed. The device sends this data to the server, which analyzes it and determines that the causes of their difficulty falling asleep are caffeine intake, high bedroom temperature, and stress. The server generates specific improvement advice, such as "avoid caffeine intake four hours before bedtime," "set the bedroom temperature to 20°C," and "try deep breathing exercises to reduce stress." The device receives this advice and notifies the user. In this way, the user can receive specific improvement measures based on their lifestyle, environment, and emotions.

[0744] This system allows users to receive specific sleep improvement measures tailored to their individual needs, improving the quality of their sleep by helping them fall asleep more easily and preventing them from waking up during the night.

[0745] The processing flow will be explained below.

[0746] Step 1:

[0747] Using a smartphone app, users input their lifestyle and sleep pattern data, including bedtime, wake-up time, meal times, exercise time, and caffeine and alcohol intake.

[0748] Step 2:

[0749] The device uses built-in sensors (such as a temperature sensor, light sensor, and microphone) to collect real-time environmental data about the bedroom, including temperature, humidity, light intensity, and noise level.

[0750] Step 3:

[0751] The terminal temporarily stores the lifestyle habit data input by the user and the collected environmental data.

[0752] Step 4:

[0753] The device sends user input data and environmental data to the emotion engine, which then analyzes the user's tone of voice and facial expressions to generate emotion data, including the user's emotional state, such as whether they are stressed or relaxed.

[0754] Step 5:

[0755] The terminal transmits the collected lifestyle habit data, sleep pattern data, environmental data, and emotional data to a server via the Internet.

[0756] Step 6:

[0757] The server receives the data sent from the terminal and stores it in a database.

[0758] Step 7:

[0759] The server analyzes the user's sleep patterns based on the stored lifestyle, sleep pattern, environmental, and emotional data, using time series analysis, cluster analysis, and machine learning algorithms.

[0760] Step 8:

[0761] The server uses the analysis results to identify the cause of the user's sleep disorder, such as difficulty falling asleep due to consuming caffeine before bedtime, frequent awakenings due to a bedroom temperature that is too high, or stress.

[0762] Step 9:

[0763] Based on the identified causes, the server generates personalized improvement advice, including suggestions for lifestyle improvements, bedroom environment adjustments, and emotional support.

[0764] Step 10:

[0765] The server transmits the generated improvement advice to the terminal.

[0766] Step 11:

[0767] The device will then notify the user of the improvement advice it has received. The notification will appear as a push notification in the app or a message on the home screen. Specific advice provided includes "limiting caffeine intake at night," "setting the bedroom temperature to 20°C," and "trying deep breathing exercises to reduce stress."

[0768] Through this series of steps, users can receive specific sleep improvement measures tailored to their individual needs, improving the quality of their sleep by helping them fall asleep more easily and preventing them from waking up during the night.

[0769] Example 2

[0770] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0771] Conventional sleep improvement systems not only collect users' lifestyle and environmental data, but also have difficulty providing personalized advice that takes into account the user's emotional state. As a result, they have been unable to identify the root causes of sleep disorders and propose effective solutions. Furthermore, it has been challenging to identify the causes of complex sleep disorders that cannot be identified through simple data analysis.

[0772] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0773] In this invention, the server includes means incorporating an emotion engine for analyzing user emotion data, means for identifying the cause of the user's sleep disorder using time series analysis, cluster analysis, and machine learning algorithms, and means for generating personalized improvement advice and sending it to the user's terminal, thereby enabling highly accurate identification of the cause of the sleep disorder that also takes the user's emotional state into consideration, and providing personalized improvement advice.

[0774] "Lifestyle data" refers to information about a user's daily activities, including bedtime, wake-up time, meal times, exercise time, caffeine and alcohol intake, etc.

[0775] "Sleep pattern data" refers to information about the user's sleep behavior and state, including the time it takes to fall asleep and the frequency of waking up during the night.

[0776] "Environmental data" refers to information about the user's bedroom environment, including temperature, humidity, light intensity, noise level, etc.

[0777] "Emotion data" is information about the user's emotional state, including stress or relaxation levels analyzed by the emotion engine from text input or voice input.

[0778] An "emotion engine" is a system that analyzes a user's text input, voice input, and facial expressions to identify the user's emotional state.

[0779] "Server" means a computer system that stores and analyzes collected data and provides specific services and information to users.

[0780] "Time series analysis" and "cluster analysis" are statistical methods used by servers to analyze collected data, analyzing fluctuations in data over time and identifying groups of similar data.

[0781] A "machine learning algorithm" is an algorithm for artificial intelligence used by servers when analyzing data. It is a technology that learns patterns based on past data and makes future predictions and classifications.

[0782] "Personalized improvement advice" is specific advice that suggests the best improvement measures for each user based on their individual lifestyle, sleep pattern, environmental, and emotional data.

[0783] "Terminal" means an electronic device used by a user to input data or receive advice from a server, including a smartphone or tablet.

[0784] The system of the present invention collects and analyzes a user's lifestyle data, sleep pattern data, environmental data, and emotional data, and combines them with an emotional engine to provide the user with personalized improvement advice. A detailed embodiment of this system is described below.

[0785] Users use devices such as smartphones or tablets to input data on their daily lifestyle habits and sleep patterns, such as bedtime, wake-up time, meal times, and exercise time. Furthermore, sensors installed on the devices are used to collect environmental data, such as temperature, humidity, light intensity, and noise levels. Along with this data, an emotion engine installed on the devices analyzes text and voice input to collect the user's emotion data. The emotion engine analyzes the text content, tone of voice, and facial expressions to identify the user's emotional state.

[0786] The device periodically transmits this lifestyle data, sleep pattern data, environmental data, and emotional data to a server via the Internet. The server receives this data and stores it in a database. The stored data is analyzed using time series analysis, cluster analysis, and machine learning algorithms. In particular, the machine learning algorithm is used to identify the cause of the user's sleep disorder based on past data.

[0787] The server uses the analysis results to identify the cause of the user's sleep disorder, such as whether consuming caffeine before bedtime is causing difficulty falling asleep, whether the bedroom temperature is too high causing frequent awakenings, or whether stress is negatively affecting sleep.

[0788] Based on the identified causes, the server uses a generative AI model to generate personalized improvement advice, such as "avoid caffeine intake four hours before bedtime," "set the bedroom temperature to 20°C," and "perform deep breathing exercises."

[0789] The server sends the generated improvement advice to the device, which then notifies the user of the received advice. This advice is displayed as a push notification or an in-app message. For example, it might say, "Reduce caffeine intake at night," "Lower the temperature in your bedroom," or "Try deep breathing exercises to reduce stress."

[0790] As a concrete example, suppose a user enters data that indicates that they go to bed at 11pm every night but are unable to fall asleep for 30 minutes. This user has the habit of drinking coffee in the evening, and their bedroom temperature is 25°C. The emotion engine also recognizes that the user is feeling stressed. The device sends this data to the server, which analyzes it and determines that the causes of their difficulty falling asleep are caffeine intake, a high bedroom temperature, and stress. The server generates specific improvement advice, such as "avoid caffeine intake four hours before bedtime," "set the bedroom temperature to 20°C," and "try deep breathing exercises," and the device notifies the user of this advice.

[0791] It is also possible to create specific advice by inputting a prompt into the generative AI model. An example of a prompt is, "I can't fall asleep for 30 minutes even though I go to bed at 11pm. I have a habit of drinking coffee in the evening, my bedroom temperature is 25°C, and I'm feeling stressed. Please provide advice to improve this situation." Based on this prompt, the generative AI model will propose the most appropriate advice.

[0792] This system allows users to receive specific sleep improvement measures tailored to their individual needs, helping them fall asleep faster, prevent waking up during the night, and improve the quality of their sleep.

[0793] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0794] Step 1:

[0795] Users input their daily lifestyle and sleep pattern data via devices such as smartphones or tablets. For example, data such as "I went to bed at 10 p.m. last night" and "I woke up at 7 a.m." In addition, sensors installed in the device are used to collect environmental data such as temperature, humidity, light intensity, and noise level. This allows lifestyle data, sleep pattern data, and environmental data to be obtained. Input data: lifestyle data, sleep pattern data, environmental data.

[0796] Step 2:

[0797] The device periodically sends collected lifestyle data, sleep pattern data, and environmental data to a server via the Internet. For example, it can be set to send data every night at 11pm. The server stores the received data in a database. Checksums and data verification protocols are used to ensure that the data being sent has been received properly. Input data: lifestyle data, sleep pattern data, environmental data. Output data: data stored in the database.

[0798] Step 3:

[0799] When a user inputs text or voice into a device, the emotion engine analyzes the content. The emotion engine analyzes the user's text content, tone of voice, and facial expressions to identify emotional states such as stress or relaxation. For example, if a user says "I'm tired today" through voice input, the emotion engine analyzes the user's emotions from the tone of voice and choice of words. Input data: text data, voice data. Output data: emotion data.

[0800] Step 4:

[0801] The device sends the analyzed emotional data to a server via the Internet, and the server stores the received emotional data in a database. This adds the user's emotional data to the database. Input data: Emotional data. Output data: Emotional data stored in the database.

[0802] Step 5:

[0803] The server analyzes the user's sleep patterns based on the lifestyle, sleep pattern, environmental, and emotional data stored in the database. This analysis uses time series analysis, cluster analysis, and machine learning algorithms. For example, the server analyzes the lifestyle data of a user who has trouble falling asleep to identify the cause. Input data: lifestyle data, sleep pattern data, environmental data, emotional data. Output data: analysis results.

[0804] Step 6:

[0805] The server identifies the cause of the user's sleep disorder based on the analysis results. For example, caffeine intake, bedroom temperature, stress, etc. are identified as possible causes. Input data: Analysis results. Output data: Cause of sleep disorder.

[0806] Step 7:

[0807] Based on the identified causes, the server uses a generative AI model to generate personalized improvement advice. For example, specific advice may be generated such as "avoid caffeine intake four hours before bedtime," "set the bedroom temperature to 20°C," or "try deep breathing exercises to relieve stress." Input data: Causes of sleep disorders. Output data: Personalized improvement advice.

[0808] Step 8:

[0809] The server sends the generated improvement advice to the device, and the device notifies the user of the received advice. Notification methods include push notifications and in-app messages. For example, messages such as "Reduce caffeine intake at night," "Lower the temperature in your bedroom," and "Try deep breathing exercises to reduce stress" are displayed. Input data: personalized improvement advice. Output data: notification to the user.

[0810] (Application example 2)

[0811] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0812] While conventional sleep improvement systems utilize users' lifestyle, sleep pattern, and environmental data, they lack real-time feedback and emotional data analysis, making it difficult to provide immediate and effective advice. Furthermore, they lack a means to comprehensively analyze users' emotional data and real-time environmental data to provide specific improvement advice. This makes it difficult for users to effectively adjust their lifestyles.

[0813] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0814] In this invention, the server includes means for collecting lifestyle habit data and sleep pattern data provided by the user, means for collecting bedroom environment data using a sensor for collecting environmental data, and means for collecting environmental data and emotion data in real time through smart glasses, thereby enabling real-time and personalized improvement advice to be provided to the user.

[0815] "User-provided lifestyle data" refers to information provided by a user regarding their daily lifestyle, including bedtime, wake-up time, meal times, exercise time, caffeine and alcohol intake, etc.

[0816] "Sleep pattern data" refers to data related to the user's sleep, such as the amount of sleep, the quality of sleep, the time it takes to fall asleep and wake up, etc.

[0817] "Environmental data" refers to data about the environment in which the user sleeps, including temperature, humidity, light intensity, noise level, etc.

[0818] "Real-time collection means" refers to a method, device, or system that collects data immediately and transmits it to a server without delay.

[0819] "Emotional data" refers to data relating to the user's emotional state, including stress levels and relaxation levels obtained by analyzing voice input and facial expressions.

[0820] A "machine learning algorithm" is a type of algorithm that learns patterns and regularities from large amounts of data and uses them to predict or classify future data.

[0821] "Personalized improvement advice" refers to specific improvement suggestions customized based on each user's individual lifestyle, sleep pattern, environmental, and emotional data.

[0822] The present invention is a system that utilizes data on a user's lifestyle habits, sleep patterns, environment, and emotions to provide personalized advice on improving sleep in real time at a brick-and-mortar store. This system is implemented using smart glasses, a server, and devices such as smartphones.

[0823] Hardware Configuration

[0824] Smart glasses: Equipped with sensors that collect real-time environmental data such as temperature, humidity, light intensity, and noise levels, as well as emotional data through sensors that analyze voice input and facial expressions.

[0825] Server: Analyzes collected data and generates personalized improvement advice using machine learning algorithms. Uses cloud computing services (e.g., AWS, Google Cloud) for large-scale data analysis.

[0826] Smartphone or tablet: Used as an interface for users to input lifestyle and sleep pattern data.

[0827] Software Configuration

[0828] Data collection module: Collects user lifestyle data, sleep pattern data, environmental data, and emotional data via smart glasses or smartphone.

[0829] Data analysis module: Runs on the server and analyzes the collected data using machine learning algorithms. Specifically, it performs time series analysis and cluster analysis to identify the causes of sleep disorders.

[0830] Emotion engine: Uses APIs (e.g., Microsoft Azure Emotion API) that analyze emotion data from user voice input and facial expressions.

[0831] Notification module: displays personalized advice in real time on the smart glasses, providing visual feedback through the smart glasses display.

[0832] Processing flow

[0833] 1. Data collection: Users input lifestyle and sleep pattern data using a smartphone or tablet, and environmental and emotional data is collected in real time through sensors installed in the smart glasses.

[0834] 2. Data transmission: The collected data is transmitted to a server via the Internet.

[0835] 3. Data analysis: The server analyzes lifestyle data, sleep pattern data, environmental data, and emotional data, and uses machine learning algorithms to identify the cause of the user's sleep disorder.

[0836] 4. Advice generation: Generate personalized improvement advice based on the cause of sleep disorders.

[0837] 5. Providing advice: Providing advice to users in real time via smart glasses.

[0838] Specific examples

[0839] For example, if a user enters data that they go to bed at 11pm every night and have trouble falling asleep, the smart glasses' sensors will determine that the room temperature is 25°C, the light intensity is 150 lux, and face recognition and voice input will confirm a high stress level.The server will analyze this data and display advice such as "limit your caffeine intake," "set the bedroom temperature to 20°C," and "do deep breathing exercises to reduce stress" on the smart glasses in real time.

[0840] Prompt Sentence Examples

[0841] "Develop an AI system that generates personalized advice for improving sleep based on a user's lifestyle data (e.g., bedtime, wake-up time, meal times), environmental data (e.g., temperature, humidity, light intensity, noise level), and emotional data (e.g., stress level). Integrate this into an application that displays advice in real time on smart glasses."

[0842] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0843] Step 1:

[0844] Users use their smartphones or tablets to input lifestyle and sleep pattern data, including bedtime, wake-up time, meal times, exercise time, and timing of caffeine and alcohol intake. The device temporarily stores this input data and prepares it to be sent to a server via the Internet.

[0845] Step 2:

[0846] The smart glasses collect environmental data in real time using built-in sensors (temperature, humidity, light, and noise sensors). In addition, the glasses use a built-in camera and microphone to collect the user's voice and facial expressions and analyze their emotional data. This data is temporarily stored inside the glasses and periodically sent to a server.

[0847] Step 3:

[0848] The server receives lifestyle data, sleep pattern data, environmental data, and emotion data sent from the device and smart glasses. The server stores this data in a database and simultaneously performs data preprocessing, such as filling in missing values ​​and removing outliers.

[0849] Step 4:

[0850] The server's analysis module uses machine learning algorithms to analyze the received data. Specifically, it performs time-series analysis to extract sleep patterns, cluster analysis to group lifestyle habits, and evaluate emotional states. Through this analysis, the server identifies the cause of the user's sleep disorder.

[0851] Step 5:

[0852] The server generates personalized improvement advice based on the analysis results. Examples of generated advice include "limit caffeine intake," "adjust the temperature in your bedroom," and "perform emotional regulation exercises if you are under high stress." The server generates this advice individually for each user and temporarily stores it.

[0853] Step 6:

[0854] The server sends the generated advice in real time to the smart glasses, which display the advice in the form of a visual notification, for example, a message pop-up on the glasses' display.

[0855] Step 7:

[0856] The user checks the notifications on the smart glasses and adjusts their lifestyle and environment accordingly. The user then changes their behavior in accordance with the advice provided, and this is reflected in future data inputs and environmental data collection by sensors.

[0857] These steps provide real-time, personalized advice on how to improve your sleep.

[0858] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0859] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0860] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0861] [Fourth embodiment]

[0862] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0863] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0864] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0865] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0866] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0867] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0868] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0869] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0870] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0871] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0872] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0873] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0874] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0875] The system of the present invention is a personalized service that utilizes AI to improve sleep, and collects and analyzes the user's lifestyle data, sleep pattern data, and environmental data to provide individualized advice on how to improve sleep. A specific embodiment of this system is described below.

[0876] User Data Collection

[0877] Users enter their daily lifestyle and sleep pattern data via devices such as smartphones or tablets. This data includes bedtime, wake-up time, meal times, exercise time, and caffeine and alcohol intake. Additionally, sensors installed in the devices are used to collect bedroom environmental data, including temperature, humidity, light intensity, and noise level.

[0878] Data transmission and storage

[0879] The device sends the collected lifestyle, sleep pattern, and environmental data to a server via the Internet. After receiving the data, the server stores it in a database. The data is sent periodically, updated daily, for example.

[0880] Data analysis

[0881] The server analyzes the user's sleep patterns based on the stored data, using time series analysis, cluster analysis, and machine learning algorithms to identify sleep disorders, such as difficulty falling asleep, frequent nighttime awakenings, or early morning awakenings.

[0882] Identifying sleep disorders

[0883] The server uses the analysis results to identify the cause of the user's sleep disorder, such as consuming caffeine before bedtime causing difficulty falling asleep, or having a bedroom that is too hot causing frequent awakenings.

[0884] Generate and provide improvement advice

[0885] The server generates specific improvement advice for the user based on the identified causes. The advice is personalized for each user and provides specific guidelines for action. For example, it may include refraining from caffeine intake four hours before going to bed and setting the bedroom temperature to 20°C. The generated improvement advice is sent from the server to the device, which notifies the user. The notification is displayed as a push notification in the app or as a message on the home screen.

[0886] Specific examples

[0887] Suppose a user enters lifestyle data stating that they go to bed at 11pm every night, but have difficulty falling asleep and are unable to fall asleep for 30 minutes. The user also has a habit of drinking coffee in the evening, and their bedroom temperature is 25°C. The device sends this data to a server, which analyzes it and determines that the cause of their difficulty falling asleep is caffeine intake and a high bedroom temperature. The server generates specific advice for improvement, such as "avoid caffeine intake four hours before bedtime" and "set the bedroom temperature to 20°C." The device receives this advice and notifies the user. In this way, the user can receive specific improvement measures tailored to their lifestyle and environment.

[0888] This system allows users to obtain sleep improvement measures tailored to their individual needs, effectively improving the quality of their sleep.

[0889] The processing flow will be explained below.

[0890] Step 1:

[0891] Using a smartphone app, users input their lifestyle and sleep pattern data, including bedtime, wake-up time, meal times, exercise time, and caffeine and alcohol intake.

[0892] Step 2:

[0893] The device uses built-in sensors to collect bedroom environmental data, including temperature, humidity, light intensity, and noise levels.

[0894] Step 3:

[0895] The terminal temporarily stores the lifestyle habit data input by the user and the collected environmental data.

[0896] Step 4:

[0897] The terminal periodically (for example, once a day) transmits the collected lifestyle habit data, sleep pattern data, and environmental data to the server.

[0898] Step 5:

[0899] The server receives the data sent from the terminal and stores it in a database.

[0900] Step 6:

[0901] The server then analyzes the user's sleep patterns based on the stored data, using time series analysis, cluster analysis, and machine learning algorithms.

[0902] Step 7:

[0903] The server uses the analysis results to identify the cause of the user's sleep disorder, such as difficulty falling asleep due to consuming caffeine before bedtime, or frequent awakenings due to a bedroom temperature that is too high.

[0904] Step 8:

[0905] The server generates personalized improvement advice based on the identified causes, including how to improve lifestyle habits and adjust the bedroom environment.

[0906] Step 9:

[0907] The server transmits the generated improvement advice to the terminal.

[0908] Step 10:

[0909] The device will notify the user of the improvement advice it has received. The advice will be displayed as a push notification in the app or as a message on the home screen.

[0910] Through this series of steps, users can receive specific sleep improvement measures tailored to their individual needs, improving the quality of their sleep by helping them fall asleep more easily and preventing them from waking up during the night.

[0911] Example 1

[0912] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0913] Previous sleep improvement services only offered general advice and were unable to provide specific advice based on individual users' lifestyles and environmental factors. Furthermore, they lacked the technological means to properly analyze collected data and identify the causes of sleep disorders. This made it difficult to effectively improve sleep.

[0914] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0915] In this invention, the server includes means for collecting daily living data and sleep data provided by the user, means for collecting bedroom environmental data using a detector for collecting environmental data, means for transmitting the daily living data, sleep data, and environmental data to the server, means for the server to analyze the collected data and identify the cause of the user's sleep disorder, means for the server to generate personalized improvement advice based on the cause of the sleep disorder and transmit the advice to the user's terminal, and means for the terminal to notify the user of the improvement advice. This enables effective sleep improvement based on the user's individual lifestyle habits and environmental factors.

[0916] "Daily Life Data" refers to data provided by users, including information such as bedtime, wake-up time, meal times, exercise time, and caffeine and alcohol intake.

[0917] "Sleep data" refers to data that includes information about a user's sleep patterns, particularly data such as the time they go to bed, the time they wake up, and the number of times they wake up during their sleep.

[0918] "Environmental data" refers to data including the temperature, humidity, light intensity, noise level, etc. of the bedroom.

[0919] A "detector" refers to a device that collects environmental data using multiple sensors, including temperature sensors, humidity sensors, light sensors, microphones, etc.

[0920] A "server" is a computer that receives, stores, analyzes, and notifies users of data.

[0921] A "terminal" is a device used by a user to input and receive data, including devices such as smartphones and tablets.

[0922] A "learning algorithm" is a computational method or algorithm used to identify patterns based on collected data and automatically learn from new information.

[0923] "Individualized Improvement Advice" refers to specific, personalized advice provided based on a user's individual lifestyle and environmental data.

[0924] The system of the present invention is a personalized service that utilizes AI to improve sleep, and collects and analyzes the user's daily life data, sleep data, and environmental data to provide individualized advice on how to improve sleep. A specific embodiment of this system is described below.

[0925] First, the user program collects daily life and sleep data. The user inputs information about their daily life through a device such as a smartphone or tablet. This information includes bedtime, wake-up time, meal times, exercise time, and caffeine and alcohol intake. Bedroom environmental data is also automatically collected using the device's built-in temperature, humidity, light, and microphone sensors. This provides information such as the bedroom's temperature, humidity, light intensity, and noise level.

[0926] The collected data is then sent over the Internet to a server, which stores the data in a database. Data is sent periodically, for example, once a day.

[0927] The server program analyzes the collected data using time series analysis, cluster analysis, and machine learning algorithms (such as the Python libraries TensorFlow and Scikit-learn) to identify the user's sleep patterns and identify any sleep disorders they may be experiencing.

[0928] Once the cause of the sleep disorder is identified, the server generates personalized recommendations based on the user's lifestyle and environmental factors. These recommendations are specific and actionable, and are based on the user's lifestyle and environmental factors. Examples include "avoid caffeine intake four hours before bedtime" and "set the bedroom temperature to 20°C."

[0929] The generated improvement advice is sent from the server to the device, and the device notifies the user via the app's push notification function or a message on the home screen.

[0930] As a concrete example, suppose a user enters daily life data stating that they go to bed at 11pm every night, but have difficulty falling asleep and are unable to fall asleep for 30 minutes. The user has the habit of drinking coffee in the evening, and their bedroom temperature is 25°C. The user's sleep pattern data and environmental data are also collected. The device sends this data to a server, which analyzes it and determines that the cause of their difficulty falling asleep is caffeine intake and a high bedroom temperature. The server generates specific improvement advice, such as "avoid caffeine intake four hours before bedtime" and "set the bedroom temperature to 20°C." The device receives this advice and notifies the user. In this way, the user can receive specific improvement measures tailored to their lifestyle and environment.

[0931] An example of a prompt sentence to input to the generative AI model is as follows:

[0932] "Generate specific advice for improving sleep based on the user's sleep data and lifestyle data. For example, if a user goes to bed at 11pm every night but has trouble falling asleep, identify the cause and provide specific guidelines for improvement. Data includes bedtime, wake-up time, caffeine and alcohol intake, bedroom temperature, etc."

[0933] The above is a detailed description of the embodiment of the present invention.

[0934] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0935] Step 1:

[0936] Users input daily life and sleep data via devices such as smartphones or tablets. Input data includes bedtime, wake-up time, meal times, exercise time, and caffeine and alcohol intake. Bedroom environmental data (temperature, humidity, light intensity, noise level) is automatically collected using detectors such as temperature, humidity, light sensors, and microphones built into the device. The input and output are as follows:

[0937] Input: User's daily life data, sleep data, environmental data

[0938] Output: Data stored on the device

[0939] Step 2:

[0940] The device sends the collected daily life data, sleep data, and environmental data to a server via the Internet. The HTTPS protocol is used for transmission, and data is sent periodically. The server stores the received data in a database.

[0941] Input: Data stored on the device

[0942] Output: Data sent to the server, data stored in the server's database

[0943] Step 3:

[0944] The server analyzes the stored data using time series analysis, cluster analysis, and machine learning algorithms (such as the Python libraries TensorFlow and Scikit-learn) to identify the user's sleep patterns and identify any sleep disorders the user may be experiencing.

[0945] Input: User data stored in a database

[0946] Output: Analysis results (identification of user's sleep patterns and sleep disorders)

[0947] Step 4:

[0948] The server then uses the analysis results to identify the cause of the user's sleep disorder. For example, it may identify that the cause is caffeine intake before bedtime or an inappropriate bedroom temperature. Specific actions are then taken based on the analysis results.

[0949] Input: Analysis results

[0950] Output: Causes of sleep disorders

[0951] Step 5:

[0952] The server generates personalized improvement advice based on the identified causes, including specific guidelines tailored to the user's lifestyle and environmental factors, such as "avoid caffeine intake four hours before bedtime" or "set the bedroom temperature to 20°C."

[0953] Input: Causes of sleep disorders

[0954] Output: Improvement advice

[0955] Step 6:

[0956] The server sends the generated improvement advice to the device, which then notifies the user of the received advice. The notification is displayed via the app's push notification function or as a message on the home screen, allowing the user to receive specific improvement measures that can be applied to their daily lives.

[0957] Input: Improvement advice

[0958] Output: User notification

[0959] (Application example 1)

[0960] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0961] Poor sleep quality and sleep disorders are serious problems faced by many people in modern society. Conventional solutions to this problem have been limited to general advice, making it difficult to provide effective improvement measures tailored to the specific conditions of each individual user. Furthermore, there has been a lack of systems that effectively utilize lifestyle and environmental data provided by users to generate detailed improvement advice. Therefore, there is a need for a new system that provides detailed and effective improvement advice based on users' personalized lifestyle and sleep pattern data.

[0962] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0963] In this invention, the server includes means for analyzing user data and identifying sleep patterns using a clustering algorithm, means for sending generated improvement advice to the user by email, and means for providing the generated improvement advice in a virtual store, thereby enabling the user to easily receive personalized, detailed sleep improvement advice.

[0964] "User-provided lifestyle data" refers to information about the user's daily activities and habits, including, for example, bedtime, wake-up time, meal times, exercise time, and caffeine and alcohol intake.

[0965] "Sleep pattern data" is detailed information about the user's sleep, specifically including the amount of time they sleep, how well they fall asleep, how many times they wake up during the night, and the time they wake up.

[0966] "Environmental data" refers to data about the user's bedroom environment, specifically including temperature, humidity, light intensity, noise level, etc.

[0967] A "sensor" is a device used to collect environmental data, including temperature sensors, humidity sensors, light sensors, noise sensors, etc.

[0968] "Server" means a central computer system that receives, stores, and analyzes data sent by users over the Internet.

[0969] A "clustering algorithm" is a computational technique for grouping (clustering) similar data, and is used in this invention to identify a user's sleep patterns.

[0970] "Email" means an electronic messaging service for notifying and communicating information to users.

[0971] A "virtual store" is a virtual sales and service space that exists on the Internet and can be accessed online by users.

[0972] "Personalized improvement advice" refers to specific improvement measures that are individually created based on collected lifestyle, sleep pattern, and environmental data.

[0973] The system of the present invention is a personalized service that utilizes AI to improve sleep, and collects and analyzes a user's lifestyle data, sleep pattern data, and environmental data to provide individualized advice on how to improve sleep. A specific embodiment of this system is described below.

[0974] First, the user uses a device such as a smartphone or tablet to input daily lifestyle and sleep pattern data. This data includes bedtime, wake-up time, meal times, exercise time, caffeine and alcohol intake, etc. Additionally, sensors installed on the device are used to collect bedroom environmental data, including temperature, humidity, light intensity, and noise level.

[0975] The device then transmits the collected lifestyle, sleep, and environmental data to a server via the Internet. The server receives the data and stores it in a database. The data is transmitted periodically, updated daily, for example.

[0976] The server analyzes the user's sleep patterns based on the stored data. The analysis uses machine learning algorithms, including clustering algorithms, to identify the user's sleep disorders. The server then identifies the cause of the user's sleep disorder based on the analysis results. For example, specific causes may be revealed, such as consuming caffeine before bedtime causing difficulty falling asleep, or the bedroom temperature being too high causing frequent awakenings.

[0977] The server then generates specific improvement advice for the user based on the identified causes. The advice is personalized for each user and provides specific guidelines for action. For example, it may include refraining from caffeine intake four hours before going to bed and setting the bedroom temperature to 20°C. The generated improvement advice is sent from the server to the device, which then notifies the user.

[0978] Furthermore, advice generated based on the lifestyle and environmental data entered by the user can be sent to the user via email. It can also be provided online in the form of a virtual store, where users can not only receive advice but also purchase related information and products.

[0979] As a specific example, suppose a user enters lifestyle habit data stating that they go to bed at 11pm every night, but have difficulty falling asleep and are unable to fall asleep for 30 minutes. The user has a habit of drinking coffee in the evening, and their bedroom temperature is 25°C. The device sends this data to the server, which analyzes it and determines that the cause of their difficulty falling asleep is caffeine intake and a high bedroom temperature. The server generates specific improvement advice, such as "avoid caffeine intake four hours before bedtime" and "set the bedroom temperature to 20°C." The device receives this advice and notifies the user, allowing them to receive specific improvement measures tailored to their lifestyle and environment.

[0980] An example prompt might have the following format:

[0981] Your sleep data:

[0982] Bedtime: 11:00 PM

[0983] Wake up time: 07:00

[0984] Meal times: 08:00, 12:00, 18:00

[0985] Exercise time: 07:30

[0986] Caffeine intake: 5:00 PM

[0987] Alcohol intake: 9:00 p.m.

[0988] Environmental data: Temperature: 25, Humidity: 50, Light intensity: 300, Noise level: 30

[0989] Analyze the above data and generate advice for improving your sleep.

[0990] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0991] Step 1:

[0992] The device inputs the user's lifestyle and sleep pattern data, including bedtime, wake-up time, meal times, exercise time, caffeine and alcohol intake, etc. By inputting this data, the device records in detail the factors that affect the user's sleep quality.

[0993] Step 2:

[0994] The device uses sensors to collect bedroom environmental data, such as temperature, humidity, light intensity, and noise level, to monitor the user's sleeping environment in detail.

[0995] Step 3:

[0996] The device sends the collected lifestyle, sleep pattern, and environmental data to a server via the Internet. The sent data is stored in a database on the server and organized for each user. Since large amounts of data are often processed at this step, efficient data transmission and storage management are important.

[0997] Step 4:

[0998] The server uses a clustering algorithm to analyze the user's sleep patterns based on the stored data. Specifically, it uses a machine learning algorithm to classify and analyze the data and identify the cause of the user's sleep disorder. Here, data with similar patterns are grouped (clustered) to make the analysis more efficient.

[0999] Step 5:

[1000] The server uses the analysis results to identify the cause of the user's sleep disorder. Based on the identified cause, the server generates personalized improvement advice that is specific and actionable, such as limiting caffeine intake for certain periods of time or setting an appropriate bedroom temperature.

[1001] Step 6:

[1002] The server generates improvement advice and sends it to the user as an email. In this process, the advice is sent in text format to the email address specified by the user, allowing the user to receive the advice on their smartphone or tablet.

[1003] Step 7:

[1004] The device will notify the user of the improvement advice received from the server. Notification methods can include push notifications, in-app messages, and even messages displayed on the home screen. This notification makes it easier for users to implement improvement measures in their daily lives.

[1005] Step 8:

[1006] In the virtual store, users can view improvement advice online and purchase related information and products. The virtual store helps users find and purchase products based on the advice, enabling them to obtain more practical solutions.

[1007] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1008] The system of the present invention is a personalized service that utilizes AI to improve sleep. It collects and analyzes the user's lifestyle data, sleep pattern data, and environmental data, and combines it with an emotion engine to provide improvement advice that takes the user's emotions into consideration. A specific embodiment of this system is described below.

[1009] User Data Collection

[1010] Users enter their daily lifestyle and sleep pattern data via devices such as smartphones or tablets. This data includes bedtime, wake-up time, meal times, exercise time, and caffeine and alcohol intake. Additionally, sensors installed on the devices are used to collect bedroom environmental data, including temperature, humidity, light intensity, and noise levels.

[1011] Data transmission and storage

[1012] The device transmits the collected lifestyle data, sleep pattern data, and environmental data to a server via the Internet. The server receives the data and stores it in a database. Data transmission is performed periodically.

[1013] Collecting Emotional Data

[1014] When a user inputs text into the device or uses the device's microphone to input voice, the emotion engine can analyze the content. The emotion engine analyzes the user's text input, tone of voice, and facial expression to identify the user's emotion. For example, it can recognize the user's emotional state, such as whether they are stressed or relaxed.

[1015] Data analysis

[1016] The server analyzes the user's sleep patterns based on the stored lifestyle, sleep pattern, environmental, and emotional data. The analysis uses time series analysis, cluster analysis, and machine learning algorithms to identify the cause of the user's sleep disorder.

[1017] Identifying sleep disorders and generating improvement advice

[1018] The server uses the analysis results to identify the cause of the user's sleep disorder. For example, consuming caffeine before bedtime can cause difficulty falling asleep, the bedroom temperature can be too high and cause frequent awakenings, or the user's stress can negatively affect sleep. Based on the identified causes, the server generates personalized improvement advice. The advice can include suggestions for improving lifestyle habits, adjusting the bedroom environment, and emotional support.

[1019] Providing advice

[1020] The server sends the generated improvement advice to the device. The device then notifies the user of the received improvement advice. The notification is displayed as a push notification in the app or a message on the home screen. For example, specific advice is provided, such as "limit caffeine intake at night," "lower the temperature in your bedroom," and "try relaxation techniques to reduce stress."

[1021] Specific examples

[1022] A user enters data that they go to bed at 11 PM every night, but have difficulty falling asleep and are unable to fall asleep for 30 minutes. They also have a habit of drinking coffee in the evening and their bedroom temperature is 25°C. The emotion engine then recognizes that the user is feeling stressed. The device sends this data to the server, which analyzes it and determines that the causes of their difficulty falling asleep are caffeine intake, high bedroom temperature, and stress. The server generates specific improvement advice, such as "avoid caffeine intake four hours before bedtime," "set the bedroom temperature to 20°C," and "try deep breathing exercises to reduce stress." The device receives this advice and notifies the user. In this way, the user can receive specific improvement measures based on their lifestyle, environment, and emotions.

[1023] This system allows users to receive specific sleep improvement measures tailored to their individual needs, improving the quality of their sleep by helping them fall asleep more easily and preventing them from waking up during the night.

[1024] The processing flow will be explained below.

[1025] Step 1:

[1026] Using a smartphone app, users input their lifestyle and sleep pattern data, including bedtime, wake-up time, meal times, exercise time, and caffeine and alcohol intake.

[1027] Step 2:

[1028] The device uses built-in sensors (such as a temperature sensor, light sensor, and microphone) to collect real-time environmental data about the bedroom, including temperature, humidity, light intensity, and noise level.

[1029] Step 3:

[1030] The terminal temporarily stores the lifestyle habit data input by the user and the collected environmental data.

[1031] Step 4:

[1032] The device sends user input data and environmental data to the emotion engine, which then analyzes the user's tone of voice and facial expressions to generate emotion data, including the user's emotional state, such as whether they are stressed or relaxed.

[1033] Step 5:

[1034] The terminal transmits the collected lifestyle habit data, sleep pattern data, environmental data, and emotional data to a server via the Internet.

[1035] Step 6:

[1036] The server receives the data sent from the terminal and stores it in a database.

[1037] Step 7:

[1038] The server analyzes the user's sleep patterns based on the stored lifestyle, sleep pattern, environmental, and emotional data, using time series analysis, cluster analysis, and machine learning algorithms.

[1039] Step 8:

[1040] The server uses the analysis results to identify the cause of the user's sleep disorder, such as difficulty falling asleep due to consuming caffeine before bedtime, frequent awakenings due to a bedroom temperature that is too high, or stress.

[1041] Step 9:

[1042] Based on the identified causes, the server generates personalized improvement advice, including suggestions for lifestyle improvements, bedroom environment adjustments, and emotional support.

[1043] Step 10:

[1044] The server transmits the generated improvement advice to the terminal.

[1045] Step 11:

[1046] The device will then notify the user of the improvement advice it has received. The notification will appear as a push notification in the app or a message on the home screen. Specific advice provided includes "limiting caffeine intake at night," "setting the bedroom temperature to 20°C," and "trying deep breathing exercises to reduce stress."

[1047] Through this series of steps, users can receive specific sleep improvement measures tailored to their individual needs, improving the quality of their sleep by helping them fall asleep more easily and preventing them from waking up during the night.

[1048] Example 2

[1049] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1050] Conventional sleep improvement systems not only collect users' lifestyle and environmental data, but also have difficulty providing personalized advice that takes into account the user's emotional state. As a result, they have been unable to identify the root causes of sleep disorders and propose effective solutions. Furthermore, it has been challenging to identify the causes of complex sleep disorders that cannot be identified through simple data analysis.

[1051] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1052] In this invention, the server includes means incorporating an emotion engine for analyzing user emotion data, means for identifying the cause of the user's sleep disorder using time series analysis, cluster analysis, and machine learning algorithms, and means for generating personalized improvement advice and sending it to the user's terminal, thereby enabling highly accurate identification of the cause of the sleep disorder that also takes the user's emotional state into consideration, and providing personalized improvement advice.

[1053] "Lifestyle data" refers to information about a user's daily activities, including bedtime, wake-up time, meal times, exercise time, caffeine and alcohol intake, etc.

[1054] "Sleep pattern data" refers to information about the user's sleep behavior and state, including the time it takes to fall asleep and the frequency of waking up during the night.

[1055] "Environmental data" refers to information about the user's bedroom environment, including temperature, humidity, light intensity, noise level, etc.

[1056] "Emotion data" is information about the user's emotional state, including stress or relaxation levels analyzed by the emotion engine from text input or voice input.

[1057] An "emotion engine" is a system that analyzes a user's text input, voice input, and facial expressions to identify the user's emotional state.

[1058] "Server" means a computer system that stores and analyzes collected data and provides specific services and information to users.

[1059] "Time series analysis" and "cluster analysis" are statistical methods used by servers to analyze collected data, analyzing fluctuations in data over time and identifying groups of similar data.

[1060] A "machine learning algorithm" is an algorithm for artificial intelligence used by servers when analyzing data. It is a technology that learns patterns based on past data and makes future predictions and classifications.

[1061] "Personalized improvement advice" is specific advice that suggests the best improvement measures for each user based on their individual lifestyle, sleep pattern, environmental, and emotional data.

[1062] "Terminal" means an electronic device used by a user to input data or receive advice from a server, including a smartphone or tablet.

[1063] The system of the present invention collects and analyzes a user's lifestyle data, sleep pattern data, environmental data, and emotional data, and combines them with an emotional engine to provide the user with personalized improvement advice. A detailed embodiment of this system is described below.

[1064] Users use devices such as smartphones or tablets to input data on their daily lifestyle habits and sleep patterns, such as bedtime, wake-up time, meal times, and exercise time. Furthermore, sensors installed on the devices are used to collect environmental data, such as temperature, humidity, light intensity, and noise levels. Along with this data, an emotion engine installed on the devices analyzes text and voice input to collect the user's emotion data. The emotion engine analyzes the text content, tone of voice, and facial expressions to identify the user's emotional state.

[1065] The device periodically transmits this lifestyle data, sleep pattern data, environmental data, and emotional data to a server via the Internet. The server receives this data and stores it in a database. The stored data is analyzed using time series analysis, cluster analysis, and machine learning algorithms. In particular, the machine learning algorithm is used to identify the cause of the user's sleep disorder based on past data.

[1066] The server uses the analysis results to identify the cause of the user's sleep disorder, such as whether consuming caffeine before bedtime is causing difficulty falling asleep, whether the bedroom temperature is too high causing frequent awakenings, or whether stress is negatively affecting sleep.

[1067] Based on the identified causes, the server uses a generative AI model to generate personalized improvement advice, such as "avoid caffeine intake four hours before bedtime," "set the bedroom temperature to 20°C," and "perform deep breathing exercises."

[1068] The server sends the generated improvement advice to the device, which then notifies the user of the received advice. This advice is displayed as a push notification or an in-app message. For example, it might say, "Reduce caffeine intake at night," "Lower the temperature in your bedroom," or "Try deep breathing exercises to reduce stress."

[1069] As a concrete example, suppose a user enters data that indicates that they go to bed at 11pm every night but are unable to fall asleep for 30 minutes. This user has the habit of drinking coffee in the evening, and their bedroom temperature is 25°C. The emotion engine also recognizes that the user is feeling stressed. The device sends this data to the server, which analyzes it and determines that the causes of their difficulty falling asleep are caffeine intake, a high bedroom temperature, and stress. The server generates specific improvement advice, such as "avoid caffeine intake four hours before bedtime," "set the bedroom temperature to 20°C," and "try deep breathing exercises," and the device notifies the user of this advice.

[1070] It is also possible to create specific advice by inputting a prompt into the generative AI model. An example of a prompt is, "I can't fall asleep for 30 minutes even though I go to bed at 11pm. I have a habit of drinking coffee in the evening, my bedroom temperature is 25°C, and I'm feeling stressed. Please provide advice to improve this situation." Based on this prompt, the generative AI model will propose the most appropriate advice.

[1071] This system allows users to receive specific sleep improvement measures tailored to their individual needs, helping them fall asleep faster, prevent waking up during the night, and improve the quality of their sleep.

[1072] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1073] Step 1:

[1074] Users input their daily lifestyle and sleep pattern data via devices such as smartphones or tablets. For example, data such as "I went to bed at 10 p.m. last night" and "I woke up at 7 a.m." In addition, sensors installed in the device are used to collect environmental data such as temperature, humidity, light intensity, and noise level. This allows lifestyle data, sleep pattern data, and environmental data to be obtained. Input data: lifestyle data, sleep pattern data, environmental data.

[1075] Step 2:

[1076] The device periodically sends collected lifestyle data, sleep pattern data, and environmental data to a server via the Internet. For example, it can be set to send data every night at 11pm. The server stores the received data in a database. Checksums and data verification protocols are used to ensure that the data being sent has been received properly. Input data: lifestyle data, sleep pattern data, environmental data. Output data: data stored in the database.

[1077] Step 3:

[1078] When a user inputs text or voice into a device, the emotion engine analyzes the content. The emotion engine analyzes the user's text content, tone of voice, and facial expressions to identify emotional states such as stress or relaxation. For example, if a user says "I'm tired today" through voice input, the emotion engine analyzes the user's emotions from the tone of voice and choice of words. Input data: text data, voice data. Output data: emotion data.

[1079] Step 4:

[1080] The device sends the analyzed emotional data to a server via the Internet, and the server stores the received emotional data in a database. This adds the user's emotional data to the database. Input data: Emotional data. Output data: Emotional data stored in the database.

[1081] Step 5:

[1082] The server analyzes the user's sleep patterns based on the lifestyle, sleep pattern, environmental, and emotional data stored in the database. This analysis uses time series analysis, cluster analysis, and machine learning algorithms. For example, the server analyzes the lifestyle data of a user who has trouble falling asleep to identify the cause. Input data: lifestyle data, sleep pattern data, environmental data, emotional data. Output data: analysis results.

[1083] Step 6:

[1084] The server identifies the cause of the user's sleep disorder based on the analysis results. For example, caffeine intake, bedroom temperature, stress, etc. are identified as possible causes. Input data: Analysis results. Output data: Cause of sleep disorder.

[1085] Step 7:

[1086] Based on the identified causes, the server uses a generative AI model to generate personalized improvement advice. For example, specific advice may be generated such as "avoid caffeine intake four hours before bedtime," "set the bedroom temperature to 20°C," or "try deep breathing exercises to relieve stress." Input data: Causes of sleep disorders. Output data: Personalized improvement advice.

[1087] Step 8:

[1088] The server sends the generated improvement advice to the device, and the device notifies the user of the received advice. Notification methods include push notifications and in-app messages. For example, messages such as "Reduce caffeine intake at night," "Lower the temperature in your bedroom," and "Try deep breathing exercises to reduce stress" are displayed. Input data: personalized improvement advice. Output data: notification to the user.

[1089] (Application example 2)

[1090] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1091] While conventional sleep improvement systems utilize users' lifestyle, sleep pattern, and environmental data, they lack real-time feedback and emotional data analysis, making it difficult to provide immediate and effective advice. Furthermore, they lack a means to comprehensively analyze users' emotional data and real-time environmental data to provide specific improvement advice. This makes it difficult for users to effectively adjust their lifestyles.

[1092] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1093] In this invention, the server includes means for collecting lifestyle habit data and sleep pattern data provided by the user, means for collecting bedroom environment data using a sensor for collecting environmental data, and means for collecting environmental data and emotion data in real time through smart glasses, thereby enabling real-time and personalized improvement advice to be provided to the user.

[1094] "User-provided lifestyle data" refers to information provided by a user regarding their daily lifestyle, including bedtime, wake-up time, meal times, exercise time, caffeine and alcohol intake, etc.

[1095] "Sleep pattern data" refers to data related to the user's sleep, such as the amount of sleep, the quality of sleep, the time it takes to fall asleep and wake up, etc.

[1096] "Environmental data" refers to data about the environment in which the user sleeps, including temperature, humidity, light intensity, noise level, etc.

[1097] "Real-time collection means" refers to a method, device, or system that collects data immediately and transmits it to a server without delay.

[1098] "Emotional data" refers to data relating to the user's emotional state, including stress levels and relaxation levels obtained by analyzing voice input and facial expressions.

[1099] A "machine learning algorithm" is a type of algorithm that learns patterns and regularities from large amounts of data and uses them to predict or classify future data.

[1100] "Personalized improvement advice" refers to specific improvement suggestions customized based on each user's individual lifestyle, sleep pattern, environmental, and emotional data.

[1101] The present invention is a system that utilizes data on a user's lifestyle habits, sleep patterns, environment, and emotions to provide personalized advice on improving sleep in real time at a brick-and-mortar store. This system is implemented using smart glasses, a server, and devices such as smartphones.

[1102] Hardware Configuration

[1103] Smart glasses: Equipped with sensors that collect real-time environmental data such as temperature, humidity, light intensity, and noise levels, as well as emotional data through sensors that analyze voice input and facial expressions.

[1104] Server: Analyzes collected data and generates personalized improvement advice using machine learning algorithms. Uses cloud computing services (e.g., AWS, Google Cloud) for large-scale data analysis.

[1105] Smartphone or tablet: Used as an interface for users to input lifestyle and sleep pattern data.

[1106] Software Configuration

[1107] Data collection module: Collects user lifestyle data, sleep pattern data, environmental data, and emotional data via smart glasses or smartphone.

[1108] Data analysis module: Runs on the server and analyzes the collected data using machine learning algorithms. Specifically, it performs time series analysis and cluster analysis to identify the causes of sleep disorders.

[1109] Emotion engine: Uses APIs (e.g., Microsoft Azure Emotion API) that analyze emotion data from user voice input and facial expressions.

[1110] Notification module: displays personalized advice in real time on the smart glasses, providing visual feedback through the smart glasses display.

[1111] Processing flow

[1112] 1. Data collection: Users input lifestyle and sleep pattern data using a smartphone or tablet, and environmental and emotional data is collected in real time through sensors installed in the smart glasses.

[1113] 2. Data transmission: The collected data is transmitted to a server via the Internet.

[1114] 3. Data analysis: The server analyzes lifestyle data, sleep pattern data, environmental data, and emotional data, and uses machine learning algorithms to identify the cause of the user's sleep disorder.

[1115] 4. Advice generation: Generate personalized improvement advice based on the cause of sleep disorders.

[1116] 5. Providing advice: Providing advice to users in real time via smart glasses.

[1117] Specific examples

[1118] For example, if a user enters data that they go to bed at 11pm every night and have trouble falling asleep, the smart glasses' sensors will determine that the room temperature is 25°C, the light intensity is 150 lux, and face recognition and voice input will confirm a high stress level.The server will analyze this data and display advice such as "limit your caffeine intake," "set the bedroom temperature to 20°C," and "do deep breathing exercises to reduce stress" on the smart glasses in real time.

[1119] Prompt Sentence Examples

[1120] "Develop an AI system that generates personalized advice for improving sleep based on a user's lifestyle data (e.g., bedtime, wake-up time, meal times), environmental data (e.g., temperature, humidity, light intensity, noise level), and emotional data (e.g., stress level). Integrate this into an application that displays advice in real time on smart glasses."

[1121] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1122] Step 1:

[1123] Users use their smartphones or tablets to input lifestyle and sleep pattern data, including bedtime, wake-up time, meal times, exercise time, and timing of caffeine and alcohol intake. The device temporarily stores this input data and prepares it to be sent to a server via the Internet.

[1124] Step 2:

[1125] The smart glasses collect environmental data in real time using built-in sensors (temperature, humidity, light, and noise sensors). In addition, the glasses use a built-in camera and microphone to collect the user's voice and facial expressions and analyze their emotional data. This data is temporarily stored inside the glasses and periodically sent to a server.

[1126] Step 3:

[1127] The server receives lifestyle data, sleep pattern data, environmental data, and emotion data sent from the device and smart glasses. The server stores this data in a database and simultaneously performs data preprocessing, such as filling in missing values ​​and removing outliers.

[1128] Step 4:

[1129] The server's analysis module uses machine learning algorithms to analyze the received data. Specifically, it performs time-series analysis to extract sleep patterns, cluster analysis to group lifestyle habits, and evaluate emotional states. Through this analysis, the server identifies the cause of the user's sleep disorder.

[1130] Step 5:

[1131] The server generates personalized improvement advice based on the analysis results. Examples of generated advice include "limit caffeine intake," "adjust the temperature in your bedroom," and "perform emotional regulation exercises if you are under high stress." The server generates this advice individually for each user and temporarily stores it.

[1132] Step 6:

[1133] The server sends the generated advice in real time to the smart glasses, which display the advice in the form of a visual notification, for example, a message pop-up on the glasses' display.

[1134] Step 7:

[1135] The user checks the notifications on the smart glasses and adjusts their lifestyle and environment accordingly. The user then changes their behavior in accordance with the advice provided, and this is reflected in future data inputs and environmental data collection by sensors.

[1136] These steps provide real-time, personalized advice on how to improve your sleep.

[1137] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1138] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1139] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1140] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1141] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1142] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1143] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1144] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1145] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1146] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1147] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1148] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1149] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1150] 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.

[1151] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1152] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1153] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1154] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1155] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1156] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1157] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1158] The following is further disclosed regarding the above embodiment.

[1159] (Claim 1)

[1160] a means for collecting user-provided lifestyle and sleep pattern data;

[1161] a means for collecting environmental data of the bedroom using a sensor for collecting environmental data;

[1162] means for transmitting the lifestyle habit data, sleep pattern data, and environmental data to a server;

[1163] means for the server to analyze the collected data and identify the cause of the user's sleep disorder;

[1164] The server generates personalized improvement advice based on the cause of the sleep disorder and transmits the advice to the user's terminal;

[1165] a means for notifying a user of the improvement advice in the terminal;

[1166] A system including:

[1167] (Claim 2)

[1168] 10. The system of claim 1, wherein the user's lifestyle data and sleep pattern data are periodically transmitted to the server.

[1169] (Claim 3)

[1170] 10. The system of claim 1, wherein the server utilizes machine learning algorithms to analyze the data and identify the cause of the user's sleep disorder.

[1171] "Example 1"

[1172] (Claim 1)

[1173] A means for collecting user-provided daily life data and sleep data;

[1174] a means for collecting environmental data in the bedroom using a detector for collecting environmental data;

[1175] means for transmitting the daily life data, sleep data, and environmental data to a server;

[1176] means for the server to analyze the collected data and identify the cause of the user's sleep disorder;

[1177] The server generates personalized improvement advice based on the cause of the sleep disorder and transmits the advice to the user's terminal;

[1178] a means for notifying a user of the improvement advice in the terminal;

[1179] A system including:

[1180] (Claim 2)

[1181] 2. The system according to claim 1, wherein the user's daily life data and sleep data are periodically transmitted to the server.

[1182] (Claim 3)

[1183] 10. The system of claim 1, wherein the server utilizes a learning algorithm to analyze the data and identify the cause of the user's sleep disorder.

[1184] "Application Example 1"

[1185] (Claim 1)

[1186] a means for collecting user-provided lifestyle and sleep pattern data;

[1187] a means for collecting environmental data of the bedroom using a sensor for collecting environmental data;

[1188] means for transmitting the lifestyle habit data, sleep pattern data, and environmental data to a server;

[1189] means for the server to analyze the collected data and identify the cause of the user's sleep disorder;

[1190] The server generates personalized improvement advice based on the cause of the sleep disorder and transmits the advice to the user's terminal;

[1191] a means for notifying a user of the improvement advice in the terminal;

[1192] means for analyzing the user's data and identifying sleep patterns using a clustering algorithm;

[1193] a means for sending the generated improvement advice to the user by email;

[1194] A means for providing the generated improvement advice in the virtual store;

[1195] A system including:

[1196] (Claim 2)

[1197] 10. The system of claim 1, wherein the user's lifestyle data and sleep pattern data are periodically transmitted to the server.

[1198] (Claim 3)

[1199] 10. The system of claim 1, wherein the server utilizes machine learning algorithms to analyze the data and identify the cause of the user's sleep disorder.

[1200] "Example 2: Combining Emotion Engines"

[1201] (Claim 1)

[1202] a means for collecting user-provided lifestyle and sleep pattern data;

[1203] means for collecting environmental data using a sensor for collecting environmental data;

[1204] means incorporating an emotion engine for analyzing user emotion data;

[1205] means for transmitting the lifestyle habit data, sleep pattern data, environmental data, and emotion data to a server;

[1206] means for the server to analyze the collected data and identify the cause of the user's sleep disorder using time series analysis, cluster analysis, and machine learning algorithms;

[1207] The server generates personalized improvement advice based on the cause of the sleep disorder and transmits the advice to the user's terminal;

[1208] a means for notifying a user of the improvement advice in the terminal;

[1209] A system including:

[1210] (Claim 2)

[1211] 10. The system of claim 1, wherein the user's lifestyle data, sleep pattern data, environmental data, and emotional data are periodically transmitted to the server.

[1212] (Claim 3)

[1213] 10. The system of claim 1, wherein the server utilizes machine learning algorithms to analyze the data and identify the cause of the user's sleep disorder.

[1214] "Application example 2 when combining emotion engines"

[1215] Rewriting of claims

[1216] (Claim 1)

[1217] a means for collecting user-provided lifestyle and sleep pattern data;

[1218] a means for collecting environmental data of the bedroom using a sensor for collecting environmental data;

[1219] means for transmitting the lifestyle habit data, sleep pattern data, and environmental data to a server;

[1220] means for the server to analyze the collected data and identify the cause of the user's sleep disorder;

[1221] The server generates personalized improvement advice based on the cause of the sleep disorder and transmits the advice to the user's terminal;

[1222] A means for collecting environmental data and emotional data in real time via smart glasses;

[1223] a means for the smart glasses to notify the user of the improvement advice in real time;

[1224] A system including:

[1225] (Claim 2)

[1226] 10. The system of claim 1, wherein the user's lifestyle data and sleep pattern data are periodically transmitted to the server.

[1227] (Claim 3)

[1228] 10. The system of claim 1, wherein the server utilizes machine learning algorithms to analyze the data and identify the cause of the user's sleep disorder. [Explanation of symbols]

[1229] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for collecting user-provided lifestyle and sleep pattern data; a means for collecting environmental data of the bedroom using a sensor for collecting environmental data; means for transmitting the lifestyle habit data, sleep pattern data, and environmental data to a server; means for the server to analyze the collected data and identify the cause of the user's sleep disorder; The server generates personalized improvement advice based on the cause of the sleep disorder and transmits the advice to the user's terminal; a means for notifying a user of the improvement advice in the terminal; A system including:

2. The system of claim 1, wherein the user's lifestyle data and sleep pattern data are periodically transmitted to the server.

3. The system of claim 1 , wherein the server utilizes machine learning algorithms to analyze the data and identify the cause of the user's sleep disorder.

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