system

Abstract

We provide the system. [Solution] A sensor device for collecting data on the user's daily activities, An artificial intelligence engine analyzes the data collected by the aforementioned sensor device to identify user behavior patterns, A visualization device for providing customized advice to the user based on the aforementioned behavioral patterns, A system that includes this.

Description

Technical Field

[0004] , , , ,

[0005] , , , ,

[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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, in order for an individual to lead a rich and healthy life, appropriate advice based on daily actions and health conditions is important. However, for many people, it is difficult to obtain individualized information on a daily basis, and it is not easy to accurately grasp their own behavior patterns and health conditions and improve them appropriately. Therefore, there is a need for a system that provides optimized advice for individuals in real time while protecting privacy.

Means for Solving the Problems

[0005] This invention provides a sensor device for collecting data on a user's daily activities, and identifies the user's behavioral patterns by analyzing the obtained data with an artificial intelligence engine. Furthermore, it provides a system that offers customized advice to the user through a visualization device and improves the analysis process by acquiring feedback information from the user. This system makes it possible to provide personalized advice and improve the user's quality of life.

[0006] A "sensor device" refers to a device used to collect data on a user's daily activities, and has the function of acquiring location information and health-related information.

[0007] An "artificial intelligence engine" refers to a system that analyzes collected data to identify user behavior patterns and generates advice based on that information.

[0008] A "visualization device" refers to a device used to provide advice to users, and has the function of visually presenting information using VR, AR, and MR technologies.

[0009] "Behavioral patterns" refer to the tendencies and regularities in user behavior identified based on user activity data, and can serve as a basis for predictions and advice.

[0010] "Health analysis tools" refer to functions that analyze user health data and provide appropriate advice regarding their health status.

[0011] A "feedback processing system" refers to a system that acquires feedback information from users, improves the analysis process, and generates more appropriate advice. [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 a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [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, a processor with a reference number (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, a RAM (Random Access Memory) with a reference number is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, a storage with a reference number 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, and the like.

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

[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] This invention is a system designed to enrich and improve the user's daily life, combining sensor devices, an artificial intelligence engine, and a visualization device to provide personalized life advice. It primarily functions as a server, a terminal (the user's device), and the user themselves.

[0034] First, when a user wears a smartphone or wearable device, these devices collect location information and health data using sensor equipment. For example, if the user is walking, the device uses an accelerometer to record step count data.

[0035] Next, the collected data is sent from the device to the server. The server uses encrypted communication to protect privacy, ensuring that the data can be handled securely. In this process, the server receives this data, first stores it in a database, and then analyzes it using an artificial intelligence engine.

[0036] The artificial intelligence engine analyzes data to identify user behavior patterns and health trends, and then generates optimal advice based on that. To achieve this, it learns user activity patterns from past data and provides specific advice for improving health. For example, if the server determines that a user has been inactive recently, it will create recommendations regarding the frequency and type of exercise.

[0037] The generated advice is sent from the server to the terminal. The terminal then presents this advice to the user at the appropriate time through visualization devices such as VR, AR, and MR. This allows the user to visually confirm specific means to modify their behavior. For example, the terminal can display changes in heart rate during jogging in real time and instruct the user on the optimal pace.

[0038] Furthermore, the system includes a feedback processing mechanism that provides a way for users to input their practical results and satisfaction levels via a terminal. The server receives this feedback to improve the accuracy of the artificial intelligence engine's analysis and make improvements to provide more appropriate advice.

[0039] In this way, the system regularly provides optimal advice tailored to the individual needs of each user, aiming to improve their quality of life.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The device collects sensor data from the user's wearable device or smartphone. This includes obtaining location information using GPS and collecting data from heart rate sensors.

[0043] Step 2:

[0044] The device collects data, bundles it into data packets at regular intervals, encrypts them, and sends them to the server. SSL / TLS and other methods are used for communication to ensure data privacy.

[0045] Step 3:

[0046] The server stores the received data in a database, and then passes the data to an artificial intelligence engine to begin analysis. The analysis uses machine learning models to identify user behavior patterns and health status.

[0047] Step 4:

[0048] The server generates personalized life advice for the user based on the analysis results. The generated advice is tailored to past data and the user's current situation.

[0049] Step 5:

[0050] The server generates advice and sends it to the terminal. The advice is received by the terminal and presented to the user through a visualization device.

[0051] Step 6:

[0052] The device displays advice to the user in real time through a visualization device. For example, if the user is jogging, the recommended pace is visually displayed using an AR device.

[0053] Step 7:

[0054] The user enters feedback on the device based on the results of implementing the advice. This feedback information is sent to the server for improvement.

[0055] Step 8:

[0056] The server analyzes user feedback and tunes the artificial intelligence engine based on survey results and health data. This process improves the accuracy of future advice.

[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 modern society, it is important to provide specific advice based on each user's lifestyle and health condition. However, existing systems have struggled to fully utilize the collected data and provide accurate instructions tailored to the individual needs of users.

[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 information gathering means for acquiring information about the user's daily activities, machine learning means for analyzing the information acquired by the information gathering means and identifying the user's behavioral patterns, and presentation means for providing personalized instructions to the user based on the behavioral patterns. This makes it possible to visually present appropriate instructions to individual users and promote improvements in the users' lifestyles.

[0062] "Information gathering means" refers to functions and technologies used to obtain information about a user's daily activities.

[0063] "Machine learning methods" refer to algorithms and technologies used to analyze acquired information and identify user behavior patterns.

[0064] "Presentation means" refers to methods or devices for providing personalized instructions to users based on analysis.

[0065] "Visualization means" refers to technologies and devices used to visually present information or instructions to users.

[0066] The embodiment of this invention is a system for enriching and improving the user's daily life. This system includes information gathering means, machine learning means, presentation means, and visualization means. It primarily functions as a server, terminal, and user.

[0067] The device uses sensors built into smartphones and wearable devices to collect information about the user's daily activities. This information includes location data and health data (e.g., steps taken, heart rate). For example, the device collects the number of steps taken each day and sends it to a server at regular intervals.

[0068] The server receives information sent from the terminal and processes it while protecting privacy through encrypted communication. The server stores the information in a database and analyzes the user's behavior patterns using machine learning. Based on the analysis results, the server "learns" the individual user's tendencies and generates appropriate instructions through an AI model. An example of a prompt message is, "The user has been determined to be sedentary recently, so please generate recommended types and frequencies of exercise."

[0069] The generated instructions are sent from the server to the terminal. The terminal receives these instructions and presents them to the user through visualization. Based on the visualized information, the user can recognize areas for improvement in their daily life and take concrete actions. For example, the terminal can use AR functionality to display exercise routes in real time, showing the user the optimal exercise course.

[0070] This system allows users to receive accurate advice regarding their health status and receive support to improve their quality of life.

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

[0072] Step 1:

[0073] The device uses the user's smartphone or wearable device to collect information related to their daily activities. It receives location information and health data (e.g., steps, heart rate) detected by sensors as input. This data is acquired in real time and temporarily stored within the device. Specifically, the device uses an accelerometer and heart rate sensor to record data points every second. The collected results are output as numerical data indicating the user's activity level.

[0074] Step 2:

[0075] The terminal sends the collected data to the server. The input is the series of data obtained in step 1. This data is encrypted and sent to the server while ensuring privacy. The output is an encrypted data packet, ensuring stable data transmission. Specifically, the terminal organizes the data at regular intervals and performs a process of uplinking it to the server.

[0076] Step 3:

[0077] The server receives data sent from the terminal and stores it in a database. It receives encrypted data packets as input. The server decrypts these packets, organizes them, and stores them in the database. The output is the decrypted raw data. Specifically, the server converts the received data into a format that can be analyzed, preparing it for analysis.

[0078] Step 4:

[0079] The server analyzes stored data using machine learning techniques. The input is user behavior data stored in a database. This data is processed to analyze user behavior patterns and health trends. The output is user behavior pattern information based on the analysis results. Specifically, the server executes machine learning algorithms to perform pattern recognition and predictive analysis.

[0080] Step 5:

[0081] The server uses a generated AI model based on the analysis results to create personalized instructions for the user. The input is the behavioral pattern information obtained in step 4. Based on this data, the program generates prompt statements and creates advice. The output is specific action instructions to be provided to the user. Specifically, the server sets prompts and documents the generated advice.

[0082] Step 6:

[0083] The server sends the generated instructions to the terminal. The input is the advice text created in step 5. This information is converted to the appropriate format and transmitted to the terminal. The output is the instruction information that arrives at the terminal. Specifically, the server sets a transmission schedule and sends the data via the appropriate communication method.

[0084] Step 7:

[0085] The terminal presents the received instructions to the user through visualization means. It receives instruction information from the server as input. The terminal performs graphics processing to display this information on the screen. The output is visual information presented in a way that is easy for the user to understand. Specifically, the terminal updates an interactive UI through its display, prompting user feedback.

[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 modern society, there is a need for approaches to health management and lifestyle improvements tailored to individual users. However, conventional health management systems are limited to advice based on general data and lack the flexibility to provide personalized advice in real time.

[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 measuring device for collecting the user's daily activity data, an artificial intelligence processing unit for analyzing the data collected by the measuring device and identifying the user's behavioral characteristics, a display unit for providing the user with customized guidance based on the behavioral characteristics, and an audio output means for providing real-time feedback on the user's activities. This makes it possible to achieve effective and personalized health management and lifestyle improvement for individual users.

[0091] "Measurement device" refers to sensors and devices used to collect data on a user's daily activities.

[0092] The "artificial intelligence processing unit" is a component that analyzes collected data and identifies user behavioral characteristics.

[0093] The "display unit" is a device that visually provides users with customized guidelines based on the analysis results.

[0094] "Voice output means" refers to a device or system for providing real-time voice feedback to a user's activities.

[0095] In implementing this invention, the server, terminal, and user play key roles. The server receives location information and health data from measuring devices such as smartphones and wearable devices to collect data on the user's daily activities. This data is transmitted encrypted, and the server is equipped with an artificial intelligence processing unit to receive and analyze the data. The artificial intelligence processing unit uses software such as Python, TENSORFLOW®, and Keras to analyze the user's behavioral characteristics and generate personalized health advice.

[0096] The device features a display unit that provides users with customized guidance. This display unit has the function of visualizing appropriate guidance based on analysis results through AR (augmented reality) and the display. The device also incorporates a voice output mechanism, enabling real-time feedback to the user. Utilizing voice assistant technology, it can provide exercise guidance and health-related suggestions via voice.

[0097] As a concrete example, when a user is jogging, the device measures and displays their heart rate, and based on that data, the server analyzes the effectiveness of the exercise and provides voice guidance on the optimal exercise pace. By using a generative AI model, personalized suggestions are generated in real time.

[0098] An example of a prompt might be, "Analyze the user's health data and suggest the optimal type and duration of exercise." This prompt allows the AI ​​to generate an optimal exercise plan based on the user's past data and real-time information.

[0099] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0100] Step 1:

[0101] The device acquires the user's daily activity data through smartphones and wearable devices. This data includes heart rate, steps taken, and location information. The acquired data is initially processed within the device and formatted so that it can be securely transmitted to the server.

[0102] Step 2:

[0103] The server receives data sent from the terminal. The received data is encrypted, and is first decrypted before being stored in the database. Here, the accuracy of the data is verified and fraudulent data is filtered out.

[0104] Step 3:

[0105] The server analyzes user activity data stored in the database using an artificial intelligence processing unit. Using Python and TensorFlow, it applies user behavioral characteristics to a model based on the data to extract health trends and signs of lack of exercise. As a result of the analysis, optimal health advice is generated that is recommended to the user.

[0106] Step 4:

[0107] Based on the generated advice, the server sends data to the terminal. The terminal visualizes this data on its display and provides real-time voice feedback using an audio output device. For example, it can provide voice guidance to a user jogging, indicating an appropriate exercise intensity based on their pace and heart rate.

[0108] Step 5:

[0109] Users perform actions according to the advice they receive and input the results as feedback information into their device. This information is then sent back to the server and used to improve the analysis process in the artificial intelligence processing unit. This feedback loop allows the AI ​​model to improve its accuracy for each user.

[0110] 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.

[0111] This invention combines an emotion engine with a system that provides customized life advice based on the user's daily activities. This system consists of a combination of a sensor device, an artificial intelligence engine, a visualization device, and an emotion recognition device.

[0112] First, the device acquires daily activity data from sensor devices via the user's wearable device or smartphone. This includes not only location information and heart rate data, but also emotional state data acquired by an emotion recognition device using a microphone and camera. For example, emotional data is collected from the voice tone when the user is on the phone or from facial expressions captured by a facial recognition camera.

[0113] Next, the device sends sensor data and emotion data to the server. The server stores this data in a database and analyzes it using an artificial intelligence engine and an emotion engine. The artificial intelligence engine identifies the user's behavioral patterns, and the emotion engine identifies the user's emotional state through voice analysis and facial expression analysis. This allows the server to comprehensively understand the user's physical and emotional state.

[0114] Based on these analysis results, the server generates personalized life advice for the user. For example, if the server determines that the user has recently been experiencing stress, it can generate advice recommending meditation or relaxation exercises. The generated advice is sent from the server to the terminal and presented to the user via a visualization device. The terminal uses VR or AR technology to visually display the advice effectively, thereby increasing user acceptance.

[0115] After a user implements the advice, they input feedback on the results and satisfaction level into their device. The device sends this feedback back to the server, which then uses the feedback to improve the analysis process of the artificial intelligence engine. This process allows the system to continuously improve the accuracy of its advice and provide users with optimized support.

[0116] This embodiment enables comprehensive health management that takes into account an individual's emotional state, providing support to improve the quality of daily life.

[0117] The following describes the processing flow.

[0118] Step 1:

[0119] The device collects the user's daily activity data using sensor devices. This includes location information, heart rate, voice tone, and facial expression data from facial recognition.

[0120] Step 2:

[0121] The device collects data, which is then compiled and sent to a server using a secure communication method. This data includes not only activity information but also information indicating emotional state.

[0122] Step 3:

[0123] The server stores the received data in a database, and an artificial intelligence engine analyzes the user's behavior patterns. Simultaneously, an emotion engine uses voice and facial expression data to identify the user's emotional state.

[0124] Step 4:

[0125] The server analyzes behavioral patterns and emotional states to generate personalized life advice for the user. For example, if stress levels are high, it will create relaxation advice.

[0126] Step 5:

[0127] The server sends the generated advice to the terminal. The terminal then presents the received advice to the user through a visualization device. The presentation method utilizes VR and AR, and is designed to allow the user to understand it intuitively.

[0128] Step 6:

[0129] Users implement the advice and input feedback on its effectiveness and their own reactions into the device. This feedback includes information about the results of implementing the advice and their satisfaction level.

[0130] Step 7:

[0131] The device collects user feedback and sends it to the server. The server analyzes this feedback and uses it to improve the accuracy of the AI ​​engine and emotion engine's analysis.

[0132] Step 8:

[0133] Based on the feedback analysis, the server adjusts the system to improve the accuracy of future advice and provide more appropriate support to the user.

[0134] (Example 2)

[0135] 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".

[0136] In modern society, managing personal health and optimizing lifestyles are crucial issues, and maintaining a balance between mind and body is particularly important. Many conventional systems provide general feedback based on the user's daily activities, but they do not offer customized advice that takes into account individual emotional states. Therefore, there is a need for technology that provides more specific and effective advice that takes individual emotional states into account, in order to improve users' health and quality of life.

[0137] 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.

[0138] In this invention, the server includes data collection means for collecting the user's daily activity data and emotional state, analysis means for analyzing the data obtained from the data collection means and identifying the user's behavioral patterns and emotional state, and generation means for generating customized life advice for the user based on the analysis results from the analysis means. This enables the user to receive specific and effective life advice tailored to their individual emotional state.

[0139] "Data collection means" refers to a device or method used to collect data on a user's daily activities and emotional state.

[0140] "Analysis means" refers to a process or technology for identifying user behavior patterns and emotional states based on collected data.

[0141] "Generation means" refers to a function or method for creating customized life advice for the user based on the analysis results.

[0142] "Presentation methods" refer to means of conveying generated life advice to the user visually or audibly, and may include the use of VR or AR technologies.

[0143] "Feedback improvement methods" are means of improving the analysis process based on feedback information obtained from users.

[0144] The system of this invention aims to improve the daily lives of users using multiple hardware and software components. Specifically, it comprehensively understands each user's emotional state and daily activities and provides advice based on that understanding.

[0145] First, the device collects data through the user's wearable device or smartphone. The sensors used include GPS for obtaining location information, biosensors for measuring heart rate, microphones for obtaining voice data, and cameras for collecting facial expression data. The device uses an emotion recognition device to determine the user's emotional state from their voice tone and facial expressions.

[0146] Next, the device securely transmits this sensor data and emotional data to the server. The server stores the received data in a database and analyzes it using an artificial intelligence engine and an emotional engine. This artificial intelligence engine implements machine learning algorithms to identify the user's behavioral patterns and emotional state.

[0147] Furthermore, the server generates life advice based on the analysis results. Leveraging a generative AI model, it can provide recommendations tailored to individual user situations. For example, if the server determines that a user is experiencing stress, it can suggest "meditating or doing relaxation exercises at the end of the day." This advice is then transferred back to the terminal and presented to the user through a visualization device. The terminal can use VR or AR technology to visually present the advice, improving user acceptance.

[0148] A concrete example would be someone who spends long hours working on their computer during the week and is feeling unwell. Based on this information, the server can generate lifestyle advice such as "stretch for 5 minutes every hour" and present it to the user via their terminal.

[0149] An example of a prompt message might be a scenario like, "The user has indicated fatigue in recent analysis. Please suggest ways to refresh them."

[0150] In this way, the system collects and analyzes user data, provides personalized advice optimized for each user, and supports the realization of a healthy lifestyle.

[0151] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0152] Step 1:

[0153] The device collects data through the user's wearable device or smartphone. Inputs include GPS location information, vital data from a heart rate sensor, voice tone from a microphone, and facial expression data from a camera. By integrating this data, the system can understand the user's daily activities and emotional state.

[0154] Step 2:

[0155] The terminal formats the collected data and sends it to the server using a secure communication protocol. The inputs here are sensor and emotion data, and the output is the data sent to the server. Data formatting involves converting to an appropriate format and filtering out unnecessary data.

[0156] Step 3:

[0157] The server stores the received data in a database and begins analysis. The input is the transmitted sensor and emotion data, and the output is the analysis results. Once the data has been stored in the database, the artificial intelligence engine identifies behavioral patterns, and the emotion engine performs voice and facial expression analysis. This allows for a comprehensive understanding of the user's state.

[0158] Step 4:

[0159] The server uses a generated AI model based on the analysis results to produce life advice optimized for the user. The input is behavioral and emotional information obtained from the analysis, and the output is specific advice. For example, if the user is determined to be in a stressed state, the server will generate advice recommending "meditating for 10 minutes every night."

[0160] Step 5:

[0161] The advice generated from the server is sent to the terminal and presented to the user via a visualization device. The input is the generated life advice, and the output is a visual or auditory presentation to the user. The terminal uses VR or AR technology to effectively display the advice and enhance user acceptance.

[0162] Step 6:

[0163] Users follow the advice provided and input feedback on the results and their satisfaction level into the device. This feedback becomes input for the system and is treated as new data.

[0164] Step 7:

[0165] The terminal sends the collected feedback information to the server, which uses it to improve the analysis process of the artificial intelligence engine. The input is user feedback, and the output is an improved analysis algorithm and increased accuracy for the next time.

[0166] (Application Example 2)

[0167] 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".

[0168] In recent years, there has been a growing need for systems that monitor an individual's emotional and health status and provide customized advice based on that data. However, conventional systems have had the problem of not being able to provide advice that adequately takes emotional states into account. Furthermore, efficiently incorporating user feedback to improve the accuracy of the advice has been a difficult challenge.

[0169] 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.

[0170] In this invention, the server includes sensing means for collecting the user's daily activity data and emotional state data, calculation means for analyzing the collected data and identifying the user's behavioral patterns and emotional state, and presentation means for providing the user with customized life advice based on the identified behavioral patterns and emotional state. This makes it possible to provide highly accurate and personalized advice that takes into account the user's emotional state.

[0171] "Sensing means" refers to devices or methods used to collect data on the user's daily activities and emotional state.

[0172] "Computation means" refers to a process or system for analyzing data collected by sensing means to identify the user's behavioral patterns and emotional state.

[0173] "Presentation means" refers to a device or method for providing a user with customized life advice, either visually or audibly, based on identified behavioral patterns and emotional states.

[0174] "Improvement measures" refer to mechanisms and systems for acquiring user feedback information and improving the analysis process of the computational means based on that feedback.

[0175] An "emotional analysis method" is a method for analyzing data related to a user's emotional state and generating customized health advice based on the results.

[0176] "Augmented reality means" are technologies and devices that visually present advice to a user and improve the acceptability of that advice based on their response.

[0177] The system for implementing this invention has a configuration comprising a "sensing means" for collecting the user's daily activity data and emotional state data, a "calculation means" for analyzing this data, a "presentation means" for providing the user with advice based on the analysis results, and an "improvement means" for improving the system based on feedback from the user.

[0178] The server first collects various data from the user's wearable device or mobile device using "sensing means." In this process, it utilizes hardware such as microphones, cameras, location sensors, and heart rate sensors to acquire activity data and emotional state data provided by these sensors.

[0179] The collected data is analyzed using "computational tools." This analysis utilizes software such as TensorFlow and OpenCV to identify user behavior patterns and emotional states using various data. The TensorFlow model integrates diverse data to understand user activities and emotions, while OpenCV performs facial recognition based on camera images.

[0180] Based on the analysis results obtained, the server provides the user with customized life advice through a "presentation means." The presentation means utilizes an AR display that leverages augmented reality technology to present information to the user in a visually appealing way.

[0181] Furthermore, users can provide feedback on the effectiveness and satisfaction level of the advice they received. This information is used as a "means of improvement," and the quality of subsequent advice is improved based on the feedback provided.

[0182] For example, if the server detects that a user is experiencing stress, it will offer advice on relaxation techniques. Exercise suggestions will appear on the AR display, allowing the user to incorporate these suggestions into their daily life. In this way, advice is provided according to the user's emotional state, contributing to an improved quality of life.

[0183] As an example of a prompt, you can use the sentence, "If the user is feeling stressed, what relaxation methods should be suggested?" This facilitates appropriate support using generative AI models.

[0184] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0185] Step 1:

[0186] The device collects daily activity data and emotional state data from the user's wearable device or mobile device through sensing mechanisms. Inputs include location information, heart rate, voice tone, and facial images. This data is temporarily stored for subsequent analysis.

[0187] Step 2:

[0188] The terminal sends the collected data to the server. The server stores the received data in a database for analysis. During this data transfer process, data format conversion and basic filtering are performed.

[0189] Step 3:

[0190] The server analyzes the data stored in the database using computational methods. Specifically, it uses a TensorFlow model to identify behavioral patterns and OpenCV to analyze emotional states from facial images. The previously collected data is used as input data, and the output provides the user's specific behavioral patterns and emotional states.

[0191] Step 4:

[0192] The server generates personalized life advice based on the analysis results. It uses prompts such as "What relaxation methods should be suggested if the user is feeling stressed?" to generate optimal advice from the AI ​​model. The output is in text format.

[0193] Step 5:

[0194] The server sends the generated advice to the terminal. The terminal visually presents the advice using a presentation method. For example, an AR display is used to visually present relaxation techniques and exercise methods to the user.

[0195] Step 6:

[0196] After implementing the provided advice, users input feedback on its effectiveness and satisfaction level into their device. This feedback is sent to the server and used to improve the accuracy of the analysis process through various improvement measures.

[0197] Step 7:

[0198] Based on the feedback, the server implements system improvements to enhance the accuracy of future analysis processes and advice. This results in more precise and effective advice for users.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] [Second Embodiment]

[0203] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0204] 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.

[0205] 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).

[0206] 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.

[0207] 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.

[0208] 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).

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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".

[0215] This invention is a system designed to enrich and improve the user's daily life, combining sensor devices, an artificial intelligence engine, and a visualization device to provide personalized life advice. It primarily functions as a server, a terminal (the user's device), and the user themselves.

[0216] First, when a user wears a smartphone or wearable device, these devices collect location information and health data using sensor equipment. For example, if the user is walking, the device uses an accelerometer to record step count data.

[0217] Next, the collected data is sent from the device to the server. The server uses encrypted communication to protect privacy, ensuring that the data can be handled securely. In this process, the server receives this data, first stores it in a database, and then analyzes it using an artificial intelligence engine.

[0218] The artificial intelligence engine analyzes data to identify user behavior patterns and health trends, and then generates optimal advice based on that. To achieve this, it learns user activity patterns from past data and provides specific advice for improving health. For example, if the server determines that a user has been inactive recently, it will create recommendations regarding the frequency and type of exercise.

[0219] The generated advice is sent from the server to the terminal. The terminal then presents this advice to the user at the appropriate time through visualization devices such as VR, AR, and MR. This allows the user to visually confirm specific means to modify their behavior. For example, the terminal can display changes in heart rate during jogging in real time and instruct the user on the optimal pace.

[0220] Furthermore, the system includes a feedback processing mechanism that provides a way for users to input their practical results and satisfaction levels via a terminal. The server receives this feedback to improve the accuracy of the artificial intelligence engine's analysis and make improvements to provide more appropriate advice.

[0221] In this way, the system regularly provides optimal advice tailored to the individual needs of each user, aiming to improve their quality of life.

[0222] The following describes the processing flow.

[0223] Step 1:

[0224] The device collects sensor data from the user's wearable device or smartphone. This includes obtaining location information using GPS and collecting data from heart rate sensors.

[0225] Step 2:

[0226] The device collects data, bundles it into data packets at regular intervals, encrypts them, and sends them to the server. SSL / TLS and other methods are used for communication to ensure data privacy.

[0227] Step 3:

[0228] The server stores the received data in a database, and then passes the data to an artificial intelligence engine to begin analysis. The analysis uses machine learning models to identify user behavior patterns and health status.

[0229] Step 4:

[0230] The server generates personalized life advice for the user based on the analysis results. The generated advice is tailored to past data and the user's current situation.

[0231] Step 5:

[0232] The server generates advice and sends it to the terminal. The advice is received by the terminal and presented to the user through a visualization device.

[0233] Step 6:

[0234] The device displays advice to the user in real time through a visualization device. For example, if the user is jogging, the recommended pace is visually displayed using an AR device.

[0235] Step 7:

[0236] The user enters feedback on the device based on the results of implementing the advice. This feedback information is sent to the server for improvement.

[0237] Step 8:

[0238] The server analyzes user feedback and tunes the artificial intelligence engine based on survey results and health data. This process improves the accuracy of future advice.

[0239] (Example 1)

[0240] 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."

[0241] In modern society, it is important to provide specific advice based on each user's lifestyle and health condition. However, existing systems have struggled to fully utilize the collected data and provide accurate instructions tailored to the individual needs of users.

[0242] 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.

[0243] In this invention, the server includes information gathering means for acquiring information about the user's daily activities, machine learning means for analyzing the information acquired by the information gathering means and identifying the user's behavioral patterns, and presentation means for providing personalized instructions to the user based on the behavioral patterns. This makes it possible to visually present appropriate instructions to individual users and promote improvements in the users' lifestyles.

[0244] "Information gathering means" refers to functions and technologies used to obtain information about a user's daily activities.

[0245] "Machine learning methods" refer to algorithms and technologies used to analyze acquired information and identify user behavior patterns.

[0246] "Presentation means" refers to methods or devices for providing personalized instructions to users based on analysis.

[0247] "Visualization means" refers to technologies and devices used to visually present information or instructions to users.

[0248] The embodiment of this invention is a system for enriching and improving the user's daily life. This system includes information gathering means, machine learning means, presentation means, and visualization means. It primarily functions as a server, terminal, and user.

[0249] The device uses sensors built into smartphones and wearable devices to collect information about the user's daily activities. This information includes location data and health data (e.g., steps taken, heart rate). For example, the device collects the number of steps taken each day and sends it to a server at regular intervals.

[0250] The server receives information sent from the terminal and processes it while protecting privacy through encrypted communication. The server stores the information in a database and analyzes the user's behavior patterns using machine learning. Based on the analysis results, the server "learns" the individual user's tendencies and generates appropriate instructions through an AI model. An example of a prompt message is, "The user has been determined to be sedentary recently, so please generate recommended types and frequencies of exercise."

[0251] The generated instructions are sent from the server to the terminal. The terminal receives these instructions and presents them to the user through visualization. Based on the visualized information, the user can recognize areas for improvement in their daily life and take concrete actions. For example, the terminal can use AR functionality to display exercise routes in real time, showing the user the optimal exercise course.

[0252] This system allows users to receive accurate advice regarding their health status and receive support to improve their quality of life.

[0253] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0254] Step 1:

[0255] The device uses the user's smartphone or wearable device to collect information related to their daily activities. It receives location information and health data (e.g., steps, heart rate) detected by sensors as input. This data is acquired in real time and temporarily stored within the device. Specifically, the device uses an accelerometer and heart rate sensor to record data points every second. The collected results are output as numerical data indicating the user's activity level.

[0256] Step 2:

[0257] The terminal sends the collected data to the server. The input is the series of data obtained in step 1. This data is encrypted and sent to the server while ensuring privacy. The output is an encrypted data packet, ensuring stable data transmission. Specifically, the terminal organizes the data at regular intervals and performs a process of uplinking it to the server.

[0258] Step 3:

[0259] The server receives data sent from the terminal and stores it in a database. It receives encrypted data packets as input. The server decrypts these packets, organizes them, and stores them in the database. The output is the decrypted raw data. Specifically, the server converts the received data into a format that can be analyzed, preparing it for analysis.

[0260] Step 4:

[0261] The server analyzes stored data using machine learning techniques. The input is user behavior data stored in a database. This data is processed to analyze user behavior patterns and health trends. The output is user behavior pattern information based on the analysis results. Specifically, the server executes machine learning algorithms to perform pattern recognition and predictive analysis.

[0262] Step 5:

[0263] The server uses a generated AI model based on the analysis results to create personalized instructions for the user. The input is the behavioral pattern information obtained in step 4. Based on this data, the program generates prompt statements and creates advice. The output is specific action instructions to be provided to the user. Specifically, the server sets prompts and documents the generated advice.

[0264] Step 6:

[0265] The server sends the generated instructions to the terminal. The input is the advice text created in step 5. This information is converted to the appropriate format and transmitted to the terminal. The output is the instruction information that arrives at the terminal. Specifically, the server sets a transmission schedule and sends the data via the appropriate communication method.

[0266] Step 7:

[0267] The terminal presents the received instructions to the user through visualization means. It receives instruction information from the server as input. The terminal performs graphics processing to display this information on the screen. The output is visual information presented in a way that is easy for the user to understand. Specifically, the terminal updates an interactive UI through its display, prompting user feedback.

[0268] (Application Example 1)

[0269] 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."

[0270] In modern society, there is a need for approaches to health management and lifestyle improvements tailored to individual users. However, conventional health management systems are limited to advice based on general data and lack the flexibility to provide personalized advice in real time.

[0271] 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.

[0272] In this invention, the server includes a measuring device for collecting the user's daily activity data, an artificial intelligence processing unit for analyzing the data collected by the measuring device and identifying the user's behavioral characteristics, a display unit for providing the user with customized guidance based on the behavioral characteristics, and an audio output means for providing real-time feedback on the user's activities. This makes it possible to achieve effective and personalized health management and lifestyle improvement for individual users.

[0273] "Measurement device" refers to sensors and devices used to collect data on a user's daily activities.

[0274] The "artificial intelligence processing unit" is a component that analyzes collected data and identifies user behavioral characteristics.

[0275] The "display unit" is a device that visually provides users with customized guidelines based on the analysis results.

[0276] "Voice output means" refers to a device or system for providing real-time voice feedback to a user's activities.

[0277] In implementing this invention, the server, terminal, and user play key roles. The server receives location information and health data from measuring devices such as smartphones and wearable devices to collect data on the user's daily activities. This data is encrypted and transmitted, and the server is equipped with an artificial intelligence processing unit to receive and analyze the data. The artificial intelligence processing unit uses software such as Python, TensorFlow, and Keras to analyze the user's behavioral characteristics and generate personalized health advice.

[0278] The device features a display unit that provides users with customized guidance. This display unit has the function of visualizing appropriate guidance based on analysis results through AR (augmented reality) and the display. The device also incorporates a voice output mechanism, enabling real-time feedback to the user. Utilizing voice assistant technology, it can provide exercise guidance and health-related suggestions via voice.

[0279] As a concrete example, when a user is jogging, the device measures and displays their heart rate, and based on that data, the server analyzes the effectiveness of the exercise and provides voice guidance on the optimal exercise pace. By using a generative AI model, personalized suggestions are generated in real time.

[0280] An example of a prompt might be, "Analyze the user's health data and suggest the optimal type and duration of exercise." This prompt allows the AI ​​to generate an optimal exercise plan based on the user's past data and real-time information.

[0281] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0282] Step 1:

[0283] The terminal acquires the user's daily activity data through smartphones and wearable devices. This data includes heart rate, number of steps, location information, etc. The acquired data is initially processed within the terminal and formatted into a form that can be securely transmitted to the server.

[0284] Step 2:

[0285] The server receives the data transmitted from the terminal. The received data is encrypted and first decrypted, and then stored in the database. Here, the accuracy of the data is confirmed and filtering of illegal data is performed.

[0286] Step 3:

[0287] The server analyzes the user's activity data stored in the database using an artificial intelligence processing unit. Using Python and TensorFlow, the user's behavior characteristics are applied to the model based on the data, and signs of health trends and lack of exercise are extracted. As an analysis result, optimal health advice for the user is generated.

[0288] Step 4:

[0289] Based on the generated advice, the server transmits data to the terminal. The terminal visualizes this data on the display unit and further provides real-time voice feedback using the voice output means. For example, for a user during jogging, the appropriate exercise intensity for the pace and heart rate is conveyed via voice guidance.

[0290] Step 5:

[0291] The user conducts activities according to the received advice and inputs the results as feedback information into the terminal. This information is transmitted to the server again and used to improve the analysis process in the artificial intelligence processing unit. Through this feedback loop, the AI model can enhance the accuracy for each user.

[0292] 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.

[0293] This invention combines an emotion engine with a system that provides customized life advice based on the user's daily activities. This system consists of a combination of a sensor device, an artificial intelligence engine, a visualization device, and an emotion recognition device.

[0294] First, the device acquires daily activity data from sensor devices via the user's wearable device or smartphone. This includes not only location information and heart rate data, but also emotional state data acquired by an emotion recognition device using a microphone and camera. For example, emotional data is collected from the voice tone when the user is on the phone or from facial expressions captured by a facial recognition camera.

[0295] Next, the device sends sensor data and emotion data to the server. The server stores this data in a database and analyzes it using an artificial intelligence engine and an emotion engine. The artificial intelligence engine identifies the user's behavioral patterns, and the emotion engine identifies the user's emotional state through voice analysis and facial expression analysis. This allows the server to comprehensively understand the user's physical and emotional state.

[0296] Based on these analysis results, the server generates personalized life advice for the user. For example, if the server determines that the user has recently been experiencing stress, it can generate advice recommending meditation or relaxation exercises. The generated advice is sent from the server to the terminal and presented to the user via a visualization device. The terminal uses VR or AR technology to visually display the advice effectively, thereby increasing user acceptance.

[0297] After the user practices the advice, feedback regarding the results and satisfaction is input into the terminal. The terminal sends this back to the server, and the server improves the analysis process of the artificial intelligence engine based on the feedback. Through this process, the system can continuously improve the accuracy of the advice and continue to provide optimized support to the user.

[0298] With this embodiment, the system enables comprehensive health management considering an individual's emotional state and provides support to improve the quality of daily life.

[0299] The following describes the processing flow.

[0300] Step 1:

[0301] The terminal collects the user's daily activity data with a sensor device. This includes location information, heart rate, voice tone, and facial expression data from face recognition.

[0302] Step 2:

[0303] The terminal consolidates the collected data and transmits it to the server by secure communication means. The data includes not only activity information but also information indicating the emotional state.

[0304] Step 3:

[0305] The server stores the received data in the database and analyzes the user's behavior pattern with an artificial intelligence engine. At the same time, the emotion engine identifies the user's emotional state using voice and facial expression data.

[0306] Step 4:

[0307] The server synthesizes the analysis results of the behavior pattern and the emotional state and generates optimal life advice for the user. For example, when stress is increasing, relaxation advice is created.

[0308] Step 5:

[0309] The server sends the generated advice to the terminal. The terminal then presents the received advice to the user through a visualization device. The presentation method utilizes VR and AR, and is designed to allow the user to understand it intuitively.

[0310] Step 6:

[0311] Users implement the advice and input feedback on its effectiveness and their own reactions into the device. This feedback includes information about the results of implementing the advice and their satisfaction level.

[0312] Step 7:

[0313] The device collects user feedback and sends it to the server. The server analyzes this feedback and uses it to improve the accuracy of the AI ​​engine and emotion engine's analysis.

[0314] Step 8:

[0315] Based on the feedback analysis, the server adjusts the system to improve the accuracy of future advice and provide more appropriate support to the user.

[0316] (Example 2)

[0317] 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".

[0318] In modern society, managing personal health and optimizing lifestyles are crucial issues, and maintaining a balance between mind and body is particularly important. Many conventional systems provide general feedback based on the user's daily activities, but they do not offer customized advice that takes into account individual emotional states. Therefore, there is a need for technology that provides more specific and effective advice that takes individual emotional states into account, in order to improve users' health and quality of life.

[0319] 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.

[0320] In this invention, the server includes data collection means for collecting the user's daily activity data and emotional state, analysis means for analyzing the data obtained from the data collection means and identifying the user's behavioral patterns and emotional state, and generation means for generating customized life advice for the user based on the analysis results from the analysis means. This enables the user to receive specific and effective life advice tailored to their individual emotional state.

[0321] "Data collection means" refers to a device or method used to collect data on a user's daily activities and emotional state.

[0322] "Analysis means" refers to a process or technology for identifying user behavior patterns and emotional states based on collected data.

[0323] "Generation means" refers to a function or method for creating customized life advice for the user based on the analysis results.

[0324] "Presentation methods" refer to means of conveying generated life advice to the user visually or audibly, and may include the use of VR or AR technologies.

[0325] "Feedback improvement methods" are means of improving the analysis process based on feedback information obtained from users.

[0326] The system of this invention aims to improve the daily lives of users using multiple hardware and software components. Specifically, it comprehensively understands each user's emotional state and daily activities and provides advice based on that understanding.

[0327] First, the device collects data through the user's wearable device or smartphone. The sensors used include GPS for obtaining location information, biosensors for measuring heart rate, microphones for obtaining voice data, and cameras for collecting facial expression data. The device uses an emotion recognition device to determine the user's emotional state from their voice tone and facial expressions.

[0328] Next, the device securely transmits this sensor data and emotional data to the server. The server stores the received data in a database and analyzes it using an artificial intelligence engine and an emotional engine. This artificial intelligence engine implements machine learning algorithms to identify the user's behavioral patterns and emotional state.

[0329] Furthermore, the server generates life advice based on the analysis results. Leveraging a generative AI model, it can provide recommendations tailored to individual user situations. For example, if the server determines that a user is experiencing stress, it can suggest "meditating or doing relaxation exercises at the end of the day." This advice is then transferred back to the terminal and presented to the user through a visualization device. The terminal can use VR or AR technology to visually present the advice, improving user acceptance.

[0330] A concrete example would be someone who spends long hours working on their computer during the week and is feeling unwell. Based on this information, the server can generate lifestyle advice such as "stretch for 5 minutes every hour" and present it to the user via their terminal.

[0331] An example of a prompt message might be a scenario like, "The user has indicated fatigue in recent analysis. Please suggest ways to refresh them."

[0332] In this way, the system collects and analyzes user data, provides personalized advice optimized for each user, and supports the realization of a healthy lifestyle.

[0333] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0334] Step 1:

[0335] The device collects data through the user's wearable device or smartphone. Inputs include GPS location information, vital data from a heart rate sensor, voice tone from a microphone, and facial expression data from a camera. By integrating this data, the system can understand the user's daily activities and emotional state.

[0336] Step 2:

[0337] The terminal formats the collected data and sends it to the server using a secure communication protocol. The inputs here are sensor and emotion data, and the output is the data sent to the server. Data formatting involves converting to an appropriate format and filtering out unnecessary data.

[0338] Step 3:

[0339] The server stores the received data in a database and begins analysis. The input is the transmitted sensor and emotion data, and the output is the analysis results. Once the data has been stored in the database, the artificial intelligence engine identifies behavioral patterns, and the emotion engine performs voice and facial expression analysis. This allows for a comprehensive understanding of the user's state.

[0340] Step 4:

[0341] The server uses a generated AI model based on the analysis results to produce life advice optimized for the user. The input is behavioral and emotional information obtained from the analysis, and the output is specific advice. For example, if the user is determined to be in a stressed state, the server will generate advice recommending "meditating for 10 minutes every night."

[0342] Step 5:

[0343] The advice generated from the server is sent to the terminal and presented to the user via a visualization device. The input is the generated life advice, and the output is a visual or auditory presentation to the user. The terminal uses VR or AR technology to effectively display the advice and enhance user acceptance.

[0344] Step 6:

[0345] Users follow the advice provided and input feedback on the results and their satisfaction level into the device. This feedback becomes input for the system and is treated as new data.

[0346] Step 7:

[0347] The terminal sends the collected feedback information to the server, which uses it to improve the analysis process of the artificial intelligence engine. The input is user feedback, and the output is an improved analysis algorithm and increased accuracy for the next time.

[0348] (Application Example 2)

[0349] 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."

[0350] In recent years, there has been a growing need for systems that monitor an individual's emotional and health status and provide customized advice based on that data. However, conventional systems have had the problem of not being able to provide advice that adequately takes emotional states into account. Furthermore, efficiently incorporating user feedback to improve the accuracy of the advice has been a difficult challenge.

[0351] 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.

[0352] In this invention, the server includes sensing means for collecting the user's daily activity data and emotional state data, calculation means for analyzing the collected data and identifying the user's behavioral patterns and emotional state, and presentation means for providing the user with customized life advice based on the identified behavioral patterns and emotional state. This makes it possible to provide highly accurate and personalized advice that takes into account the user's emotional state.

[0353] "Sensing means" refers to devices or methods used to collect data on the user's daily activities and emotional state.

[0354] "Computation means" refers to a process or system for analyzing data collected by sensing means to identify the user's behavioral patterns and emotional state.

[0355] "Presentation means" refers to a device or method for providing a user with customized life advice, either visually or audibly, based on identified behavioral patterns and emotional states.

[0356] "Improvement measures" refer to mechanisms and systems for acquiring user feedback information and improving the analysis process of the computational means based on that feedback.

[0357] An "emotional analysis method" is a method for analyzing data related to a user's emotional state and generating customized health advice based on the results.

[0358] "Augmented reality means" are technologies and devices that visually present advice to a user and improve the acceptability of that advice based on their response.

[0359] The system for implementing this invention has a configuration comprising a "sensing means" for collecting the user's daily activity data and emotional state data, a "calculation means" for analyzing this data, a "presentation means" for providing the user with advice based on the analysis results, and an "improvement means" for improving the system based on feedback from the user.

[0360] The server first collects various data from the user's wearable device or mobile device using "sensing means." In this process, it utilizes hardware such as microphones, cameras, location sensors, and heart rate sensors to acquire activity data and emotional state data provided by these sensors.

[0361] The collected data is analyzed using "computational tools." This analysis utilizes software such as TensorFlow and OpenCV to identify user behavior patterns and emotional states using various data. The TensorFlow model integrates diverse data to understand user activities and emotions, while OpenCV performs facial recognition based on camera images.

[0362] Based on the analysis results obtained, the server provides the user with customized life advice through a "presentation means." The presentation means utilizes an AR display that leverages augmented reality technology to present information to the user in a visually appealing way.

[0363] Furthermore, users can provide feedback on the effectiveness and satisfaction level of the advice they received. This information is used as a "means of improvement," and the quality of subsequent advice is improved based on the feedback provided.

[0364] For example, if the server detects that a user is experiencing stress, it will offer advice on relaxation techniques. Exercise suggestions will appear on the AR display, allowing the user to incorporate these suggestions into their daily life. In this way, advice is provided according to the user's emotional state, contributing to an improved quality of life.

[0365] As an example of a prompt, you can use the sentence, "If the user is feeling stressed, what relaxation methods should be suggested?" This facilitates appropriate support using generative AI models.

[0366] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0367] Step 1:

[0368] The device collects daily activity data and emotional state data from the user's wearable device or mobile device through sensing mechanisms. Inputs include location information, heart rate, voice tone, and facial images. This data is temporarily stored for subsequent analysis.

[0369] Step 2:

[0370] The terminal sends the collected data to the server. The server stores the received data in a database for analysis. During this data transfer process, data format conversion and basic filtering are performed.

[0371] Step 3:

[0372] The server analyzes the data stored in the database using computational methods. Specifically, it uses a TensorFlow model to identify behavioral patterns and OpenCV to analyze emotional states from facial images. The previously collected data is used as input data, and the output provides the user's specific behavioral patterns and emotional states.

[0373] Step 4:

[0374] The server generates personalized life advice based on the analysis results. It uses prompts such as "What relaxation methods should be suggested if the user is feeling stressed?" to generate optimal advice from the AI ​​model. The output is in text format.

[0375] Step 5:

[0376] The server sends the generated advice to the terminal. The terminal visually presents the advice using a presentation method. For example, an AR display is used to visually present relaxation techniques and exercise methods to the user.

[0377] Step 6:

[0378] After implementing the provided advice, users input feedback on its effectiveness and satisfaction level into their device. This feedback is sent to the server and used to improve the accuracy of the analysis process through various improvement measures.

[0379] Step 7:

[0380] Based on the feedback, the server implements system improvements to enhance the accuracy of future analysis processes and advice. This results in more precise and effective advice for users.

[0381] 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.

[0382] 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.

[0383] 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.

[0384] [Third Embodiment]

[0385] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0386] 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.

[0387] 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).

[0388] 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.

[0389] 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.

[0390] 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).

[0391] 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.

[0392] 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.

[0393] 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.

[0394] 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.

[0395] 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.

[0396] 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".

[0397] This invention is a system designed to enrich and improve the user's daily life, combining sensor devices, an artificial intelligence engine, and a visualization device to provide personalized life advice. It primarily functions as a server, a terminal (the user's device), and the user themselves.

[0398] First, when a user wears a smartphone or wearable device, these devices collect location information and health data using sensor equipment. For example, if the user is walking, the device uses an accelerometer to record step count data.

[0399] Next, the collected data is sent from the device to the server. The server uses encrypted communication to protect privacy, ensuring that the data can be handled securely. In this process, the server receives this data, first stores it in a database, and then analyzes it using an artificial intelligence engine.

[0400] The artificial intelligence engine analyzes data to identify user behavior patterns and health trends, and then generates optimal advice based on that. To achieve this, it learns user activity patterns from past data and provides specific advice for improving health. For example, if the server determines that a user has been inactive recently, it will create recommendations regarding the frequency and type of exercise.

[0401] The generated advice is sent from the server to the terminal. The terminal then presents this advice to the user at the appropriate time through visualization devices such as VR, AR, and MR. This allows the user to visually confirm specific means to modify their behavior. For example, the terminal can display changes in heart rate during jogging in real time and instruct the user on the optimal pace.

[0402] Furthermore, the system includes a feedback processing mechanism that provides a way for users to input their practical results and satisfaction levels via a terminal. The server receives this feedback to improve the accuracy of the artificial intelligence engine's analysis and make improvements to provide more appropriate advice.

[0403] In this way, the system regularly provides optimal advice tailored to the individual needs of each user, aiming to improve their quality of life.

[0404] The following describes the processing flow.

[0405] Step 1:

[0406] The device collects sensor data from the user's wearable device or smartphone. This includes obtaining location information using GPS and collecting data from heart rate sensors.

[0407] Step 2:

[0408] The device collects data, bundles it into data packets at regular intervals, encrypts them, and sends them to the server. SSL / TLS and other methods are used for communication to ensure data privacy.

[0409] Step 3:

[0410] The server stores the received data in a database, and then passes the data to an artificial intelligence engine to begin analysis. The analysis uses machine learning models to identify user behavior patterns and health status.

[0411] Step 4:

[0412] The server generates personalized life advice for the user based on the analysis results. The generated advice is tailored to past data and the user's current situation.

[0413] Step 5:

[0414] The server generates advice and sends it to the terminal. The advice is received by the terminal and presented to the user through a visualization device.

[0415] Step 6:

[0416] The device displays advice to the user in real time through a visualization device. For example, if the user is jogging, the recommended pace is visually displayed using an AR device.

[0417] Step 7:

[0418] The user enters feedback on the device based on the results of implementing the advice. This feedback information is sent to the server for improvement.

[0419] Step 8:

[0420] The server analyzes user feedback and tunes the artificial intelligence engine based on survey results and health data. This process improves the accuracy of future advice.

[0421] (Example 1)

[0422] 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."

[0423] In modern society, it is important to provide specific advice based on each user's lifestyle and health condition. However, existing systems have struggled to fully utilize the collected data and provide accurate instructions tailored to the individual needs of users.

[0424] 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.

[0425] In this invention, the server includes information gathering means for acquiring information about the user's daily activities, machine learning means for analyzing the information acquired by the information gathering means and identifying the user's behavioral patterns, and presentation means for providing personalized instructions to the user based on the behavioral patterns. This makes it possible to visually present appropriate instructions to individual users and promote improvements in the users' lifestyles.

[0426] "Information gathering means" refers to functions and technologies used to obtain information about a user's daily activities.

[0427] "Machine learning methods" refer to algorithms and technologies used to analyze acquired information and identify user behavior patterns.

[0428] "Presentation means" refers to methods or devices for providing personalized instructions to users based on analysis.

[0429] "Visualization means" refers to technologies and devices used to visually present information or instructions to users.

[0430] The embodiment of this invention is a system for enriching and improving the user's daily life. This system includes information gathering means, machine learning means, presentation means, and visualization means. It primarily functions as a server, terminal, and user.

[0431] The device uses sensors built into smartphones and wearable devices to collect information about the user's daily activities. This information includes location data and health data (e.g., steps taken, heart rate). For example, the device collects the number of steps taken each day and sends it to a server at regular intervals.

[0432] The server receives information sent from the terminal and processes it while protecting privacy through encrypted communication. The server stores the information in a database and analyzes the user's behavior patterns using machine learning. Based on the analysis results, the server "learns" the individual user's tendencies and generates appropriate instructions through an AI model. An example of a prompt message is, "The user has been determined to be sedentary recently, so please generate recommended types and frequencies of exercise."

[0433] The generated instructions are sent from the server to the terminal. The terminal receives these instructions and presents them to the user through visualization. Based on the visualized information, the user can recognize areas for improvement in their daily life and take concrete actions. For example, the terminal can use AR functionality to display exercise routes in real time, showing the user the optimal exercise course.

[0434] This system allows users to receive accurate advice regarding their health status and receive support to improve their quality of life.

[0435] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0436] Step 1:

[0437] The device uses the user's smartphone or wearable device to collect information related to their daily activities. It receives location information and health data (e.g., steps, heart rate) detected by sensors as input. This data is acquired in real time and temporarily stored within the device. Specifically, the device uses an accelerometer and heart rate sensor to record data points every second. The collected results are output as numerical data indicating the user's activity level.

[0438] Step 2:

[0439] The terminal sends the collected data to the server. The input is the series of data obtained in step 1. This data is encrypted and sent to the server while ensuring privacy. The output is an encrypted data packet, ensuring stable data transmission. Specifically, the terminal organizes the data at regular intervals and performs a process of uplinking it to the server.

[0440] Step 3:

[0441] The server receives data sent from the terminal and stores it in a database. It receives encrypted data packets as input. The server decrypts these packets, organizes them, and stores them in the database. The output is the decrypted raw data. Specifically, the server converts the received data into a format that can be analyzed, preparing it for analysis.

[0442] Step 4:

[0443] The server analyzes stored data using machine learning techniques. The input is user behavior data stored in a database. This data is processed to analyze user behavior patterns and health trends. The output is user behavior pattern information based on the analysis results. Specifically, the server executes machine learning algorithms to perform pattern recognition and predictive analysis.

[0444] Step 5:

[0445] The server uses a generated AI model based on the analysis results to create personalized instructions for the user. The input is the behavioral pattern information obtained in step 4. Based on this data, the program generates prompt statements and creates advice. The output is specific action instructions to be provided to the user. Specifically, the server sets prompts and documents the generated advice.

[0446] Step 6:

[0447] The server sends the generated instructions to the terminal. The input is the advice text created in step 5. This information is converted to the appropriate format and transmitted to the terminal. The output is the instruction information that arrives at the terminal. Specifically, the server sets a transmission schedule and sends the data via the appropriate communication method.

[0448] Step 7:

[0449] The terminal presents the received instructions to the user through visualization means. It receives instruction information from the server as input. The terminal performs graphics processing to display this information on the screen. The output is visual information presented in a way that is easy for the user to understand. Specifically, the terminal updates an interactive UI through its display, prompting user feedback.

[0450] (Application Example 1)

[0451] 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."

[0452] In modern society, there is a need for approaches to health management and lifestyle improvements tailored to individual users. However, conventional health management systems are limited to advice based on general data and lack the flexibility to provide personalized advice in real time.

[0453] 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.

[0454] In this invention, the server includes a measuring device for collecting the user's daily activity data, an artificial intelligence processing unit for analyzing the data collected by the measuring device and identifying the user's behavioral characteristics, a display unit for providing the user with customized guidance based on the behavioral characteristics, and an audio output means for providing real-time feedback on the user's activities. This makes it possible to achieve effective and personalized health management and lifestyle improvement for individual users.

[0455] "Measurement device" refers to sensors and devices used to collect data on a user's daily activities.

[0456] The "artificial intelligence processing unit" is a component that analyzes collected data and identifies user behavioral characteristics.

[0457] The "display unit" is a device that visually provides users with customized guidelines based on the analysis results.

[0458] "Voice output means" refers to a device or system for providing real-time voice feedback to a user's activities.

[0459] In implementing this invention, the server, terminal, and user play key roles. The server receives location information and health data from measuring devices such as smartphones and wearable devices to collect data on the user's daily activities. This data is encrypted and transmitted, and the server is equipped with an artificial intelligence processing unit to receive and analyze the data. The artificial intelligence processing unit uses software such as Python, TensorFlow, and Keras to analyze the user's behavioral characteristics and generate personalized health advice.

[0460] The device features a display unit that provides users with customized guidance. This display unit has the function of visualizing appropriate guidance based on analysis results through AR (augmented reality) and the display. The device also incorporates a voice output mechanism, enabling real-time feedback to the user. Utilizing voice assistant technology, it can provide exercise guidance and health-related suggestions via voice.

[0461] As a concrete example, when a user is jogging, the device measures and displays their heart rate, and based on that data, the server analyzes the effectiveness of the exercise and provides voice guidance on the optimal exercise pace. By using a generative AI model, personalized suggestions are generated in real time.

[0462] An example of a prompt might be, "Analyze the user's health data and suggest the optimal type and duration of exercise." This prompt allows the AI ​​to generate an optimal exercise plan based on the user's past data and real-time information.

[0463] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0464] Step 1:

[0465] The device acquires the user's daily activity data through smartphones and wearable devices. This data includes heart rate, steps taken, and location information. The acquired data is initially processed within the device and formatted so that it can be securely transmitted to the server.

[0466] Step 2:

[0467] The server receives data sent from the terminal. The received data is encrypted, and is first decrypted before being stored in the database. Here, the accuracy of the data is verified and fraudulent data is filtered out.

[0468] Step 3:

[0469] The server analyzes user activity data stored in the database using an artificial intelligence processing unit. Using Python and TensorFlow, it applies user behavioral characteristics to a model based on the data to extract health trends and signs of lack of exercise. As a result of the analysis, optimal health advice is generated that is recommended to the user.

[0470] Step 4:

[0471] Based on the generated advice, the server sends data to the terminal. The terminal visualizes this data on its display and provides real-time voice feedback using an audio output device. For example, it can provide voice guidance to a user jogging, indicating an appropriate exercise intensity based on their pace and heart rate.

[0472] Step 5:

[0473] Users perform actions according to the advice they receive and input the results as feedback information into their device. This information is then sent back to the server and used to improve the analysis process in the artificial intelligence processing unit. This feedback loop allows the AI ​​model to improve its accuracy for each user.

[0474] 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.

[0475] This invention combines an emotion engine with a system that provides customized life advice based on the user's daily activities. This system consists of a combination of a sensor device, an artificial intelligence engine, a visualization device, and an emotion recognition device.

[0476] First, the device acquires daily activity data from sensor devices via the user's wearable device or smartphone. This includes not only location information and heart rate data, but also emotional state data acquired by an emotion recognition device using a microphone and camera. For example, emotional data is collected from the voice tone when the user is on the phone or from facial expressions captured by a facial recognition camera.

[0477] Next, the device sends sensor data and emotion data to the server. The server stores this data in a database and analyzes it using an artificial intelligence engine and an emotion engine. The artificial intelligence engine identifies the user's behavioral patterns, and the emotion engine identifies the user's emotional state through voice analysis and facial expression analysis. This allows the server to comprehensively understand the user's physical and emotional state.

[0478] Based on these analysis results, the server generates personalized life advice for the user. For example, if the server determines that the user has recently been experiencing stress, it can generate advice recommending meditation or relaxation exercises. The generated advice is sent from the server to the terminal and presented to the user via a visualization device. The terminal uses VR or AR technology to visually display the advice effectively, thereby increasing user acceptance.

[0479] After a user implements the advice, they input feedback on the results and satisfaction level into their device. The device sends this feedback back to the server, which then uses the feedback to improve the analysis process of the artificial intelligence engine. This process allows the system to continuously improve the accuracy of its advice and provide users with optimized support.

[0480] This embodiment enables comprehensive health management that takes into account an individual's emotional state, providing support to improve the quality of daily life.

[0481] The following describes the processing flow.

[0482] Step 1:

[0483] The device collects the user's daily activity data using sensor devices. This includes location information, heart rate, voice tone, and facial expression data from facial recognition.

[0484] Step 2:

[0485] The device collects data, which is then compiled and sent to a server using a secure communication method. This data includes not only activity information but also information indicating emotional state.

[0486] Step 3:

[0487] The server stores the received data in a database, and an artificial intelligence engine analyzes the user's behavior patterns. Simultaneously, an emotion engine uses voice and facial expression data to identify the user's emotional state.

[0488] Step 4:

[0489] The server analyzes behavioral patterns and emotional states to generate personalized life advice for the user. For example, if stress levels are high, it will create relaxation advice.

[0490] Step 5:

[0491] The server sends the generated advice to the terminal. The terminal then presents the received advice to the user through a visualization device. The presentation method utilizes VR and AR, and is designed to allow the user to understand it intuitively.

[0492] Step 6:

[0493] Users implement the advice and input feedback on its effectiveness and their own reactions into the device. This feedback includes information about the results of implementing the advice and their satisfaction level.

[0494] Step 7:

[0495] The device collects user feedback and sends it to the server. The server analyzes this feedback and uses it to improve the accuracy of the AI ​​engine and emotion engine's analysis.

[0496] Step 8:

[0497] Based on the feedback analysis, the server adjusts the system to improve the accuracy of future advice and provide more appropriate support to the user.

[0498] (Example 2)

[0499] 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."

[0500] In modern society, managing personal health and optimizing lifestyles are crucial issues, and maintaining a balance between mind and body is particularly important. Many conventional systems provide general feedback based on the user's daily activities, but they do not offer customized advice that takes into account individual emotional states. Therefore, there is a need for technology that provides more specific and effective advice that takes individual emotional states into account, in order to improve users' health and quality of life.

[0501] 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.

[0502] In this invention, the server includes data collection means for collecting the user's daily activity data and emotional state, analysis means for analyzing the data obtained from the data collection means and identifying the user's behavioral patterns and emotional state, and generation means for generating customized life advice for the user based on the analysis results from the analysis means. This enables the user to receive specific and effective life advice tailored to their individual emotional state.

[0503] "Data collection means" refers to a device or method used to collect data on a user's daily activities and emotional state.

[0504] "Analysis means" refers to a process or technology for identifying user behavior patterns and emotional states based on collected data.

[0505] "Generation means" refers to a function or method for creating customized life advice for the user based on the analysis results.

[0506] "Presentation methods" refer to means of conveying generated life advice to the user visually or audibly, and may include the use of VR or AR technologies.

[0507] "Feedback improvement methods" are means of improving the analysis process based on feedback information obtained from users.

[0508] The system of this invention aims to improve the daily lives of users using multiple hardware and software components. Specifically, it comprehensively understands each user's emotional state and daily activities and provides advice based on that understanding.

[0509] First, the device collects data through the user's wearable device or smartphone. The sensors used include GPS for obtaining location information, biosensors for measuring heart rate, microphones for obtaining voice data, and cameras for collecting facial expression data. The device uses an emotion recognition device to determine the user's emotional state from their voice tone and facial expressions.

[0510] Next, the device securely transmits this sensor data and emotional data to the server. The server stores the received data in a database and analyzes it using an artificial intelligence engine and an emotional engine. This artificial intelligence engine implements machine learning algorithms to identify the user's behavioral patterns and emotional state.

[0511] Furthermore, the server generates life advice based on the analysis results. Leveraging a generative AI model, it can provide recommendations tailored to individual user situations. For example, if the server determines that a user is experiencing stress, it can suggest "meditating or doing relaxation exercises at the end of the day." This advice is then transferred back to the terminal and presented to the user through a visualization device. The terminal can use VR or AR technology to visually present the advice, improving user acceptance.

[0512] A concrete example would be someone who spends long hours working on their computer during the week and is feeling unwell. Based on this information, the server can generate lifestyle advice such as "stretch for 5 minutes every hour" and present it to the user via their terminal.

[0513] An example of a prompt message might be a scenario like, "The user has indicated fatigue in recent analysis. Please suggest ways to refresh them."

[0514] In this way, the system collects and analyzes user data, provides personalized advice optimized for each user, and supports the realization of a healthy lifestyle.

[0515] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0516] Step 1:

[0517] The device collects data through the user's wearable device or smartphone. Inputs include GPS location information, vital data from a heart rate sensor, voice tone from a microphone, and facial expression data from a camera. By integrating this data, the system can understand the user's daily activities and emotional state.

[0518] Step 2:

[0519] The terminal formats the collected data and sends it to the server using a secure communication protocol. The inputs here are sensor and emotion data, and the output is the data sent to the server. Data formatting involves converting to an appropriate format and filtering out unnecessary data.

[0520] Step 3:

[0521] The server stores the received data in a database and begins analysis. The input is the transmitted sensor and emotion data, and the output is the analysis results. Once the data has been stored in the database, the artificial intelligence engine identifies behavioral patterns, and the emotion engine performs voice and facial expression analysis. This allows for a comprehensive understanding of the user's state.

[0522] Step 4:

[0523] The server uses a generated AI model based on the analysis results to produce life advice optimized for the user. The input is behavioral and emotional information obtained from the analysis, and the output is specific advice. For example, if the user is determined to be in a stressed state, the server will generate advice recommending "meditating for 10 minutes every night."

[0524] Step 5:

[0525] The advice generated from the server is sent to the terminal and presented to the user via a visualization device. The input is the generated life advice, and the output is a visual or auditory presentation to the user. The terminal uses VR or AR technology to effectively display the advice and enhance user acceptance.

[0526] Step 6:

[0527] Users follow the advice provided and input feedback on the results and their satisfaction level into the device. This feedback becomes input for the system and is treated as new data.

[0528] Step 7:

[0529] The terminal sends the collected feedback information to the server, which uses it to improve the analysis process of the artificial intelligence engine. The input is user feedback, and the output is an improved analysis algorithm and increased accuracy for the next time.

[0530] (Application Example 2)

[0531] 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."

[0532] In recent years, there has been a growing need for systems that monitor an individual's emotional and health status and provide customized advice based on that data. However, conventional systems have had the problem of not being able to provide advice that adequately takes emotional states into account. Furthermore, efficiently incorporating user feedback to improve the accuracy of the advice has been a difficult challenge.

[0533] 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.

[0534] In this invention, the server includes sensing means for collecting the user's daily activity data and emotional state data, calculation means for analyzing the collected data and identifying the user's behavioral patterns and emotional state, and presentation means for providing the user with customized life advice based on the identified behavioral patterns and emotional state. This makes it possible to provide highly accurate and personalized advice that takes into account the user's emotional state.

[0535] "Sensing means" refers to devices or methods used to collect data on the user's daily activities and emotional state.

[0536] "Computation means" refers to a process or system for analyzing data collected by sensing means to identify the user's behavioral patterns and emotional state.

[0537] "Presentation means" refers to a device or method for providing a user with customized life advice, either visually or audibly, based on identified behavioral patterns and emotional states.

[0538] "Improvement measures" refer to mechanisms and systems for acquiring user feedback information and improving the analysis process of the computational means based on that feedback.

[0539] An "emotional analysis method" is a method for analyzing data related to a user's emotional state and generating customized health advice based on the results.

[0540] "Augmented reality means" are technologies and devices that visually present advice to a user and improve the acceptability of that advice based on their response.

[0541] The system for implementing this invention has a configuration comprising a "sensing means" for collecting the user's daily activity data and emotional state data, a "calculation means" for analyzing this data, a "presentation means" for providing the user with advice based on the analysis results, and an "improvement means" for improving the system based on feedback from the user.

[0542] The server first collects various data from the user's wearable device or mobile device using "sensing means." In this process, it utilizes hardware such as microphones, cameras, location sensors, and heart rate sensors to acquire activity data and emotional state data provided by these sensors.

[0543] The collected data is analyzed using "computational tools." This analysis utilizes software such as TensorFlow and OpenCV to identify user behavior patterns and emotional states using various data. The TensorFlow model integrates diverse data to understand user activities and emotions, while OpenCV performs facial recognition based on camera images.

[0544] Based on the analysis results obtained, the server provides the user with customized life advice through a "presentation means." The presentation means utilizes an AR display that leverages augmented reality technology to present information to the user in a visually appealing way.

[0545] Furthermore, users can provide feedback on the effectiveness and satisfaction level of the advice they received. This information is used as a "means of improvement," and the quality of subsequent advice is improved based on the feedback provided.

[0546] For example, if the server detects that a user is experiencing stress, it will offer advice on relaxation techniques. Exercise suggestions will appear on the AR display, allowing the user to incorporate these suggestions into their daily life. In this way, advice is provided according to the user's emotional state, contributing to an improved quality of life.

[0547] As an example of a prompt, you can use the sentence, "If the user is feeling stressed, what relaxation methods should be suggested?" This facilitates appropriate support using generative AI models.

[0548] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0549] Step 1:

[0550] The device collects daily activity data and emotional state data from the user's wearable device or mobile device through sensing mechanisms. Inputs include location information, heart rate, voice tone, and facial images. This data is temporarily stored for subsequent analysis.

[0551] Step 2:

[0552] The terminal sends the collected data to the server. The server stores the received data in a database for analysis. During this data transfer process, data format conversion and basic filtering are performed.

[0553] Step 3:

[0554] The server analyzes the data stored in the database using computational methods. Specifically, it uses a TensorFlow model to identify behavioral patterns and OpenCV to analyze emotional states from facial images. The previously collected data is used as input data, and the output provides the user's specific behavioral patterns and emotional states.

[0555] Step 4:

[0556] The server generates personalized life advice based on the analysis results. It uses prompts such as "What relaxation methods should be suggested if the user is feeling stressed?" to generate optimal advice from the AI ​​model. The output is in text format.

[0557] Step 5:

[0558] The server sends the generated advice to the terminal. The terminal visually presents the advice using a presentation method. For example, an AR display is used to visually present relaxation techniques and exercise methods to the user.

[0559] Step 6:

[0560] After implementing the provided advice, users input feedback on its effectiveness and satisfaction level into their device. This feedback is sent to the server and used to improve the accuracy of the analysis process through various improvement measures.

[0561] Step 7:

[0562] Based on the feedback, the server implements system improvements to enhance the accuracy of future analysis processes and advice. This results in more precise and effective advice for users.

[0563] 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.

[0564] 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.

[0565] 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.

[0566] [Fourth Embodiment]

[0567] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0568] 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.

[0569] 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).

[0570] 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.

[0571] 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.

[0572] 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).

[0573] 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.

[0574] 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.

[0575] 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.

[0576] 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.

[0577] 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.

[0578] 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.

[0579] 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".

[0580] This invention is a system designed to enrich and improve the user's daily life, combining sensor devices, an artificial intelligence engine, and a visualization device to provide personalized life advice. It primarily functions as a server, a terminal (the user's device), and the user themselves.

[0581] First, when a user wears a smartphone or wearable device, these devices collect location information and health data using sensor equipment. For example, if the user is walking, the device uses an accelerometer to record step count data.

[0582] Next, the collected data is sent from the device to the server. The server uses encrypted communication to protect privacy, ensuring that the data can be handled securely. In this process, the server receives this data, first stores it in a database, and then analyzes it using an artificial intelligence engine.

[0583] The artificial intelligence engine analyzes data to identify user behavior patterns and health trends, and then generates optimal advice based on that. To achieve this, it learns user activity patterns from past data and provides specific advice for improving health. For example, if the server determines that a user has been inactive recently, it will create recommendations regarding the frequency and type of exercise.

[0584] The generated advice is sent from the server to the terminal. The terminal then presents this advice to the user at the appropriate time through visualization devices such as VR, AR, and MR. This allows the user to visually confirm specific means to modify their behavior. For example, the terminal can display changes in heart rate during jogging in real time and instruct the user on the optimal pace.

[0585] Furthermore, the system includes a feedback processing mechanism that provides a way for users to input their practical results and satisfaction levels via a terminal. The server receives this feedback to improve the accuracy of the artificial intelligence engine's analysis and make improvements to provide more appropriate advice.

[0586] In this way, the system regularly provides optimal advice tailored to the individual needs of each user, aiming to improve their quality of life.

[0587] The following describes the processing flow.

[0588] Step 1:

[0589] The device collects sensor data from the user's wearable device or smartphone. This includes obtaining location information using GPS and collecting data from heart rate sensors.

[0590] Step 2:

[0591] The device collects data, bundles it into data packets at regular intervals, encrypts them, and sends them to the server. SSL / TLS and other methods are used for communication to ensure data privacy.

[0592] Step 3:

[0593] The server stores the received data in a database, and then passes the data to an artificial intelligence engine to begin analysis. The analysis uses machine learning models to identify user behavior patterns and health status.

[0594] Step 4:

[0595] The server generates personalized life advice for the user based on the analysis results. The generated advice is tailored to past data and the user's current situation.

[0596] Step 5:

[0597] The server generates advice and sends it to the terminal. The advice is received by the terminal and presented to the user through a visualization device.

[0598] Step 6:

[0599] The device displays advice to the user in real time through a visualization device. For example, if the user is jogging, the recommended pace is visually displayed using an AR device.

[0600] Step 7:

[0601] The user enters feedback on the device based on the results of implementing the advice. This feedback information is sent to the server for improvement.

[0602] Step 8:

[0603] The server analyzes user feedback and tunes the artificial intelligence engine based on survey results and health data. This process improves the accuracy of future advice.

[0604] (Example 1)

[0605] 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".

[0606] In modern society, it is important to provide specific advice based on each user's lifestyle and health condition. However, existing systems have struggled to fully utilize the collected data and provide accurate instructions tailored to the individual needs of users.

[0607] 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.

[0608] In this invention, the server includes information gathering means for acquiring information about the user's daily activities, machine learning means for analyzing the information acquired by the information gathering means and identifying the user's behavioral patterns, and presentation means for providing personalized instructions to the user based on the behavioral patterns. This makes it possible to visually present appropriate instructions to individual users and promote improvements in the users' lifestyles.

[0609] "Information gathering means" refers to functions and technologies used to obtain information about a user's daily activities.

[0610] "Machine learning methods" refer to algorithms and technologies used to analyze acquired information and identify user behavior patterns.

[0611] "Presentation means" refers to methods or devices for providing personalized instructions to users based on analysis.

[0612] "Visualization means" refers to technologies and devices used to visually present information or instructions to users.

[0613] The embodiment of this invention is a system for enriching and improving the user's daily life. This system includes information gathering means, machine learning means, presentation means, and visualization means. It primarily functions as a server, terminal, and user.

[0614] The device uses sensors built into smartphones and wearable devices to collect information about the user's daily activities. This information includes location data and health data (e.g., steps taken, heart rate). For example, the device collects the number of steps taken each day and sends it to a server at regular intervals.

[0615] The server receives information sent from the terminal and processes it while protecting privacy through encrypted communication. The server stores the information in a database and analyzes the user's behavior patterns using machine learning. Based on the analysis results, the server "learns" the individual user's tendencies and generates appropriate instructions through an AI model. An example of a prompt message is, "The user has been determined to be sedentary recently, so please generate recommended types and frequencies of exercise."

[0616] The generated instructions are sent from the server to the terminal. The terminal receives these instructions and presents them to the user through visualization. Based on the visualized information, the user can recognize areas for improvement in their daily life and take concrete actions. For example, the terminal can use AR functionality to display exercise routes in real time, showing the user the optimal exercise course.

[0617] This system allows users to receive accurate advice regarding their health status and receive support to improve their quality of life.

[0618] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0619] Step 1:

[0620] The device uses the user's smartphone or wearable device to collect information related to their daily activities. It receives location information and health data (e.g., steps, heart rate) detected by sensors as input. This data is acquired in real time and temporarily stored within the device. Specifically, the device uses an accelerometer and heart rate sensor to record data points every second. The collected results are output as numerical data indicating the user's activity level.

[0621] Step 2:

[0622] The terminal sends the collected data to the server. The input is the series of data obtained in step 1. This data is encrypted and sent to the server while ensuring privacy. The output is an encrypted data packet, ensuring stable data transmission. Specifically, the terminal organizes the data at regular intervals and performs a process of uplinking it to the server.

[0623] Step 3:

[0624] The server receives data sent from the terminal and stores it in a database. It receives encrypted data packets as input. The server decrypts these packets, organizes them, and stores them in the database. The output is the decrypted raw data. Specifically, the server converts the received data into a format that can be analyzed, preparing it for analysis.

[0625] Step 4:

[0626] The server analyzes stored data using machine learning techniques. The input is user behavior data stored in a database. This data is processed to analyze user behavior patterns and health trends. The output is user behavior pattern information based on the analysis results. Specifically, the server executes machine learning algorithms to perform pattern recognition and predictive analysis.

[0627] Step 5:

[0628] The server uses a generated AI model based on the analysis results to create personalized instructions for the user. The input is the behavioral pattern information obtained in step 4. Based on this data, the program generates prompt statements and creates advice. The output is specific action instructions to be provided to the user. Specifically, the server sets prompts and documents the generated advice.

[0629] Step 6:

[0630] The server sends the generated instructions to the terminal. The input is the advice text created in step 5. This information is converted to the appropriate format and transmitted to the terminal. The output is the instruction information that arrives at the terminal. Specifically, the server sets a transmission schedule and sends the data via the appropriate communication method.

[0631] Step 7:

[0632] The terminal presents the received instructions to the user through visualization means. It receives instruction information from the server as input. The terminal performs graphics processing to display this information on the screen. The output is visual information presented in a way that is easy for the user to understand. Specifically, the terminal updates an interactive UI through its display, prompting user feedback.

[0633] (Application Example 1)

[0634] 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".

[0635] In modern society, there is a need for approaches to health management and lifestyle improvements tailored to individual users. However, conventional health management systems are limited to advice based on general data and lack the flexibility to provide personalized advice in real time.

[0636] 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.

[0637] In this invention, the server includes a measuring device for collecting the user's daily activity data, an artificial intelligence processing unit for analyzing the data collected by the measuring device and identifying the user's behavioral characteristics, a display unit for providing the user with customized guidance based on the behavioral characteristics, and an audio output means for providing real-time feedback on the user's activities. This makes it possible to achieve effective and personalized health management and lifestyle improvement for individual users.

[0638] "Measurement device" refers to sensors and devices used to collect data on a user's daily activities.

[0639] The "artificial intelligence processing unit" is a component that analyzes collected data and identifies user behavioral characteristics.

[0640] The "display unit" is a device that visually provides users with customized guidelines based on the analysis results.

[0641] "Voice output means" refers to a device or system for providing real-time voice feedback to a user's activities.

[0642] In implementing this invention, the server, terminal, and user play key roles. The server receives location information and health data from measuring devices such as smartphones and wearable devices to collect data on the user's daily activities. This data is encrypted and transmitted, and the server is equipped with an artificial intelligence processing unit to receive and analyze the data. The artificial intelligence processing unit uses software such as Python, TensorFlow, and Keras to analyze the user's behavioral characteristics and generate personalized health advice.

[0643] The device features a display unit that provides users with customized guidance. This display unit has the function of visualizing appropriate guidance based on analysis results through AR (augmented reality) and the display. The device also incorporates a voice output mechanism, enabling real-time feedback to the user. Utilizing voice assistant technology, it can provide exercise guidance and health-related suggestions via voice.

[0644] As a concrete example, when a user is jogging, the device measures and displays their heart rate, and based on that data, the server analyzes the effectiveness of the exercise and provides voice guidance on the optimal exercise pace. By using a generative AI model, personalized suggestions are generated in real time.

[0645] An example of a prompt might be, "Analyze the user's health data and suggest the optimal type and duration of exercise." This prompt allows the AI ​​to generate an optimal exercise plan based on the user's past data and real-time information.

[0646] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0647] Step 1:

[0648] The device acquires the user's daily activity data through smartphones and wearable devices. This data includes heart rate, steps taken, and location information. The acquired data is initially processed within the device and formatted so that it can be securely transmitted to the server.

[0649] Step 2:

[0650] The server receives data sent from the terminal. The received data is encrypted, and is first decrypted before being stored in the database. Here, the accuracy of the data is verified and fraudulent data is filtered out.

[0651] Step 3:

[0652] The server analyzes user activity data stored in the database using an artificial intelligence processing unit. Using Python and TensorFlow, it applies user behavioral characteristics to a model based on the data to extract health trends and signs of lack of exercise. As a result of the analysis, optimal health advice is generated that is recommended to the user.

[0653] Step 4:

[0654] Based on the generated advice, the server sends data to the terminal. The terminal visualizes this data on its display and provides real-time voice feedback using an audio output device. For example, it can provide voice guidance to a user jogging, indicating an appropriate exercise intensity based on their pace and heart rate.

[0655] Step 5:

[0656] Users perform actions according to the advice they receive and input the results as feedback information into their device. This information is then sent back to the server and used to improve the analysis process in the artificial intelligence processing unit. This feedback loop allows the AI ​​model to improve its accuracy for each user.

[0657] 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.

[0658] This invention combines an emotion engine with a system that provides customized life advice based on the user's daily activities. This system consists of a combination of a sensor device, an artificial intelligence engine, a visualization device, and an emotion recognition device.

[0659] First, the device acquires daily activity data from sensor devices via the user's wearable device or smartphone. This includes not only location information and heart rate data, but also emotional state data acquired by an emotion recognition device using a microphone and camera. For example, emotional data is collected from the voice tone when the user is on the phone or from facial expressions captured by a facial recognition camera.

[0660] Next, the device sends sensor data and emotion data to the server. The server stores this data in a database and analyzes it using an artificial intelligence engine and an emotion engine. The artificial intelligence engine identifies the user's behavioral patterns, and the emotion engine identifies the user's emotional state through voice analysis and facial expression analysis. This allows the server to comprehensively understand the user's physical and emotional state.

[0661] Based on these analysis results, the server generates personalized life advice for the user. For example, if the server determines that the user has recently been experiencing stress, it can generate advice recommending meditation or relaxation exercises. The generated advice is sent from the server to the terminal and presented to the user via a visualization device. The terminal uses VR or AR technology to visually display the advice effectively, thereby increasing user acceptance.

[0662] After a user implements the advice, they input feedback on the results and satisfaction level into their device. The device sends this feedback back to the server, which then uses the feedback to improve the analysis process of the artificial intelligence engine. This process allows the system to continuously improve the accuracy of its advice and provide users with optimized support.

[0663] This embodiment enables comprehensive health management that takes into account an individual's emotional state, providing support to improve the quality of daily life.

[0664] The following describes the processing flow.

[0665] Step 1:

[0666] The device collects the user's daily activity data using sensor devices. This includes location information, heart rate, voice tone, and facial expression data from facial recognition.

[0667] Step 2:

[0668] The device collects data, which is then compiled and sent to a server using a secure communication method. This data includes not only activity information but also information indicating emotional state.

[0669] Step 3:

[0670] The server stores the received data in a database, and an artificial intelligence engine analyzes the user's behavior patterns. Simultaneously, an emotion engine uses voice and facial expression data to identify the user's emotional state.

[0671] Step 4:

[0672] The server analyzes behavioral patterns and emotional states to generate personalized life advice for the user. For example, if stress levels are high, it will create relaxation advice.

[0673] Step 5:

[0674] The server sends the generated advice to the terminal. The terminal then presents the received advice to the user through a visualization device. The presentation method utilizes VR and AR, and is designed to allow the user to understand it intuitively.

[0675] Step 6:

[0676] Users implement the advice and input feedback on its effectiveness and their own reactions into the device. This feedback includes information about the results of implementing the advice and their satisfaction level.

[0677] Step 7:

[0678] The device collects user feedback and sends it to the server. The server analyzes this feedback and uses it to improve the accuracy of the AI ​​engine and emotion engine's analysis.

[0679] Step 8:

[0680] Based on the feedback analysis, the server adjusts the system to improve the accuracy of future advice and provide more appropriate support to the user.

[0681] (Example 2)

[0682] 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".

[0683] In modern society, managing personal health and optimizing lifestyles are crucial issues, and maintaining a balance between mind and body is particularly important. Many conventional systems provide general feedback based on the user's daily activities, but they do not offer customized advice that takes into account individual emotional states. Therefore, there is a need for technology that provides more specific and effective advice that takes individual emotional states into account, in order to improve users' health and quality of life.

[0684] 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.

[0685] In this invention, the server includes data collection means for collecting the user's daily activity data and emotional state, analysis means for analyzing the data obtained from the data collection means and identifying the user's behavioral patterns and emotional state, and generation means for generating customized life advice for the user based on the analysis results from the analysis means. This enables the user to receive specific and effective life advice tailored to their individual emotional state.

[0686] "Data collection means" refers to a device or method used to collect data on a user's daily activities and emotional state.

[0687] "Analysis means" refers to a process or technology for identifying user behavior patterns and emotional states based on collected data.

[0688] "Generation means" refers to a function or method for creating customized life advice for the user based on the analysis results.

[0689] "Presentation methods" refer to means of conveying generated life advice to the user visually or audibly, and may include the use of VR or AR technologies.

[0690] "Feedback improvement methods" are means of improving the analysis process based on feedback information obtained from users.

[0691] The system of this invention aims to improve the daily lives of users using multiple hardware and software components. Specifically, it comprehensively understands each user's emotional state and daily activities and provides advice based on that understanding.

[0692] First, the device collects data through the user's wearable device or smartphone. The sensors used include GPS for obtaining location information, biosensors for measuring heart rate, microphones for obtaining voice data, and cameras for collecting facial expression data. The device uses an emotion recognition device to determine the user's emotional state from their voice tone and facial expressions.

[0693] Next, the device securely transmits this sensor data and emotional data to the server. The server stores the received data in a database and analyzes it using an artificial intelligence engine and an emotional engine. This artificial intelligence engine implements machine learning algorithms to identify the user's behavioral patterns and emotional state.

[0694] Furthermore, the server generates life advice based on the analysis results. Leveraging a generative AI model, it can provide recommendations tailored to individual user situations. For example, if the server determines that a user is experiencing stress, it can suggest "meditating or doing relaxation exercises at the end of the day." This advice is then transferred back to the terminal and presented to the user through a visualization device. The terminal can use VR or AR technology to visually present the advice, improving user acceptance.

[0695] A concrete example would be someone who spends long hours working on their computer during the week and is feeling unwell. Based on this information, the server can generate lifestyle advice such as "stretch for 5 minutes every hour" and present it to the user via their terminal.

[0696] An example of a prompt message might be a scenario like, "The user has indicated fatigue in recent analysis. Please suggest ways to refresh them."

[0697] In this way, the system collects and analyzes user data, provides personalized advice optimized for each user, and supports the realization of a healthy lifestyle.

[0698] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0699] Step 1:

[0700] The device collects data through the user's wearable device or smartphone. Inputs include GPS location information, vital data from a heart rate sensor, voice tone from a microphone, and facial expression data from a camera. By integrating this data, the system can understand the user's daily activities and emotional state.

[0701] Step 2:

[0702] The terminal formats the collected data and sends it to the server using a secure communication protocol. The inputs here are sensor and emotion data, and the output is the data sent to the server. Data formatting involves converting to an appropriate format and filtering out unnecessary data.

[0703] Step 3:

[0704] The server stores the received data in a database and begins analysis. The input is the transmitted sensor and emotion data, and the output is the analysis results. Once the data has been stored in the database, the artificial intelligence engine identifies behavioral patterns, and the emotion engine performs voice and facial expression analysis. This allows for a comprehensive understanding of the user's state.

[0705] Step 4:

[0706] The server uses a generated AI model based on the analysis results to produce life advice optimized for the user. The input is behavioral and emotional information obtained from the analysis, and the output is specific advice. For example, if the user is determined to be in a stressed state, the server will generate advice recommending "meditating for 10 minutes every night."

[0707] Step 5:

[0708] The advice generated from the server is sent to the terminal and presented to the user via a visualization device. The input is the generated life advice, and the output is a visual or auditory presentation to the user. The terminal uses VR or AR technology to effectively display the advice and enhance user acceptance.

[0709] Step 6:

[0710] Users follow the advice provided and input feedback on the results and their satisfaction level into the device. This feedback becomes input for the system and is treated as new data.

[0711] Step 7:

[0712] The terminal sends the collected feedback information to the server, which uses it to improve the analysis process of the artificial intelligence engine. The input is user feedback, and the output is an improved analysis algorithm and increased accuracy for the next time.

[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] In recent years, there has been a growing need for systems that monitor an individual's emotional and health status and provide customized advice based on that data. However, conventional systems have had the problem of not being able to provide advice that adequately takes emotional states into account. Furthermore, efficiently incorporating user feedback to improve the accuracy of the advice has been a difficult challenge.

[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 sensing means for collecting the user's daily activity data and emotional state data, calculation means for analyzing the collected data and identifying the user's behavioral patterns and emotional state, and presentation means for providing the user with customized life advice based on the identified behavioral patterns and emotional state. This makes it possible to provide highly accurate and personalized advice that takes into account the user's emotional state.

[0718] "Sensing means" refers to devices or methods used to collect data on the user's daily activities and emotional state.

[0719] "Computation means" refers to a process or system for analyzing data collected by sensing means to identify the user's behavioral patterns and emotional state.

[0720] "Presentation means" refers to a device or method for providing a user with customized life advice, either visually or audibly, based on identified behavioral patterns and emotional states.

[0721] "Improvement measures" refer to mechanisms and systems for acquiring user feedback information and improving the analysis process of the computational means based on that feedback.

[0722] An "emotional analysis method" is a method for analyzing data related to a user's emotional state and generating customized health advice based on the results.

[0723] "Augmented reality means" are technologies and devices that visually present advice to users and improve the acceptability of that advice based on their reactions.

[0724] The system for implementing this invention has a configuration comprising a "sensing means" for collecting the user's daily activity data and emotional state data, a "calculation means" for analyzing this data, a "presentation means" for providing the user with advice based on the analysis results, and an "improvement means" for improving the system based on feedback from the user.

[0725] The server first collects various data from the user's wearable device or mobile device using "sensing means." In this process, it utilizes hardware such as microphones, cameras, location sensors, and heart rate sensors to acquire activity data and emotional state data provided by these sensors.

[0726] The collected data is analyzed using "computational tools." This analysis utilizes software such as TensorFlow and OpenCV to identify user behavior patterns and emotional states using various data. The TensorFlow model integrates diverse data to understand user activities and emotions, while OpenCV performs facial recognition based on camera images.

[0727] Based on the analysis results obtained, the server provides the user with customized life advice through a "presentation means." The presentation means utilizes an AR display that leverages augmented reality technology to present information to the user in a visually appealing way.

[0728] Furthermore, users can provide feedback on the effectiveness and satisfaction level of the advice they received. This information is used as a "means of improvement," and the quality of subsequent advice is improved based on the feedback provided.

[0729] For example, if the server detects that a user is experiencing stress, it will offer advice on relaxation techniques. Exercise suggestions will appear on the AR display, allowing the user to incorporate these suggestions into their daily life. In this way, advice is provided according to the user's emotional state, contributing to an improved quality of life.

[0730] As an example of a prompt, you can use the sentence, "If the user is feeling stressed, what relaxation methods should be suggested?" This facilitates appropriate support using generative AI models.

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

[0732] Step 1:

[0733] The device collects daily activity data and emotional state data from the user's wearable device or mobile device through sensing mechanisms. Inputs include location information, heart rate, voice tone, and facial images. This data is temporarily stored for subsequent analysis.

[0734] Step 2:

[0735] The terminal sends the collected data to the server. The server stores the received data in a database for analysis. During this data transfer process, data format conversion and basic filtering are performed.

[0736] Step 3:

[0737] The server analyzes the data stored in the database using computational methods. Specifically, it uses a TensorFlow model to identify behavioral patterns and OpenCV to analyze emotional states from facial images. The previously collected data is used as input data, and the output provides the user's specific behavioral patterns and emotional states.

[0738] Step 4:

[0739] The server generates personalized life advice based on the analysis results. It uses prompts such as "What relaxation methods should be suggested if the user is feeling stressed?" to generate optimal advice from the AI ​​model. The output is in text format.

[0740] Step 5:

[0741] The server sends the generated advice to the terminal. The terminal visually presents the advice using a presentation method. For example, an AR display is used to visually present relaxation techniques and exercise methods to the user.

[0742] Step 6:

[0743] After implementing the provided advice, users input feedback on its effectiveness and satisfaction level into their device. This feedback is sent to the server and used to improve the accuracy of the analysis process through various improvement measures.

[0744] Step 7:

[0745] Based on the feedback, the server implements system improvements to enhance the accuracy of future analysis processes and advice. This results in more precise and effective advice for users.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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."

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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 as being incorporated by reference.

[0767] The following is further disclosed regarding the embodiments described above.

[0768] (Claim 1)

[0769] A sensor device for collecting data on the user's daily activities,

[0770] An artificial intelligence engine analyzes the data collected by the aforementioned sensor device to identify user behavior patterns,

[0771] A visualization device for providing customized advice to the user based on the aforementioned behavioral patterns,

[0772] A system that includes this.

[0773] (Claim 2)

[0774] The system according to claim 1, comprising a health analysis means for analyzing user health data and providing advice on health status.

[0775] (Claim 3)

[0776] The system according to claim 1, further comprising a feedback processing means for acquiring user feedback information and improving the analysis process of the artificial intelligence engine.

[0777] "Example 1"

[0778] (Claim 1)

[0779] Information gathering means for obtaining information about the user's daily activities,

[0780] A machine learning method for analyzing the information obtained by the aforementioned information gathering means and identifying user behavior patterns,

[0781] A presentation means for providing personalized instructions to the user based on the aforementioned behavioral pattern,

[0782] A visualization means for visually presenting the personalized instructions to the user,

[0783] A system that includes this.

[0784] (Claim 2)

[0785] The system according to claim 1, comprising health analysis means for analyzing information related to the user's health and providing instructions regarding their health status.

[0786] (Claim 3)

[0787] The system according to claim 1, further comprising a reaction processing means for acquiring user reaction information and improving the analysis process of the machine learning means.

[0788] "Application Example 1"

[0789] (Claim 1)

[0790] A measuring device for collecting data on the user's daily activities,

[0791] An artificial intelligence processing unit analyzes the data collected by the aforementioned measuring device to identify the user's behavioral characteristics,

[0792] A display unit for providing customized guidance to the user based on the aforementioned behavioral characteristics,

[0793] A means of providing real-time feedback on user activity,

[0794] A system that includes this.

[0795] (Claim 2)

[0796] The system according to claim 1, comprising a health analysis means for analyzing user health data and providing guidance on health status.

[0797] (Claim 3)

[0798] The system according to claim 1, further comprising a reaction processing means for acquiring user reaction information and improving the analysis method of the artificial intelligence processing unit.

[0799] "Example 2 of combining an emotion engine"

[0800] (Claim 1)

[0801] A data collection method for collecting user daily activity data and emotional state,

[0802] Analysis means for analyzing data obtained from the aforementioned data collection means to identify user behavior patterns and emotional states,

[0803] A generation means that generates customized life advice for the user based on the analysis results from the aforementioned analysis means,

[0804] A presentation means for presenting life advice generated by the generation means to the user,

[0805] A feedback improvement means for acquiring user feedback information based on the advice presented by the presentation means and for improving the process of the analysis means,

[0806] A system that includes this.

[0807] (Claim 2)

[0808] The system according to claim 1, comprising a health management means for providing health management advice that takes into account the user's emotional state.

[0809] (Claim 3)

[0810] The system according to claim 1, wherein the data collection means includes a voice recognition means and a facial expression recognition means for recording voice tone and facial expression.

[0811] "Application example 2 when combining with an emotional engine"

[0812] (Claim 1)

[0813] A sensing means for collecting user's daily activity data and emotional state data,

[0814] A calculation means for analyzing the data collected by the sensing means and identifying the user's behavioral patterns and emotional state,

[0815] A means of providing customized life advice to the user based on identified behavioral patterns and emotional states,

[0816] An improvement means for obtaining user feedback information and improving the analysis process of the calculation means,

[0817] A system that includes this.

[0818] (Claim 2)

[0819] The system according to claim 1, comprising an emotional analysis means for providing customized health advice according to the user's emotional state.

[0820] (Claim 3)

[0821] The system according to claim 1, comprising augmented reality means for visually presenting advice to a user and improving the acceptability of the advice based on user response data. [Explanation of symbols]

[0822] 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 sensor device for collecting data on the user's daily activities, An artificial intelligence engine analyzes the data collected by the aforementioned sensor device to identify user behavior patterns, A visualization device for providing customized advice to the user based on the aforementioned behavioral patterns, A system that includes this.

2. The system according to claim 1, comprising a health analysis means for analyzing user health data and providing advice on health status.

3. The system according to claim 1, further comprising a feedback processing means for acquiring user feedback information and improving the analysis process of the artificial intelligence engine.

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