system

Abstract

We provide the system. [Solution] Means for acquiring physiological information and behavioral data of living organisms, Means for storing the acquired information in a distributed storage device in association with a specific user, A means for analyzing data stored in the aforementioned distributed storage device to identify the user's behavioral model and emotional tendencies, A means for generating advice optimized for a specific user based on identified behavioral models and emotional tendencies, Means for providing the generated advice to the user, Means of providing physical interaction within the user's living space to support the improvement of their emotions and behaviors, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, with the diversification of individual lifestyles, advice for personalized life improvement is in demand. However, it is difficult for conventional general advice and support to adapt to the characteristics of individuals and daily changes, and thus it may not fully exert its effects. Against this background, there is a demand for the development of a system that enables more individualized feedback and support according to the state of each user.

Means for Solving the Problems

[0005] This invention involves collecting biometric and behavioral information through wearable devices and storing it in the cloud. By analyzing user-specific data within the cloud, it identifies individual user behavioral patterns and emotional tendencies. Based on the analysis results, it uses generative AI technology to generate personalized advice and provides that advice to the user. In this way, the invention realizes a system that can provide specific and effective feedback tailored to the individual needs of each user in real time.

[0006] "Biometric information" refers to data that indicates the physical condition of an individual user, and includes information such as heart rate, sleep data, and body temperature.

[0007] "Behavioral information" refers to data that shows a user's daily activities, including information such as step count, location information, and activity logs.

[0008] A "cloud environment" is a virtual computer system that allows data to be stored and processed via the internet.

[0009] "Means of analysis" refers to methods or devices for processing collected data and extracting user patterns and trends.

[0010] "Means of generation" refers to a method or apparatus for creating new data or information based on the analysis results.

[0011] "Optimized advice" refers to customized advice and suggestions based on analyzed data, tailored to the specific characteristics and circumstances of a particular user.

[0012] "Means of delivery" refers to a method or device for communicating the generated advice to the user. [Brief explanation of the drawing]

[0013] [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] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards 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.

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention is a system for realizing personalized life coaching for users, and is configured as follows: A wearable device worn by the user acquires biometric and behavioral information in real time. This acquired information is temporarily stored on the user's smartphone or a dedicated terminal. This terminal periodically encrypts the data and transmits it to a server in a cloud environment.

[0035] The server organizes and stores received data for each user and performs analysis using machine learning models. This analysis allows the server to identify users' behavioral patterns and emotional tendencies. Based on the identified behavioral patterns and emotional tendencies, the server uses generative AI technology to generate personalized advice for each user.

[0036] The generated advice is sent to the device via the cloud. Users can interactively receive the advice through their smartphones or VR / MR / AR devices. This allows users to receive personalized feedback in real time, which can lead to behavioral improvements and enhanced mental health.

[0037] For example, if the system detects that a user is experiencing a certain level of stress on a daily basis, the server generates specific advice, such as relaxation techniques or appropriate exercise, and presents it to the user's device. Furthermore, if progress toward the user's long-term goals is stalled, advice prompting reassessment is also provided. This system allows users to improve themselves and cope with obstacles in a more efficient and effective way.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The wearable device worn by the user acquires biometric and behavioral information such as heart rate, steps taken, location, and sleep duration. The wearable device transmits this data to the terminal, updating it at regular intervals.

[0041] Step 2:

[0042] The device temporarily stores biometric and behavioral information received from the wearable device in its storage. When a certain amount of data is reached or a predetermined amount of time has elapsed, the device prepares to encrypt the data and send it to a server in the cloud.

[0043] Step 3:

[0044] The server organizes the data received by each user and stores it in a database. The server stores the data chronologically and formats it into a usable format for later analysis.

[0045] Step 4:

[0046] The server analyzes the data using a machine learning model. The server identifies behavioral patterns, abnormal heart rates, and emotional tendencies, and extracts user-specific characteristics based on this.

[0047] Step 5:

[0048] Based on the analysis results, the server uses generative AI technology to generate personalized advice for the user. This advice includes suggestions for health management, stress reduction, and achieving long-term goals.

[0049] Step 6:

[0050] The server sends the generated advice to the device via the cloud. The device receives this advice and formats it for display.

[0051] Step 7:

[0052] Users can view advice provided from their devices using their smartphones or VR / MR / AR devices. Users can receive advice in an interactive way and use it to improve their daily actions and habits.

[0053] (Example 1)

[0054] 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."

[0055] In today's busy lifestyle, it is a challenge for individual users to accurately understand their own health status, behavioral patterns, and emotional tendencies, and to take appropriate corrective measures based on this understanding. Furthermore, general advice does not address individual needs, and there is a need to provide users with effective feedback and action plans.

[0056] 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.

[0057] In this invention, the server includes means for acquiring biometric data and activity data, means for encrypting the acquired data, associating it with a specific user, and storing it in an information storage device, and means for analyzing the data stored in the information storage device using a machine learning model to identify the user's behavioral characteristics and emotional tendencies. This makes it possible to provide personalized and appropriate advice to the user in real time.

[0058] "Biometric data" refers to information obtained from the human body, including measurements related to health conditions such as heart rate, body temperature, and blood pressure.

[0059] "Activity data" refers to information related to human movement and behavior, including measurements of physical activity such as steps taken, sitting time, and distance traveled.

[0060] An "information storage device" is a device or system for recording and storing digital data, and includes cloud environments and local storage.

[0061] A "machine learning model" refers to an algorithm or framework designed to analyze data and extract patterns and features, and includes technologies such as TENSORFLOW® and PyTorch.

[0062] "Behavioral characteristics" refer to features that indicate a consistent behavioral pattern or habit of a particular user, and are identified based on past behavioral data.

[0063] "Emotional tendencies" refer to the tendencies that indicate the emotional fluctuations or general mental state of a particular user, and are inferred through data analysis.

[0064] "Advice" refers to behavioral guidelines and improvement measures provided to specific users, and is generated based on analysis results.

[0065] A "body-worn device" refers to a measuring device that a user can continuously wear in their daily life, and includes wristband-type health monitors, among others.

[0066] "Knowledge processing technology" refers to technologies that generate meaningful information and content from data using natural language processing and generative AI models.

[0067] The purpose of this system is to provide users with personalized health management and behavioral improvement guidelines. Specifically, the server, terminal, and user work together to perform the following processes.

[0068] The server utilizes cloud computing services as its hardware and operates a database system as its information storage device. Specific examples include common cloud platform services and database management systems. Furthermore, frameworks such as TensorFlow and PyTorch are used to implement machine learning models. By combining this hardware and software, the server analyzes biometric and activity data sent by users to identify behavioral characteristics and emotional tendencies.

[0069] The terminals include smartphones and tablets, which collect data from users and securely communicate it to the server. The terminals acquire biometric and activity data from wearable devices via Bluetooth or Wi-Fi and temporarily record the data in local storage. The data is encrypted using the AES 256-bit encryption algorithm and sent to the server via the HTTPS protocol.

[0070] Users unconsciously collect data by wearing body-worn devices during their daily lives. Users themselves receive advice generated using the device and use it to improve their behavior. The advice is provided in an interactive format, such as smartphone notifications, voice guidance, or interfaces using VR / MR / AR technology.

[0071] For example, if a high-stress state is detected based on the user's activity data, the server will run a generation AI model using a prompt such as "The user is feeling stressed, please suggest ways to relax," generate specific advice for relaxation, and notify the user of this advice through their device.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] The user wears a body-worn device.

[0075] Input: User biometric data and activity data

[0076] Specific operation: The device uses sensors to detect heart rate, body temperature, steps taken, etc., in real time. This data is acquired instantly and reflects recent activity.

[0077] Output: Acquired biometric data and activity data

[0078] Step 2:

[0079] The terminal receives data from the device.

[0080] Input: Biometric data and activity data transmitted from the device

[0081] Specific operation: The device receives data via Bluetooth and temporarily stores it in local storage. AES256-bit encryption is used to ensure data security.

[0082] Output: Encrypted temporary data

[0083] Step 3:

[0084] The device sends the saved data to the server.

[0085] Input: Encrypted temporary data

[0086] Specific operation: The device uses the HTTPS protocol to securely send encrypted data to a server in the cloud. This process is performed regularly and is automated.

[0087] Output: Encrypted data sent to the server

[0088] Step 4:

[0089] The server analyzes the data.

[0090] Input: Encrypted data sent to the server

[0091] Specific operation: The server first decrypts the data and organizes it by user. It then uses machine learning models to analyze the data and extract behavioral characteristics and emotional tendencies. The models are built using TensorFlow and PyTorch.

[0092] Output: Behavioral characteristics and emotional tendencies as analysis results

[0093] Step 5:

[0094] The server generates advice using an AI model.

[0095] Input: Behavioral characteristics and emotional tendencies as analysis results

[0096] Specific operation: The server inputs a prompt message, "Requesting the generation of advice based on behavioral characteristics and emotional tendencies," into the AI ​​model, which then generates advice optimized for the user.

[0097] Output: Generated advice

[0098] Step 6:

[0099] The server sends the generated advice to the terminal.

[0100] Input: Generated advice

[0101] Specific operation: The server sends the generated advice back to the user's terminal via the cloud, delivering it to the user immediately.

[0102] Output: Advice sent to the user terminal

[0103] Step 7:

[0104] The user receives advice from the device.

[0105] Input: Advice sent to the device

[0106] Specific operation: Users receive advice via smartphone notifications or applications and review the content. It is often displayed in an interactive format.

[0107] Output: Opportunity to implement behavioral improvements based on received advice

[0108] (Application Example 1)

[0109] 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."

[0110] Traditional systems lack the ability to provide real-time, personalized support for behavioral improvement and mental health enhancement, as they cannot offer physical feedback within individual living environments. This results in a challenge in effectively implementing interactive interventions in users' daily lives.

[0111] 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.

[0112] In this invention, the server includes means for acquiring physiological information and behavioral data of a living organism, means for associating the acquired information with a specific user and storing it in a distributed storage device, and means for analyzing the data stored in the distributed storage device to identify the user's behavioral model and emotional tendencies. This enables physical interaction within the user's living space to support the improvement of their emotions and behavior.

[0113] "Physiological information of a living organism" refers to measurable data related to bodily functions and conditions, such as heart rate, body temperature, blood pressure, and respiratory rate.

[0114] "Behavioral data" refers to information about human movement and activity, including step count, location, and exercise level.

[0115] A "distributed storage device" is a system that stores data on various nodes, such as the cloud, and is characterized by its decentralized data storage rather than centralized management.

[0116] A "behavioral model" refers to a predictable behavioral pattern based on a user's past actions.

[0117] "Emotional tendencies" refer to the patterns in which a user's emotions manifest, clearly indicating the frequency and tendency of positive or negative emotions.

[0118] "Advice" refers to the provision of information, including suggestions and measures for improvement aimed at a specific purpose, and is used to improve the user's behavior or emotional state.

[0119] "Physical interaction" refers to the feedback process that takes place in the user's living environment through actual actions and conversations, and is carried out via voice, movement, displays, etc.

[0120] The system for implementing this invention is built primarily using wearable devices, distributed storage devices, and artificial intelligence technologies.

[0121] The server activates sensors attached to the user's body to acquire physiological and behavioral data. These wearable devices record heart rate, body temperature, and movement in real time, and temporarily store the collected data via a smartphone. Periodically, this data is encrypted and sent to distributed storage, where it is organized for each user.

[0122] The server applies machine learning algorithms when analyzing data stored in memory. This allows it to identify the user's behavioral models and emotional tendencies, and generate personalized advice. The generated advice is then delivered to the user in the most effective way possible using a generative AI model. For example, this could include suggestions for relaxation exercises suitable for a specific situation or measures to improve daily behaviors.

[0123] Users receive advice through a robot installed in their home. This robot interacts with users using voice and a display, providing support to help them make concrete changes in their daily lives.

[0124] For example, if a user indicates a high stress level over the past few days, the robot can advise the user by saying, "I'll play some calming music so you can take a deep breath and relax."

[0125] An example of a prompt message is: "This user has averaged less than 6 hours of sleep over the past 3 days. Based on this information, generate sleep improvement advice."

[0126] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0127] Step 1:

[0128] The device acquires the user's physiological information and behavioral data from wearable devices. The input is real-time data from sensors, and the output is temporarily stored raw data. This data includes heart rate, body temperature, and movement.

[0129] Step 2:

[0130] The terminal encrypts this temporarily stored raw data and periodically sends it to a distributed storage device in the cloud. The input is the temporarily stored raw data, and the output is the encrypted data stored in the cloud. This process is performed via an internet connection, ensuring data security.

[0131] Step 3:

[0132] The server retrieves data from distributed storage devices and performs analysis using machine learning algorithms. The input is encrypted data stored in the cloud, and the output is the identified behavioral model and emotional tendencies. Data processing utilizes pattern recognition and statistical methods.

[0133] Step 4:

[0134] The server generates user-optimized advice using a generative AI model based on identified behavioral models and sentiment tendencies. The input is the analyzed behavioral models and sentiment tendencies, and the output is personalized advice. Prompt statements are used in this generation process.

[0135] Step 5:

[0136] The server sends the generated advice to a robot installed in the home. The input is the generated advice, and the output is the robot providing advice via voice and display. Specifically, the robot makes announcements to the user, such as "I will play music to help you relax."

[0137] 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.

[0138] This invention provides a system that supports users' personalized lives. By combining it with an emotion engine, it recognizes the user's emotional state and improves the accuracy and effectiveness of advice. The wearable device worn by the user acquires biometric and behavioral information in real time, and also collects voice and facial expression data. This information is temporarily stored on the device and periodically transmitted to a server in a cloud environment.

[0139] The server uses a machine learning model to analyze the received data. The analysis results include the user's behavior patterns, fluctuations in physical condition, and emotional states identified using the emotion engine. Generative AI technology is then used to generate optimized advice based on this data. The emotion engine detects the user's emotions from voice and facial expression data and estimates stress levels, happiness levels, and other factors. This emotional data is integrated with other behavioral information to generate more specific and personalized advice.

[0140] For example, if the emotion engine determines that a user is experiencing high levels of stress, it will offer advice on relaxation methods and rest plans. Conversely, if positive emotions are detected, suggestions will be made to promote further positive experiences. This allows users to receive more consistent support in improving their daily lives and mental health. The system is designed with full user privacy in mind, and individual data is encrypted and securely protected.

[0141] The following describes the processing flow.

[0142] Step 1:

[0143] The wearable device worn by the user acquires biometric information such as heart rate, steps taken, location information, and sleep data in real time. Furthermore, it also acquires voice data and facial expression data using the device's microphone and camera.

[0144] Step 2:

[0145] The device temporarily stores biometric information, behavioral information, and voice / facial expression data received from the wearable device. The stored data is encrypted at regular intervals and prepared for transmission to a server in the cloud environment.

[0146] Step 3:

[0147] The server organizes and stores the received data for each user. The server then formats the data chronologically for analysis.

[0148] Step 4:

[0149] The server analyzes the data using machine learning models and an emotion engine. The server identifies user behavior patterns and recognizes the user's emotional state from voice and facial expression data.

[0150] Step 5:

[0151] Based on the analysis results, the server uses AI-generated technology to create personalized advice for the user. The advice is individually tailored to the user's emotional state and behavioral patterns, focusing on health promotion and stress management.

[0152] Step 6:

[0153] The server generates advice and sends it to the device via the cloud. The device receives this advice and formats it in a way that is easy for the user to understand.

[0154] Step 7:

[0155] Users can use their smartphones or VR / MR / AR devices to review the advice they receive. Based on this advice, users adjust their behavior and use it to improve the quality of their daily lives.

[0156] (Example 2)

[0157] 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 will be referred to as the "terminal."

[0158] Conventional lifestyle support systems have struggled to provide individualized advice based on users' emotions and behaviors, offering only general suggestions and failing to deliver optimal support for each user. Furthermore, there has been a lack of technology to monitor emotional fluctuations in real time and provide appropriate support accordingly. There is a need to address these challenges and enable more personalized lifestyle support.

[0159] 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.

[0160] In this invention, the server includes an acquisition device for acquiring biometric and behavioral information, a transmission device for associating the acquired information with individual users and transmitting it to the external environment, and an analysis device for analyzing the data transmitted to the external environment and determining the user's behavioral characteristics and emotional state. This makes it possible to generate optimal guidelines for users in real time and provide specific and effective advice tailored to individual circumstances.

[0161] An "acquisition device" is a device that collects biological and behavioral information in real time and provides the data for analysis.

[0162] A "transmission device" is a device that securely transmits information collected by an acquisition device to an external environment, associating it with a specific user.

[0163] An "analysis device" is a device that processes data transmitted to the external environment and extracts and identifies the user's behavioral characteristics and emotional state.

[0164] A "generating device" is a device that generates optimized guidelines and advice for users based on identified behavioral characteristics and emotional states.

[0165] A "presentation device" is a device that directly presents guidelines and advice created by a generation device to the user.

[0166] "Biometric information" refers to data about the user's physical condition, including heart rate and body temperature.

[0167] "Behavioral information" refers to data about the user's behavior, including distance traveled and activity patterns.

[0168] This invention is a system that provides personalized life support to users, collecting biometric and behavioral information using various sensors. In a specific embodiment, a server and a terminal play a central role in this system.

[0169] The user wears a wearable device that measures heart rate, body temperature, and other parameters. This device also records activity levels and location information, and has the capability to collect voice and facial expression data. The device temporarily stores this data in real time and periodically transmits it to a cloud server using a transmission device. The cloud server, to which the data is received, is equipped with an analysis device for analyzing the data.

[0170] The server uses machine learning models and an emotion recognition engine to analyze the acquired data. These allow the server to determine the user's behavioral characteristics and emotional state, and then use generative AI technology to generate optimal guidance based on the analysis results. This guidance is optimized for each individual user's situation. For example, if the server determines that the user is experiencing high stress, it might generate specific advice such as, "Taking a 15-minute walk in a nearby park will help you relax."

[0171] The device presents the generated advice to the user via voice and notification functions. This allows users to receive more effective and personalized advice in their real lives. The generated advice takes into account the user's emotional state and supports lifestyle improvement and mental health enhancement.

[0172] An example of a prompt to the generating AI model would be, "Please suggest effective ways for the user to relax, but please consider external circumstances and time constraints." By providing specific instructions like this, appropriate advice can be generated.

[0173] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0174] Step 1:

[0175] The user wears a wearable device. This device collects biometric information such as heart rate, body temperature, and activity level, as well as behavioral information such as location, voice, and facial expression data. It acquires biometric and behavioral information as input and transmits this data to the terminal in real time as output. Specifically, sensors on the device detect data and transfer it to the terminal wirelessly.

[0176] Step 2:

[0177] The terminal temporarily stores data transmitted from the user's device. The input is biometric and behavioral information collected in step 1, and the output is data stored in a database or local storage. This storage process efficiently handles the data in preparation for later analysis while maintaining data integrity.

[0178] Step 3:

[0179] The device periodically sends temporarily stored data to a cloud server. The input is the data in the storage area, and the output is encrypted data stored on the cloud server. Encryption technology is applied during the transmission process to protect data privacy. Specifically, HTTPS is used as the transmission protocol.

[0180] Step 4:

[0181] The server analyzes data received on the cloud. The input is encrypted user data, and the output is the user's behavioral characteristics, estimated physical condition, and emotional state. Machine learning models are used for data processing, and these models perform feature extraction and inference. Specifically, stress levels are considered based on heart rate and activity levels, and emotions are determined from voice and facial expressions.

[0182] Step 5:

[0183] The server runs a generative AI model based on the analysis results to generate personalized advice for the user. The input is the user's behavioral characteristics and emotional information, and the output is optimized advice. The prompt is an instruction to the generative AI, "Please provide suggestions to reduce the user's current stress." In terms of operation, the prompt is input to the AI ​​model, and advice in natural language is output.

[0184] Step 6:

[0185] The device provides the user with generated advice. The input is the advice sent from the server, and the output is the information presented to the user. Specifically, the advice is displayed on the device's screen or presented to the user verbally via speech synthesis. This allows the user to learn about specific ways to improve their life.

[0186] (Application Example 2)

[0187] 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".

[0188] There is a growing need to appropriately monitor the emotions and health status of the elderly and those requiring care, and to provide support tailored to their individual needs. However, conventional technologies struggle to accurately recognize real-time changes in health and emotional states and provide optimized care support. Therefore, there is a need for technologies that enable more effective and personalized care support.

[0189] 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.

[0190] In this invention, the server includes means for acquiring biometric and behavioral information; means for associating the acquired information with a specific user and storing it in a cloud environment; means for analyzing the data stored in the cloud environment and identifying the user's behavioral patterns and emotional tendencies; means for generating optimized advice for the specific user based on the identified behavioral patterns and emotional tendencies; means for providing the generated advice to the user; and means for monitoring the user's health status in real time and providing optimized suggestions for care support. This makes it possible to effectively provide support tailored to the individual needs of the elderly and people requiring care, thereby improving their quality of life.

[0191] "Biometric information" refers to data related to an individual's health status and the maintenance of life, including physical conditions such as heart rate, blood pressure, and body temperature.

[0192] "Behavioral information" refers to data about an individual's social or physical movements and activities, including walking distance, activity time, and location travel history.

[0193] A "cloud environment" is a system that utilizes computing resources and data storage provided via the internet, and serves as a foundation for distributed storage and processing of information.

[0194] "Analysis" refers to the process of scrutinizing collected data and extracting useful information and patterns from it.

[0195] "Behavioral patterns" refer to data that shows typical models or tendencies of how a particular individual usually behaves.

[0196] "Emotional tendencies" refer to data that shows an individual's sustained emotional state and its changes, including trends in happiness and stress levels.

[0197] "Optimized advice" refers to recommended actions and information that are tailored to the specific condition and needs of an individual.

[0198] "User" refers to an individual who uses this system and enjoys its benefits.

[0199] "Healthcare support" refers to services and information provided to maintain or improve an individual's health.

[0200] To realize this invention, it is necessary to acquire the user's biometric and behavioral information in real time using a wearable device. The acquired information is temporarily stored on the user's terminal and transmitted to a cloud environment. A server located in the cloud environment analyzes the user's behavioral patterns and emotional tendencies based on this information.

[0201] The analysis uses the machine learning library TensorFlow to evaluate the emotional state of individual users based on biometric and behavioral information. The evaluation also includes analysis of voice and facial expression data to determine stress levels and happiness tendencies. Based on these analysis results, a generative AI model is used to generate personalized advice.

[0202] The generated advice is delivered to the user through devices such as smartphones and smart glasses. If the user is experiencing high levels of stress, relaxation methods and rest plans are provided. Conversely, if positive emotions are detected, suggestions are made to encourage positive experiences.

[0203] For example, we might suggest using an application that allows users to listen to nature sounds to those experiencing high levels of stress in their daily lives. We might also offer advice on how to promote relaxed sleep in preparation for an important event the following day.

[0204] Through this process, users will receive support in improving their daily lives and mental health. An example of a prompt for the generating AI model is: "Your current emotional state is [emotional state]. Please provide an optimal care plan that takes this state into consideration."

[0205] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0206] Step 1:

[0207] The device collects the user's biometric and behavioral information in real time from the wearable device. Biometric information includes heart rate and body temperature, while behavioral information includes movement data. This data is temporarily stored in the device's local storage.

[0208] Step 2:

[0209] The device periodically transmits collected biometric and behavioral information to a server in a cloud environment. A secure protocol is used for transmission, and the data is transferred in an encrypted state.

[0210] Step 3:

[0211] The server collects and stores the received data and performs analysis using the machine learning library TensorFlow. Here, the data is processed to identify user behavior patterns and emotional tendencies. The output includes the user's stress level and degree of happiness.

[0212] Step 4:

[0213] The server uses a generative AI model to generate personalized advice based on identified emotional tendencies. The generated advice is constructed based on pre-defined protocol statements. The output is personalized advice in text format.

[0214] Step 5:

[0215] The generated advice is sent from the cloud environment to the device, which then notifies the user. Specifically, the user is notified using the smartphone's notification function or voice assistant. The user is supported in managing stress and promoting positive experiences in their daily life.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] [Second Embodiment]

[0220] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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".

[0232] This invention is a system for realizing personalized life coaching for users, and is configured as follows: A wearable device worn by the user acquires biometric and behavioral information in real time. This acquired information is temporarily stored on the user's smartphone or a dedicated terminal. This terminal periodically encrypts the data and transmits it to a server in a cloud environment.

[0233] The server organizes and stores received data for each user and performs analysis using machine learning models. This analysis allows the server to identify users' behavioral patterns and emotional tendencies. Based on the identified behavioral patterns and emotional tendencies, the server uses generative AI technology to generate personalized advice for each user.

[0234] The generated advice is sent to the device via the cloud. Users can interactively receive the advice through their smartphones or VR / MR / AR devices. This allows users to receive personalized feedback in real time, which can lead to behavioral improvements and enhanced mental health.

[0235] For example, if the system detects that a user is experiencing a certain level of stress on a daily basis, the server generates specific advice, such as relaxation techniques or appropriate exercise, and presents it to the user's device. Furthermore, if progress toward the user's long-term goals is stalled, advice prompting reassessment is also provided. This system allows users to improve themselves and cope with obstacles in a more efficient and effective way.

[0236] The following describes the processing flow.

[0237] Step 1:

[0238] The wearable device worn by the user acquires biometric and behavioral information such as heart rate, steps taken, location, and sleep duration. The wearable device transmits this data to the terminal, updating it at regular intervals.

[0239] Step 2:

[0240] The device temporarily stores biometric and behavioral information received from the wearable device in its storage. When a certain amount of data is reached or a predetermined amount of time has elapsed, the device prepares to encrypt the data and send it to a server in the cloud.

[0241] Step 3:

[0242] The server organizes the data received by each user and stores it in a database. The server stores the data chronologically and formats it into a usable format for later analysis.

[0243] Step 4:

[0244] The server analyzes the data using a machine learning model. The server identifies behavioral patterns, abnormal heart rates, and emotional tendencies, and extracts user-specific characteristics based on this.

[0245] Step 5:

[0246] Based on the analysis results, the server uses generative AI technology to generate personalized advice for the user. This advice includes suggestions for health management, stress reduction, and achieving long-term goals.

[0247] Step 6:

[0248] The server sends the generated advice to the device via the cloud. The device receives this advice and formats it for display.

[0249] Step 7:

[0250] Users can view advice provided from their devices using their smartphones or VR / MR / AR devices. Users can receive advice in an interactive way and use it to improve their daily actions and habits.

[0251] (Example 1)

[0252] 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."

[0253] In today's busy lifestyle, it is a challenge for individual users to accurately understand their own health status, behavioral patterns, and emotional tendencies, and to take appropriate corrective measures based on this understanding. Furthermore, general advice does not address individual needs, and there is a need to provide users with effective feedback and action plans.

[0254] 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.

[0255] In this invention, the server includes means for acquiring biometric data and activity data, means for encrypting the acquired data, associating it with a specific user, and storing it in an information storage device, and means for analyzing the data stored in the information storage device using a machine learning model to identify the user's behavioral characteristics and emotional tendencies. This makes it possible to provide personalized and appropriate advice to the user in real time.

[0256] "Biometric data" refers to information obtained from the human body, including measurements related to health conditions such as heart rate, body temperature, and blood pressure.

[0257] "Activity data" refers to information related to human movement and behavior, including measurements of physical activity such as steps taken, sitting time, and distance traveled.

[0258] An "information storage device" is a device or system for recording and storing digital data, and includes cloud environments and local storage.

[0259] A "machine learning model" refers to an algorithm or framework designed to analyze data and extract patterns and features, and includes technologies such as TensorFlow and PyTorch.

[0260] "Behavioral characteristics" refer to features that indicate a consistent behavioral pattern or habit of a particular user, and are identified based on past behavioral data.

[0261] "Emotional tendencies" refer to the tendencies that indicate the emotional fluctuations or general mental state of a particular user, and are inferred through data analysis.

[0262] "Advice" refers to behavioral guidelines and improvement measures provided to specific users, and is generated based on analysis results.

[0263] A "body-worn device" refers to a measuring device that a user can continuously wear in their daily life, and includes wristband-type health monitors, among others.

[0264] "Knowledge processing technology" refers to technologies that generate meaningful information and content from data using natural language processing and generative AI models.

[0265] The purpose of this system is to provide users with personalized health management and behavioral improvement guidelines. Specifically, the server, terminal, and user work together to perform the following processes.

[0266] The server utilizes cloud computing services as its hardware and operates a database system as its information storage device. Specific examples include common cloud platform services and database management systems. Furthermore, frameworks such as TensorFlow and PyTorch are used to implement machine learning models. By combining this hardware and software, the server analyzes biometric and activity data sent by users to identify behavioral characteristics and emotional tendencies.

[0267] The terminals include smartphones and tablets, which collect data from users and securely communicate it to the server. The terminals acquire biometric and activity data from wearable devices via Bluetooth or Wi-Fi and temporarily record the data in local storage. The data is encrypted using the AES 256-bit encryption algorithm and sent to the server via the HTTPS protocol.

[0268] Users unconsciously collect data by wearing body-worn devices during their daily lives. Users themselves receive advice generated using the device and use it to improve their behavior. The advice is provided in an interactive format, such as smartphone notifications, voice guidance, or interfaces using VR / MR / AR technology.

[0269] For example, if a high-stress state is detected based on the user's activity data, the server will run a generation AI model using a prompt such as "The user is feeling stressed, please suggest ways to relax," generate specific advice for relaxation, and notify the user of this advice through their device.

[0270] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0271] Step 1:

[0272] The user wears a body-worn device.

[0273] Input: User biometric data and activity data

[0274] Specific operation: The device uses sensors to detect heart rate, body temperature, steps taken, etc., in real time. This data is acquired instantly and reflects recent activity.

[0275] Output: Acquired biometric data and activity data

[0276] Step 2:

[0277] The terminal receives data from the device.

[0278] Input: Biometric data and activity data sent from the device

[0279] Specific operation: The terminal receives data via Bluetooth and temporarily stores it in local storage. AES 256-bit is used as the encryption means to ensure data security.

[0280] Output: Encrypted temporarily stored data

[0281] Step 3:

[0282] The terminal sends the stored data to the server.

[0283] Input: Encrypted temporarily stored data

[0284] Specific operation: The terminal uses the HTTPS protocol to securely send the encrypted data to the server on the cloud. This process is performed periodically and is automated.

[0285] Output: Encrypted data sent to the server

[0286] Step 4:

[0287] The server analyzes the data.

[0288] Input: Encrypted data sent to the server

[0289] Specific operation: The server first decrypts the data and organizes it for each user. Using a machine learning model, analysis is performed to extract behavior characteristics and emotional tendencies from the data. The model is built using TensorFlow or PyTorch.

[0290] Output: Behavior characteristics and emotional tendencies as analysis results

[0291] Step 5:

[0292] The server generates advice using an AI model.

[0293] Input: Behavioral characteristics and emotional tendencies as analysis results

[0294] Specific operation: The server inputs a prompt message, "Requesting the generation of advice based on behavioral characteristics and emotional tendencies," into the AI ​​model, which then generates advice optimized for the user.

[0295] Output: Generated advice

[0296] Step 6:

[0297] The server sends the generated advice to the terminal.

[0298] Input: Generated advice

[0299] Specific operation: The server sends the generated advice back to the user's terminal via the cloud, delivering it to the user immediately.

[0300] Output: Advice sent to the user terminal

[0301] Step 7:

[0302] The user receives advice from the device.

[0303] Input: Advice sent to the device

[0304] Specific operation: Users receive advice via smartphone notifications or applications and review the content. It is often displayed in an interactive format.

[0305] Output: Opportunity to implement behavioral improvements based on received advice

[0306] (Application Example 1)

[0307] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0308] In providing support for improving behavior and enhancing mental health optimized for individual users in real time, the conventional system lacks the ability to provide physical feedback in individual living environments. For this reason, there is a problem that interactive intervention in the daily life of users is not effectively carried out.

[0309] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0310] In this invention, the server includes means for acquiring physiological information and behavior data of a living body, means for associating the acquired information with a specific user and storing it in a distributed storage device, and means for analyzing the data stored in the distributed storage device to identify the behavior model and emotional tendency of the user. Thereby, physical interaction for supporting the improvement of emotions and behavior within the living space of the user becomes possible.

[0311] "Physiological information of a living body" refers to measurable data related to the functions and states of the body, such as heart rate, body temperature, blood pressure, and respiratory rate.

[0312] "Behavior data" refers to information related to human movements and activities, including the number of steps, moving position, amount of exercise, and the like.

[0313] "Distributed storage device" is a system that stores data in various nodes such as the cloud, and is characterized by decentralized data storage rather than centralization.

[0314] "Behavior model" refers to a behavior pattern predicted based on the past behavior of the user.

[0315] "Emotional tendencies" refer to the patterns in which a user's emotions manifest, clearly indicating the frequency and tendency of positive or negative emotions.

[0316] "Advice" refers to the provision of information, including suggestions and measures for improvement aimed at a specific purpose, and is used to improve the user's behavior or emotional state.

[0317] "Physical interaction" refers to the feedback process that takes place in the user's living environment through actual actions and conversations, and is carried out via voice, movement, displays, etc.

[0318] The system for implementing this invention is built primarily using wearable devices, distributed storage devices, and artificial intelligence technologies.

[0319] The server activates sensors attached to the user's body to acquire physiological and behavioral data. These wearable devices record heart rate, body temperature, and movement in real time, and temporarily store the collected data via a smartphone. Periodically, this data is encrypted and sent to distributed storage, where it is organized for each user.

[0320] The server applies machine learning algorithms when analyzing data stored in memory. This allows it to identify the user's behavioral models and emotional tendencies, and generate personalized advice. The generated advice is then delivered to the user in the most effective way possible using a generative AI model. For example, this could include suggestions for relaxation exercises suitable for a specific situation or measures to improve daily behaviors.

[0321] Users receive advice through a robot installed in their home. This robot interacts with users using voice and a display, providing support to help them make concrete changes in their daily lives.

[0322] For example, if a user indicates a high stress level over the past few days, the robot can advise the user by saying, "I'll play some calming music so you can take a deep breath and relax."

[0323] An example of a prompt message is: "This user has averaged less than 6 hours of sleep over the past 3 days. Based on this information, generate sleep improvement advice."

[0324] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0325] Step 1:

[0326] The device acquires the user's physiological information and behavioral data from wearable devices. The input is real-time data from sensors, and the output is temporarily stored raw data. This data includes heart rate, body temperature, and movement.

[0327] Step 2:

[0328] The terminal encrypts this temporarily stored raw data and periodically sends it to a distributed storage device in the cloud. The input is the temporarily stored raw data, and the output is the encrypted data stored in the cloud. This process is performed via an internet connection, ensuring data security.

[0329] Step 3:

[0330] The server retrieves data from distributed storage devices and performs analysis using machine learning algorithms. The input is encrypted data stored in the cloud, and the output is the identified behavioral model and emotional tendencies. Data processing utilizes pattern recognition and statistical methods.

[0331] Step 4:

[0332] The server generates user-optimized advice using a generative AI model based on identified behavioral models and sentiment tendencies. The input is the analyzed behavioral models and sentiment tendencies, and the output is personalized advice. Prompt statements are used in this generation process.

[0333] Step 5:

[0334] The server sends the generated advice to a robot installed in the home. The input is the generated advice, and the output is the robot providing advice via voice and display. Specifically, the robot makes announcements to the user, such as "I will play music to help you relax."

[0335] 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.

[0336] This invention provides a system that supports users' personalized lives. By combining it with an emotion engine, it recognizes the user's emotional state and improves the accuracy and effectiveness of advice. The wearable device worn by the user acquires biometric and behavioral information in real time, and also collects voice and facial expression data. This information is temporarily stored on the device and periodically transmitted to a server in a cloud environment.

[0337] The server uses a machine learning model to analyze the received data. The analysis results include the user's behavior patterns, fluctuations in physical condition, and emotional states identified using the emotion engine. Generative AI technology is then used to generate optimized advice based on this data. The emotion engine detects the user's emotions from voice and facial expression data and estimates stress levels, happiness levels, and other factors. This emotional data is integrated with other behavioral information to generate more specific and personalized advice.

[0338] For example, if the emotion engine determines that a user is experiencing high levels of stress, it will offer advice on relaxation methods and rest plans. Conversely, if positive emotions are detected, suggestions will be made to promote further positive experiences. This allows users to receive more consistent support in improving their daily lives and mental health. The system is designed with full user privacy in mind, and individual data is encrypted and securely protected.

[0339] The following describes the processing flow.

[0340] Step 1:

[0341] The wearable device worn by the user acquires biometric information such as heart rate, steps taken, location information, and sleep data in real time. Furthermore, it also acquires voice data and facial expression data using the device's microphone and camera.

[0342] Step 2:

[0343] The device temporarily stores biometric information, behavioral information, and voice / facial expression data received from the wearable device. The stored data is encrypted at regular intervals and prepared for transmission to a server in the cloud environment.

[0344] Step 3:

[0345] The server organizes and stores the received data for each user. The server then formats the data chronologically for analysis.

[0346] Step 4:

[0347] The server analyzes the data using machine learning models and an emotion engine. The server identifies user behavior patterns and recognizes the user's emotional state from voice and facial expression data.

[0348] Step 5:

[0349] Based on the analysis results, the server uses AI-generated technology to create personalized advice for the user. The advice is individually tailored to the user's emotional state and behavioral patterns, focusing on health promotion and stress management.

[0350] Step 6:

[0351] The server generates advice and sends it to the device via the cloud. The device receives this advice and formats it in a way that is easy for the user to understand.

[0352] Step 7:

[0353] Users can use their smartphones or VR / MR / AR devices to review the advice they receive. Based on this advice, users adjust their behavior and use it to improve the quality of their daily lives.

[0354] (Example 2)

[0355] 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".

[0356] Conventional lifestyle support systems have struggled to provide individualized advice based on users' emotions and behaviors, offering only general suggestions and failing to deliver optimal support for each user. Furthermore, there has been a lack of technology to monitor emotional fluctuations in real time and provide appropriate support accordingly. There is a need to address these challenges and enable more personalized lifestyle support.

[0357] 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.

[0358] In this invention, the server includes an acquisition device for acquiring biometric and behavioral information, a transmission device for associating the acquired information with individual users and transmitting it to the external environment, and an analysis device for analyzing the data transmitted to the external environment and determining the user's behavioral characteristics and emotional state. This makes it possible to generate optimal guidelines for users in real time and provide specific and effective advice tailored to individual circumstances.

[0359] An "acquisition device" is a device that collects biological and behavioral information in real time and provides the data for analysis.

[0360] A "transmission device" is a device that securely transmits information collected by an acquisition device to an external environment, associating it with a specific user.

[0361] An "analysis device" is a device that processes data transmitted to the external environment and extracts and identifies the user's behavioral characteristics and emotional state.

[0362] A "generating device" is a device that generates optimized guidelines and advice for users based on identified behavioral characteristics and emotional states.

[0363] A "presentation device" is a device that directly presents guidelines and advice created by a generation device to the user.

[0364] "Biometric information" refers to data about the user's physical condition, including heart rate and body temperature.

[0365] "Behavioral information" refers to data about the user's behavior, including distance traveled and activity patterns.

[0366] This invention is a system that provides personalized life support to users, collecting biometric and behavioral information using various sensors. In a specific embodiment, a server and a terminal play a central role in this system.

[0367] The user wears a wearable device that measures heart rate, body temperature, and other parameters. This device also records activity levels and location information, and has the capability to collect voice and facial expression data. The device temporarily stores this data in real time and periodically transmits it to a cloud server using a transmission device. The cloud server, to which the data is received, is equipped with an analysis device for analyzing the data.

[0368] The server uses machine learning models and an emotion recognition engine to analyze the acquired data. These allow the server to determine the user's behavioral characteristics and emotional state, and then use generative AI technology to generate optimal guidance based on the analysis results. This guidance is optimized for each individual user's situation. For example, if the server determines that the user is experiencing high stress, it might generate specific advice such as, "Taking a 15-minute walk in a nearby park will help you relax."

[0369] The device presents the generated advice to the user via voice and notification functions. This allows users to receive more effective and personalized advice in their real lives. The generated advice takes into account the user's emotional state and supports lifestyle improvement and mental health enhancement.

[0370] An example of a prompt to the generating AI model would be, "Please suggest effective ways for the user to relax, but please consider external circumstances and time constraints." By providing specific instructions like this, appropriate advice can be generated.

[0371] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0372] Step 1:

[0373] The user wears a wearable device. This device collects biometric information such as heart rate, body temperature, and activity level, as well as behavioral information such as location, voice, and facial expression data. It acquires biometric and behavioral information as input and transmits this data to the terminal in real time as output. Specifically, sensors on the device detect data and transfer it to the terminal wirelessly.

[0374] Step 2:

[0375] The terminal temporarily stores data transmitted from the user's device. The input is biometric and behavioral information collected in step 1, and the output is data stored in a database or local storage. This storage process efficiently handles the data in preparation for later analysis while maintaining data integrity.

[0376] Step 3:

[0377] The device periodically sends temporarily stored data to a cloud server. The input is the data in the storage area, and the output is encrypted data stored on the cloud server. Encryption technology is applied during the transmission process to protect data privacy. Specifically, HTTPS is used as the transmission protocol.

[0378] Step 4:

[0379] The server analyzes data received on the cloud. The input is encrypted user data, and the output is the user's behavioral characteristics, estimated physical condition, and emotional state. Machine learning models are used for data processing, and these models perform feature extraction and inference. Specifically, stress levels are considered based on heart rate and activity levels, and emotions are determined from voice and facial expressions.

[0380] Step 5:

[0381] The server runs a generative AI model based on the analysis results to generate personalized advice for the user. The input is the user's behavioral characteristics and emotional information, and the output is optimized advice. The prompt is an instruction to the generative AI, "Please provide suggestions to reduce the user's current stress." In terms of operation, the prompt is input to the AI ​​model, and advice in natural language is output.

[0382] Step 6:

[0383] The device provides the user with generated advice. The input is the advice sent from the server, and the output is the information presented to the user. Specifically, the advice is displayed on the device's screen or presented to the user verbally via speech synthesis. This allows the user to learn about specific ways to improve their life.

[0384] (Application Example 2)

[0385] 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 as the "terminal".

[0386] There is a growing need to appropriately monitor the emotions and health status of the elderly and those requiring care, and to provide support tailored to their individual needs. However, conventional technologies struggle to accurately recognize real-time changes in health and emotional states and provide optimized care support. Therefore, there is a need for technologies that enable more effective and personalized care support.

[0387] 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.

[0388] In this invention, the server includes means for acquiring biometric and behavioral information; means for associating the acquired information with a specific user and storing it in a cloud environment; means for analyzing the data stored in the cloud environment and identifying the user's behavioral patterns and emotional tendencies; means for generating optimized advice for the specific user based on the identified behavioral patterns and emotional tendencies; means for providing the generated advice to the user; and means for monitoring the user's health status in real time and providing optimized suggestions for care support. This makes it possible to effectively provide support tailored to the individual needs of the elderly and people requiring care, thereby improving their quality of life.

[0389] "Biometric information" refers to data related to an individual's health status and the maintenance of life, including physical conditions such as heart rate, blood pressure, and body temperature.

[0390] "Behavioral information" refers to data about an individual's social or physical movements and activities, including walking distance, activity time, and location travel history.

[0391] A "cloud environment" is a system that utilizes computing resources and data storage provided via the internet, and serves as a foundation for distributed storage and processing of information.

[0392] "Analysis" refers to the process of scrutinizing collected data and extracting useful information and patterns from it.

[0393] "Behavioral patterns" refer to data that shows typical models or tendencies of how a particular individual usually behaves.

[0394] "Emotional tendencies" refer to data that shows an individual's sustained emotional state and its changes, including trends in happiness and stress levels.

[0395] "Optimized advice" refers to recommended actions and information that are tailored to the specific condition and needs of an individual.

[0396] "User" refers to an individual who uses this system and enjoys its benefits.

[0397] "Healthcare support" refers to services and information provided to maintain or improve an individual's health.

[0398] To realize this invention, it is necessary to acquire the user's biometric and behavioral information in real time using a wearable device. The acquired information is temporarily stored on the user's terminal and transmitted to a cloud environment. A server located in the cloud environment analyzes the user's behavioral patterns and emotional tendencies based on this information.

[0399] The analysis uses the machine learning library TensorFlow to evaluate the emotional state of individual users based on biometric and behavioral information. The evaluation also includes analysis of voice and facial expression data to determine stress levels and happiness tendencies. Based on these analysis results, a generative AI model is used to generate personalized advice.

[0400] The generated advice is delivered to the user through devices such as smartphones and smart glasses. If the user is experiencing high levels of stress, relaxation methods and rest plans are provided. Conversely, if positive emotions are detected, suggestions are made to encourage positive experiences.

[0401] For example, we might suggest using an application that allows users to listen to nature sounds to those experiencing high levels of stress in their daily lives. We might also offer advice on how to promote relaxed sleep in preparation for an important event the following day.

[0402] Through this process, users will receive support in improving their daily lives and mental health. An example of a prompt for the generating AI model is: "Your current emotional state is [emotional state]. Please provide an optimal care plan that takes this state into consideration."

[0403] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0404] Step 1:

[0405] The device collects the user's biometric and behavioral information in real time from the wearable device. Biometric information includes heart rate and body temperature, while behavioral information includes movement data. This data is temporarily stored in the device's local storage.

[0406] Step 2:

[0407] The device periodically transmits collected biometric and behavioral information to a server in a cloud environment. A secure protocol is used for transmission, and the data is transferred in an encrypted state.

[0408] Step 3:

[0409] The server collects and stores the received data and performs analysis using the machine learning library TensorFlow. Here, the data is processed to identify user behavior patterns and emotional tendencies. The output includes the user's stress level and degree of happiness.

[0410] Step 4:

[0411] The server uses a generative AI model to generate personalized advice based on identified emotional tendencies. The generated advice is constructed based on pre-defined protocol statements. The output is personalized advice in text format.

[0412] Step 5:

[0413] The generated advice is sent from the cloud environment to the device, which then notifies the user. Specifically, the user is notified using the smartphone's notification function or voice assistant. The user is supported in managing stress and promoting positive experiences in their daily life.

[0414] 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.

[0415] 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.

[0416] 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.

[0417] [Third Embodiment]

[0418] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0419] 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.

[0420] 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).

[0421] 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.

[0422] 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.

[0423] 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).

[0424] 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.

[0425] 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.

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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".

[0430] This invention is a system for realizing personalized life coaching for users, and is configured as follows: A wearable device worn by the user acquires biometric and behavioral information in real time. This acquired information is temporarily stored on the user's smartphone or a dedicated terminal. This terminal periodically encrypts the data and transmits it to a server in a cloud environment.

[0431] The server organizes and stores received data for each user and performs analysis using machine learning models. This analysis allows the server to identify users' behavioral patterns and emotional tendencies. Based on the identified behavioral patterns and emotional tendencies, the server uses generative AI technology to generate personalized advice for each user.

[0432] The generated advice is sent to the device via the cloud. Users can interactively receive the advice through their smartphones or VR / MR / AR devices. This allows users to receive personalized feedback in real time, which can lead to behavioral improvements and enhanced mental health.

[0433] For example, if the system detects that a user is experiencing a certain level of stress on a daily basis, the server generates specific advice, such as relaxation techniques or appropriate exercise, and presents it to the user's device. Furthermore, if progress toward the user's long-term goals is stalled, advice prompting reassessment is also provided. This system allows users to improve themselves and cope with obstacles in a more efficient and effective way.

[0434] The following describes the processing flow.

[0435] Step 1:

[0436] The wearable device worn by the user acquires biometric and behavioral information such as heart rate, steps taken, location, and sleep duration. The wearable device transmits this data to the terminal, updating it at regular intervals.

[0437] Step 2:

[0438] The device temporarily stores biometric and behavioral information received from the wearable device in its storage. When a certain amount of data is reached or a predetermined amount of time has elapsed, the device prepares to encrypt the data and send it to a server in the cloud.

[0439] Step 3:

[0440] The server organizes the data received by each user and stores it in a database. The server stores the data chronologically and formats it into a usable format for later analysis.

[0441] Step 4:

[0442] The server analyzes the data using a machine learning model. The server identifies behavioral patterns, abnormal heart rates, and emotional tendencies, and extracts user-specific characteristics based on this.

[0443] Step 5:

[0444] Based on the analysis results, the server uses generative AI technology to generate personalized advice for the user. This advice includes suggestions for health management, stress reduction, and achieving long-term goals.

[0445] Step 6:

[0446] The server sends the generated advice to the device via the cloud. The device receives this advice and formats it for display.

[0447] Step 7:

[0448] Users can view advice provided from their devices using their smartphones or VR / MR / AR devices. Users can receive advice in an interactive way and use it to improve their daily actions and habits.

[0449] (Example 1)

[0450] 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."

[0451] In today's busy lifestyle, it is a challenge for individual users to accurately understand their own health status, behavioral patterns, and emotional tendencies, and to take appropriate corrective measures based on this understanding. Furthermore, general advice does not address individual needs, and there is a need to provide users with effective feedback and action plans.

[0452] 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.

[0453] In this invention, the server includes means for acquiring biometric data and activity data, means for encrypting the acquired data, associating it with a specific user, and storing it in an information storage device, and means for analyzing the data stored in the information storage device using a machine learning model to identify the user's behavioral characteristics and emotional tendencies. This makes it possible to provide personalized and appropriate advice to the user in real time.

[0454] "Biometric data" refers to information obtained from the human body, including measurements related to health conditions such as heart rate, body temperature, and blood pressure.

[0455] "Activity data" refers to information related to human movement and behavior, including measurements of physical activity such as steps taken, sitting time, and distance traveled.

[0456] An "information storage device" is a device or system for recording and storing digital data, and includes cloud environments and local storage.

[0457] A "machine learning model" refers to an algorithm or framework designed to analyze data and extract patterns and features, and includes technologies such as TensorFlow and PyTorch.

[0458] "Behavioral characteristics" refer to features that indicate a consistent behavioral pattern or habit of a particular user, and are identified based on past behavioral data.

[0459] "Emotional tendencies" refer to the tendencies that indicate the emotional fluctuations or general mental state of a particular user, and are inferred through data analysis.

[0460] "Advice" refers to behavioral guidelines and improvement measures provided to specific users, and is generated based on analysis results.

[0461] A "body-worn device" refers to a measuring device that a user can continuously wear in their daily life, and includes wristband-type health monitors, among others.

[0462] "Knowledge processing technology" refers to technologies that generate meaningful information and content from data using natural language processing and generative AI models.

[0463] The purpose of this system is to provide users with personalized health management and behavioral improvement guidelines. Specifically, the server, terminal, and user work together to perform the following processes.

[0464] The server utilizes cloud computing services as its hardware and operates a database system as its information storage device. Specific examples include common cloud platform services and database management systems. Furthermore, frameworks such as TensorFlow and PyTorch are used to implement machine learning models. By combining this hardware and software, the server analyzes biometric and activity data sent by users to identify behavioral characteristics and emotional tendencies.

[0465] The terminals include smartphones and tablets, which collect data from users and securely communicate it to the server. The terminals acquire biometric and activity data from wearable devices via Bluetooth or Wi-Fi and temporarily record the data in local storage. The data is encrypted using the AES 256-bit encryption algorithm and sent to the server via the HTTPS protocol.

[0466] Users unconsciously collect data by wearing body-worn devices during their daily lives. Users themselves receive advice generated using the device and use it to improve their behavior. The advice is provided in an interactive format, such as smartphone notifications, voice guidance, or interfaces using VR / MR / AR technology.

[0467] For example, if a high-stress state is detected based on the user's activity data, the server will run a generation AI model using a prompt such as "The user is feeling stressed, please suggest ways to relax," generate specific advice for relaxation, and notify the user of this advice through their device.

[0468] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0469] Step 1:

[0470] The user wears a body-worn device.

[0471] Input: User biometric data and activity data

[0472] Specific operation: The device uses sensors to detect heart rate, body temperature, steps taken, etc., in real time. This data is acquired instantly and reflects recent activity.

[0473] Output: Acquired biometric data and activity data

[0474] Step 2:

[0475] The terminal receives data from the device.

[0476] Input: Biometric data and activity data transmitted from the device

[0477] Specific operation: The device receives data via Bluetooth and temporarily stores it in local storage. AES256-bit encryption is used to ensure data security.

[0478] Output: Encrypted temporary data

[0479] Step 3:

[0480] The device sends the saved data to the server.

[0481] Input: Encrypted temporary data

[0482] Specific operation: The device uses the HTTPS protocol to securely send encrypted data to a server in the cloud. This process is performed regularly and is automated.

[0483] Output: Encrypted data sent to the server

[0484] Step 4:

[0485] The server analyzes the data.

[0486] Input: Encrypted data sent to the server

[0487] Specific operation: The server first decrypts the data and organizes it by user. It then uses machine learning models to analyze the data and extract behavioral characteristics and emotional tendencies. The models are built using TensorFlow and PyTorch.

[0488] Output: Behavioral characteristics and emotional tendencies as analysis results

[0489] Step 5:

[0490] The server generates advice using an AI model.

[0491] Input: Behavioral characteristics and emotional tendencies as analysis results

[0492] Specific operation: The server inputs a prompt message, "Requesting the generation of advice based on behavioral characteristics and emotional tendencies," into the AI ​​model, which then generates advice optimized for the user.

[0493] Output: Generated advice

[0494] Step 6:

[0495] The server sends the generated advice to the terminal.

[0496] Input: Generated advice

[0497] Specific operation: The server sends the generated advice back to the user's terminal via the cloud, delivering it to the user immediately.

[0498] Output: Advice sent to the user terminal

[0499] Step 7:

[0500] The user receives advice from the device.

[0501] Input: Advice sent to the device

[0502] Specific operation: Users receive advice via smartphone notifications or applications and review the content. It is often displayed in an interactive format.

[0503] Output: Opportunity to implement behavioral improvements based on received advice

[0504] (Application Example 1)

[0505] 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."

[0506] Traditional systems lack the ability to provide real-time, personalized support for behavioral improvement and mental health enhancement, as they cannot offer physical feedback within individual living environments. This results in a challenge in effectively implementing interactive interventions in users' daily lives.

[0507] 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.

[0508] In this invention, the server includes means for acquiring physiological information and behavioral data of a living organism, means for associating the acquired information with a specific user and storing it in a distributed storage device, and means for analyzing the data stored in the distributed storage device to identify the user's behavioral model and emotional tendencies. This enables physical interaction within the user's living space to support the improvement of their emotions and behavior.

[0509] "Physiological information of a living organism" refers to measurable data related to bodily functions and conditions, such as heart rate, body temperature, blood pressure, and respiratory rate.

[0510] "Behavioral data" refers to information about human movement and activity, including step count, location, and exercise level.

[0511] A "distributed storage device" is a system that stores data on various nodes, such as the cloud, and is characterized by its decentralized data storage rather than centralized management.

[0512] A "behavioral model" refers to a predictable behavioral pattern based on a user's past actions.

[0513] "Emotional tendencies" refer to the patterns in which a user's emotions manifest, clearly indicating the frequency and tendency of positive or negative emotions.

[0514] "Advice" refers to the provision of information, including suggestions and measures for improvement aimed at a specific purpose, and is used to improve the user's behavior or emotional state.

[0515] "Physical interaction" refers to the feedback process that takes place in the user's living environment through actual actions and conversations, and is carried out via voice, movement, displays, etc.

[0516] The system for implementing this invention is built primarily using wearable devices, distributed storage devices, and artificial intelligence technologies.

[0517] The server activates sensors attached to the user's body to acquire physiological and behavioral data. These wearable devices record heart rate, body temperature, and movement in real time, and temporarily store the collected data via a smartphone. Periodically, this data is encrypted and sent to distributed storage, where it is organized for each user.

[0518] The server applies machine learning algorithms when analyzing data stored in memory. This allows it to identify the user's behavioral models and emotional tendencies, and generate personalized advice. The generated advice is then delivered to the user in the most effective way possible using a generative AI model. For example, this could include suggestions for relaxation exercises suitable for a specific situation or measures to improve daily behaviors.

[0519] Users receive advice through a robot installed in their home. This robot interacts with users using voice and a display, providing support to help them make concrete changes in their daily lives.

[0520] For example, if a user indicates a high stress level over the past few days, the robot can advise the user by saying, "I'll play some calming music so you can take a deep breath and relax."

[0521] An example of a prompt message is: "This user has averaged less than 6 hours of sleep over the past 3 days. Based on this information, generate sleep improvement advice."

[0522] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0523] Step 1:

[0524] The device acquires the user's physiological information and behavioral data from wearable devices. The input is real-time data from sensors, and the output is temporarily stored raw data. This data includes heart rate, body temperature, and movement.

[0525] Step 2:

[0526] The terminal encrypts this temporarily stored raw data and periodically sends it to a distributed storage device in the cloud. The input is the temporarily stored raw data, and the output is the encrypted data stored in the cloud. This process is performed via an internet connection, ensuring data security.

[0527] Step 3:

[0528] The server retrieves data from distributed storage devices and performs analysis using machine learning algorithms. The input is encrypted data stored in the cloud, and the output is the identified behavioral model and emotional tendencies. Data processing utilizes pattern recognition and statistical methods.

[0529] Step 4:

[0530] The server generates user-optimized advice using a generative AI model based on identified behavioral models and sentiment tendencies. The input is the analyzed behavioral models and sentiment tendencies, and the output is personalized advice. Prompt statements are used in this generation process.

[0531] Step 5:

[0532] The server sends the generated advice to a robot installed in the home. The input is the generated advice, and the output is the robot providing advice via voice and display. Specifically, the robot makes announcements to the user, such as "I will play music to help you relax."

[0533] 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.

[0534] This invention provides a system that supports users' personalized lives. By combining it with an emotion engine, it recognizes the user's emotional state and improves the accuracy and effectiveness of advice. The wearable device worn by the user acquires biometric and behavioral information in real time, and also collects voice and facial expression data. This information is temporarily stored on the device and periodically transmitted to a server in a cloud environment.

[0535] The server uses a machine learning model to analyze the received data. The analysis results include the user's behavior patterns, fluctuations in physical condition, and emotional states identified using the emotion engine. Generative AI technology is then used to generate optimized advice based on this data. The emotion engine detects the user's emotions from voice and facial expression data and estimates stress levels, happiness levels, and other factors. This emotional data is integrated with other behavioral information to generate more specific and personalized advice.

[0536] For example, if the emotion engine determines that a user is experiencing high levels of stress, it will offer advice on relaxation methods and rest plans. Conversely, if positive emotions are detected, suggestions will be made to promote further positive experiences. This allows users to receive more consistent support in improving their daily lives and mental health. The system is designed with full user privacy in mind, and individual data is encrypted and securely protected.

[0537] The following describes the processing flow.

[0538] Step 1:

[0539] The wearable device worn by the user acquires biometric information such as heart rate, steps taken, location information, and sleep data in real time. Furthermore, it also acquires voice data and facial expression data using the device's microphone and camera.

[0540] Step 2:

[0541] The device temporarily stores biometric information, behavioral information, and voice / facial expression data received from the wearable device. The stored data is encrypted at regular intervals and prepared for transmission to a server in the cloud environment.

[0542] Step 3:

[0543] The server organizes and stores the received data for each user. The server then formats the data chronologically for analysis.

[0544] Step 4:

[0545] The server analyzes the data using machine learning models and an emotion engine. The server identifies user behavior patterns and recognizes the user's emotional state from voice and facial expression data.

[0546] Step 5:

[0547] Based on the analysis results, the server uses AI-generated technology to create personalized advice for the user. The advice is individually tailored to the user's emotional state and behavioral patterns, focusing on health promotion and stress management.

[0548] Step 6:

[0549] The server generates advice and sends it to the device via the cloud. The device receives this advice and formats it in a way that is easy for the user to understand.

[0550] Step 7:

[0551] Users can use their smartphones or VR / MR / AR devices to review the advice they receive. Based on this advice, users adjust their behavior and use it to improve the quality of their daily lives.

[0552] (Example 2)

[0553] 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."

[0554] Conventional lifestyle support systems have struggled to provide individualized advice based on users' emotions and behaviors, offering only general suggestions and failing to deliver optimal support for each user. Furthermore, there has been a lack of technology to monitor emotional fluctuations in real time and provide appropriate support accordingly. There is a need to address these challenges and enable more personalized lifestyle support.

[0555] 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.

[0556] In this invention, the server includes an acquisition device for acquiring biometric and behavioral information, a transmission device for associating the acquired information with individual users and transmitting it to the external environment, and an analysis device for analyzing the data transmitted to the external environment and determining the user's behavioral characteristics and emotional state. This makes it possible to generate optimal guidelines for users in real time and provide specific and effective advice tailored to individual circumstances.

[0557] An "acquisition device" is a device that collects biological and behavioral information in real time and provides the data for analysis.

[0558] A "transmission device" is a device that securely transmits information collected by an acquisition device to an external environment, associating it with a specific user.

[0559] An "analysis device" is a device that processes data transmitted to the external environment and extracts and identifies the user's behavioral characteristics and emotional state.

[0560] A "generating device" is a device that generates optimized guidelines and advice for users based on identified behavioral characteristics and emotional states.

[0561] A "presentation device" is a device that directly presents guidelines and advice created by a generation device to the user.

[0562] "Biometric information" refers to data about the user's physical condition, including heart rate and body temperature.

[0563] "Behavioral information" refers to data about the user's behavior, including distance traveled and activity patterns.

[0564] This invention is a system that provides personalized life support to users, collecting biometric and behavioral information using various sensors. In a specific embodiment, a server and a terminal play a central role in this system.

[0565] The user wears a wearable device that measures heart rate, body temperature, and other parameters. This device also records activity levels and location information, and has the capability to collect voice and facial expression data. The device temporarily stores this data in real time and periodically transmits it to a cloud server using a transmission device. The cloud server, to which the data is received, is equipped with an analysis device for analyzing the data.

[0566] The server uses machine learning models and an emotion recognition engine to analyze the acquired data. These allow the server to determine the user's behavioral characteristics and emotional state, and then use generative AI technology to generate optimal guidance based on the analysis results. This guidance is optimized for each individual user's situation. For example, if the server determines that the user is experiencing high stress, it might generate specific advice such as, "Taking a 15-minute walk in a nearby park will help you relax."

[0567] The device presents the generated advice to the user via voice and notification functions. This allows users to receive more effective and personalized advice in their real lives. The generated advice takes into account the user's emotional state and supports lifestyle improvement and mental health enhancement.

[0568] An example of a prompt to the generating AI model would be, "Please suggest effective ways for the user to relax, but please consider external circumstances and time constraints." By providing specific instructions like this, appropriate advice can be generated.

[0569] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0570] Step 1:

[0571] The user wears a wearable device. This device collects biometric information such as heart rate, body temperature, and activity level, as well as behavioral information such as location, voice, and facial expression data. It acquires biometric and behavioral information as input and transmits this data to the terminal in real time as output. Specifically, sensors on the device detect data and transfer it to the terminal wirelessly.

[0572] Step 2:

[0573] The terminal temporarily stores data transmitted from the user's device. The input is biometric and behavioral information collected in step 1, and the output is data stored in a database or local storage. This storage process efficiently handles the data in preparation for later analysis while maintaining data integrity.

[0574] Step 3:

[0575] The device periodically sends temporarily stored data to a cloud server. The input is the data in the storage area, and the output is encrypted data stored on the cloud server. Encryption technology is applied during the transmission process to protect data privacy. Specifically, HTTPS is used as the transmission protocol.

[0576] Step 4:

[0577] The server analyzes data received on the cloud. The input is encrypted user data, and the output is the user's behavioral characteristics, estimated physical condition, and emotional state. Machine learning models are used for data processing, and these models perform feature extraction and inference. Specifically, stress levels are considered based on heart rate and activity levels, and emotions are determined from voice and facial expressions.

[0578] Step 5:

[0579] The server runs a generative AI model based on the analysis results to generate personalized advice for the user. The input is the user's behavioral characteristics and emotional information, and the output is optimized advice. The prompt is an instruction to the generative AI, "Please provide suggestions to reduce the user's current stress." In terms of operation, the prompt is input to the AI ​​model, and advice in natural language is output.

[0580] Step 6:

[0581] The device provides the user with generated advice. The input is the advice sent from the server, and the output is the information presented to the user. Specifically, the advice is displayed on the device's screen or presented to the user verbally via speech synthesis. This allows the user to learn about specific ways to improve their life.

[0582] (Application Example 2)

[0583] 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."

[0584] There is a growing need to appropriately monitor the emotions and health status of the elderly and those requiring care, and to provide support tailored to their individual needs. However, conventional technologies struggle to accurately recognize real-time changes in health and emotional states and provide optimized care support. Therefore, there is a need for technologies that enable more effective and personalized care support.

[0585] 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.

[0586] In this invention, the server includes means for acquiring biometric and behavioral information; means for associating the acquired information with a specific user and storing it in a cloud environment; means for analyzing the data stored in the cloud environment and identifying the user's behavioral patterns and emotional tendencies; means for generating optimized advice for the specific user based on the identified behavioral patterns and emotional tendencies; means for providing the generated advice to the user; and means for monitoring the user's health status in real time and providing optimized suggestions for care support. This makes it possible to effectively provide support tailored to the individual needs of the elderly and people requiring care, thereby improving their quality of life.

[0587] "Biometric information" refers to data related to an individual's health status and the maintenance of life, including physical conditions such as heart rate, blood pressure, and body temperature.

[0588] "Behavioral information" refers to data about an individual's social or physical movements and activities, including walking distance, activity time, and location travel history.

[0589] A "cloud environment" is a system that utilizes computing resources and data storage provided via the internet, and serves as a foundation for distributed storage and processing of information.

[0590] "Analysis" refers to the process of scrutinizing collected data and extracting useful information and patterns from it.

[0591] "Behavioral patterns" refer to data that shows typical models or tendencies of how a particular individual usually behaves.

[0592] "Emotional tendencies" refer to data that shows an individual's sustained emotional state and its changes, including trends in happiness and stress levels.

[0593] "Optimized advice" refers to recommended actions and information that are tailored to the specific condition and needs of an individual.

[0594] "User" refers to an individual who uses this system and enjoys its benefits.

[0595] "Healthcare support" refers to services and information provided to maintain or improve an individual's health.

[0596] To realize this invention, it is necessary to acquire the user's biometric and behavioral information in real time using a wearable device. The acquired information is temporarily stored on the user's terminal and transmitted to a cloud environment. A server located in the cloud environment analyzes the user's behavioral patterns and emotional tendencies based on this information.

[0597] The analysis uses the machine learning library TensorFlow to evaluate the emotional state of individual users based on biometric and behavioral information. The evaluation also includes analysis of voice and facial expression data to determine stress levels and happiness tendencies. Based on these analysis results, a generative AI model is used to generate personalized advice.

[0598] The generated advice is delivered to the user through devices such as smartphones and smart glasses. If the user is experiencing high levels of stress, relaxation methods and rest plans are provided. Conversely, if positive emotions are detected, suggestions are made to encourage positive experiences.

[0599] For example, we might suggest using an application that allows users to listen to nature sounds to those experiencing high levels of stress in their daily lives. We might also offer advice on how to promote relaxed sleep in preparation for an important event the following day.

[0600] Through this process, users will receive support in improving their daily lives and mental health. An example of a prompt for the generating AI model is: "Your current emotional state is [emotional state]. Please provide an optimal care plan that takes this state into consideration."

[0601] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0602] Step 1:

[0603] The device collects the user's biometric and behavioral information in real time from the wearable device. Biometric information includes heart rate and body temperature, while behavioral information includes movement data. This data is temporarily stored in the device's local storage.

[0604] Step 2:

[0605] The device periodically transmits collected biometric and behavioral information to a server in a cloud environment. A secure protocol is used for transmission, and the data is transferred in an encrypted state.

[0606] Step 3:

[0607] The server collects and stores the received data and performs analysis using the machine learning library TensorFlow. Here, the data is processed to identify user behavior patterns and emotional tendencies. The output includes the user's stress level and degree of happiness.

[0608] Step 4:

[0609] The server uses a generative AI model to generate personalized advice based on identified emotional tendencies. The generated advice is constructed based on pre-defined protocol statements. The output is personalized advice in text format.

[0610] Step 5:

[0611] The generated advice is sent from the cloud environment to the device, which then notifies the user. Specifically, the user is notified using the smartphone's notification function or voice assistant. The user is supported in managing stress and promoting positive experiences in their daily life.

[0612] 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.

[0613] 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.

[0614] 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.

[0615] [Fourth Embodiment]

[0616] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0617] 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.

[0618] 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).

[0619] 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.

[0620] 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.

[0621] 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).

[0622] 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.

[0623] 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.

[0624] 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.

[0625] 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.

[0626] 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.

[0627] 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.

[0628] 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".

[0629] This invention is a system for realizing personalized life coaching for users, and is configured as follows: A wearable device worn by the user acquires biometric and behavioral information in real time. This acquired information is temporarily stored on the user's smartphone or a dedicated terminal. This terminal periodically encrypts the data and transmits it to a server in a cloud environment.

[0630] The server organizes and stores received data for each user and performs analysis using machine learning models. This analysis allows the server to identify users' behavioral patterns and emotional tendencies. Based on the identified behavioral patterns and emotional tendencies, the server uses generative AI technology to generate personalized advice for each user.

[0631] The generated advice is sent to the device via the cloud. Users can interactively receive the advice through their smartphones or VR / MR / AR devices. This allows users to receive personalized feedback in real time, which can lead to behavioral improvements and enhanced mental health.

[0632] For example, if the system detects that a user is experiencing a certain level of stress on a daily basis, the server generates specific advice, such as relaxation techniques or appropriate exercise, and presents it to the user's device. Furthermore, if progress toward the user's long-term goals is stalled, advice prompting reassessment is also provided. This system allows users to improve themselves and cope with obstacles in a more efficient and effective way.

[0633] The following describes the processing flow.

[0634] Step 1:

[0635] The wearable device worn by the user acquires biometric and behavioral information such as heart rate, steps taken, location, and sleep duration. The wearable device transmits this data to the terminal, updating it at regular intervals.

[0636] Step 2:

[0637] The device temporarily stores biometric and behavioral information received from the wearable device in its storage. When a certain amount of data is reached or a predetermined amount of time has elapsed, the device prepares to encrypt the data and send it to a server in the cloud.

[0638] Step 3:

[0639] The server organizes the data received by each user and stores it in a database. The server stores the data chronologically and formats it into a usable format for later analysis.

[0640] Step 4:

[0641] The server analyzes the data using a machine learning model. The server identifies behavioral patterns, abnormal heart rates, and emotional tendencies, and extracts user-specific characteristics based on this.

[0642] Step 5:

[0643] Based on the analysis results, the server uses generative AI technology to generate personalized advice for the user. This advice includes suggestions for health management, stress reduction, and achieving long-term goals.

[0644] Step 6:

[0645] The server sends the generated advice to the device via the cloud. The device receives this advice and formats it for display.

[0646] Step 7:

[0647] Users can view advice provided from their devices using their smartphones or VR / MR / AR devices. Users can receive advice in an interactive way and use it to improve their daily actions and habits.

[0648] (Example 1)

[0649] 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".

[0650] In today's busy lifestyle, it is a challenge for individual users to accurately understand their own health status, behavioral patterns, and emotional tendencies, and to take appropriate corrective measures based on this understanding. Furthermore, general advice does not address individual needs, and there is a need to provide users with effective feedback and action plans.

[0651] 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.

[0652] In this invention, the server includes means for acquiring biometric data and activity data, means for encrypting the acquired data, associating it with a specific user, and storing it in an information storage device, and means for analyzing the data stored in the information storage device using a machine learning model to identify the user's behavioral characteristics and emotional tendencies. This makes it possible to provide personalized and appropriate advice to the user in real time.

[0653] "Biometric data" refers to information obtained from the human body, including measurements related to health conditions such as heart rate, body temperature, and blood pressure.

[0654] "Activity data" refers to information related to human movement and behavior, including measurements of physical activity such as steps taken, sitting time, and distance traveled.

[0655] An "information storage device" is a device or system for recording and storing digital data, and includes cloud environments and local storage.

[0656] A "machine learning model" refers to an algorithm or framework designed to analyze data and extract patterns and features, and includes technologies such as TensorFlow and PyTorch.

[0657] "Behavioral characteristics" refer to features that indicate a consistent behavioral pattern or habit of a particular user, and are identified based on past behavioral data.

[0658] "Emotional tendencies" refer to the tendencies that indicate the emotional fluctuations or general mental state of a particular user, and are inferred through data analysis.

[0659] "Advice" refers to behavioral guidelines and improvement measures provided to specific users, and is generated based on analysis results.

[0660] A "body-worn device" refers to a measuring device that a user can continuously wear in their daily life, and includes wristband-type health monitors, among others.

[0661] "Knowledge processing technology" refers to technologies that generate meaningful information and content from data using natural language processing and generative AI models.

[0662] The purpose of this system is to provide users with personalized health management and behavioral improvement guidelines. Specifically, the server, terminal, and user work together to perform the following processes.

[0663] The server utilizes cloud computing services as its hardware and operates a database system as its information storage device. Specific examples include common cloud platform services and database management systems. Furthermore, frameworks such as TensorFlow and PyTorch are used to implement machine learning models. By combining this hardware and software, the server analyzes biometric and activity data sent by users to identify behavioral characteristics and emotional tendencies.

[0664] The terminals include smartphones and tablets, which collect data from users and securely communicate it to the server. The terminals acquire biometric and activity data from wearable devices via Bluetooth or Wi-Fi and temporarily record the data in local storage. The data is encrypted using the AES 256-bit encryption algorithm and sent to the server via the HTTPS protocol.

[0665] Users unconsciously collect data by wearing body-worn devices during their daily lives. Users themselves receive advice generated using the device and use it to improve their behavior. The advice is provided in an interactive format, such as smartphone notifications, voice guidance, or interfaces using VR / MR / AR technology.

[0666] For example, if a high-stress state is detected based on the user's activity data, the server will run a generation AI model using a prompt such as "The user is feeling stressed, please suggest ways to relax," generate specific advice for relaxation, and notify the user of this advice through their device.

[0667] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0668] Step 1:

[0669] The user wears a body-worn device.

[0670] Input: User biometric data and activity data

[0671] Specific operation: The device uses sensors to detect heart rate, body temperature, steps taken, etc., in real time. This data is acquired instantly and reflects recent activity.

[0672] Output: Acquired biometric data and activity data

[0673] Step 2:

[0674] The terminal receives data from the device.

[0675] Input: Biometric data and activity data transmitted from the device

[0676] Specific operation: The device receives data via Bluetooth and temporarily stores it in local storage. AES256-bit encryption is used to ensure data security.

[0677] Output: Encrypted temporary data

[0678] Step 3:

[0679] The device sends the saved data to the server.

[0680] Input: Encrypted temporary data

[0681] Specific operation: The device uses the HTTPS protocol to securely send encrypted data to a server in the cloud. This process is performed regularly and is automated.

[0682] Output: Encrypted data sent to the server

[0683] Step 4:

[0684] The server analyzes the data.

[0685] Input: Encrypted data sent to the server

[0686] Specific operation: The server first decrypts the data and organizes it by user. It then uses machine learning models to analyze the data and extract behavioral characteristics and emotional tendencies. The models are built using TensorFlow and PyTorch.

[0687] Output: Behavioral characteristics and emotional tendencies as analysis results

[0688] Step 5:

[0689] The server generates advice using an AI model.

[0690] Input: Behavioral characteristics and emotional tendencies as analysis results

[0691] Specific operation: The server inputs a prompt message, "Requesting the generation of advice based on behavioral characteristics and emotional tendencies," into the AI ​​model, which then generates advice optimized for the user.

[0692] Output: Generated advice

[0693] Step 6:

[0694] The server sends the generated advice to the terminal.

[0695] Input: Generated advice

[0696] Specific operation: The server sends the generated advice back to the user's terminal via the cloud, delivering it to the user immediately.

[0697] Output: Advice sent to the user terminal

[0698] Step 7:

[0699] The user receives advice from the device.

[0700] Input: Advice sent to the device

[0701] Specific operation: Users receive advice via smartphone notifications or applications and review the content. It is often displayed in an interactive format.

[0702] Output: Opportunity to implement behavioral improvements based on received advice

[0703] (Application Example 1)

[0704] 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".

[0705] Traditional systems lack the ability to provide real-time, personalized support for behavioral improvement and mental health enhancement, as they cannot offer physical feedback within individual living environments. This results in a challenge in effectively implementing interactive interventions in users' daily lives.

[0706] 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.

[0707] In this invention, the server includes means for acquiring physiological information and behavioral data of a living organism, means for associating the acquired information with a specific user and storing it in a distributed storage device, and means for analyzing the data stored in the distributed storage device to identify the user's behavioral model and emotional tendencies. This enables physical interaction within the user's living space to support the improvement of their emotions and behavior.

[0708] "Physiological information of a living organism" refers to measurable data related to bodily functions and conditions, such as heart rate, body temperature, blood pressure, and respiratory rate.

[0709] "Behavioral data" refers to information about human movement and activity, including step count, location, and exercise level.

[0710] A "distributed storage device" is a system that stores data on various nodes, such as the cloud, and is characterized by its decentralized data storage rather than centralized management.

[0711] A "behavioral model" refers to a predictable behavioral pattern based on a user's past actions.

[0712] "Emotional tendencies" refer to the patterns in which a user's emotions manifest, clearly indicating the frequency and tendency of positive or negative emotions.

[0713] "Advice" refers to the provision of information, including suggestions and measures for improvement aimed at a specific purpose, and is used to improve the user's behavior or emotional state.

[0714] "Physical interaction" refers to the feedback process that takes place in the user's living environment through actual actions and conversations, and is carried out via voice, movement, displays, etc.

[0715] The system for implementing this invention is built primarily using wearable devices, distributed storage devices, and artificial intelligence technologies.

[0716] The server activates sensors attached to the user's body to acquire physiological and behavioral data. These wearable devices record heart rate, body temperature, and movement in real time, and temporarily store the collected data via a smartphone. Periodically, this data is encrypted and sent to distributed storage, where it is organized for each user.

[0717] The server applies machine learning algorithms when analyzing data stored in memory. This allows it to identify the user's behavioral models and emotional tendencies, and generate personalized advice. The generated advice is then delivered to the user in the most effective way possible using a generative AI model. For example, this could include suggestions for relaxation exercises suitable for a specific situation or measures to improve daily behaviors.

[0718] Users receive advice through a robot installed in their home. This robot interacts with users using voice and a display, providing support to help them make concrete changes in their daily lives.

[0719] For example, if a user indicates a high stress level over the past few days, the robot can advise the user by saying, "I'll play some calming music so you can take a deep breath and relax."

[0720] An example of a prompt message is: "This user has averaged less than 6 hours of sleep over the past 3 days. Based on this information, generate sleep improvement advice."

[0721] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0722] Step 1:

[0723] The device acquires the user's physiological information and behavioral data from wearable devices. The input is real-time data from sensors, and the output is temporarily stored raw data. This data includes heart rate, body temperature, and movement.

[0724] Step 2:

[0725] The terminal encrypts this temporarily stored raw data and periodically sends it to a distributed storage device in the cloud. The input is the temporarily stored raw data, and the output is the encrypted data stored in the cloud. This process is performed via an internet connection, ensuring data security.

[0726] Step 3:

[0727] The server retrieves data from distributed storage devices and performs analysis using machine learning algorithms. The input is encrypted data stored in the cloud, and the output is the identified behavioral model and emotional tendencies. Data processing utilizes pattern recognition and statistical methods.

[0728] Step 4:

[0729] The server generates user-optimized advice using a generative AI model based on identified behavioral models and sentiment tendencies. The input is the analyzed behavioral models and sentiment tendencies, and the output is personalized advice. Prompt statements are used in this generation process.

[0730] Step 5:

[0731] The server sends the generated advice to a robot installed in the home. The input is the generated advice, and the output is the robot providing advice via voice and display. Specifically, the robot makes announcements to the user, such as "I will play music to help you relax."

[0732] 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.

[0733] This invention provides a system that supports users' personalized lives. By combining it with an emotion engine, it recognizes the user's emotional state and improves the accuracy and effectiveness of advice. The wearable device worn by the user acquires biometric and behavioral information in real time, and also collects voice and facial expression data. This information is temporarily stored on the device and periodically transmitted to a server in a cloud environment.

[0734] The server uses a machine learning model to analyze the received data. The analysis results include the user's behavior patterns, fluctuations in physical condition, and emotional states identified using the emotion engine. Generative AI technology is then used to generate optimized advice based on this data. The emotion engine detects the user's emotions from voice and facial expression data and estimates stress levels, happiness levels, and other factors. This emotional data is integrated with other behavioral information to generate more specific and personalized advice.

[0735] For example, if the emotion engine determines that a user is experiencing high levels of stress, it will offer advice on relaxation methods and rest plans. Conversely, if positive emotions are detected, suggestions will be made to promote further positive experiences. This allows users to receive more consistent support in improving their daily lives and mental health. The system is designed with full user privacy in mind, and individual data is encrypted and securely protected.

[0736] The following describes the processing flow.

[0737] Step 1:

[0738] The wearable device worn by the user acquires biometric information such as heart rate, steps taken, location information, and sleep data in real time. Furthermore, it also acquires voice data and facial expression data using the device's microphone and camera.

[0739] Step 2:

[0740] The device temporarily stores biometric information, behavioral information, and voice / facial expression data received from the wearable device. The stored data is encrypted at regular intervals and prepared for transmission to a server in the cloud environment.

[0741] Step 3:

[0742] The server organizes and stores the received data for each user. The server then formats the data chronologically for analysis.

[0743] Step 4:

[0744] The server analyzes the data using machine learning models and an emotion engine. The server identifies user behavior patterns and recognizes the user's emotional state from voice and facial expression data.

[0745] Step 5:

[0746] Based on the analysis results, the server uses AI-generated technology to create personalized advice for the user. The advice is individually tailored to the user's emotional state and behavioral patterns, focusing on health promotion and stress management.

[0747] Step 6:

[0748] The server generates advice and sends it to the device via the cloud. The device receives this advice and formats it in a way that is easy for the user to understand.

[0749] Step 7:

[0750] Users can use their smartphones or VR / MR / AR devices to review the advice they receive. Based on this advice, users adjust their behavior and use it to improve the quality of their daily lives.

[0751] (Example 2)

[0752] 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".

[0753] Conventional lifestyle support systems have struggled to provide individualized advice based on users' emotions and behaviors, offering only general suggestions and failing to deliver optimal support for each user. Furthermore, there has been a lack of technology to monitor emotional fluctuations in real time and provide appropriate support accordingly. There is a need to address these challenges and enable more personalized lifestyle support.

[0754] 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.

[0755] In this invention, the server includes an acquisition device for acquiring biometric and behavioral information, a transmission device for associating the acquired information with individual users and transmitting it to the external environment, and an analysis device for analyzing the data transmitted to the external environment and determining the user's behavioral characteristics and emotional state. This makes it possible to generate optimal guidelines for users in real time and provide specific and effective advice tailored to individual circumstances.

[0756] An "acquisition device" is a device that collects biological and behavioral information in real time and provides the data for analysis.

[0757] A "transmission device" is a device that securely transmits information collected by an acquisition device to an external environment, associating it with a specific user.

[0758] An "analysis device" is a device that processes data transmitted to the external environment and extracts and identifies the user's behavioral characteristics and emotional state.

[0759] A "generating device" is a device that generates optimized guidelines and advice for users based on identified behavioral characteristics and emotional states.

[0760] A "presentation device" is a device that directly presents guidelines and advice created by a generation device to the user.

[0761] "Biometric information" refers to data about the user's physical condition, including heart rate and body temperature.

[0762] "Behavioral information" refers to data about the user's behavior, including distance traveled and activity patterns.

[0763] This invention is a system that provides personalized life support to users, collecting biometric and behavioral information using various sensors. In a specific embodiment, a server and a terminal play a central role in this system.

[0764] The user wears a wearable device that measures heart rate, body temperature, and other parameters. This device also records activity levels and location information, and has the capability to collect voice and facial expression data. The device temporarily stores this data in real time and periodically transmits it to a cloud server using a transmission device. The cloud server, to which the data is received, is equipped with an analysis device for analyzing the data.

[0765] The server uses machine learning models and an emotion recognition engine to analyze the acquired data. These allow the server to determine the user's behavioral characteristics and emotional state, and then use generative AI technology to generate optimal guidance based on the analysis results. This guidance is optimized for each individual user's situation. For example, if the server determines that the user is experiencing high stress, it might generate specific advice such as, "Taking a 15-minute walk in a nearby park will help you relax."

[0766] The device presents the generated advice to the user via voice and notification functions. This allows users to receive more effective and personalized advice in their real lives. The generated advice takes into account the user's emotional state and supports lifestyle improvement and mental health enhancement.

[0767] An example of a prompt to the generating AI model would be, "Please suggest effective ways for the user to relax, but please consider external circumstances and time constraints." By providing specific instructions like this, appropriate advice can be generated.

[0768] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0769] Step 1:

[0770] The user wears a wearable device. This device collects biometric information such as heart rate, body temperature, and activity level, as well as behavioral information such as location, voice, and facial expression data. It acquires biometric and behavioral information as input and transmits this data to the terminal in real time as output. Specifically, sensors on the device detect data and transfer it to the terminal wirelessly.

[0771] Step 2:

[0772] The terminal temporarily stores data transmitted from the user's device. The input is biometric and behavioral information collected in step 1, and the output is data stored in a database or local storage. This storage process efficiently handles the data in preparation for later analysis while maintaining data integrity.

[0773] Step 3:

[0774] The device periodically sends temporarily stored data to a cloud server. The input is the data in the storage area, and the output is encrypted data stored on the cloud server. Encryption technology is applied during the transmission process to protect data privacy. Specifically, HTTPS is used as the transmission protocol.

[0775] Step 4:

[0776] The server analyzes data received on the cloud. The input is encrypted user data, and the output is the user's behavioral characteristics, estimated physical condition, and emotional state. Machine learning models are used for data processing, and these models perform feature extraction and inference. Specifically, stress levels are considered based on heart rate and activity levels, and emotions are determined from voice and facial expressions.

[0777] Step 5:

[0778] The server runs a generative AI model based on the analysis results to generate personalized advice for the user. The input is the user's behavioral characteristics and emotional information, and the output is optimized advice. The prompt is an instruction to the generative AI, "Please provide suggestions to reduce the user's current stress." In terms of operation, the prompt is input to the AI ​​model, and advice in natural language is output.

[0779] Step 6:

[0780] The device provides the user with generated advice. The input is the advice sent from the server, and the output is the information presented to the user. Specifically, the advice is displayed on the device's screen or presented to the user verbally via speech synthesis. This allows the user to learn about specific ways to improve their life.

[0781] (Application Example 2)

[0782] 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".

[0783] There is a growing need to appropriately monitor the emotions and health status of the elderly and those requiring care, and to provide support tailored to their individual needs. However, conventional technologies struggle to accurately recognize real-time changes in health and emotional states and provide optimized care support. Therefore, there is a need for technologies that enable more effective and personalized care support.

[0784] 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.

[0785] In this invention, the server includes means for acquiring biometric and behavioral information; means for associating the acquired information with a specific user and storing it in a cloud environment; means for analyzing the data stored in the cloud environment and identifying the user's behavioral patterns and emotional tendencies; means for generating optimized advice for the specific user based on the identified behavioral patterns and emotional tendencies; means for providing the generated advice to the user; and means for monitoring the user's health status in real time and providing optimized suggestions for care support. This makes it possible to effectively provide support tailored to the individual needs of the elderly and people requiring care, thereby improving their quality of life.

[0786] "Biometric information" refers to data related to an individual's health status and the maintenance of life, including physical conditions such as heart rate, blood pressure, and body temperature.

[0787] "Behavioral information" refers to data about an individual's social or physical movements and activities, including walking distance, activity time, and location travel history.

[0788] A "cloud environment" is a system that utilizes computing resources and data storage provided via the internet, and serves as a foundation for distributed storage and processing of information.

[0789] "Analysis" refers to the process of scrutinizing collected data and extracting useful information and patterns from it.

[0790] "Behavioral patterns" refer to data that shows typical models or tendencies of how a particular individual usually behaves.

[0791] "Emotional tendencies" refer to data that shows an individual's sustained emotional state and its changes, including trends in happiness and stress levels.

[0792] "Optimized advice" refers to recommended actions and information that are tailored to the specific condition and needs of an individual.

[0793] "User" refers to an individual who uses this system and enjoys its benefits.

[0794] "Healthcare support" refers to services and information provided to maintain or improve an individual's health.

[0795] To realize this invention, it is necessary to acquire the user's biometric and behavioral information in real time using a wearable device. The acquired information is temporarily stored on the user's terminal and transmitted to a cloud environment. A server located in the cloud environment analyzes the user's behavioral patterns and emotional tendencies based on this information.

[0796] The analysis uses the machine learning library TensorFlow to evaluate the emotional state of individual users based on biometric and behavioral information. The evaluation also includes analysis of voice and facial expression data to determine stress levels and happiness tendencies. Based on these analysis results, a generative AI model is used to generate personalized advice.

[0797] The generated advice is delivered to the user through devices such as smartphones and smart glasses. If the user is experiencing high levels of stress, relaxation methods and rest plans are provided. Conversely, if positive emotions are detected, suggestions are made to encourage positive experiences.

[0798] For example, we might suggest using an application that allows users to listen to nature sounds to those experiencing high levels of stress in their daily lives. We might also offer advice on how to promote relaxed sleep in preparation for an important event the following day.

[0799] Through this process, users will receive support in improving their daily lives and mental health. An example of a prompt for the generating AI model is: "Your current emotional state is [emotional state]. Please provide an optimal care plan that takes this state into consideration."

[0800] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0801] Step 1:

[0802] The device collects the user's biometric and behavioral information in real time from the wearable device. Biometric information includes heart rate and body temperature, while behavioral information includes movement data. This data is temporarily stored in the device's local storage.

[0803] Step 2:

[0804] The device periodically transmits collected biometric and behavioral information to a server in a cloud environment. A secure protocol is used for transmission, and the data is transferred in an encrypted state.

[0805] Step 3:

[0806] The server collects and stores the received data and performs analysis using the machine learning library TensorFlow. Here, the data is processed to identify user behavior patterns and emotional tendencies. The output includes the user's stress level and degree of happiness.

[0807] Step 4:

[0808] The server uses a generative AI model to generate personalized advice based on identified emotional tendencies. The generated advice is constructed based on pre-defined protocol statements. The output is personalized advice in text format.

[0809] Step 5:

[0810] The generated advice is sent from the cloud environment to the device, which then notifies the user. Specifically, the user is notified using the smartphone's notification function or voice assistant. The user is supported in managing stress and promoting positive experiences in their daily life.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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."

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0832] The following is further disclosed regarding the embodiments described above.

[0833] (Claim 1)

[0834] Means for acquiring biometric and behavioral information,

[0835] A means for storing the acquired information in a cloud environment, associated with a specific user,

[0836] A means for analyzing data stored in the aforementioned cloud environment to identify user behavior patterns and emotional tendencies,

[0837] A means for generating optimized advice for a specific user based on the identified behavioral patterns and emotional tendencies,

[0838] Means for providing the generated advice to the user,

[0839] A system that includes this.

[0840] (Claim 2)

[0841] The system according to claim 1, which acquires biometric information and behavioral information using a wearable device.

[0842] (Claim 3)

[0843] The system according to claim 1, which generates advice for a specific user using generative AI technology.

[0844] "Example 1"

[0845] (Claim 1)

[0846] Means for acquiring biometric data and activity data,

[0847] Means for encrypting the acquired data, associating it with a specific user, and storing it in an information storage device,

[0848] A means for analyzing data stored in the aforementioned information storage device using a machine learning model to identify the user's behavioral characteristics and emotional tendencies,

[0849] A means for generating advice optimized for a specific user based on the identified behavioral characteristics and emotional tendencies,

[0850] Means for interactively providing the generated advice to the user,

[0851] A system that includes this.

[0852] (Claim 2)

[0853] The system according to claim 1, which acquires biometric data and activity data using a body-worn device.

[0854] (Claim 3)

[0855] The system according to claim 1, which generates advice for a specific user using knowledge processing technology.

[0856] "Application Example 1"

[0857] (Claim 1)

[0858] Means for acquiring physiological information and behavioral data of living organisms,

[0859] Means for storing the acquired information in a distributed storage device in association with a specific user,

[0860] A means for analyzing data stored in the aforementioned distributed storage device to identify the user's behavioral model and emotional tendencies,

[0861] A means for generating advice optimized for a specific user based on identified behavioral models and emotional tendencies,

[0862] Means for providing the generated advice to the user,

[0863] Means of providing physical interaction within the user's living space to support the improvement of their emotions and behaviors,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The system according to claim 1, which acquires physiological information and behavioral data using a portable electronic device.

[0867] (Claim 3)

[0868] The system according to claim 1, which uses a generated artificial intelligence model to generate advice for a specific user.

[0869] "Example 2 of combining an emotion engine"

[0870] (Claim 1)

[0871] A device for acquiring biological information and behavioral information,

[0872] A transmission device for transmitting acquired information to an external environment, associating it with individual users,

[0873] An analysis device for analyzing data transmitted to the external environment and determining the user's behavioral characteristics and emotional state,

[0874] A generation device for generating individually optimized guidelines for each user based on identified behavioral characteristics and emotional states,

[0875] A display device for presenting the generated guidelines to the user,

[0876] A system that includes this.

[0877] (Claim 2)

[0878] The system according to claim 1, which acquires biological information and behavioral information using a wearable device.

[0879] (Claim 3)

[0880] The system according to claim 1, which generates user-specific guidelines using generative artificial intelligence technology.

[0881] "Application example 2 when combining with an emotional engine"

[0882] (Claim 1)

[0883] Means for acquiring biometric and behavioral information,

[0884] A means for storing the acquired information in a cloud environment, associated with a specific user,

[0885] A means for analyzing data stored in the aforementioned cloud environment to identify user behavior patterns and emotional tendencies,

[0886] A means for generating optimized advice for a specific user based on the identified behavioral patterns and emotional tendencies,

[0887] Means for providing the generated advice to the user,

[0888] A means of monitoring the user's health status in real time and providing optimized suggestions for care support,

[0889] A system that includes this.

[0890] (Claim 2)

[0891] The system according to claim 1, which acquires biometric information and behavioral information using a wearable device.

[0892] (Claim 3)

[0893] The system according to claim 1, which generates advice for a specific user using generative AI technology. [Explanation of Symbols]

[0894] 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. Means for acquiring physiological information and behavioral data of living organisms, Means for storing the acquired information in a distributed storage device in association with a specific user, A means for analyzing data stored in the aforementioned distributed storage device to identify the user's behavioral model and emotional tendencies, A means for generating advice optimized for a specific user based on identified behavioral models and emotional tendencies, Means for providing the generated advice to the user, Means of providing physical interaction within the user's living space to support the improvement of their emotions and behaviors, A system that includes this.

2. The system according to claim 1, which acquires physiological information and behavioral data using a portable electronic device.

3. The system according to claim 1, which generates advice for a specific user using a generated artificial intelligence model.

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