system

Abstract

We provide the system. [Solution] A means of collecting data from users, An analysis means that analyzes the aforementioned data to generate an optimal schedule for the user, A system including means for presenting the aforementioned schedule to the user.

Description

Technical Field

[0004] , , , ,

[0005] , , , , ,

[0003] , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern life, many people find it difficult to ensure high-quality sleep due to irregular lifestyles and high stress levels. Also, there is a lack of means to obtain appropriate sleep improvement advice based on individual life rhythms and habits. To solve such problems, a system that provides a personalized sleep schedule based on individual data is needed.

Means for Solving the Problems

[0005] This invention provides a system that performs analysis based on data acquired from users and generates a schedule optimized for the user. The system includes means for collecting, analyzing, and providing user data, and further includes a function that enables the evaluation of stress levels. By incorporating feedback that reflects the analysis results, the system continuously provides optimal advice to the user and effectively solves the above-mentioned problems.

[0006] "Means of collecting data from users" refers to functions that acquire necessary information using devices and sensors that record users' physiological and behavioral data.

[0007] "Analysis means" refers to a function that evaluates the user's lifestyle and physical condition based on collected data and processes it to derive the optimal improvement measures.

[0008] "Analysis tools for generating optimal schedules for users" refers to a function that creates and provides schedules tailored to the individual characteristics of each user based on the analysis results.

[0009] "Means of delivery" refer to interfaces and notification functions that present generated schedules and advice to users in an easy-to-understand manner.

[0010] A "means for evaluating stress levels" refers to a function that quantitatively determines the degree of stress based on the user's psychological and physiological data.

[0011] "Means for obtaining feedback and incorporating it into the next analysis" refers to a function that incorporates user evaluations and opinions into the system, adjusts the analysis algorithm based on that information, and improves the accuracy of recommendations in subsequent analyses. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]This is a conceptual diagram showing an example of the essential functions of the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0018] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] In order to implement the present invention, the user, server, and terminal must cooperate as follows so that the entire system can operate.

[0034] The server is connected to various sensors and smart devices that acquire data from users. This group of devices includes wearable devices, smartphone sensors, and environmental sensors. The server receives physiological data (heart rate and body temperature), environmental data (room temperature and noise level), and behavioral data (distance traveled and activity level) transmitted from these devices in real time.

[0035] The server analyzes incoming data and generates an optimal sleep schedule for the user. Machine learning algorithms are used for the analysis, which assesses the user's lifestyle and stress levels. Specifically, the analysis utilizes a database with a large amount of historical data to generate customized suggestions based on each user's characteristics.

[0036] The device presents the user with an optimal sleep schedule received from the server. Specifically, the smartphone app provides the schedule along with appropriate relaxation guides and ambient sound options. An intuitive interface is designed to make it easy for users to operate the device and access the presented information.

[0037] Users can review the information displayed on their device and incorporate it into their daily schedule. For example, they can follow suggestions for bedtime and wake-up times, or use breathing exercises to reduce stress. Furthermore, users can input feedback on their sleep quality into the device, and this information is sent to the server.

[0038] The server uses the feedback received from users to learn what is needed for the next analysis. This makes the recommendations provided more tailored to the individual needs of each user.

[0039] With the configuration described above, this invention can provide users with a high-quality sleep experience as a whole system. For example, if a user suffers from insomnia, the server can identify the cause and recommend relaxation activities before bedtime to improve their condition. In this way, this system achieves comprehensive sleep improvement for users.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server acquires physiological and environmental data in real time from the user's various sensors and smart devices. This data is securely stored in the cloud.

[0043] Step 2:

[0044] The server preprocesses and cleans up the acquired data to prepare it for analysis. This step involves removing noise and imputing missing values.

[0045] Step 3:

[0046] The server uses machine learning algorithms to analyze data and determine the user's sleep patterns and stress levels. Based on the analysis results, it generates a customized, optimal sleep schedule for each user.

[0047] Step 4:

[0048] The server sends the optimized schedule it generates to the terminal. The terminal receives this information and prepares to present it visually to the user.

[0049] Step 5:

[0050] The device provides users with sleep schedules and relaxation guides through its user interface. An intuitive and easy-to-use design has been considered.

[0051] Step 6:

[0052] Users can view the schedule presented through their device and incorporate it into their daily lives. Relaxation guides and ambient sound options are available to support sleep.

[0053] Step 7:

[0054] Users input and submit feedback on their sleep quality and schedule via their device. This feedback is then sent to the server.

[0055] Step 8:

[0056] The server receives feedback from users and uses it to improve the next data analysis and schedule generation process. This allows for the continuous provision of personalized advice to individual users.

[0057] (Example 1)

[0058] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0059] In recent years, the quality of people's sleep has been declining due to disrupted sleep patterns and increased stress. This negatively impacts health and daily life, creating a need for a system that can provide sleep schedules tailored to individual users. However, existing systems struggle to adequately consider user characteristics in their suggestions, and personalization through effective feedback is insufficient.

[0060] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0061] In this invention, the server includes a data collection means for acquiring data from the user, an analysis means for analyzing the data using a machine learning algorithm and comparing it with past information to generate an optimal schedule for the user, a provision means for presenting information to the user, including relaxation guides and ambient sounds, based on the schedule, and a learning means for acquiring user feedback and updating the generated AI model to reflect in the next analysis. This makes it possible to propose highly accurate, personalized sleep improvement plans for each individual user.

[0062] "Data collection means" refers to devices and methods for acquiring physiological data, environmental data, and behavioral data from users, and includes various sensors and smart devices.

[0063] "Analysis means" refers to a process or system for analyzing data obtained through collection means using machine learning algorithms, comparing it with past information, and generating an optimal schedule for each individual user.

[0064] "Means of provision" refers to methods and devices for transmitting information such as schedules, relaxation guides, and ambient sounds generated by analysis means to the user, and includes systems equipped with an intuitive interface.

[0065] A "learning method" is a mechanism or process for obtaining user feedback, updating the generated AI model, and reflecting that feedback in the next analysis.

[0066] This invention is a system for improving sleep quality by presenting a personalized and optimized sleep schedule to the user. First, the server works in conjunction with wearable devices worn by the user, smartphone sensors, and environmental sensors to collect physiological data (e.g., heart rate, body temperature), environmental data (e.g., room temperature, noise level), and behavioral data (e.g., distance traveled, activity level). This system uses sensor technology and IoT devices for data collection.

[0067] Next, the server analyzes this data using machine learning algorithms. The analysis uses historical data stored in the server's database to evaluate the user's lifestyle and stress levels. Based on this evaluation, the server generates a personalized sleep schedule for the user. A generative AI model is used for the analysis, making optimal suggestions based on pattern recognition and prediction of the data.

[0068] Next, the device notifies the user of the sleep schedule received from the server. The notification is delivered via a smartphone app and presented to the user in an easy-to-understand format, along with relaxation guides and ambient sound options. The application has an intuitive interface, allowing users to easily access and utilize the information.

[0069] Users can use their devices to view the presented schedule and incorporate it into their daily lives. For example, they can follow suggested bedtimes or utilize breathing exercises to relieve tension. Furthermore, users can input feedback on their sleep quality into their devices and send it to the server. This feedback is used as important data for future analysis.

[0070] Based on this feedback, the server can update its AI model to provide suggestions that are better suited to the user's needs in subsequent uses. An example of a specific prompt might be, "User A wants to know relaxation methods that can help improve their insomnia." In this way, the system aims to provide comprehensive sleep improvement for the user.

[0071] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0072] Step 1:

[0073] The server acquires physiological, environmental, and behavioral data from the user's wearable devices, smartphone sensors, and environmental sensors. Inputs include heart rate, body temperature, room temperature, noise level, distance traveled, and activity level. This data is transmitted to the server in real time and recorded in a database. Specifically, the system uses communication protocols between devices to ensure accurate data transfer.

[0074] Step 2:

[0075] The server analyzes the collected data using machine learning algorithms. In particular, it combines historical and current data to evaluate lifestyle habits and stress levels. The input is the data acquired in step 1, and the output is an evaluation of the user's health status and stress level based on the analysis. In this process, the algorithm recognizes patterns in the data and forms a new predictive model.

[0076] Step 3:

[0077] The server generates an optimal sleep schedule for the user based on the analysis results. The generated schedule includes recommended bedtimes and wake-up times, relaxation activities, and ambient sound options. The input is the evaluation results from step 2, and the output is a personalized sleep schedule. In this process, a generative AI model is utilized to provide suggestions tailored to the user's needs.

[0078] Step 4:

[0079] The device displays the sleep schedule sent from the server to the user. The information is displayed on the device screen in an intuitive and easy-to-understand format. The input is schedule data from the server, and the output is specific schedule information displayed on the user's smartphone. The application features an interface designed to enhance the user experience.

[0080] Step 5:

[0081] Users incorporate the schedule presented on their device into their daily lives. They can prepare to sleep according to the suggested bedtime and try the relaxation guide. The input is information from the device, and the output is the change in the user's behavior itself. As a result, users can experience reduced stress and improved sleep quality in their real lives.

[0082] Step 6:

[0083] Users input feedback about their sleep quality into a device and send it to a server. The input is feedback information based on the user's experience, and the output is feedback data sent to the server. The device provides an interface that allows users to easily input feedback.

[0084] Step 7:

[0085] The server updates the generated AI model based on user feedback and incorporates it into the next analysis. The input is the feedback data from step 6, and the output is the updated AI model. This process ensures that the system can always provide optimized suggestions based on the latest user information.

[0086] (Application Example 1)

[0087] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0088] In users' daily lives, the relationship between sleep quality and economic behavior is rarely considered. As a result, optimal spending management based on sleep patterns is not implemented, sometimes leading to wasteful spending and impulsive purchasing. This invention aims to support personalized spending management and achieve a comfortable lifestyle by integrating the management of a user's physiological state and economic behavior.

[0089] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0090] In this invention, the server includes a collection means for acquiring biometric information and economic behavior information from the user, an analysis means for analyzing the information and generating an optimal activity plan for the user, a provision means for presenting the plan to the user, and a control means for generating support information for managing the user's economic behavior based on the biometric information. This makes it possible to optimize expenditure management based on the user's sleep state.

[0091] "Biometric information" refers to data that indicates a user's health status and physical activity, including heart rate, body temperature, and sleep patterns.

[0092] "Economic behavior information" refers to data about a user's daily spending and purchasing activities, including purchase history and spending trends.

[0093] An "activity plan" is a set of behavioral guidelines created based on the user's biometric and economic behavioral information, suggesting optimal spending and lifestyle habits.

[0094] "Collection means" refers to devices and methods for obtaining necessary information from users, including those using smartphone applications and sensors.

[0095] "Analysis means" refers to processes and devices that perform analysis based on acquired information and generate content to be provided to the user.

[0096] "Means of delivery" refers to methods and devices for communicating the generated activity plan to the user, and includes applications for mobile devices.

[0097] "Control means" refers to methods or devices for adjusting economic behavior based on biometric information and generating recommendations regarding expenditure management.

[0098] To implement this invention, a system is configured in which a server, a terminal (such as a smartphone), and a user work together. The server is connected to a device equipped with the function of collecting biometric information and economic behavioral information. This includes wearable devices and smartphone applications. Specifically, data on sleep patterns and activity levels is acquired from the smartphone's sensors and transmitted to the server. Furthermore, information on the user's purchasing history and spending trends is also collected by the server.

[0099] The server uses collected information to perform machine learning (e.g., TENSORFLOW®) and generate an optimal activity plan for each individual user. This plan takes into account the user's sleep patterns and daily spending habits to promote planned shopping and reduce unnecessary spending.

[0100] The device is responsible for providing the user with activity plans sent from the server. For example, on days when the user is sleep-deprived, it will notify them of a reminder to refrain from certain expenses. On days when they have had a good night's sleep, it will recommend that they consider a previously planned purchase.

[0101] Users improve their daily lives based on activity plans provided by their devices. For example, on mornings when they've had a good night's sleep, they can proceed with their shopping according to a planned shopping list. Furthermore, user responses are sent to a server, which can be used to improve future suggestions.

[0102] An example of a prompt message generated using an AI model is: "Based on User A's past sleep data and spending history, please show predicted spending trends for the next 24 hours and generate appropriate advice."

[0103] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0104] Step 1:

[0105] The server acquires biometric and economic activity data from the user's smartphone and wearable devices. Inputs include heart rate, sleep duration, and purchase history. The server collects this data and stores it for use in the next step.

[0106] Step 2:

[0107] The server analyzes the acquired data using a machine learning algorithm (e.g., TensorFlow). The input for the analysis is the data collected in the previous step. Based on this, the server performs data calculations to evaluate the correlation between the user's physiological state and spending behavior and generate an optimized activity plan. The output is an activity plan customized for each user.

[0108] Step 3:

[0109] The server sends the generated activity plan to the terminal. The input is the activity plan obtained in the previous step. By transferring this plan to the terminal, the server helps the user act according to the plan. The output is the activity plan delivered to the terminal.

[0110] Step 4:

[0111] The terminal presents the received activity plan to the user. The input to the terminal is the activity plan sent from the server. The terminal visually displays the plan through an intuitive interface that the user can easily understand and provides notifications as needed. The output is the screen display of the activity plan that the user sees.

[0112] Step 5:

[0113] The user inputs feedback on the activity plan provided via the terminal. This user input reflects their satisfaction with and adaptation to the activity plan. This feedback is sent to the server for use in subsequent analyses. The input is the user's feedback, and the output is its transfer to the server.

[0114] Step 6:

[0115] The server receives feedback from the user and incorporates this information into the next analysis. The server's input is the feedback provided by the user. Based on the received feedback, the analysis model is updated to generate an activity plan that better suits the user. The output is the improved analysis model.

[0116] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0117] This invention combines conventional functions of collecting and analyzing user data and presenting optimal schedules with an emotion engine that recognizes user emotions. The aim of this system is to achieve even higher accuracy in personalization and further improve users' health and quality of life.

[0118] The server acquires not only physiological and behavioral data, but also emotional signals such as facial expressions and voice tone, through the user's various sensors and smart devices. This data is processed by an emotion engine to identify the user's emotional state. In this process, for example, stress levels and satisfaction levels can be estimated from the user's voice tone.

[0119] The analysis method uses comprehensive information, including acquired emotional data, to evaluate the user's lifestyle and sleep patterns. This evaluation generates the most effective sleep schedule and stress management methods for the user. For example, if irritability due to sleep deprivation is detected, the system may suggest earlier bedtime and provide relaxation music.

[0120] The device has an interface that presents the user with schedules and advice generated by the server. This interface displays emotionally-based, customized messages designed to boost user motivation. Based on this information, users can review their lifestyle habits or implement relaxation techniques.

[0121] Furthermore, user feedback, including changes in emotions, is sent to the server. The server uses this feedback to further refine its analysis algorithms and improve future schedule suggestions.

[0122] This invention utilizes an emotion engine to take into account the user's subjective health status and needs, which could not be grasped through conventional pattern analysis alone, thereby achieving more precise personalization. As a result, users can manage their sleep and health in a way that is best suited to them.

[0123] The following describes the processing flow.

[0124] Step 1:

[0125] The server acquires physiological data, behavioral data, and emotional signals from the user's smart devices and sensors. Emotional signals include facial expression data and voice tone. This allows for a comprehensive understanding of the user's state.

[0126] Step 2:

[0127] An emotion engine installed within the server analyzes acquired emotional signals to identify the user's emotional state. For example, it can calculate stress levels from the tone of the user's voice.

[0128] Step 3:

[0129] The server integrates emotional data with other physiological data and performs analysis using machine learning algorithms. This comprehensively evaluates the user's sleep patterns and lifestyle habits. As a result, an optimal sleep schedule and suggestions for improvement are generated for each individual user.

[0130] Step 4:

[0131] The server sends the optimal schedule and suggestions it generates to the terminal. The terminal receives this data and prepares to display it in the user interface.

[0132] Step 5:

[0133] The device presents the user with schedules, relaxation guides, and emotion-based messages. The user can then review the information presented and decide how to apply it to their real life.

[0134] Step 6:

[0135] Users submit feedback using their devices. This feedback includes their thoughts after the scheduled execution and any changes in their newly experienced emotions.

[0136] Step 7:

[0137] The server receives user feedback and adjusts the algorithm to utilize it in future analyses. This enables the delivery of more personalized services.

[0138] (Example 2)

[0139] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0140] Conventional user data analysis systems rely on objective data to analyze users' lifestyles and health conditions, making it difficult to accurately reflect users' subjective emotions and stress levels. This resulted in a problem where the schedules and advice provided did not match the user's actual situation, failing to effectively improve their quality of life.

[0141] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0142] In this invention, the server includes a data collection means for acquiring data from the user, an analysis means for analyzing the data to generate an optimal schedule for the user, an emotion analysis means for identifying the user's emotional state from the analyzed data, a provision means for presenting the schedule to the user, a feedback means for acquiring user feedback and reflecting it in the next analysis, and a means for making optimal suggestions according to the user's emotional state using a generation AI model. This enables sophisticated personalization that takes into account the user's subjective health condition and needs.

[0143] "Data collection methods" refer to devices and systems used to acquire physiological data, behavioral data, emotional signals, and other information from users.

[0144] "Analysis methods" refer to techniques and technologies that analyze collected data to generate optimal schedules and advice for users.

[0145] "Emotional analysis methods" refer to methods and technologies for processing emotional signals such as the tone of a user's voice and facial expressions to identify their emotional state.

[0146] "Means of delivery" refers to the interface or method for presenting the generated schedule and advice to the user.

[0147] A "feedback method" is a way of collecting user reactions and opinions and incorporating them into future analyses and suggestions.

[0148] A "generative AI model" is an artificial intelligence technology that automatically generates optimal advice and suggestions based on the user's emotional state and feedback.

[0149] The present invention is a system that improves the quality of life of users by effectively collecting and analyzing user data. This system includes a "collection means" for acquiring data from users, an "analysis means" for performing analysis, an "emotion analysis means" for identifying the user's emotional state, a "provision means" for presenting the results to the user, a "feedback means" for acquiring feedback from the user and incorporating it into the next time, and a means for making suggestions that correspond to the user's emotions using a generative AI model.

[0150] The server collects data such as heart rate, activity levels, sleep data, facial expressions, and voice tone from the user's smart device. Smartwatches and smartphones play a crucial role in this data collection, providing detailed physiological data through their sensors.

[0151] This data is used to determine the user's emotional state through emotion analysis. The emotion engine utilizes speech recognition and natural language processing to analyze the user's voice tone and word choice, identifying levels of stress and satisfaction.

[0152] Next, the analysis system analyzes all the acquired data and evaluates the user's lifestyle. Based on this evaluation, the server generates a personalized schedule and advice. Here, a generative AI model is used to provide suggestions adapted to the user's stress level and emotions. For example, if the user is experiencing high stress, suggestions such as reviewing bedtime or meditating may be made.

[0153] The device is designed to present these suggestions to the user through an intuitive interface. This interface displays customized messages tailored to the user's emotions, encouraging behavioral change. Furthermore, the rich use of visual elements ensures clear and easy-to-understand instructions.

[0154] Users send feedback on the advice they receive via their device. The server analyzes this feedback to further improve future suggestions and provide a more optimal schedule for the user. This feedback loop ensures that the system is always tailored to the user's needs.

[0155] A concrete example of a prompt message might be, "Consider the user's recent sleep patterns and stress levels to generate a lifestyle suggestion that is best suited to them." This further facilitates the system's provision of personalized suggestions based on individual circumstances.

[0156] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0157] Step 1:

[0158] The server collects physiological data and emotional signals from the user's smart device. This input includes heart rate, steps taken, sleep duration, voice tone, and facial expression data obtained from smartwatches and smartphones. This data is sent to the server in raw form. The server receives this data and converts it into an appropriate format for analysis.

[0159] Step 2:

[0160] The server performs sentiment analysis using the collected data. Inputs include voice tone and facial expression data. The sentiment analysis tool uses an AI model to identify the user's emotional state, such as stress or happiness, from this data. The analysis results are output, generating information that indicates the user's emotional pattern.

[0161] Step 3:

[0162] The server integrates emotion analysis results and physiological data to evaluate the user's lifestyle. Inputs include emotional state, heart rate, steps taken, and sleep duration. This data is evaluated through analysis tools, and insights into the user's activity patterns, sleep quality, and stress levels are output.

[0163] Step 4:

[0164] The server generates optimal schedules and advice based on the analysis results. The user's emotional state and lifestyle data are used as input. The generating AI model uses this data to output customized suggestions tailored to the user, such as recommendations for going to bed early or selections of relaxation music.

[0165] Step 5:

[0166] The terminal presents the user with schedules and advice received from the server. The input consists of customized messages and suggestions, clearly displayed to the user through the terminal's user interface. As a result, the user receives visually and emotionally appropriate suggestions.

[0167] Step 6:

[0168] Users submit feedback on the advice they receive via their device. The input consists of the user's thoughts and reactions to the suggestions, which are sent to the server as feedback. The server analyzes this information and uses it to improve future suggestions. The output is the feedback data, which is then incorporated into the next analysis algorithm.

[0169] (Application Example 2)

[0170] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0171] Traditional meal suggestion systems often failed to consider user emotions, instead offering manual suggestions based solely on general health information and past data. This resulted in a lack of appropriate meal suggestions adapted to the user's real-time emotional state, leading to shortcomings in user satisfaction and improved health.

[0172] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0173] In this invention, the server includes a collection means for acquiring the user's physiological data, including emotional signals; an analysis means for analyzing the data to identify the user's emotional state and generate optimal meal options based on that emotional state; and a provision means for presenting the meal options to the user. This enables more personalized meal suggestions tailored to the user's real-time emotional state.

[0174] "Emotional signals" are data that indicates a user's emotional state, extracted from physiological data including the user's facial expressions and tone of voice.

[0175] "Physiological data" refers to information that represents the user's physical state, including heart rate, skin conductance, body temperature, facial expression, and voice tone.

[0176] "Data collection methods" refer to sensors and devices used to acquire physiological data and emotional signals from users.

[0177] "Analysis means" refers to an algorithm or software that evaluates the user's emotional state and health status based on acquired physiological data and emotional signals, and generates optimal dietary options.

[0178] "Means of delivery" refers to interfaces or devices used to display or present generated meal options or suggestions to the user.

[0179] "Feedback" refers to information including responses, opinions, and impressions that users provide to the system through various means.

[0180] A "generative AI model" is an artificial intelligence model that automatically generates meal suggestions based on user data.

[0181] A "prompt message" is text that presents specific suggestions to the user based on the suggestions obtained from the generative AI model.

[0182] The system that realizes this invention consists of three main components: a server, a terminal, and a user.

[0183] The server utilizes various sensors and smart devices to acquire physiological data, including the user's emotional signals. This data includes heart rate, facial expressions, and voice tone, and emotion recognition software is used to identify the user's emotional state. A specific example of this software is "emotion_recognition." As an analysis method, the server uses a generative AI model based on this data to generate meal options appropriate to the user's current emotions.

[0184] The device (e.g., a smartphone) has an interface for presenting server-generated meal options to the user. Through this method, the user can receive personalized meal suggestions based on their emotional state. This information is displayed in a visually easy-to-understand format.

[0185] Users send feedback to the server regarding meal suggestions received through the service. This feedback is incorporated into the system's analysis algorithm, helping to refine future suggestions. An example of a specific prompt is, "Please suggest relaxing foods if the user is feeling stressed."

[0186] In this way, emotion-based meal suggestions can contribute to reducing user stress and improving their health, thereby enhancing the user's quality of life.

[0187] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0188] Step 1:

[0189] The server collects physiological data from the user's smart devices and sensors. This collected data includes information such as heart rate, facial expressions, and voice tone. The server inputs this data into the "emotion_recognition" software to analyze the user's emotional signals. As a result of the analysis, the server identifies the user's emotional state and obtains a quantified emotion score associated with that state.

[0190] Step 2:

[0191] The server inputs the emotion score obtained in Step 1 into the generating AI model. The generating AI model calculates the optimal meal plan based on the user's emotional state. This plan also takes into account the user's current health information and past eating history. The model outputs a list of meal options that are best suited to the emotional state and health data.

[0192] Step 3:

[0193] The server sends the generated meal plan to the device. The device visually presents the meal options to the user through its user interface. Specifically, it displays the options on the smartphone screen and provides detailed explanations of ingredients and dishes that have a relaxing effect.

[0194] Step 4:

[0195] Users provide feedback on the presented meal options via their device. For example, this feedback may include information such as whether the selected dish suited their taste and whether they felt it was effective. User feedback is collected via the device and sent to a server.

[0196] Step 5:

[0197] The server incorporates the user feedback it receives into its analysis algorithm, using it to refine future suggestions. Based on the feedback, the analysis results are used to adjust the prompt text of the generated AI model, improving the next meal suggestion to better fit the user's emotional state.

[0198] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0199] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0200] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0201] [Second Embodiment]

[0202] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0203] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0204] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0205] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0206] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0207] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0208] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0209] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0210] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0211] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0212] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0213] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0214] In order to implement the present invention, the user, server, and terminal must cooperate as follows so that the entire system can operate.

[0215] The server is connected to various sensors and smart devices that acquire data from users. This group of devices includes wearable devices, smartphone sensors, and environmental sensors. The server receives physiological data (heart rate and body temperature), environmental data (room temperature and noise level), and behavioral data (distance traveled and activity level) transmitted from these devices in real time.

[0216] The server analyzes incoming data and generates an optimal sleep schedule for the user. Machine learning algorithms are used for the analysis, which assesses the user's lifestyle and stress levels. Specifically, the analysis utilizes a database with a large amount of historical data to generate customized suggestions based on each user's characteristics.

[0217] The device presents the user with an optimal sleep schedule received from the server. Specifically, the smartphone app provides the schedule along with appropriate relaxation guides and ambient sound options. An intuitive interface is designed to make it easy for users to operate the device and access the presented information.

[0218] Users can review the information displayed on their device and incorporate it into their daily schedule. For example, they can follow suggestions for bedtime and wake-up times, or use breathing exercises to reduce stress. Furthermore, users can input feedback on their sleep quality into the device, and this information is sent to the server.

[0219] The server uses the feedback received from users to learn what is needed for the next analysis. This makes the recommendations provided more tailored to the individual needs of each user.

[0220] With the configuration described above, this invention can provide users with a high-quality sleep experience as a whole system. For example, if a user suffers from insomnia, the server can identify the cause and recommend relaxation activities before bedtime to improve their condition. In this way, this system achieves comprehensive sleep improvement for users.

[0221] The following describes the processing flow.

[0222] Step 1:

[0223] The server acquires physiological and environmental data in real time from the user's various sensors and smart devices. This data is securely stored in the cloud.

[0224] Step 2:

[0225] The server preprocesses and cleans up the acquired data to prepare it for analysis. This step involves removing noise and imputing missing values.

[0226] Step 3:

[0227] The server uses machine learning algorithms to analyze data and determine the user's sleep patterns and stress levels. Based on the analysis results, it generates a customized, optimal sleep schedule for each user.

[0228] Step 4:

[0229] The server sends the optimized schedule it generates to the terminal. The terminal receives this information and prepares to present it visually to the user.

[0230] Step 5:

[0231] The device provides users with sleep schedules and relaxation guides through its user interface. An intuitive and easy-to-use design has been considered.

[0232] Step 6:

[0233] Users can view the schedule presented through their device and incorporate it into their daily lives. Relaxation guides and ambient sound options are available to support sleep.

[0234] Step 7:

[0235] Users input and submit feedback on their sleep quality and schedule via their device. This feedback is then sent to the server.

[0236] Step 8:

[0237] The server receives feedback from users and uses it to improve the next data analysis and schedule generation process. This allows for the continuous provision of personalized advice to individual users.

[0238] (Example 1)

[0239] Next, we will describe Example 1. 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."

[0240] In recent years, the quality of people's sleep has been declining due to disrupted sleep patterns and increased stress. This negatively impacts health and daily life, creating a need for a system that can provide sleep schedules tailored to individual users. However, existing systems struggle to adequately consider user characteristics in their suggestions, and personalization through effective feedback is insufficient.

[0241] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0242] In this invention, the server includes a data collection means for acquiring data from the user, an analysis means for analyzing the data using a machine learning algorithm and comparing it with past information to generate an optimal schedule for the user, a provision means for presenting information to the user, including relaxation guides and ambient sounds, based on the schedule, and a learning means for acquiring user feedback and updating the generated AI model to reflect in the next analysis. This makes it possible to propose highly accurate, personalized sleep improvement plans for each individual user.

[0243] "Data collection means" refers to devices and methods for acquiring physiological data, environmental data, and behavioral data from users, and includes various sensors and smart devices.

[0244] "Analysis means" refers to a process or system for analyzing data obtained through collection means using machine learning algorithms, comparing it with past information, and generating an optimal schedule for each individual user.

[0245] "Means of provision" refers to methods and devices for transmitting information such as schedules, relaxation guides, and ambient sounds generated by analysis means to the user, and includes systems equipped with an intuitive interface.

[0246] A "learning method" is a mechanism or process for obtaining user feedback, updating the generated AI model, and reflecting that feedback in the next analysis.

[0247] This invention is a system for improving sleep quality by presenting a personalized and optimized sleep schedule to the user. First, the server works in conjunction with wearable devices worn by the user, smartphone sensors, and environmental sensors to collect physiological data (e.g., heart rate, body temperature), environmental data (e.g., room temperature, noise level), and behavioral data (e.g., distance traveled, activity level). This system uses sensor technology and IoT devices for data collection.

[0248] Next, the server analyzes this data using machine learning algorithms. The analysis uses historical data stored in the server's database to evaluate the user's lifestyle and stress levels. Based on this evaluation, the server generates a personalized sleep schedule for the user. A generative AI model is used for the analysis, making optimal suggestions based on pattern recognition and prediction of the data.

[0249] Next, the device notifies the user of the sleep schedule received from the server. The notification is delivered via a smartphone app and presented to the user in an easy-to-understand format, along with relaxation guides and ambient sound options. The application has an intuitive interface, allowing users to easily access and utilize the information.

[0250] Users can use their devices to view the presented schedule and incorporate it into their daily lives. For example, they can follow suggested bedtimes or utilize breathing exercises to relieve tension. Furthermore, users can input feedback on their sleep quality into their devices and send it to the server. This feedback is used as important data for future analysis.

[0251] Based on this feedback, the server can update its AI model to provide suggestions that are better suited to the user's needs in subsequent uses. An example of a specific prompt might be, "User A wants to know relaxation methods that can help improve their insomnia." In this way, the system aims to provide comprehensive sleep improvement for the user.

[0252] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0253] Step 1:

[0254] The server acquires physiological, environmental, and behavioral data from the user's wearable devices, smartphone sensors, and environmental sensors. Inputs include heart rate, body temperature, room temperature, noise level, distance traveled, and activity level. This data is transmitted to the server in real time and recorded in a database. Specifically, the system uses communication protocols between devices to ensure accurate data transfer.

[0255] Step 2:

[0256] The server analyzes the collected data using machine learning algorithms. In particular, it combines historical and current data to evaluate lifestyle habits and stress levels. The input is the data acquired in step 1, and the output is an evaluation of the user's health status and stress level based on the analysis. In this process, the algorithm recognizes patterns in the data and forms a new predictive model.

[0257] Step 3:

[0258] The server generates an optimal sleep schedule for the user based on the analysis results. The generated schedule includes recommended bedtimes and wake-up times, relaxation activities, and ambient sound options. The input is the evaluation results from step 2, and the output is a personalized sleep schedule. In this process, a generative AI model is utilized to provide suggestions tailored to the user's needs.

[0259] Step 4:

[0260] The device displays the sleep schedule sent from the server to the user. The information is displayed on the device screen in an intuitive and easy-to-understand format. The input is schedule data from the server, and the output is specific schedule information displayed on the user's smartphone. The application features an interface designed to enhance the user experience.

[0261] Step 5:

[0262] Users incorporate the schedule presented on their device into their daily lives. They can prepare to sleep according to the suggested bedtime and try the relaxation guide. The input is information from the device, and the output is the change in the user's behavior itself. As a result, users can experience reduced stress and improved sleep quality in their real lives.

[0263] Step 6:

[0264] Users input feedback about their sleep quality into a device and send it to a server. The input is feedback information based on the user's experience, and the output is feedback data sent to the server. The device provides an interface that allows users to easily input feedback.

[0265] Step 7:

[0266] The server updates the generated AI model based on user feedback and incorporates it into the next analysis. The input is the feedback data from step 6, and the output is the updated AI model. This process ensures that the system can always provide optimized suggestions based on the latest user information.

[0267] (Application Example 1)

[0268] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0269] In users' daily lives, the relationship between sleep quality and economic behavior is rarely considered. As a result, optimal spending management based on sleep patterns is not implemented, sometimes leading to wasteful spending and impulsive purchasing. This invention aims to support personalized spending management and achieve a comfortable lifestyle by integrating the management of a user's physiological state and economic behavior.

[0270] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0271] In this invention, the server includes a collection means for acquiring biometric information and economic behavior information from the user, an analysis means for analyzing the information and generating an optimal activity plan for the user, a provision means for presenting the plan to the user, and a control means for generating support information for managing the user's economic behavior based on the biometric information. This makes it possible to optimize expenditure management based on the user's sleep state.

[0272] "Biometric information" refers to data that indicates a user's health status and physical activity, including heart rate, body temperature, and sleep patterns.

[0273] "Economic behavior information" refers to data about a user's daily spending and purchasing activities, including purchase history and spending trends.

[0274] An "activity plan" is a set of behavioral guidelines created based on the user's biometric and economic behavioral information, suggesting optimal spending and lifestyle habits.

[0275] "Collection means" refers to devices and methods for obtaining necessary information from users, including those using smartphone applications and sensors.

[0276] "Analysis means" refers to processes and devices that perform analysis based on acquired information and generate content to be provided to the user.

[0277] "Means of delivery" refers to methods and devices for communicating the generated activity plan to the user, and includes applications for mobile devices.

[0278] "Control means" refers to methods or devices for adjusting economic behavior based on biometric information and generating recommendations regarding expenditure management.

[0279] To implement this invention, a system is configured in which a server, a terminal (such as a smartphone), and a user work together. The server is connected to a device equipped with the function of collecting biometric information and economic behavioral information. This includes wearable devices and smartphone applications. Specifically, data on sleep patterns and activity levels is acquired from the smartphone's sensors and transmitted to the server. Furthermore, information on the user's purchasing history and spending trends is also collected by the server.

[0280] The server performs machine learning (e.g., TensorFlow) using the collected information and generates an optimal activity plan for each individual user. This plan takes into account the user's sleep state and daily spending behavior, promotes planned shopping, and suppresses wasteful spending.

[0281] The terminal is responsible for providing the user with the activity plan sent from the server. As a specific example, when the user has a day of insufficient sleep, a reminder is notified to refrain from certain expenditures. Also, on a day when sufficient sleep has been obtained, it is recommended to consider purchases planned previously.

[0282] The user improves their daily life based on the activity plan provided by the terminal. For example, in the morning when good-quality sleep has been obtained, purchases can be made according to the planned shopping list. Also, response information from the user can be sent to the server and used to improve subsequent proposals.

[0283] As an example of a prompt sentence using a generative AI model, "Based on the past sleep data and spending history of User A, show the predicted spending trend in the next 24 hours and generate appropriate advice." can be cited.

[0284] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0285] Step 1:

[0286] The server acquires biometric information and economic behavior information from the user's smartphone and wearable device. Input includes heart rate, sleep time, and purchase history, etc. The server collects these data and stores them for use in the next step.

[0287] Step 2:

[0288] The server analyzes the acquired data using a machine learning algorithm (e.g., TensorFlow). The input for the analysis is the data collected in the previous step. Based on this, the server performs data calculations to evaluate the correlation between the user's physiological state and spending behavior and generate an optimized activity plan. The output is an activity plan customized for each user.

[0289] Step 3:

[0290] The server sends the generated activity plan to the terminal. The input is the activity plan obtained in the previous step. By transferring this plan to the terminal, the server helps the user act according to the plan. The output is the activity plan delivered to the terminal.

[0291] Step 4:

[0292] The terminal presents the received activity plan to the user. The input to the terminal is the activity plan sent from the server. The terminal visually displays the plan through an intuitive interface that the user can easily understand and provides notifications as needed. The output is the screen display of the activity plan that the user sees.

[0293] Step 5:

[0294] The user inputs feedback on the activity plan provided via the terminal. This user input reflects their satisfaction with and adaptation to the activity plan. This feedback is sent to the server for use in subsequent analyses. The input is the user's feedback, and the output is its transfer to the server.

[0295] Step 6:

[0296] The server receives feedback from the user and incorporates this information into the next analysis. The server's input is the feedback provided by the user. Based on the received feedback, the analysis model is updated to generate an activity plan that better suits the user. The output is the improved analysis model.

[0297] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0298] This invention combines conventional functions of collecting and analyzing user data and presenting optimal schedules with an emotion engine that recognizes user emotions. The aim of this system is to achieve even higher accuracy in personalization and further improve users' health and quality of life.

[0299] The server acquires not only physiological and behavioral data, but also emotional signals such as facial expressions and voice tone, through the user's various sensors and smart devices. This data is processed by an emotion engine to identify the user's emotional state. In this process, for example, stress levels and satisfaction levels can be estimated from the user's voice tone.

[0300] The analysis method uses comprehensive information, including acquired emotional data, to evaluate the user's lifestyle and sleep patterns. This evaluation generates the most effective sleep schedule and stress management methods for the user. For example, if irritability due to sleep deprivation is detected, the system may suggest earlier bedtime and provide relaxation music.

[0301] The terminal has an interface for presenting the user with schedules and advice generated by the server. This interface displays customized messages based on emotions and is designed to boost the user's motivation. Based on this information, the user can review their lifestyle and practice relaxation techniques.

[0302] In addition, the feedback from the user, which includes changes in emotions, is sent to the server. The server uses this feedback to further adjust the analysis algorithm and improve the schedule proposals for subsequent times.

[0303] By utilizing an emotion engine, the present invention can take into account the user's subjective health status and needs that could not be grasped by conventional pattern analysis alone, thereby achieving more refined personalization. As a result, the user can perform sleep and health management that is most suitable for themselves.

[0304] The processing flow will be described below.

[0305] Step 1:

[0306] The server acquires physiological data, behavioral data, and emotion signals from the user's smart device and sensors. The emotion signals include facial expression data and voice tone. This enables a broad understanding of the user's state.

[0307] Step 2:

[0308] The emotion engine installed in the server analyzes the acquired emotion signals and identifies the user's emotional state. For example, it is possible to calculate the stress level from the user's voice tone.

[0309] Step 3:

[0310] The server integrates emotional data with other physiological data and performs analysis using machine learning algorithms. This comprehensively evaluates the user's sleep patterns and lifestyle habits. As a result, an optimal sleep schedule and suggestions for improvement are generated for each individual user.

[0311] Step 4:

[0312] The server sends the optimal schedule and suggestions it generates to the terminal. The terminal receives this data and prepares to display it in the user interface.

[0313] Step 5:

[0314] The device presents the user with schedules, relaxation guides, and emotion-based messages. The user can then review the information presented and decide how to apply it to their real life.

[0315] Step 6:

[0316] Users submit feedback using their devices. This feedback includes their thoughts after the scheduled execution and any changes in their newly experienced emotions.

[0317] Step 7:

[0318] The server receives user feedback and adjusts the algorithm to utilize it in future analyses. This enables the delivery of more personalized services.

[0319] (Example 2)

[0320] Next, we will describe Example 2. 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".

[0321] Conventional user data analysis systems rely on objective data to analyze users' lifestyles and health conditions, making it difficult to accurately reflect users' subjective emotions and stress levels. This resulted in a problem where the schedules and advice provided did not match the user's actual situation, failing to effectively improve their quality of life.

[0322] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0323] In this invention, the server includes a data collection means for acquiring data from the user, an analysis means for analyzing the data to generate an optimal schedule for the user, an emotion analysis means for identifying the user's emotional state from the analyzed data, a provision means for presenting the schedule to the user, a feedback means for acquiring user feedback and reflecting it in the next analysis, and a means for making optimal suggestions according to the user's emotional state using a generation AI model. This enables sophisticated personalization that takes into account the user's subjective health condition and needs.

[0324] "Data collection methods" refer to devices and systems used to acquire physiological data, behavioral data, emotional signals, and other information from users.

[0325] "Analysis methods" refer to techniques and technologies that analyze collected data to generate optimal schedules and advice for users.

[0326] "Emotional analysis methods" refer to methods and technologies for processing emotional signals such as the tone of a user's voice and facial expressions to identify their emotional state.

[0327] "Means of delivery" refers to the interface or method for presenting the generated schedule and advice to the user.

[0328] A "feedback method" is a way of collecting user reactions and opinions and incorporating them into future analyses and suggestions.

[0329] A "generative AI model" is an artificial intelligence technology that automatically generates optimal advice and suggestions based on the user's emotional state and feedback.

[0330] The present invention is a system that improves the quality of life of users by effectively collecting and analyzing user data. This system includes a "collection means" for acquiring data from users, an "analysis means" for performing analysis, an "emotion analysis means" for identifying the user's emotional state, a "provision means" for presenting the results to the user, a "feedback means" for acquiring feedback from the user and incorporating it into the next time, and a means for making suggestions that correspond to the user's emotions using a generative AI model.

[0331] The server collects data such as heart rate, activity levels, sleep data, facial expressions, and voice tone from the user's smart device. Smartwatches and smartphones play a crucial role in this data collection, providing detailed physiological data through their sensors.

[0332] This data is used to determine the user's emotional state through emotion analysis. The emotion engine utilizes speech recognition and natural language processing to analyze the user's voice tone and word choice, identifying levels of stress and satisfaction.

[0333] Next, the analysis system analyzes all the acquired data and evaluates the user's lifestyle. Based on this evaluation, the server generates a personalized schedule and advice. Here, a generative AI model is used to provide suggestions adapted to the user's stress level and emotions. For example, if the user is experiencing high stress, suggestions such as reviewing bedtime or meditating may be made.

[0334] The device is designed to present these suggestions to the user through an intuitive interface. This interface displays customized messages tailored to the user's emotions, encouraging behavioral change. Furthermore, the rich use of visual elements ensures clear and easy-to-understand instructions.

[0335] Users send feedback on the advice they receive via their device. The server analyzes this feedback to further improve future suggestions and provide a more optimal schedule for the user. This feedback loop ensures that the system is always tailored to the user's needs.

[0336] A concrete example of a prompt message might be, "Consider the user's recent sleep patterns and stress levels to generate a lifestyle suggestion that is best suited to them." This further facilitates the system's provision of personalized suggestions based on individual circumstances.

[0337] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0338] Step 1:

[0339] The server collects physiological data and emotional signals from the user's smart device. This input includes heart rate, steps taken, sleep duration, voice tone, and facial expression data obtained from smartwatches and smartphones. This data is sent to the server in raw form. The server receives this data and converts it into an appropriate format for analysis.

[0340] Step 2:

[0341] The server performs sentiment analysis using the collected data. Inputs include voice tone and facial expression data. The sentiment analysis tool uses an AI model to identify the user's emotional state, such as stress or happiness, from this data. The analysis results are output, generating information that indicates the user's emotional pattern.

[0342] Step 3:

[0343] The server integrates emotion analysis results and physiological data to evaluate the user's lifestyle. Inputs include emotional state, heart rate, steps taken, and sleep duration. This data is evaluated through analysis tools, and insights into the user's activity patterns, sleep quality, and stress levels are output.

[0344] Step 4:

[0345] The server generates optimal schedules and advice based on the analysis results. The user's emotional state and lifestyle data are used as input. The generating AI model uses this data to output customized suggestions tailored to the user, such as recommendations for going to bed early or selections of relaxation music.

[0346] Step 5:

[0347] The terminal presents the user with schedules and advice received from the server. The input consists of customized messages and suggestions, clearly displayed to the user through the terminal's user interface. As a result, the user receives visually and emotionally appropriate suggestions.

[0348] Step 6:

[0349] Users submit feedback on the advice they receive via their device. The input consists of the user's thoughts and reactions to the suggestions, which are sent to the server as feedback. The server analyzes this information and uses it to improve future suggestions. The output is the feedback data, which is then incorporated into the next analysis algorithm.

[0350] (Application Example 2)

[0351] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0352] Traditional meal suggestion systems often failed to consider user emotions, instead offering manual suggestions based solely on general health information and past data. This resulted in a lack of appropriate meal suggestions adapted to the user's real-time emotional state, leading to shortcomings in user satisfaction and improved health.

[0353] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0354] In this invention, the server includes a collection means for acquiring the user's physiological data, including emotional signals; an analysis means for analyzing the data to identify the user's emotional state and generate optimal meal options based on that emotional state; and a provision means for presenting the meal options to the user. This enables more personalized meal suggestions tailored to the user's real-time emotional state.

[0355] "Emotional signals" are data that indicates a user's emotional state, extracted from physiological data including the user's facial expressions and tone of voice.

[0356] "Physiological data" refers to information that represents the user's physical state, including heart rate, skin conductance, body temperature, facial expression, and voice tone.

[0357] "Data collection methods" refer to sensors and devices used to acquire physiological data and emotional signals from users.

[0358] "Analysis means" refers to an algorithm or software that evaluates the user's emotional state and health status based on acquired physiological data and emotional signals, and generates optimal dietary options.

[0359] "Means of delivery" refers to interfaces or devices used to display or present generated meal options or suggestions to the user.

[0360] "Feedback" refers to information including responses, opinions, and impressions that users provide to the system through various means.

[0361] A "generative AI model" is an artificial intelligence model that automatically generates meal suggestions based on user data.

[0362] A "prompt message" is text that presents specific suggestions to the user based on the suggestions obtained from the generative AI model.

[0363] The system that realizes this invention consists of three main components: a server, a terminal, and a user.

[0364] The server utilizes various sensors and smart devices to acquire physiological data, including the user's emotional signals. This data includes heart rate, facial expressions, and voice tone, and emotion recognition software is used to identify the user's emotional state. A specific example of this software is "emotion_recognition." As an analysis method, the server uses a generative AI model based on this data to generate meal options appropriate to the user's current emotions.

[0365] The device (e.g., a smartphone) has an interface for presenting server-generated meal options to the user. Through this method, the user can receive personalized meal suggestions based on their emotional state. This information is displayed in a visually easy-to-understand format.

[0366] Users send feedback to the server regarding meal suggestions received through the service. This feedback is incorporated into the system's analysis algorithm, helping to refine future suggestions. An example of a specific prompt is, "Please suggest relaxing foods if the user is feeling stressed."

[0367] In this way, emotion-based meal suggestions can contribute to reducing user stress and improving their health, thereby enhancing the user's quality of life.

[0368] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0369] Step 1:

[0370] The server collects physiological data from the user's smart devices and sensors. This collected data includes information such as heart rate, facial expressions, and voice tone. The server inputs this data into the "emotion_recognition" software to analyze the user's emotional signals. As a result of the analysis, the server identifies the user's emotional state and obtains a quantified emotion score associated with that state.

[0371] Step 2:

[0372] The server inputs the emotion score obtained in Step 1 into the generating AI model. The generating AI model calculates the optimal meal plan based on the user's emotional state. This plan also takes into account the user's current health information and past eating history. The model outputs a list of meal options that are best suited to the emotional state and health data.

[0373] Step 3:

[0374] The server sends the generated meal plan to the device. The device visually presents the meal options to the user through its user interface. Specifically, it displays the options on the smartphone screen and provides detailed explanations of ingredients and dishes that have a relaxing effect.

[0375] Step 4:

[0376] Users provide feedback on the presented meal options via their device. For example, this feedback may include information such as whether the selected dish suited their taste and whether they felt it was effective. User feedback is collected via the device and sent to a server.

[0377] Step 5:

[0378] The server incorporates the user feedback it receives into its analysis algorithm, using it to refine future suggestions. Based on the feedback, the analysis results are used to adjust the prompt text of the generated AI model, improving the next meal suggestion to better fit the user's emotional state.

[0379] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0380] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0381] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0382] [Third Embodiment]

[0383] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0384] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0385] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0386] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0387] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0388] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0389] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0390] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0391] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0392] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0393] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0394] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0395] In order to implement the present invention, the user, server, and terminal must cooperate as follows so that the entire system can operate.

[0396] The server is connected to various sensors and smart devices that acquire data from users. This group of devices includes wearable devices, smartphone sensors, and environmental sensors. The server receives physiological data (heart rate and body temperature), environmental data (room temperature and noise level), and behavioral data (distance traveled and activity level) transmitted from these devices in real time.

[0397] The server analyzes incoming data and generates an optimal sleep schedule for the user. Machine learning algorithms are used for the analysis, which assesses the user's lifestyle and stress levels. Specifically, the analysis utilizes a database with a large amount of historical data to generate customized suggestions based on each user's characteristics.

[0398] The device presents the user with an optimal sleep schedule received from the server. Specifically, the smartphone app provides the schedule along with appropriate relaxation guides and ambient sound options. An intuitive interface is designed to make it easy for users to operate the device and access the presented information.

[0399] Users can review the information displayed on their device and incorporate it into their daily schedule. For example, they can follow suggestions for bedtime and wake-up times, or use breathing exercises to reduce stress. Furthermore, users can input feedback on their sleep quality into the device, and this information is sent to the server.

[0400] The server uses the feedback received from users to learn what is needed for the next analysis. This makes the recommendations provided more tailored to the individual needs of each user.

[0401] With the configuration described above, this invention can provide users with a high-quality sleep experience as a whole system. For example, if a user suffers from insomnia, the server can identify the cause and recommend relaxation activities before bedtime to improve their condition. In this way, this system achieves comprehensive sleep improvement for users.

[0402] The following describes the processing flow.

[0403] Step 1:

[0404] The server acquires physiological and environmental data in real time from the user's various sensors and smart devices. This data is securely stored in the cloud.

[0405] Step 2:

[0406] The server preprocesses and cleans up the acquired data to prepare it for analysis. This step involves removing noise and imputing missing values.

[0407] Step 3:

[0408] The server uses machine learning algorithms to analyze data and determine the user's sleep patterns and stress levels. Based on the analysis results, it generates a customized, optimal sleep schedule for each user.

[0409] Step 4:

[0410] The server sends the optimized schedule it generates to the terminal. The terminal receives this information and prepares to present it visually to the user.

[0411] Step 5:

[0412] The device provides users with sleep schedules and relaxation guides through its user interface. An intuitive and easy-to-use design has been considered.

[0413] Step 6:

[0414] Users can view the schedule presented through their device and incorporate it into their daily lives. Relaxation guides and ambient sound options are available to support sleep.

[0415] Step 7:

[0416] Users input and submit feedback on their sleep quality and schedule via their device. This feedback is then sent to the server.

[0417] Step 8:

[0418] The server receives feedback from users and uses it to improve the next data analysis and schedule generation process. This allows for the continuous provision of personalized advice to individual users.

[0419] (Example 1)

[0420] Next, we will describe Example 1. 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."

[0421] In recent years, the quality of people's sleep has been declining due to disrupted sleep patterns and increased stress. This negatively impacts health and daily life, creating a need for a system that can provide sleep schedules tailored to individual users. However, existing systems struggle to adequately consider user characteristics in their suggestions, and personalization through effective feedback is insufficient.

[0422] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0423] In this invention, the server includes a data collection means for acquiring data from the user, an analysis means for analyzing the data using a machine learning algorithm and comparing it with past information to generate an optimal schedule for the user, a provision means for presenting information to the user, including relaxation guides and ambient sounds, based on the schedule, and a learning means for acquiring user feedback and updating the generated AI model to reflect in the next analysis. This makes it possible to propose highly accurate, personalized sleep improvement plans for each individual user.

[0424] "Data collection means" refers to devices and methods for acquiring physiological data, environmental data, and behavioral data from users, and includes various sensors and smart devices.

[0425] "Analysis means" refers to a process or system for analyzing data obtained through collection means using machine learning algorithms, comparing it with past information, and generating an optimal schedule for each individual user.

[0426] "Means of provision" refers to methods and devices for transmitting information such as schedules, relaxation guides, and ambient sounds generated by analysis means to the user, and includes systems equipped with an intuitive interface.

[0427] A "learning method" is a mechanism or process for obtaining user feedback, updating the generated AI model, and reflecting that feedback in the next analysis.

[0428] This invention is a system for improving sleep quality by presenting a personalized and optimized sleep schedule to the user. First, the server works in conjunction with wearable devices worn by the user, smartphone sensors, and environmental sensors to collect physiological data (e.g., heart rate, body temperature), environmental data (e.g., room temperature, noise level), and behavioral data (e.g., distance traveled, activity level). This system uses sensor technology and IoT devices for data collection.

[0429] Next, the server analyzes this data using machine learning algorithms. The analysis uses historical data stored in the server's database to evaluate the user's lifestyle and stress levels. Based on this evaluation, the server generates a personalized sleep schedule for the user. A generative AI model is used for the analysis, making optimal suggestions based on pattern recognition and prediction of the data.

[0430] Next, the device notifies the user of the sleep schedule received from the server. The notification is delivered via a smartphone app and presented to the user in an easy-to-understand format, along with relaxation guides and ambient sound options. The application has an intuitive interface, allowing users to easily access and utilize the information.

[0431] Users can use their devices to view the presented schedule and incorporate it into their daily lives. For example, they can follow suggested bedtimes or utilize breathing exercises to relieve tension. Furthermore, users can input feedback on their sleep quality into their devices and send it to the server. This feedback is used as important data for future analysis.

[0432] Based on this feedback, the server can update its AI model to provide suggestions that are better suited to the user's needs in subsequent uses. An example of a specific prompt might be, "User A wants to know relaxation methods that can help improve their insomnia." In this way, the system aims to provide comprehensive sleep improvement for the user.

[0433] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0434] Step 1:

[0435] The server acquires physiological, environmental, and behavioral data from the user's wearable devices, smartphone sensors, and environmental sensors. Inputs include heart rate, body temperature, room temperature, noise level, distance traveled, and activity level. This data is transmitted to the server in real time and recorded in a database. Specifically, the system uses communication protocols between devices to ensure accurate data transfer.

[0436] Step 2:

[0437] The server analyzes the collected data using machine learning algorithms. In particular, it combines historical and current data to evaluate lifestyle habits and stress levels. The input is the data acquired in step 1, and the output is an evaluation of the user's health status and stress level based on the analysis. In this process, the algorithm recognizes patterns in the data and forms a new predictive model.

[0438] Step 3:

[0439] The server generates an optimal sleep schedule for the user based on the analysis results. The generated schedule includes recommended bedtimes and wake-up times, relaxation activities, and ambient sound options. The input is the evaluation results from step 2, and the output is a personalized sleep schedule. In this process, a generative AI model is utilized to provide suggestions tailored to the user's needs.

[0440] Step 4:

[0441] The device displays the sleep schedule sent from the server to the user. The information is displayed on the device screen in an intuitive and easy-to-understand format. The input is schedule data from the server, and the output is specific schedule information displayed on the user's smartphone. The application features an interface designed to enhance the user experience.

[0442] Step 5:

[0443] Users incorporate the schedule presented on their device into their daily lives. They can prepare to sleep according to the suggested bedtime and try the relaxation guide. The input is information from the device, and the output is the change in the user's behavior itself. As a result, users can experience reduced stress and improved sleep quality in their real lives.

[0444] Step 6:

[0445] Users input feedback about their sleep quality into a device and send it to a server. The input is feedback information based on the user's experience, and the output is feedback data sent to the server. The device provides an interface that allows users to easily input feedback.

[0446] Step 7:

[0447] The server updates the generated AI model based on user feedback and incorporates it into the next analysis. The input is the feedback data from step 6, and the output is the updated AI model. This process ensures that the system can always provide optimized suggestions based on the latest user information.

[0448] (Application Example 1)

[0449] Next, we will explain Application Example 1. In the following explanation, 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."

[0450] In users' daily lives, the relationship between sleep quality and economic behavior is rarely considered. As a result, optimal spending management based on sleep patterns is not implemented, sometimes leading to wasteful spending and impulsive purchasing. This invention aims to support personalized spending management and achieve a comfortable lifestyle by integrating the management of a user's physiological state and economic behavior.

[0451] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0452] In this invention, the server includes a collection means for acquiring biometric information and economic behavior information from the user, an analysis means for analyzing the information and generating an optimal activity plan for the user, a provision means for presenting the plan to the user, and a control means for generating support information for managing the user's economic behavior based on the biometric information. This makes it possible to optimize expenditure management based on the user's sleep state.

[0453] "Biometric information" refers to data that indicates a user's health status and physical activity, including heart rate, body temperature, and sleep patterns.

[0454] "Economic behavior information" refers to data about a user's daily spending and purchasing activities, including purchase history and spending trends.

[0455] An "activity plan" is a set of behavioral guidelines created based on the user's biometric and economic behavioral information, suggesting optimal spending and lifestyle habits.

[0456] "Collection means" refers to devices and methods for obtaining necessary information from users, including those using smartphone applications and sensors.

[0457] "Analysis means" refers to processes and devices that perform analysis based on acquired information and generate content to be provided to the user.

[0458] "Means of delivery" refers to methods and devices for communicating the generated activity plan to the user, and includes applications for mobile devices.

[0459] "Control means" refers to methods or devices for adjusting economic behavior based on biometric information and generating recommendations regarding expenditure management.

[0460] To implement this invention, a system is configured in which a server, a terminal (such as a smartphone), and a user work together. The server is connected to a device equipped with the function of collecting biometric information and economic behavioral information. This includes wearable devices and smartphone applications. Specifically, data on sleep patterns and activity levels is acquired from the smartphone's sensors and transmitted to the server. Furthermore, information on the user's purchasing history and spending trends is also collected by the server.

[0461] The server uses the collected information to perform machine learning (e.g., TensorFlow) and generate an optimal activity plan for each individual user. This plan takes into account the user's sleep patterns and daily spending habits to promote planned shopping and reduce unnecessary spending.

[0462] The device is responsible for providing the user with activity plans sent from the server. For example, on days when the user is sleep-deprived, it will notify them of a reminder to refrain from certain expenses. On days when they have had a good night's sleep, it will recommend that they consider a previously planned purchase.

[0463] Users improve their daily lives based on activity plans provided by their devices. For example, on mornings when they've had a good night's sleep, they can proceed with their shopping according to a planned shopping list. Furthermore, user responses are sent to a server, which can be used to improve future suggestions.

[0464] An example of a prompt message generated using an AI model is: "Based on User A's past sleep data and spending history, please show predicted spending trends for the next 24 hours and generate appropriate advice."

[0465] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0466] Step 1:

[0467] The server acquires biometric and economic activity data from the user's smartphone and wearable devices. Inputs include heart rate, sleep duration, and purchase history. The server collects this data and stores it for use in the next step.

[0468] Step 2:

[0469] The server analyzes the acquired data using a machine learning algorithm (e.g., TensorFlow). The input for the analysis is the data collected in the previous step. Based on this, the server performs data calculations to evaluate the correlation between the user's physiological state and spending behavior and generate an optimized activity plan. The output is an activity plan customized for each user.

[0470] Step 3:

[0471] The server sends the generated activity plan to the terminal. The input is the activity plan obtained in the previous step. By transferring this plan to the terminal, the server helps the user act according to the plan. The output is the activity plan delivered to the terminal.

[0472] Step 4:

[0473] The terminal presents the received activity plan to the user. The input to the terminal is the activity plan sent from the server. The terminal visually displays the plan through an intuitive interface that the user can easily understand and provides notifications as needed. The output is the screen display of the activity plan that the user sees.

[0474] Step 5:

[0475] The user inputs feedback on the activity plan provided via the terminal. This user input reflects their satisfaction with and adaptation to the activity plan. This feedback is sent to the server for use in subsequent analyses. The input is the user's feedback, and the output is its transfer to the server.

[0476] Step 6:

[0477] The server receives feedback from the user and incorporates this information into the next analysis. The server's input is the feedback provided by the user. Based on the received feedback, the analysis model is updated to generate an activity plan that better suits the user. The output is the improved analysis model.

[0478] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0479] This invention combines conventional functions of collecting and analyzing user data and presenting optimal schedules with an emotion engine that recognizes user emotions. The aim of this system is to achieve even higher accuracy in personalization and further improve users' health and quality of life.

[0480] The server acquires not only physiological and behavioral data, but also emotional signals such as facial expressions and voice tone, through the user's various sensors and smart devices. This data is processed by an emotion engine to identify the user's emotional state. In this process, for example, stress levels and satisfaction levels can be estimated from the user's voice tone.

[0481] The analysis method uses comprehensive information, including acquired emotional data, to evaluate the user's lifestyle and sleep patterns. This evaluation generates the most effective sleep schedule and stress management methods for the user. For example, if irritability due to sleep deprivation is detected, the system may suggest earlier bedtime and provide relaxation music.

[0482] The device has an interface that presents the user with schedules and advice generated by the server. This interface displays emotionally-based, customized messages designed to boost user motivation. Based on this information, users can review their lifestyle habits or implement relaxation techniques.

[0483] Furthermore, user feedback, including changes in emotions, is sent to the server. The server uses this feedback to further refine its analysis algorithms and improve future schedule suggestions.

[0484] This invention utilizes an emotion engine to take into account the user's subjective health status and needs, which could not be grasped through conventional pattern analysis alone, thereby achieving more precise personalization. As a result, users can manage their sleep and health in a way that is best suited to them.

[0485] The following describes the processing flow.

[0486] Step 1:

[0487] The server acquires physiological data, behavioral data, and emotional signals from the user's smart devices and sensors. Emotional signals include facial expression data and voice tone. This allows for a comprehensive understanding of the user's state.

[0488] Step 2:

[0489] An emotion engine installed within the server analyzes acquired emotional signals to identify the user's emotional state. For example, it can calculate stress levels from the tone of the user's voice.

[0490] Step 3:

[0491] The server integrates emotional data with other physiological data and performs analysis using machine learning algorithms. This comprehensively evaluates the user's sleep patterns and lifestyle habits. As a result, an optimal sleep schedule and suggestions for improvement are generated for each individual user.

[0492] Step 4:

[0493] The server sends the optimal schedule and suggestions it generates to the terminal. The terminal receives this data and prepares to display it in the user interface.

[0494] Step 5:

[0495] The device presents the user with schedules, relaxation guides, and emotion-based messages. The user can then review the information presented and decide how to apply it to their real life.

[0496] Step 6:

[0497] Users submit feedback using their devices. This feedback includes their thoughts after the scheduled execution and any changes in their newly experienced emotions.

[0498] Step 7:

[0499] The server receives user feedback and adjusts the algorithm to utilize it in future analyses. This enables the delivery of more personalized services.

[0500] (Example 2)

[0501] Next, we will describe Example 2. 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."

[0502] Conventional user data analysis systems rely on objective data to analyze users' lifestyles and health conditions, making it difficult to accurately reflect users' subjective emotions and stress levels. This resulted in a problem where the schedules and advice provided did not match the user's actual situation, failing to effectively improve their quality of life.

[0503] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0504] In this invention, the server includes a data collection means for acquiring data from the user, an analysis means for analyzing the data to generate an optimal schedule for the user, an emotion analysis means for identifying the user's emotional state from the analyzed data, a provision means for presenting the schedule to the user, a feedback means for acquiring user feedback and reflecting it in the next analysis, and a means for making optimal suggestions according to the user's emotional state using a generation AI model. This enables sophisticated personalization that takes into account the user's subjective health condition and needs.

[0505] "Data collection methods" refer to devices and systems used to acquire physiological data, behavioral data, emotional signals, and other information from users.

[0506] "Analysis methods" refer to techniques and technologies that analyze collected data to generate optimal schedules and advice for users.

[0507] "Emotional analysis methods" refer to methods and technologies for processing emotional signals such as the tone of a user's voice and facial expressions to identify their emotional state.

[0508] "Means of delivery" refers to the interface or method for presenting the generated schedule and advice to the user.

[0509] A "feedback method" is a way of collecting user reactions and opinions and incorporating them into future analyses and suggestions.

[0510] A "generative AI model" is an artificial intelligence technology that automatically generates optimal advice and suggestions based on the user's emotional state and feedback.

[0511] The present invention is a system that improves the quality of life of users by effectively collecting and analyzing user data. This system includes a "collection means" for acquiring data from users, an "analysis means" for performing analysis, an "emotion analysis means" for identifying the user's emotional state, a "provision means" for presenting the results to the user, a "feedback means" for acquiring feedback from the user and incorporating it into the next time, and a means for making suggestions that correspond to the user's emotions using a generative AI model.

[0512] The server collects data such as heart rate, activity levels, sleep data, facial expressions, and voice tone from the user's smart device. Smartwatches and smartphones play a crucial role in this data collection, providing detailed physiological data through their sensors.

[0513] This data is used to determine the user's emotional state through emotion analysis. The emotion engine utilizes speech recognition and natural language processing to analyze the user's voice tone and word choice, identifying levels of stress and satisfaction.

[0514] Next, the analysis system analyzes all the acquired data and evaluates the user's lifestyle. Based on this evaluation, the server generates a personalized schedule and advice. Here, a generative AI model is used to provide suggestions adapted to the user's stress level and emotions. For example, if the user is experiencing high stress, suggestions such as reviewing bedtime or meditating may be made.

[0515] The device is designed to present these suggestions to the user through an intuitive interface. This interface displays customized messages tailored to the user's emotions, encouraging behavioral change. Furthermore, the rich use of visual elements ensures clear and easy-to-understand instructions.

[0516] Users send feedback on the advice they receive via their device. The server analyzes this feedback to further improve future suggestions and provide a more optimal schedule for the user. This feedback loop ensures that the system is always tailored to the user's needs.

[0517] A concrete example of a prompt message might be, "Consider the user's recent sleep patterns and stress levels to generate a lifestyle suggestion that is best suited to them." This further facilitates the system's provision of personalized suggestions based on individual circumstances.

[0518] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0519] Step 1:

[0520] The server collects physiological data and emotional signals from the user's smart device. This input includes heart rate, steps taken, sleep duration, voice tone, and facial expression data obtained from smartwatches and smartphones. This data is sent to the server in raw form. The server receives this data and converts it into an appropriate format for analysis.

[0521] Step 2:

[0522] The server performs sentiment analysis using the collected data. Inputs include voice tone and facial expression data. The sentiment analysis tool uses an AI model to identify the user's emotional state, such as stress or happiness, from this data. The analysis results are output, generating information that indicates the user's emotional pattern.

[0523] Step 3:

[0524] The server integrates emotion analysis results and physiological data to evaluate the user's lifestyle. Inputs include emotional state, heart rate, steps taken, and sleep duration. This data is evaluated through analysis tools, and insights into the user's activity patterns, sleep quality, and stress levels are output.

[0525] Step 4:

[0526] The server generates optimal schedules and advice based on the analysis results. The user's emotional state and lifestyle data are used as input. The generating AI model uses this data to output customized suggestions tailored to the user, such as recommendations for going to bed early or selections of relaxation music.

[0527] Step 5:

[0528] The terminal presents the user with schedules and advice received from the server. The input consists of customized messages and suggestions, clearly displayed to the user through the terminal's user interface. As a result, the user receives visually and emotionally appropriate suggestions.

[0529] Step 6:

[0530] Users submit feedback on the advice they receive via their device. The input consists of the user's thoughts and reactions to the suggestions, which are sent to the server as feedback. The server analyzes this information and uses it to improve future suggestions. The output is the feedback data, which is then incorporated into the next analysis algorithm.

[0531] (Application Example 2)

[0532] Next, we will explain application example 2. In the following explanation, 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."

[0533] Traditional meal suggestion systems often failed to consider user emotions, instead offering manual suggestions based solely on general health information and past data. This resulted in a lack of appropriate meal suggestions adapted to the user's real-time emotional state, leading to shortcomings in user satisfaction and improved health.

[0534] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0535] In this invention, the server includes a collection means for acquiring the user's physiological data, including emotional signals; an analysis means for analyzing the data to identify the user's emotional state and generate optimal meal options based on that emotional state; and a provision means for presenting the meal options to the user. This enables more personalized meal suggestions tailored to the user's real-time emotional state.

[0536] "Emotional signals" are data that indicates a user's emotional state, extracted from physiological data including the user's facial expressions and tone of voice.

[0537] "Physiological data" refers to information that represents the user's physical state, including heart rate, skin conductance, body temperature, facial expression, and voice tone.

[0538] "Data collection methods" refer to sensors and devices used to acquire physiological data and emotional signals from users.

[0539] "Analysis means" refers to an algorithm or software that evaluates the user's emotional state and health status based on acquired physiological data and emotional signals, and generates optimal dietary options.

[0540] "Means of delivery" refers to interfaces or devices used to display or present generated meal options or suggestions to the user.

[0541] "Feedback" refers to information including responses, opinions, and impressions that users provide to the system through various means.

[0542] A "generative AI model" is an artificial intelligence model that automatically generates meal suggestions based on user data.

[0543] A "prompt message" is text that presents specific suggestions to the user based on the suggestions obtained from the generative AI model.

[0544] The system that realizes this invention consists of three main components: a server, a terminal, and a user.

[0545] The server utilizes various sensors and smart devices to acquire physiological data, including the user's emotional signals. This data includes heart rate, facial expressions, and voice tone, and emotion recognition software is used to identify the user's emotional state. A specific example of this software is "emotion_recognition." As an analysis method, the server uses a generative AI model based on this data to generate meal options appropriate to the user's current emotions.

[0546] The device (e.g., a smartphone) has an interface for presenting server-generated meal options to the user. Through this method, the user can receive personalized meal suggestions based on their emotional state. This information is displayed in a visually easy-to-understand format.

[0547] Users send feedback to the server regarding meal suggestions received through the service. This feedback is incorporated into the system's analysis algorithm, helping to refine future suggestions. An example of a specific prompt is, "Please suggest relaxing foods if the user is feeling stressed."

[0548] In this way, emotion-based meal suggestions can contribute to reducing user stress and improving their health, thereby enhancing the user's quality of life.

[0549] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0550] Step 1:

[0551] The server collects physiological data from the user's smart devices and sensors. This collected data includes information such as heart rate, facial expressions, and voice tone. The server inputs this data into the "emotion_recognition" software to analyze the user's emotional signals. As a result of the analysis, the server identifies the user's emotional state and obtains a quantified emotion score associated with that state.

[0552] Step 2:

[0553] The server inputs the emotion score obtained in Step 1 into the generating AI model. The generating AI model calculates the optimal meal plan based on the user's emotional state. This plan also takes into account the user's current health information and past eating history. The model outputs a list of meal options that are best suited to the emotional state and health data.

[0554] Step 3:

[0555] The server sends the generated meal plan to the device. The device visually presents the meal options to the user through its user interface. Specifically, it displays the options on the smartphone screen and provides detailed explanations of ingredients and dishes that have a relaxing effect.

[0556] Step 4:

[0557] Users provide feedback on the presented meal options via their device. For example, this feedback may include information such as whether the selected dish suited their taste and whether they felt it was effective. User feedback is collected via the device and sent to a server.

[0558] Step 5:

[0559] The server incorporates the user feedback it receives into its analysis algorithm, using it to refine future suggestions. Based on the feedback, the analysis results are used to adjust the prompt text of the generated AI model, improving the next meal suggestion to better fit the user's emotional state.

[0560] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0561] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0562] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0563] [Fourth Embodiment]

[0564] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0565] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0566] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0567] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0568] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0569] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0570] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0571] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0572] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0573] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0574] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0575] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0576] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0577] In order to implement the present invention, the user, server, and terminal must cooperate as follows so that the entire system can operate.

[0578] The server is connected to various sensors and smart devices that acquire data from users. This group of devices includes wearable devices, smartphone sensors, and environmental sensors. The server receives physiological data (heart rate and body temperature), environmental data (room temperature and noise level), and behavioral data (distance traveled and activity level) transmitted from these devices in real time.

[0579] The server analyzes incoming data and generates an optimal sleep schedule for the user. Machine learning algorithms are used for the analysis, which assesses the user's lifestyle and stress levels. Specifically, the analysis utilizes a database with a large amount of historical data to generate customized suggestions based on each user's characteristics.

[0580] The device presents the user with an optimal sleep schedule received from the server. Specifically, the smartphone app provides the schedule along with appropriate relaxation guides and ambient sound options. An intuitive interface is designed to make it easy for users to operate the device and access the presented information.

[0581] Users can review the information displayed on their device and incorporate it into their daily schedule. For example, they can follow suggestions for bedtime and wake-up times, or use breathing exercises to reduce stress. Furthermore, users can input feedback on their sleep quality into the device, and this information is sent to the server.

[0582] The server uses the feedback received from users to learn what is needed for the next analysis. This makes the recommendations provided more tailored to the individual needs of each user.

[0583] With the configuration described above, this invention can provide users with a high-quality sleep experience as a whole system. For example, if a user suffers from insomnia, the server can identify the cause and recommend relaxation activities before bedtime to improve their condition. In this way, this system achieves comprehensive sleep improvement for users.

[0584] The following describes the processing flow.

[0585] Step 1:

[0586] The server acquires physiological and environmental data in real time from the user's various sensors and smart devices. This data is securely stored in the cloud.

[0587] Step 2:

[0588] The server preprocesses and cleans up the acquired data to prepare it for analysis. This step involves removing noise and imputing missing values.

[0589] Step 3:

[0590] The server uses machine learning algorithms to analyze data and determine the user's sleep patterns and stress levels. Based on the analysis results, it generates a customized, optimal sleep schedule for each user.

[0591] Step 4:

[0592] The server sends the optimized schedule it generates to the terminal. The terminal receives this information and prepares to present it visually to the user.

[0593] Step 5:

[0594] The device provides users with sleep schedules and relaxation guides through its user interface. An intuitive and easy-to-use design has been considered.

[0595] Step 6:

[0596] Users can view the schedule presented through their device and incorporate it into their daily lives. Relaxation guides and ambient sound options are available to support sleep.

[0597] Step 7:

[0598] Users input and submit feedback on their sleep quality and schedule via their device. This feedback is then sent to the server.

[0599] Step 8:

[0600] The server receives feedback from users and uses it to improve the next data analysis and schedule generation process. This allows for the continuous provision of personalized advice to individual users.

[0601] (Example 1)

[0602] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0603] In recent years, the quality of people's sleep has been declining due to disrupted sleep patterns and increased stress. This negatively impacts health and daily life, creating a need for a system that can provide sleep schedules tailored to individual users. However, existing systems struggle to adequately consider user characteristics in their suggestions, and personalization through effective feedback is insufficient.

[0604] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0605] In this invention, the server includes a data collection means for acquiring data from the user, an analysis means for analyzing the data using a machine learning algorithm and comparing it with past information to generate an optimal schedule for the user, a provision means for presenting information to the user, including relaxation guides and ambient sounds, based on the schedule, and a learning means for acquiring user feedback and updating the generated AI model to reflect in the next analysis. This makes it possible to propose highly accurate, personalized sleep improvement plans for each individual user.

[0606] "Data collection means" refers to devices and methods for acquiring physiological data, environmental data, and behavioral data from users, and includes various sensors and smart devices.

[0607] "Analysis means" refers to a process or system for analyzing data obtained through collection means using machine learning algorithms, comparing it with past information, and generating an optimal schedule for each individual user.

[0608] "Means of provision" refers to methods and devices for transmitting information such as schedules, relaxation guides, and ambient sounds generated by analysis means to the user, and includes systems equipped with an intuitive interface.

[0609] A "learning method" is a mechanism or process for obtaining user feedback, updating the generated AI model, and reflecting that feedback in the next analysis.

[0610] This invention is a system for improving sleep quality by presenting a personalized and optimized sleep schedule to the user. First, the server works in conjunction with wearable devices worn by the user, smartphone sensors, and environmental sensors to collect physiological data (e.g., heart rate, body temperature), environmental data (e.g., room temperature, noise level), and behavioral data (e.g., distance traveled, activity level). This system uses sensor technology and IoT devices for data collection.

[0611] Next, the server analyzes this data using machine learning algorithms. The analysis uses historical data stored in the server's database to evaluate the user's lifestyle and stress levels. Based on this evaluation, the server generates a personalized sleep schedule for the user. A generative AI model is used for the analysis, making optimal suggestions based on pattern recognition and prediction of the data.

[0612] Next, the device notifies the user of the sleep schedule received from the server. The notification is delivered via a smartphone app and presented to the user in an easy-to-understand format, along with relaxation guides and ambient sound options. The application has an intuitive interface, allowing users to easily access and utilize the information.

[0613] Users can use their devices to view the presented schedule and incorporate it into their daily lives. For example, they can follow suggested bedtimes or utilize breathing exercises to relieve tension. Furthermore, users can input feedback on their sleep quality into their devices and send it to the server. This feedback is used as important data for future analysis.

[0614] Based on this feedback, the server can update its AI model to provide suggestions that are better suited to the user's needs in subsequent uses. An example of a specific prompt might be, "User A wants to know relaxation methods that can help improve their insomnia." In this way, the system aims to provide comprehensive sleep improvement for the user.

[0615] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0616] Step 1:

[0617] The server acquires physiological, environmental, and behavioral data from the user's wearable devices, smartphone sensors, and environmental sensors. Inputs include heart rate, body temperature, room temperature, noise level, distance traveled, and activity level. This data is transmitted to the server in real time and recorded in a database. Specifically, the system uses communication protocols between devices to ensure accurate data transfer.

[0618] Step 2:

[0619] The server analyzes the collected data using machine learning algorithms. In particular, it combines historical and current data to evaluate lifestyle habits and stress levels. The input is the data acquired in step 1, and the output is an evaluation of the user's health status and stress level based on the analysis. In this process, the algorithm recognizes patterns in the data and forms a new predictive model.

[0620] Step 3:

[0621] The server generates an optimal sleep schedule for the user based on the analysis results. The generated schedule includes recommended bedtimes and wake-up times, relaxation activities, and ambient sound options. The input is the evaluation results from step 2, and the output is a personalized sleep schedule. In this process, a generative AI model is utilized to provide suggestions tailored to the user's needs.

[0622] Step 4:

[0623] The device displays the sleep schedule sent from the server to the user. The information is displayed on the device screen in an intuitive and easy-to-understand format. The input is schedule data from the server, and the output is specific schedule information displayed on the user's smartphone. The application features an interface designed to enhance the user experience.

[0624] Step 5:

[0625] Users incorporate the schedule presented on their device into their daily lives. They can prepare to sleep according to the suggested bedtime and try the relaxation guide. The input is information from the device, and the output is the change in the user's behavior itself. As a result, users can experience reduced stress and improved sleep quality in their real lives.

[0626] Step 6:

[0627] Users input feedback about their sleep quality into a device and send it to a server. The input is feedback information based on the user's experience, and the output is feedback data sent to the server. The device provides an interface that allows users to easily input feedback.

[0628] Step 7:

[0629] The server updates the generated AI model based on user feedback and incorporates it into the next analysis. The input is the feedback data from step 6, and the output is the updated AI model. This process ensures that the system can always provide optimized suggestions based on the latest user information.

[0630] (Application Example 1)

[0631] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0632] In users' daily lives, the relationship between sleep quality and economic behavior is rarely considered. As a result, optimal spending management based on sleep patterns is not implemented, sometimes leading to wasteful spending and impulsive purchasing. This invention aims to support personalized spending management and achieve a comfortable lifestyle by integrating the management of a user's physiological state and economic behavior.

[0633] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0634] In this invention, the server includes a collection means for acquiring biometric information and economic behavior information from the user, an analysis means for analyzing the information and generating an optimal activity plan for the user, a provision means for presenting the plan to the user, and a control means for generating support information for managing the user's economic behavior based on the biometric information. This makes it possible to optimize expenditure management based on the user's sleep state.

[0635] "Biometric information" refers to data that indicates a user's health status and physical activity, including heart rate, body temperature, and sleep patterns.

[0636] "Economic behavior information" refers to data about a user's daily spending and purchasing activities, including purchase history and spending trends.

[0637] An "activity plan" is a set of behavioral guidelines created based on the user's biometric and economic behavioral information, suggesting optimal spending and lifestyle habits.

[0638] "Collection means" refers to devices and methods for obtaining necessary information from users, including those using smartphone applications and sensors.

[0639] "Analysis means" refers to processes and devices that perform analysis based on acquired information and generate content to be provided to the user.

[0640] "Means of delivery" refers to methods and devices for communicating the generated activity plan to the user, and includes applications for mobile devices.

[0641] "Control means" refers to methods or devices for adjusting economic behavior based on biometric information and generating recommendations regarding expenditure management.

[0642] To implement this invention, a system is configured in which a server, a terminal (such as a smartphone), and a user work together. The server is connected to a device equipped with the function of collecting biometric information and economic behavioral information. This includes wearable devices and smartphone applications. Specifically, data on sleep patterns and activity levels is acquired from the smartphone's sensors and transmitted to the server. Furthermore, information on the user's purchasing history and spending trends is also collected by the server.

[0643] The server uses the collected information to perform machine learning (e.g., TensorFlow) and generate an optimal activity plan for each individual user. This plan takes into account the user's sleep patterns and daily spending habits to promote planned shopping and reduce unnecessary spending.

[0644] The device is responsible for providing the user with activity plans sent from the server. For example, on days when the user is sleep-deprived, it will notify them of a reminder to refrain from certain expenses. On days when they have had a good night's sleep, it will recommend that they consider a previously planned purchase.

[0645] Users improve their daily lives based on activity plans provided by their devices. For example, on mornings when they've had a good night's sleep, they can proceed with their shopping according to a planned shopping list. Furthermore, user responses are sent to a server, which can be used to improve future suggestions.

[0646] An example of a prompt message generated using an AI model is: "Based on User A's past sleep data and spending history, please show predicted spending trends for the next 24 hours and generate appropriate advice."

[0647] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0648] Step 1:

[0649] The server acquires biometric and economic activity data from the user's smartphone and wearable devices. Inputs include heart rate, sleep duration, and purchase history. The server collects this data and stores it for use in the next step.

[0650] Step 2:

[0651] The server analyzes the acquired data using a machine learning algorithm (e.g., TensorFlow). The input for the analysis is the data collected in the previous step. Based on this, the server performs data calculations to evaluate the correlation between the user's physiological state and spending behavior and generate an optimized activity plan. The output is an activity plan customized for each user.

[0652] Step 3:

[0653] The server sends the generated activity plan to the terminal. The input is the activity plan obtained in the previous step. By transferring this plan to the terminal, the server helps the user act according to the plan. The output is the activity plan delivered to the terminal.

[0654] Step 4:

[0655] The terminal presents the received activity plan to the user. The input to the terminal is the activity plan sent from the server. The terminal visually displays the plan through an intuitive interface that the user can easily understand and provides notifications as needed. The output is the screen display of the activity plan that the user sees.

[0656] Step 5:

[0657] The user inputs feedback on the activity plan provided via the terminal. This user input reflects their satisfaction with and adaptation to the activity plan. This feedback is sent to the server for use in subsequent analyses. The input is the user's feedback, and the output is its transfer to the server.

[0658] Step 6:

[0659] The server receives feedback from the user and incorporates this information into the next analysis. The server's input is the feedback provided by the user. Based on the received feedback, the analysis model is updated to generate an activity plan that better suits the user. The output is the improved analysis model.

[0660] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0661] This invention combines conventional functions of collecting and analyzing user data and presenting optimal schedules with an emotion engine that recognizes user emotions. The aim of this system is to achieve even higher accuracy in personalization and further improve users' health and quality of life.

[0662] The server acquires not only physiological and behavioral data, but also emotional signals such as facial expressions and voice tone, through the user's various sensors and smart devices. This data is processed by an emotion engine to identify the user's emotional state. In this process, for example, stress levels and satisfaction levels can be estimated from the user's voice tone.

[0663] The analysis method uses comprehensive information, including acquired emotional data, to evaluate the user's lifestyle and sleep patterns. This evaluation generates the most effective sleep schedule and stress management methods for the user. For example, if irritability due to sleep deprivation is detected, the system may suggest earlier bedtime and provide relaxation music.

[0664] The device has an interface that presents the user with schedules and advice generated by the server. This interface displays emotionally-based, customized messages designed to boost user motivation. Based on this information, users can review their lifestyle habits or implement relaxation techniques.

[0665] Furthermore, user feedback, including changes in emotions, is sent to the server. The server uses this feedback to further refine its analysis algorithms and improve future schedule suggestions.

[0666] This invention utilizes an emotion engine to take into account the user's subjective health status and needs, which could not be grasped through conventional pattern analysis alone, thereby achieving more precise personalization. As a result, users can manage their sleep and health in a way that is best suited to them.

[0667] The following describes the processing flow.

[0668] Step 1:

[0669] The server acquires physiological data, behavioral data, and emotional signals from the user's smart devices and sensors. Emotional signals include facial expression data and voice tone. This allows for a comprehensive understanding of the user's state.

[0670] Step 2:

[0671] An emotion engine installed within the server analyzes acquired emotional signals to identify the user's emotional state. For example, it can calculate stress levels from the tone of the user's voice.

[0672] Step 3:

[0673] The server integrates emotional data with other physiological data and performs analysis using machine learning algorithms. This comprehensively evaluates the user's sleep patterns and lifestyle habits. As a result, an optimal sleep schedule and suggestions for improvement are generated for each individual user.

[0674] Step 4:

[0675] The server sends the optimal schedule and suggestions it generates to the terminal. The terminal receives this data and prepares to display it in the user interface.

[0676] Step 5:

[0677] The device presents the user with schedules, relaxation guides, and emotion-based messages. The user can then review the information presented and decide how to apply it to their real life.

[0678] Step 6:

[0679] Users submit feedback using their devices. This feedback includes their thoughts after the scheduled execution and any changes in their newly experienced emotions.

[0680] Step 7:

[0681] The server receives user feedback and adjusts the algorithm to utilize it in future analyses. This enables the delivery of more personalized services.

[0682] (Example 2)

[0683] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0684] Conventional user data analysis systems rely on objective data to analyze users' lifestyles and health conditions, making it difficult to accurately reflect users' subjective emotions and stress levels. This resulted in a problem where the schedules and advice provided did not match the user's actual situation, failing to effectively improve their quality of life.

[0685] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0686] In this invention, the server includes a data collection means for acquiring data from the user, an analysis means for analyzing the data to generate an optimal schedule for the user, an emotion analysis means for identifying the user's emotional state from the analyzed data, a provision means for presenting the schedule to the user, a feedback means for acquiring user feedback and reflecting it in the next analysis, and a means for making optimal suggestions according to the user's emotional state using a generation AI model. This enables sophisticated personalization that takes into account the user's subjective health condition and needs.

[0687] "Data collection methods" refer to devices and systems used to acquire physiological data, behavioral data, emotional signals, and other information from users.

[0688] "Analysis methods" refer to techniques and technologies that analyze collected data to generate optimal schedules and advice for users.

[0689] "Emotional analysis methods" refer to methods and technologies for processing emotional signals such as the tone of a user's voice and facial expressions to identify their emotional state.

[0690] "Means of delivery" refers to the interface or method for presenting the generated schedule and advice to the user.

[0691] A "feedback method" is a way of collecting user reactions and opinions and incorporating them into future analyses and suggestions.

[0692] A "generative AI model" is an artificial intelligence technology that automatically generates optimal advice and suggestions based on the user's emotional state and feedback.

[0693] The present invention is a system that improves the quality of life of users by effectively collecting and analyzing user data. This system includes a "collection means" for acquiring data from users, an "analysis means" for performing analysis, an "emotion analysis means" for identifying the user's emotional state, a "provision means" for presenting the results to the user, a "feedback means" for acquiring feedback from the user and incorporating it into the next time, and a means for making suggestions that correspond to the user's emotions using a generative AI model.

[0694] The server collects data such as heart rate, activity levels, sleep data, facial expressions, and voice tone from the user's smart device. Smartwatches and smartphones play a crucial role in this data collection, providing detailed physiological data through their sensors.

[0695] This data is used to determine the user's emotional state through emotion analysis. The emotion engine utilizes speech recognition and natural language processing to analyze the user's voice tone and word choice, identifying levels of stress and satisfaction.

[0696] Next, the analysis system analyzes all the acquired data and evaluates the user's lifestyle. Based on this evaluation, the server generates a personalized schedule and advice. Here, a generative AI model is used to provide suggestions adapted to the user's stress level and emotions. For example, if the user is experiencing high stress, suggestions such as reviewing bedtime or meditating may be made.

[0697] The device is designed to present these suggestions to the user through an intuitive interface. This interface displays customized messages tailored to the user's emotions, encouraging behavioral change. Furthermore, the rich use of visual elements ensures clear and easy-to-understand instructions.

[0698] Users send feedback on the advice they receive via their device. The server analyzes this feedback to further improve future suggestions and provide a more optimal schedule for the user. This feedback loop ensures that the system is always tailored to the user's needs.

[0699] A concrete example of a prompt message might be, "Consider the user's recent sleep patterns and stress levels to generate a lifestyle suggestion that is best suited to them." This further facilitates the system's provision of personalized suggestions based on individual circumstances.

[0700] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0701] Step 1:

[0702] The server collects physiological data and emotional signals from the user's smart device. This input includes heart rate, steps taken, sleep duration, voice tone, and facial expression data obtained from smartwatches and smartphones. This data is sent to the server in raw form. The server receives this data and converts it into an appropriate format for analysis.

[0703] Step 2:

[0704] The server performs sentiment analysis using the collected data. Inputs include voice tone and facial expression data. The sentiment analysis tool uses an AI model to identify the user's emotional state, such as stress or happiness, from this data. The analysis results are output, generating information that indicates the user's emotional pattern.

[0705] Step 3:

[0706] The server integrates emotion analysis results and physiological data to evaluate the user's lifestyle. Inputs include emotional state, heart rate, steps taken, and sleep duration. This data is evaluated through analysis tools, and insights into the user's activity patterns, sleep quality, and stress levels are output.

[0707] Step 4:

[0708] The server generates optimal schedules and advice based on the analysis results. The user's emotional state and lifestyle data are used as input. The generating AI model uses this data to output customized suggestions tailored to the user, such as recommendations for going to bed early or selections of relaxation music.

[0709] Step 5:

[0710] The terminal presents the user with schedules and advice received from the server. The input consists of customized messages and suggestions, clearly displayed to the user through the terminal's user interface. As a result, the user receives visually and emotionally appropriate suggestions.

[0711] Step 6:

[0712] Users submit feedback on the advice they receive via their device. The input consists of the user's thoughts and reactions to the suggestions, which are sent to the server as feedback. The server analyzes this information and uses it to improve future suggestions. The output is the feedback data, which is then incorporated into the next analysis algorithm.

[0713] (Application Example 2)

[0714] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0715] Traditional meal suggestion systems often failed to consider user emotions, instead offering manual suggestions based solely on general health information and past data. This resulted in a lack of appropriate meal suggestions adapted to the user's real-time emotional state, leading to shortcomings in user satisfaction and improved health.

[0716] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0717] In this invention, the server includes a collection means for acquiring the user's physiological data, including emotional signals; an analysis means for analyzing the data to identify the user's emotional state and generate optimal meal options based on that emotional state; and a provision means for presenting the meal options to the user. This enables more personalized meal suggestions tailored to the user's real-time emotional state.

[0718] "Emotional signals" are data that indicates a user's emotional state, extracted from physiological data including the user's facial expressions and tone of voice.

[0719] "Physiological data" refers to information that represents the user's physical state, including heart rate, skin conductance, body temperature, facial expression, and voice tone.

[0720] "Data collection methods" refer to sensors and devices used to acquire physiological data and emotional signals from users.

[0721] "Analysis means" refers to an algorithm or software that evaluates the user's emotional state and health status based on acquired physiological data and emotional signals, and generates optimal dietary options.

[0722] "Means of delivery" refers to interfaces or devices used to display or present generated meal options or suggestions to the user.

[0723] "Feedback" refers to information including responses, opinions, and impressions that users provide to the system through various means.

[0724] A "generative AI model" is an artificial intelligence model that automatically generates meal suggestions based on user data.

[0725] A "prompt message" is text that presents specific suggestions to the user based on the suggestions obtained from the generative AI model.

[0726] The system that realizes this invention consists of three main components: a server, a terminal, and a user.

[0727] The server utilizes various sensors and smart devices to acquire physiological data, including the user's emotional signals. This data includes heart rate, facial expressions, and voice tone, and emotion recognition software is used to identify the user's emotional state. A specific example of this software is "emotion_recognition." As an analysis method, the server uses a generative AI model based on this data to generate meal options appropriate to the user's current emotions.

[0728] The device (e.g., a smartphone) has an interface for presenting server-generated meal options to the user. Through this method, the user can receive personalized meal suggestions based on their emotional state. This information is displayed in a visually easy-to-understand format.

[0729] Users send feedback to the server regarding meal suggestions received through the service. This feedback is incorporated into the system's analysis algorithm, helping to refine future suggestions. An example of a specific prompt is, "Please suggest relaxing foods if the user is feeling stressed."

[0730] In this way, emotion-based meal suggestions can contribute to reducing user stress and improving their health, thereby enhancing the user's quality of life.

[0731] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0732] Step 1:

[0733] The server collects physiological data from the user's smart devices and sensors. This collected data includes information such as heart rate, facial expressions, and voice tone. The server inputs this data into the "emotion_recognition" software to analyze the user's emotional signals. As a result of the analysis, the server identifies the user's emotional state and obtains a quantified emotion score associated with that state.

[0734] Step 2:

[0735] The server inputs the emotion score obtained in Step 1 into the generating AI model. The generating AI model calculates the optimal meal plan based on the user's emotional state. This plan also takes into account the user's current health information and past eating history. The model outputs a list of meal options that are best suited to the emotional state and health data.

[0736] Step 3:

[0737] The server sends the generated meal plan to the device. The device visually presents the meal options to the user through its user interface. Specifically, it displays the options on the smartphone screen and provides detailed explanations of ingredients and dishes that have a relaxing effect.

[0738] Step 4:

[0739] Users provide feedback on the presented meal options via their device. For example, this feedback may include information such as whether the selected dish suited their taste and whether they felt it was effective. User feedback is collected via the device and sent to a server.

[0740] Step 5:

[0741] The server incorporates the user feedback it receives into its analysis algorithm, using it to refine future suggestions. Based on the feedback, the analysis results are used to adjust the prompt text of the generated AI model, improving the next meal suggestion to better fit the user's emotional state.

[0742] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0743] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0744] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0745] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0746] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0747] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0748] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0749] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0750] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0751] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0752] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0753] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0754] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0755] 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.

[0756] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0757] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0758] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0759] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0760] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0761] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0762] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0763] The following is further disclosed regarding the embodiments described above.

[0764] (Claim 1)

[0765] A means of collecting data from users,

[0766] An analysis means that analyzes the aforementioned data to generate an optimal schedule for the user,

[0767] A system including means for presenting the aforementioned schedule to the user.

[0768] (Claim 2)

[0769] The system according to claim 1, comprising means for evaluating the user's stress level based on the analysis results.

[0770] (Claim 3)

[0771] The system according to claim 1, further comprising means for obtaining user feedback and reflecting said feedback in the next analysis.

[0772] "Example 1"

[0773] (Claim 1)

[0774] A means of collecting data from users,

[0775] An analysis means that analyzes the aforementioned data using a machine learning algorithm and generates an optimal schedule for the user by comparing it with past information,

[0776] A means of providing information to the user, including relaxation guides and ambient sounds, based on the aforementioned schedule,

[0777] A learning method that obtains user feedback, updates the generated AI model, and reflects it in the next analysis,

[0778] A system that includes this.

[0779] (Claim 2)

[0780] The system according to claim 1, comprising means for evaluating the user's lifestyle and stress level based on the analysis results.

[0781] (Claim 3)

[0782] The system according to claim 1, comprising an intuitive interface for assisting a user in adjusting their life based on the information presented by the aforementioned means.

[0783] "Application Example 1"

[0784] (Claim 1)

[0785] A means for collecting biometric information and economic behavioral information from users,

[0786] An analysis means that analyzes the aforementioned information to generate an optimal activity plan for the user,

[0787] A means for presenting the aforementioned plan to the user,

[0788] A system including control means for generating support information to manage the user's economic behavior based on the aforementioned biometric information.

[0789] (Claim 2)

[0790] The system according to claim 1, comprising means for evaluating the user's psychological burden based on the analysis results and supporting their spending behavior.

[0791] (Claim 3)

[0792] The system according to claim 1, comprising means for acquiring response information from a user, reflecting the response information in the next analysis, and optimizing the support information.

[0793] "Example 2 of combining an emotion engine"

[0794] (Claim 1)

[0795] A means of collecting data from users,

[0796] An analysis means that analyzes the aforementioned data to generate an optimal schedule for the user,

[0797] An emotion analysis method that identifies the user's emotional state from the analyzed data,

[0798] A means for presenting the aforementioned schedule to the user,

[0799] A feedback mechanism to obtain user feedback and incorporate it into the next analysis,

[0800] A method for providing optimal suggestions based on the user's emotional state using a generative AI model,

[0801] A system that includes this.

[0802] (Claim 2)

[0803] The system according to claim 1, comprising means for evaluating the user's stress level based on the analysis results and proposing measures to improve their lifestyle.

[0804] (Claim 3)

[0805] The system according to claim 1, comprising means for generating customized messages based on the user's emotional state and supporting the user's life guidance and motivation.

[0806] "Application example 2 when combining with an emotional engine"

[0807] (Claim 1)

[0808] A means of collecting user physiological data, including emotional signals,

[0809] An analytical means for analyzing the aforementioned data to identify the user's emotional state and generating optimal meal options based on that emotional state,

[0810] A means for presenting the aforementioned meal options to the user,

[0811] A means for obtaining user feedback and reflecting said feedback in the analysis algorithm,

[0812] A system that includes this.

[0813] (Claim 2)

[0814] The system according to claim 1, comprising means for evaluating the user's stress level based on the analysis results and providing a healthy meal plan based on the evaluation.

[0815] (Claim 3)

[0816] The system according to claim 1, comprising means for converting suggestions obtained from a generative AI model into prompt sentences and improving meal suggestions to the user. [Explanation of symbols]

[0817] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting data from users, An analysis means that analyzes the aforementioned data to generate an optimal schedule for the user, A system including means for presenting the aforementioned schedule to the user.

2. The system according to claim 1, further comprising means for evaluating the user's stress level based on the analysis results.

3. The system according to claim 1, further comprising means for obtaining user feedback and reflecting said feedback in the next analysis.

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