System

Abstract

To provide systems.SOLUTION: A system includes the means for: receiving pieces of data that a wearable device a user wears collects; by a generative AI, analyzing the data, evaluating a wellness state of the user, and suggesting a meal menu optimal for the user; notifying a terminal of the user of the meal menu; and placing an order for the meal menu with a food delivery service.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Generative AI requires a lot of effort in inputting data, making it difficult for users to immediately call on the AI ​​when they need it to get help with a problem or advice to enrich their daily lives. [Means for solving the problem]

[0005] They developed a system that uses wearable devices to constantly collect data and inputs that data into generative AI, allowing users to call on the AI ​​immediately when they need it to provide help with problems and advice to enrich their daily lives. Furthermore, by adding a function to collect healthcare-related data and a function to input data from a device that reads the user's thoughts into the generative AI, it has become possible to provide more specific advice. [Brief description of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 2 is a sequence diagram showing a process flow of the data processing system in the first embodiment of the first form example. [Figure 12]FIG. 11 is a sequence diagram showing a process flow of the data processing system in the application example 1 of the first embodiment. [Figure 13] FIG. 11 is a sequence diagram showing a process flow of the data processing system in the second embodiment of the second embodiment. [Figure 14] FIG. 13 is a sequence diagram showing a process flow of the data processing system in application example 2 of embodiment 2. [Figure 15] FIG. 13 is a sequence diagram showing a process flow of a data processing system in a third embodiment of the present invention. [Figure 16] FIG. 13 is a sequence diagram showing a process flow of the data processing system in the third application example of the third embodiment. [Figure 17] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when combined with an emotion engine. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0007] 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.

[0008] do.

[0009] First, the terms used in the following description will be explained.

[0010] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0011] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0012] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0013] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0014] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

[0015] [First embodiment]

[0016] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (e.g., voice 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 voice according to instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Fig. 2, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific process program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0026] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores a reception output program 60. The reception output program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads out 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 the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0027] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0028] "Example 1"

[0029] The present invention is a system that constantly collects biometric and behavioral data of a user using a wearable device and inputs the data into a generative AI. Specifically, the wearable device collects healthcare-related data such as heart rate, blood pressure, and number of steps, and transmits the data to the generative AI. The generative AI analyzes the received data and provides advice on the user's health condition and lifestyle habits.

[0030] "Example 2"

[0031] In addition, in another aspect of the present invention, a system is provided that inputs data from a device that reads a user's thoughts into a generative AI. Specifically, a device such as a headset that reads brainwaves is used to read the user's thoughts and transmit the data to the generative AI. The generative AI analyzes the received data and provides advice corresponding to the user's thoughts and feelings.

[0032] The process flow of each embodiment will be described below.

[0033] "Example 1"

[0034] Step 1: The wearable device constantly collects the user's biometric and behavioral data. Step 2: The collected data is sent from the wearable device to the generative AI.

[0035] Step 3: The generative AI analyzes the received data and provides advice on the user's health and lifestyle.

[0036] "Example 2"

[0037] Step 1: A mind-reading device (e.g. a brainwave-reading headset) reads the user's thoughts.

[0038] Step 2: The read data is sent from the device to the generative AI.

[0039] Step 3: The generative AI analyzes the received data and provides advice that corresponds to the user's thoughts and feelings.

[0040] Example 1

[0041] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal".

[0042] Conventional wearable devices can collect biometric and behavioral data of users, but they lack a system that can effectively analyze the data and provide specific advice to users. Another issue is that it is difficult to analyze the collected data in real time and provide quick feedback to users.

[0043] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0044] In this invention, the server includes a means for constantly collecting data by the wearable device, a means for transmitting the collected data to the server, a means for transmitting the data received by the server to the generative AI, a means for the generative AI to analyze the received data, a means for the generative AI to generate advice on the user's health condition and lifestyle habits based on the analysis results, a means for the server to transmit the generated advice to the user's terminal, and a means for the user to check the advice on the terminal. This allows the user to understand their own health condition in real time and quickly receive appropriate advice.

[0045] A "wearable device" is a device that is worn by the user to constantly collect biometric information such as heart rate, blood pressure, and number of steps taken, as well as behavioral data.

[0046] The "server" is a computer system that acts as an intermediary to receive data sent from the wearable device and pass it on to the generative AI.

[0047] "Generative AI" is an artificial intelligence system that analyzes data received from a server and generates advice regarding the user's health and lifestyle habits.

[0048] "Means of continuous data collection" refers to the function of a wearable device to continuously collect the user's biometric information and behavioral data.

[0049] "Data transmission means" is a function that transmits data collected by the wearable device to a server.

[0050] "Data receiving means" is a function by which the server receives data transmitted from the wearable device.

[0051] "Data analysis means" refers to the function by which the generative AI analyzes the data it receives from the server.

[0052] The "advice generation means" is a function in which the generative AI generates advice regarding the user's health condition and lifestyle habits based on the analysis results.

[0053] "Advice sending means" refers to a function whereby the server sends advice generated by the generative AI to the user's device.

[0054] "Advice confirmation means" refers to a function that allows a user to confirm advice provided by the generative AI on their device.

[0055] This invention is a system that uses a wearable device to constantly collect a user's biometric and behavioral data and inputs that data into a generative AI. Specifically, the wearable device collects healthcare-related data such as heart rate, blood pressure, and number of steps, and transmits that data to the generative AI. The generative AI analyzes the received data and provides advice on the user's health condition and lifestyle habits.

[0056] Hardware and Software Used

[0057] Wearable device: A device that collects health-related data such as heart rate, blood pressure, and number of steps (e.g., Apple® Watch, Fitbit®).

[0058] Server: Receives data sent from the wearable device and acts as a relay to pass it on to the generative AI.

[0059] Generative AI: An AI system that analyzes the data it receives and provides advice to users about their health and lifestyle habits (e.g., GPT-4(registered trademark) by OpenAI(registered trademark).

[0060] Program Processing Procedure

[0061] 1. Data collection:

[0062] The user puts on the wearable device.

[0063] Wearable devices constantly collect the user's biometric information, such as heart rate, blood pressure, and number of steps, as well as behavioral data.

[0064] 2. Data transmission:

[0065] The wearable device transmits the collected data to a server.

[0066] The server sends the received data to the generative AI.

[0067] 3. Data Analysis:

[0068] The generative AI analyzes the data received from the server.

[0069] Based on the analysis results, the generative AI generates advice regarding the user's health condition and lifestyle habits.

[0070] 4. Advice provided:

[0071] The advice generated by the generative AI is sent to the server.

[0072] The server sends the advice to the user's device (such as a smartphone or PC).

[0073] The user checks the advice on the device.

[0074] Examples

[0075] Example 1: When the user is wearing an Apple Watch

[0076] The user wears the Apple Watch.

[0077] The Apple Watch collects the user's heart rate and steps.

[0078] The data collected by Apple Watch is sent to the user's iPhone.

[0079] The iPhone uploads the data to the server.

[0080] The server sends the data to the generative AI (GPT-4).

[0081] Generative AI analyzes the data and generates advice such as, "Your heart rate has been high recently. To reduce stress, try spending more time relaxing."

[0082] The server sends the advice to the user's iPhone.

[0083] The user checks the advice on their iPhone.

[0084] Examples of prompt statements

[0085] User heart rate data: [80, 85, 90, 95, 100]

[0086] User step count data: [1000, 2000, 3000, 4000, 5000]

[0087] Analyze this data and provide advice regarding the user's health status.

[0088] In this way, users can constantly monitor their own health status through their wearable devices and receive appropriate advice.

[0089] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0090] Step 1:

[0091] Data collection

[0092] The user puts on the wearable device.

[0093] Wearable devices constantly collect the user's biometric information, such as heart rate, blood pressure, and number of steps, as well as behavioral data.

[0094] Input: User biometric information (heart rate, blood pressure, number of steps, etc.).

[0095] Output: Collected biometric data.

[0096] What it does: For example, your Apple Watch measures your heart rate every minute and counts your steps in real time.

[0097] Step 2:

[0098] Data transmission

[0099] The wearable device transmits the data it collects to the user's smartphone.

[0100] The smartphone uploads the data to the server.

[0101] Input: Collected biometric data.

[0102] Output: Biometric data sent to the server.

[0103] Specific operations: For example, Apple Watch sends collected data via Bluetooth or Wi-Fi to the user's iPhone, which then uploads the data to a server.

[0104] Step 3:

[0105] Data Reception

[0106] The server receives the data transmitted from the wearable device.

[0107] Input: Biometric data sent from your smartphone.

[0108] Output: Biometric data stored on the server.

[0109] Specific operation: The server receives the data in real time and stores it in a database.

[0110] Step 4:

[0111] 4. Data Analysis

[0112] The server sends the received data to the generative AI.

[0113] The generative AI analyzes the data received from the server.

[0114] Input: Biometric data stored on the server.

[0115] Output: Analysis results (information about the user's health and lifestyle).

[0116] How it works: Generative AI analyzes heart rate and step count data to detect outliers and trends.

[0117] Step 5:

[0118] Advice Generation

[0119] Based on the analysis results, the generative AI generates advice regarding the user's health condition and lifestyle habits.

[0120] Input: The analysis results.

[0121] Output: Advice (e.g. "Your heart rate has been high lately. Try spending more time relaxing to reduce stress").

[0122] Specific behavior: The generative AI generates specific advice for the user based on the analysis results.

[0123] Step 6:

[0124] Send Advice

[0125] The advice generated by the generative AI is sent to the server.

[0126] The server sends the advice to the user's device (such as a smartphone or PC).

[0127] Input: The generated advice.

[0128] Output: Advice sent to the user's terminal.

[0129] Specific operation: The server sends the advice received from the generative AI to the user's smartphone via push notification.

[0130] Step 7:

[0131] Advice confirmation

[0132] The user checks the advice on the device.

[0133] Input: The advice sent to the user's terminal.

[0134] Output: The advice the user confirmed.

[0135] Specific action: The user taps on the notification on their smartphone to view the advice.

[0136] (Application example 1)

[0137] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0138] Conventional healthcare systems using wearable devices were limited to collecting users' biometric and behavioral data, and monitoring their health status and providing advice. However, these systems did not adequately address specific behavioral suggestions based on the user's health status or improving convenience in daily life. In particular, there was a lack of suggestions for meal menus optimal for the user's health status and integration with meal delivery services, resulting in insufficient specific support for users to live a healthy lifestyle.

[0139] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means. In this invention, the server includes a means for constantly collecting data by the wearable device, a means for inputting the collected data to the generative AI, a means for the generative AI to provide help for the user's problems and advice for enriching daily life, a means for proposing an optimal meal menu based on the user's biometric information and behavioral data, and a means for ordering the proposed meal menu from a food delivery service. This enables specific meal suggestions based on the user's health condition to be linked with a meal delivery service.

[0140] A "wearable device" is a device that is worn on the user's body and constantly collects biometric information such as heart rate, blood pressure, and number of steps taken, as well as behavioral data.

[0141] "Means of continuous data collection" refers to a function that uses a wearable device to continuously obtain a user's biometric information and behavioral data.

[0142] "Generative AI" is an artificial intelligence system that analyzes collected data and generates advice and suggestions based on the user's health and lifestyle habits.

[0143] "Means of input" refers to the function of sending data collected from a wearable device to the generative AI.

[0144] "Means of providing advice" refers to a function that provides users with advice regarding their health status and lifestyle habits based on data analyzed by the generative AI.

[0145] The "means of suggesting meal menus" refers to a function in which generative AI analyzes the user's biometric information and behavioral data, and suggests to the user the meal menu that is optimal for their health condition.

[0146] The "means for ordering from a food delivery service" is a function for automatically ordering the suggested meal menu from a food delivery service.

[0147] The system for implementing this invention is configured by combining a wearable device, generative AI, and a food delivery service API. A specific embodiment is shown below.

[0148] System Configuration

[0149] Hardware:

[0150] Wearable device: A device that is worn on the user's body and constantly collects biometric and behavioral data such as heart rate, blood pressure, and number of steps taken. Examples include smart watches and fitness trackers.

[0151] Server: A computer system for running generative AI, analyzing data and proposing meal menus.

[0152] User device: A device operated by a user, such as a smartphone or tablet, on which a food delivery service application is installed.

[0153] software:

[0154] Generative AI: An artificial intelligence system that analyzes collected data and generates advice and meal menus based on the user's health condition and lifestyle habits.

[0155] Food Delivery Service API: An interface for ordering suggested food menus.

[0156] Data collection and analysis

[0157] Wearable devices constantly collect biometric information such as the user's heart rate, blood pressure, number of steps, etc. This data is sent to a server via the user's terminal.

[0158] The server inputs the received data into the generative AI, which then analyzes the data and evaluates the user's health condition. Based on the analysis results, the generative AI then suggests the optimal meal menu for the user.

[0159] Food menu suggestions and ordering

[0160] Generative AI can generate meal plans based on a user's health status. For example, if a user's heart rate is high, it can suggest low-calorie, high-protein meals.

[0161] The user device receives the meal menu suggestions from the generative AI and places the order via the food delivery service's API, allowing users to easily order meals that are optimal for their health.

[0162] Examples

[0163] For example, when a user wears a smartwatch, data such as heart rate and number of steps are automatically collected. This data is sent to a server, and the generative AI provides advice such as "Based on your current health condition, we recommend a low-calorie, high-protein meal." The user's device then automatically orders the suggested meal through a food delivery service.

[0164] Examples of prompt statements

[0165] User heart rate: 75

[0166] User's blood pressure: 120 / 80

[0167] User steps: 10000

[0168] Based on this data, suggest the best meal menu for your users.

[0169] In this way, it becomes possible to provide specific meal suggestions based on the user's health condition and link them to a meal delivery service.

[0170] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0171] Step 1:

[0172] Collecting data

[0173] A user wears a wearable device and constantly collects biometric information such as heart rate, blood pressure, and number of steps. This data is sent to the user's terminal such as a smartphone or tablet. The input is the biometric data from the wearable device, and the output is the biometric data sent to the terminal.

[0174] Step 2:

[0175] Sending Data

[0176] The terminal transmits the collected biometric data to the server. The input is the biometric data stored in the terminal, and the output is the biometric data transmitted to the server. In concrete terms, the terminal transmits data to the server using an HTTP request.

[0177] Step 3:

[0178] Analyzing data

[0179] The server inputs the received biometric data into the generative AI. The generative AI analyzes this data and evaluates the user's health condition. The input is the biometric data sent to the server, and the output is the analysis results regarding the user's health condition. In concrete terms, the generative AI analyzes the data and detects abnormalities in heart rate and blood pressure.

[0180] Step 4:

[0181] Meal menu suggestions

[0182] The generative AI proposes the optimal meal menu for the user based on the analysis results. The input is the analysis result on the user's health condition, and the output is the proposed meal menu. In concrete terms, the generative AI generates a prompt sentence and proposes a meal menu suitable for the user.

[0183] Step 5:

[0184] Meal menu notification

[0185] The server notifies the user's device of the meal menu proposed by the generative AI. The input is the proposed meal menu, and the output is the meal menu notified to the user's device. In concrete terms, the server sends the menu to the device using push notifications.

[0186] Step 6:

[0187] Ordering food from the menu

[0188] The user's device orders the proposed meal menu via the API of the food delivery service. The input is the notified meal menu, and the output is the order data sent to the food delivery service. In concrete terms, the device sends the order using an API request.

[0189] Step 7:

[0190] Review Order

[0191] The food delivery service receives the order and sends a confirmation to the user's terminal. The input is the order data sent to the food delivery service, and the output is the order confirmation sent to the user's terminal. In particular, the food delivery service processes the order and sends a confirmation to the terminal.

[0192] In this way, it becomes possible to provide specific meal suggestions based on the user's health condition and link them to a meal delivery service.

[0193] Example 2

[0194] Next, a second embodiment of the second embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0195] Conventional systems have had difficulty analyzing a user's thoughts and feelings in real time and providing appropriate advice based on them. In addition, there was a lack of means to efficiently process data collected from wearable devices and provide immediate feedback to users. This made it difficult to provide specific advice to improve users' problems and daily lives.

[0196] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0197] In this invention, the server includes a means for constantly collecting data by a wearable device worn by the user, a means for transmitting the collected data to the terminal, a means for the terminal to transmit the collected data to the server, a means for inputting the data collected by the server to the generative AI model, a means for the generative AI model to analyze the user's thoughts and emotions, a means for the generative AI model to generate advice based on the analysis results, a means for transmitting the generated advice to the terminal, and a means for the terminal to display the advice to the user. This makes it possible to provide appropriate advice based on the user's thoughts and emotions in real time.

[0198] A "wearable device" is an electronic device that can be worn by a user and is used to collect biometric and behavioral data of the user.

[0199] A "terminal" is an electronic device that receives and processes data transmitted from a wearable device, and includes smartphones, personal computers, etc.

[0200] The "server" is a central processing unit that receives data sent from the terminal and inputs it into the generative AI model.

[0201] A "generative AI model" is an artificial intelligence algorithm that analyzes input data and generates advice based on the user's thoughts and feelings.

[0202] "Means for continuous data collection" refers to a function that enables a wearable device to continuously collect the user's biometric and behavioral data.

[0203] "Means for transmitting data to a terminal" refers to a function for transmitting data collected by a wearable device to a terminal via wireless communication or the like.

[0204] The "means for transmitting data to a server" is a function for transmitting data received by a terminal to a server.

[0205] "Means for inputting data into a generative AI model" refers to a function for inputting data received by the server into a generative AI model.

[0206] "Means of analyzing the user's thoughts and emotions" refers to the function that enables the generative AI model to analyze input data and understand the user's thoughts and emotions.

[0207] "Means for generating advice" refers to the function that enables the generative AI model to generate advice for the user based on the analysis results.

[0208] The "means for transmitting advice to a terminal" is a function for the server to transmit the generated advice to the terminal.

[0209] The "means for displaying advice to a user" refers to a function for visually or audibly displaying the advice received by the terminal to the user.

[0210] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0211] This invention is a system that analyzes a user's thoughts and feelings in real time and provides appropriate advice based on the results. This system is implemented using a wearable device, a terminal, a server, and a generative AI model.

[0212] Hardware and Software Used

[0213] 1. Wearable Devices

[0214] The user wears a wearable device (e.g., an EEG headset) that reads the user's brainwaves, and the device collects the user's brainwave data in real time.

[0215] 2. Terminal

[0216] A terminal (e.g., a smartphone or a personal computer) receives the brainwave data transmitted from the wearable device. A dedicated application for processing the brainwave data is installed on the terminal.

[0217] 3. Server

[0218] The server receives the EEG data sent from the device and inputs it into the generative AI model. The server also pre-processes the data and manages the analysis results.

[0219] 4. Generative AI Models

[0220] A generative AI model (e.g., GPT-4) analyzes the brainwave data input from the server to understand the user's thoughts and emotions. Based on the analysis results, it generates advice for the user.

[0221] Data processing and calculation

[0222] 1. Data Collection

[0223] When a user wears the wearable device, it collects brainwave data and transmits it to a terminal via Bluetooth.

[0224] 2. Data Receipt and Transmission

[0225] The terminal receives the brainwave data sent from the wearable device and transmits the data to a server using a dedicated application.

[0226] 3. Data Analysis

[0227] The server inputs the brainwave data sent from the device into a generative AI model, which then analyzes the brainwave data to understand the user's thoughts and emotions.

[0228] 4. Advice Generation

[0229] The generative AI model generates advice for the user based on the analysis results. For example, if the user is feeling stressed, it generates advice such as "Try taking a deep breath to relax."

[0230] 5. Sending and Displaying Advice

[0231] The server transmits the generated advice to the terminal, which displays the received advice to the user.

[0232] Examples

[0233] For example, consider a case where a user feels stressed at work. The user puts on a wearable device and brainwave data is collected. The device sends this data to a server, which inputs the data into a generative AI model. The generative AI model analyzes that the user is feeling stressed and generates advice such as "Try taking a deep breath to relax." This advice is sent to the device and displayed to the user.

[0234] Example prompt:

[0235] “What advice should we offer our users if they are stressed?”

[0236] The system allows users to receive appropriate advice in real time based on their thoughts and feelings.

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

[0238] Program processing flow

[0239] Step 1: User puts on headset

[0240] A user wears a wearable device (e.g., an EEG headset) on their head to read their brainwaves, and the device collects the user's brainwave data in real time.

[0241] Input: User's brainwaves

[0242] Output: EEG data

[0243] Step 2: The headset collects brainwave data

[0244] The headset collects the user's brainwave data and transmits it to the device via Bluetooth.

[0245] Input: User's brainwaves

[0246] Output: EEG data sent to the terminal

[0247] Step 3: The device receives the EEG data

[0248] A terminal (e.g., a smartphone or a personal computer) receives the brainwave data transmitted from the headset. A dedicated application for processing the brainwave data is installed on the terminal.

[0249] Input: EEG data transmitted from the headset

[0250] Output: EEG data stored on the device

[0251] Step 4: The device transmits the EEG data to the server.

[0252] The device then transmits the received brainwave data to a server, where it is pre-processed for input into a generative AI model.

[0253] Input: EEG data stored on the device

[0254] Output: EEG data sent to the server

[0255] Step 5: The server inputs the EEG data into the generative AI model

[0256] The server receives the brainwave data sent from the device and inputs it into the generative AI model, which has an algorithm for analyzing this data.

[0257] Input: EEG data sent to the server

[0258] Output: EEG data input into a generative AI model

[0259] Step 6: The generative AI model analyzes the EEG data

[0260] The generative AI model analyzes the input brainwave data to understand the user's thoughts and emotions. For example, if the user is feeling stressed, it will extract that information.

[0261] Input: EEG data input to the generative AI model

[0262] Output: Analysis results (user's thoughts and feelings)

[0263] Step 7: The generative AI model generates advice

[0264] The generative AI model generates advice for the user based on the analysis results, such as "Try taking a deep breath to relax."

[0265] Input: Analysis results (user's thoughts and feelings)

[0266] Output: Generated advice

[0267] Step 8: The server sends the generated advice to the device

[0268] The server transmits the advice generated by the generative AI model to the terminal, and the advice is provided to the user in an appropriate format.

[0269] Input: Generated advice

[0270] Output: Advice sent to terminal

[0271] Step 9: The device displays the advice to the user

[0272] The terminal receives the advice sent from the server and displays it to the user, who can then confirm and act on the advice.

[0273] Input: Advice sent to terminal

[0274] Output: The advice displayed to the user

[0275] In this way, users can receive relevant advice in real time based on their own thoughts and feelings.

[0276] (Application example 2)

[0277] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0278] Conventional systems using wearable devices and generative AI can provide help for users' problems and advice to enrich their daily lives, but they cannot read the user's brainwaves, detect anxiety or tension in real time, and provide appropriate security advice. This makes it difficult to respond immediately when a user feels anxious, and there is an issue of remaining concerns about security.

[0279] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0280] In this invention, the server includes a means for constantly collecting data from a wearable device, a means for inputting the collected data to a generative AI, a means for the generative AI to provide help for a user's troubles and advice for enriching daily life, a means for inputting data from a device that reads the user's brainwaves to the generative AI, and a means for detecting the user's anxiety or tension and providing appropriate security advice. This makes it possible to detect the situation in real time when the user feels anxious and provide appropriate security advice.

[0281] A "wearable device" is an electronic device that can be worn by the user and has the ability to collect data continuously.

[0282] "Generative AI" is an artificial intelligence system that analyzes collected data and provides users with help and advice for their problems.

[0283] A "brainwave reading device" is a device that detects the user's brainwaves and collects the data.

[0284] The "means for detecting anxiety or tension" refers to a function that analyzes the user's brainwave data and detects the anxiety or tension felt by the user in real time.

[0285] "Security advice" is advice provided to users on appropriate actions and precautions when they feel anxious or tense.

[0286] The following system configuration will be described as an embodiment of the present invention.

[0287] System Configuration

[0288] The system consists of a wearable device, a device that reads brainwaves, a generative AI, and a server. The wearable device can be worn by the user and has the ability to constantly collect data. The device that reads brainwaves is a device that detects the user's brainwaves and collects that data. The generative AI is an artificial intelligence system that analyzes the collected data and offers help and advice for the user's problems. The server is responsible for receiving data from these devices and inputting it into the generative AI.

[0289] Hardware and Software

[0290] Hardware:

[0291] Wearable devices (e.g. smart glasses)

[0292] Brainwave Sensor

[0293] server

[0294] software:

[0295] Python

[0296] Keras (deep learning library)

[0297] Scikit-learn (data preprocessing library)

[0298] Requests (HTTP request library)

[0299] Data processing and calculation

[0300] 1. Data collection:

[0301] Wearable devices and brainwave sensors constantly collect the user's biometric and brainwave data.

[0302] The collected data is sent to a server.

[0303] 2. Data preprocessing:

[0304] The server uses Scikit-learn to standardize the data it receives.

[0305] The standardized data is input into the generative AI.

[0306] 3. Data analysis and advice generation:

[0307] The generative AI uses Keras to analyze data and detect the user's concerns, anxieties, and tensions.

[0308] Based on the detected information, appropriate security advice is generated.

[0309] 4. Advice provided:

[0310] The generated advice is provided to the user in real time via a wearable device.

[0311] Examples

[0312] If a user feels anxious while walking down a dark street at night, the EEG sensor will detect that anxiety, and the generative AI will analyze the data and provide advice such as, "Pay attention to your surroundings. Suspicious activity has been detected nearby."

[0313] Examples of prompt statements

[0314] "Please develop an application that analyzes the surrounding situation and provides appropriate security advice when the user feels uneasy. The system reads brainwave data, transmits it to a generative AI, and receives advice."

[0315] The above is an embodiment of the present invention.

[0316] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0317] Step 1:

[0318] A user wears a wearable device and an EEG sensor. The wearable device constantly collects the user's biometric data (e.g., heart rate and body temperature). The EEG sensor collects the user's EEG data in real time. These data are sent from the wearable device and the EEG sensor to a server.

[0319] Input: User's biometric data, brainwave data

[0320] Output: Biometric data and EEG data sent to the server

[0321] Step 2:

[0322] The server standardizes the received biometric data and EEG data using Scikit-learn's StandardScaler. The standardized data is then input to the generative AI.

[0323] Input: Biometric data and EEG data sent to the server

[0324] Output: Standardized biometric data, EEG data

[0325] Step 3:

[0326] The server sends the standardized data to the generative AI, which uses Keras to analyze the data and detects the user's concerns, anxieties, and tensions. Based on the analysis results, it generates appropriate security advice.

[0327] Input: Standardized biometric data, EEG data

[0328] Output: Analysis results by generative AI, security advice

[0329] Step 4:

[0330] The security advice generated by the generative AI is sent to the wearable device via a server. The wearable device displays the advice to the user in real time. For example, a message such as "Pay attention to your surroundings. Suspicious activity has been detected nearby" is displayed.

[0331] Input: Analysis results by generative AI, security advice

[0332] Output: The security advice displayed to the user

[0333] Step 5:

[0334] The user acts according to the security advice displayed on the wearable device, for example by checking the surroundings and moving to a safe place.

[0335] Input: Security advice displayed to the user

[0336] Output: User actions

[0337] The above are the specific processing steps of this system.

[0338] Furthermore, an emotion engine that estimates the emotion of the user may be combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[0339] "Example 1"

[0340] As one embodiment of the present invention, a system is provided that includes a means for constantly collecting data by a wearable device, a means for inputting the collected data to a generative AI, a means for the generative AI to provide help for a user's troubles and advice to enrich daily life, and an emotion engine in which the generative AI recognizes the user's emotions. Specifically, the wearable device constantly collects biometric information and behavioral data of the user and transmits the data to the generative AI. The generative AI analyzes the received data and provides advice on the user's health condition and lifestyle habits. Furthermore, the generative AI uses the emotion engine to recognize the user's emotions and provides advice according to the emotions. For example, if it recognizes that the user is feeling stressed, it provides advice to encourage relaxation.

[0341] "Example 2"

[0342] As another embodiment of the present invention, a system is provided that includes a means for a wearable device to collect healthcare-related data and an emotion engine for a generative AI to recognize a user's emotions. Specifically, the wearable device constantly collects the user's biometric and behavioral data, particularly healthcare-related data, and transmits the data to the generative AI. The generative AI analyzes the received data and provides advice regarding the user's health condition and lifestyle habits. Furthermore, the generative AI uses the emotion engine to recognize the user's emotions and provides advice according to the emotions. For example, if it recognizes that the user is feeling stressed, it provides advice to encourage relaxation.

[0343] "Example 5"

[0344] As yet another embodiment of the present invention, a system is provided that includes a means for inputting data from a device that reads a user's thoughts into a generative AI, and an emotion engine that allows the generative AI to recognize the user's emotions. Specifically, a device that reads the user's thoughts (e.g., a headset that reads brainwaves) reads the user's thoughts and transmits the data to the generative AI. The generative AI analyzes the received data and provides advice corresponding to the user's thoughts and emotions. Furthermore, the generative AI uses the emotion engine to recognize the user's emotions and provides advice according to the emotions. For example, if it recognizes that the user is feeling stressed, it provides advice encouraging relaxation.

[0345] The process flow of each embodiment will be described below.

[0346] "Example 1"

[0347] Step 1: The wearable device constantly collects the user's biometric and behavioral data. Step 2: The wearable device sends the collected data to the generative AI.

[0348] Step 3: The generative AI analyzes the received data.

[0349] Step 4: Generative AI provides advice on the user's health and lifestyle.

[0350] Step 5: The generative AI uses the emotion engine to recognize the user's emotions.

[0351] Step 6: Generative AI provides advice based on emotions.

[0352] "Example 2"

[0353] Step 1: Wearable devices constantly collect biometric and behavioral data from users, especially health-related data.

[0354] Step 2: The wearable device sends the data it collects to the generative AI.

[0355] Step 3: The generative AI analyzes the received data.

[0356] Step 4: Generative AI provides advice on the user's health and lifestyle.

[0357] Step 5: The generative AI uses the emotion engine to recognize the user's emotions.

[0358] Step 6: Generative AI provides advice based on emotions.

[0359] "Example 5"

[0360] Step 1: A mind-reading device (e.g. a brainwave-reading headset) reads the user's thoughts.

[0361] Step 2: The device that reads the user's thoughts sends the data it has read to the generative AI.

[0362] Step 3: The generative AI analyzes the received data.

[0363] Step 4: Generative AI provides advice that responds to the user's thoughts and feelings.

[0364] Step 5: The generative AI uses the emotion engine to recognize the user's emotions.

[0365] Step 6: Generative AI provides advice based on emotions.

[0366] Example 1

[0367] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal".

[0368] Conventional health management systems using wearable devices often do not analyze collected data or provide feedback to users in real time, making it difficult to provide appropriate advice based on the user's health condition and emotions. In addition, they lacked the ability to recognize the user's emotions and provide advice based on them, making it difficult to improve the user's quality of life.

[0369] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0370] In this invention, the server includes a means for receiving and storing data, a means for analyzing the data using a generative AI model, and a means for recognizing the user's emotions using an emotion engine, which makes it possible to analyze the collected data in real time and provide appropriate advice according to the user's health condition and emotions.

[0371] A "wearable device" is a device that can be worn by the user and constantly collects biometric information such as heart rate, blood pressure, and number of steps taken, as well as behavioral data.

[0372] A "terminal" is a device that receives data sent from a wearable device and transmits it to a server, and examples of this include smartphones and tablets.

[0373] The "server" is a central processing unit that receives data sent from the terminals, stores it, analyzes it, and generates advice.

[0374] A "generative AI model" is an artificial intelligence model that analyzes collected data, identifies patterns related to the user's health and lifestyle habits, and generates appropriate advice.

[0375] An "emotion engine" is an algorithm or software that recognizes a user's emotions based on their biometric and behavioral data.

[0376] A "database" is a storage device for storing data received by a server.

[0377] "Advice" refers to instructions or suggestions that the generative AI model provides to the user based on the results of analysis and emotion recognition.

[0378] "Notification" is a function that notifies the user of advice that the terminal has received from the server.

[0379] This invention is a system that combines a wearable device, a terminal, a server, and a generative AI model to provide advice based on the user's health condition and emotions.

[0380] Hardware and Software Used

[0381] Wearable Devices

[0382] The user wears a wearable device that constantly collects biometric and behavioral data, such as heart rate, blood pressure, and number of steps taken. Specific examples of such devices include common smart watches and fitness trackers.

[0383] Terminal

[0384] The wearable device transmits the collected data to a terminal (such as a smartphone or tablet). The terminal receives the data and transmits it to a server. An application for receiving and transmitting data is installed on the terminal.

[0385] server

[0386] The server receives the data sent from the device and stores it in a database. The server analyzes the data using a generative AI model to identify patterns related to the user's health and lifestyle. It also uses an emotion engine to recognize the user's emotions.

[0387] Generative AI Models

[0388] The generative AI model analyzes the data stored on the server and generates advice on the user's health condition and lifestyle habits. The generative AI model predicts the user's health condition based on past data and provides appropriate advice.

[0389] Emotion Engine

[0390] An emotion engine is an algorithm or software that recognizes a user's emotions based on their biometric and behavioral data. For example, it can determine whether a user is feeling stressed based on fluctuations in their heart rate or blood pressure.

[0391] Examples

[0392] Health monitoring

[0393] Suppose a user is wearing a smartwatch. The smartwatch constantly collects heart rate and step counts and sends them to the device. The device then sends the received data to a server. The server uses a generative AI model to analyze the data and advises the user, "Your step count is low today, so you should walk a little more."

[0394] Emotion Recognition and Advice

[0395] Suppose a user is wearing a fitness tracker. The fitness tracker constantly collects heart rate and blood pressure and sends them to the device. The device then sends the received data to the server. The generative AI model analyzes the data and recognizes that the user is feeling stressed. The generative AI model advises, "Take a deep breath and relax."

[0396] Examples of prompt statements

[0397] "If the user's heart rate is higher than normal, provide them with advice on how to relax."

[0398] By combining a wearable device with generative AI, the system can provide appropriate advice based on the user's health condition and emotions.

[0399] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0400] Step 1:

[0401] A user wears a wearable device. The wearable device constantly collects biometric and behavioral data, such as heart rate, blood pressure, and number of steps. Specifically, the wearable device uses sensors to measure these data and stores them in its internal memory. The input is the user's biometric information, and the output is the collected data.

[0402] Step 2:

[0403] The wearable device transmits the collected data to the terminal. Specifically, the data is transferred to the terminal using wireless communication technologies such as Bluetooth and Wi-Fi. The input is the data stored in the wearable device, and the output is the data transmitted to the terminal.

[0404] Step 3:

[0405] The terminal sends the received data to the server. Specifically, the terminal uploads data to the server using an Internet connection. The input is the data stored on the terminal, and the output is the data sent to the server.

[0406] Step 4:

[0407] The server receives data sent from the terminal and stores it in a database. Specifically, the server converts the data into a format that is easy to analyze and stores it in the database. The input is the data sent to the server, and the output is the data stored in the database.

[0408] Step 5:

[0409] The server analyzes the stored data using a generative AI model. Specifically, the generative AI model identifies patterns related to the user's health and lifestyle based on past data. The input is the data stored in the database, and the output is the analysis results.

[0410] Step 6:

[0411] The server uses an emotion engine to recognize the user's emotions. Specifically, the emotion engine analyzes fluctuations in heart rate and blood pressure to determine whether the user is feeling stressed. The input is the analysis result, and the output is the result of emotion recognition.

[0412] Step 7:

[0413] The server generates advice for the user based on the analysis results and emotion recognition results. In concrete terms, the generative AI model generates advice such as "Take a deep breath and relax." The input is the analysis results and emotion recognition results, and the output is the generated advice.

[0414] Step 8:

[0415] The server sends the generated advice to the terminal. In particular, the server uses an Internet connection to send the advice to the terminal. The input is the generated advice, and the output is the advice sent to the terminal.

[0416] Step 9:

[0417] The terminal notifies the user of the received advice. In concrete terms, the terminal uses a notification function to display the advice to the user. The input is the advice sent to the terminal, and the output is the advice notified to the user.

[0418] (Application example 1)

[0419] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0420] In modern society, it is important for each user to properly manage their own health condition and lifestyle habits. However, with conventional systems, it has been difficult to collect a user's biometric and behavioral data in real time and provide appropriate advice based on that data. Furthermore, there have been no systems that recognize a user's emotions and provide advice based on those emotions. This has resulted in users being unable to take appropriate measures based on their own health condition and emotions, which could lead to a decline in their quality of life.

[0421] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0422] In this invention, the server includes a means for constantly collecting data by the wearable device, a means for inputting the collected data to the generative AI, a means for the generative AI to provide help for the user's problems and advice for enriching daily life, a means for the generative AI to recognize the user's emotions and provide advice according to the emotions, and a means for the generative AI to provide advice on the user's health condition and lifestyle habits. This makes it possible to collect the user's biometric information and behavioral data in real time and provide appropriate advice on the health condition and lifestyle habits based on the collected data. In addition, by recognizing the user's emotions and providing advice according to the emotions, the quality of the user's life can be improved.

[0423] A "wearable device" is an electronic device that is worn to constantly collect the user's biometric and behavioral data.

[0424] "Generative AI" is an artificial intelligence system that analyzes collected data and provides appropriate advice to users.

[0425] "Means of continuous data collection" refers to a function that uses a wearable device to continuously collect a user's biometric information and behavioral data.

[0426] "Means of input" is a function for sending collected data to the generative AI.

[0427] "Means of providing advice" refers to the function by which the generative AI provides appropriate advice and suggestions to the user based on the results of analysis.

[0428] "Means of recognizing emotions" refers to the function whereby the generative AI analyzes the user's emotional state and provides advice accordingly.

[0429] "Means of providing advice regarding health status and lifestyle habits" refers to a function in which generative AI analyzes a user's health and behavioral data and provides advice regarding health management and improving lifestyle habits.

[0430] A system for carrying out the present invention includes a wearable device, a generative AI, an emotion engine, and a communication means for linking these components. The specific configuration and operation of the system are described below.

[0431] System Configuration

[0432] 1. Wearable devices:

[0433] Constantly collect user biometric information (heart rate, blood pressure, number of steps, etc.) and behavioral data.

[0434] Example: Fitbit, Apple Watch, etc.

[0435] 2. Generative AI:

[0436] The collected data is analysed to generate advice on the user's health and lifestyle habits.

[0437] Examples: OpenAI GPT-3(registered trademark), Google(registered trademark) Cloud AI, etc.

[0438] 3. Emotion Engine:

[0439] Recognize user emotions and provide advice accordingly.

[0440] Examples: Affectiva, IBM Watson(R) Tone Analyzer, etc.

[0441] 4. Means of communication:

[0442] Network communication to send data from wearable devices to the generative AI and emotion engine.

[0443] Examples: Bluetooth, Wi-Fi, cellular, etc.

[0444] System Operation

[0445] 1. Data collection:

[0446] Wearable devices collect the user's biometric information, such as heart rate, blood pressure, and number of steps, in real time.

[0447] The collected data is sent to the generative AI at regular intervals.

[0448] 2. Data Analysis:

[0449] The generative AI analyzes the biometric and behavioral data it receives and generates advice about the user's health and lifestyle habits.

[0450] The analysis results are displayed on the user's smartphone app.

[0451] 3. Emotion recognition:

[0452] The emotion engine recognizes the user's emotions based on data sent from the generative AI.

[0453] Generate and provide emotion-based advice to users.

[0454] Examples

[0455] Health Tip: "Your heart rate is high. Take a deep breath and relax."

[0456] Emotional Advice: "You seem to be stressed. Try meditating for five minutes to help you relax."

[0457] Examples of prompt statements

[0458] "Generate advice for when a user's heart rate is high and they are feeling stressed."

[0459] In this way, it is possible to provide appropriate advice in real time based on the user's biometric information and emotional state. This system allows users to take appropriate measures according to their own health condition and emotions, improving their quality of life.

[0460] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0461] Step 1:

[0462] A wearable device collects the user's biometric information (heart rate, blood pressure, number of steps, etc.) in real time. The input is the user's biometric information, and the output is the collected data. In concrete terms, the wearable device's sensors collect data from the user's body and store it in its internal memory.

[0463] Step 2:

[0464] The wearable device transmits the collected data to a smartphone. The input is the biometric information stored in the wearable device, and the output is the data transferred to the smartphone. Specifically, the data is transmitted to the smartphone using Bluetooth or Wi-Fi.

[0465] Step 3:

[0466] The smartphone sends the received data to the generative AI. The input is the biometric information transferred to the smartphone, and the output is the data sent to the generative AI. In concrete terms, the smartphone application sends data to the generative AI server via the internet.

[0467] Step 4:

[0468] The generative AI analyzes the data it receives and generates advice about the user's health condition and lifestyle habits. The input is the biometric information sent to the generative AI, and the output is the generated advice. In concrete terms, the generative AI analyzes the data and generates advice based on the user's health condition and lifestyle habits.

[0469] Step 5:

[0470] The generative AI sends the analysis results to the emotion engine, which recognizes the user's emotions. The input is the analysis results of the generative AI, and the output is the user's emotions recognized by the emotion engine. In concrete terms, the emotion engine identifies the user's emotions based on the analysis results.

[0471] Step 6:

[0472] The emotion engine generates advice according to the user's emotion. The input is the user's emotion recognized by the emotion engine, and the output is advice according to the emotion. In concrete terms, the emotion engine generates appropriate advice based on the user's emotional state.

[0473] Step 7:

[0474] The advice generated by the generative AI and emotion engine is sent to the smartphone. The input is the generated advice, and the output is the advice transferred to the smartphone. In concrete terms, the generative AI and emotion engine send advice to the smartphone via the Internet.

[0475] Step 8:

[0476] The smartphone displays the received advice to the user. The input is the advice transferred to the smartphone, and the output is the advice displayed to the user. In concrete terms, the smartphone application displays the advice on the screen and the user confirms it.

[0477] Example 2

[0478] Next, a second embodiment of the second embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0479] Conventional systems have had difficulty analyzing a user's biometric information and thoughts in real time and providing appropriate advice. In addition, there was a lack of means to accurately recognize a user's emotions and thoughts and provide advice based on them. This resulted in insufficient help for users' problems and advice to enrich their daily lives.

[0480] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for constantly collecting biometric information of the user, a means for inputting the collected biometric information to the generative AI, a means for the generative AI to analyze the user's emotions and thoughts and provide help for the user's troubles and advice for enriching the user's daily life, a means for the generative AI to generate appropriate advice for the user based on the analysis result and transmit the advice to the terminal, and a means for the terminal to display the advice to the user. This makes it possible to analyze the user's biometric information and thoughts in real time and provide appropriate advice.

[0481] "User" means an individual or organization that uses the system.

[0482] "Biometric information" is data obtained from the user's body, and includes heart rate, brain waves, body temperature, and the like.

[0483] A "wearable device" is an electronic device that can be worn by a user and is equipped with sensors for collecting biometric information.

[0484] "Generative AI" is an artificial intelligence system that analyzes collected data and generates appropriate advice for users.

[0485] An "emotion engine" is a part of generative AI and is a module that contains algorithms for analyzing user emotions.

[0486] A "terminal" is an electronic device used by a user, and includes a smartphone, tablet, etc.

[0487] "Advice" refers to advice or suggestions that the generative AI provides to the user based on the results of its analysis.

[0488] A "thought-reading device" is a device for capturing data related to a user's thoughts, such as brainwaves.

[0489] This invention is a system that analyzes a user's biological information and thoughts in real time and provides appropriate advice. The system includes a wearable device, a terminal, a server, and a generative AI.

[0490] The user wears a wearable device, such as a headset, that reads brainwaves. The device captures the user's brainwaves and other biometric information and transmits it to the user's device (e.g., a smartphone or tablet) via Bluetooth or Wi-Fi. The device then transmits the received biometric information to a server via the Internet. The data is encrypted before being transmitted, ensuring security.

[0491] The server passes the received biometric information to a generative AI, which is hosted within the server or resides on the cloud. The generative AI uses an emotion engine to analyze the user's brainwave patterns to identify the emotions and thoughts the user is feeling. Based on the analysis results, the generative AI generates appropriate advice for the user. For example, if the user is recognized as feeling stressed, the generative AI generates advice to encourage relaxation.

[0492] The server sends the advice received from the generative AI to the user's device. The device notifies the user of the advice received. For example, the smartphone's notification function can be used to display a message such as "We recommend that you take a deep breath and take some time to relax."

[0493] As a concrete example, consider a situation where a user is wearing a headset that reads brainwaves. The headset captures the stress the user feels while working in real time. The server receives this data and sends it to a generative AI. The generative AI analyzes the data and determines that the user is at a high stress level. As a result, the generative AI provides advice such as, "I recommend that you take a moment to take a deep breath and relax."

[0494] Examples of prompt sentences to be input to a generative AI model include the following:

[0495] "Analyze the user's brainwave data to identify the emotions the user is feeling. If the user is feeling stressed, provide advice to promote relaxation."

[0496] By using this prompt, the generative AI can generate appropriate advice based on the user's brainwave data.

[0497] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0498] Step 1:

[0499] A user wears a headset that reads brainwaves. The user puts the headset on their head to capture brainwaves and turns on the device. The headset starts capturing the user's brainwave data in real time. The input is the user's brainwaves and the output is the captured brainwave data.

[0500] Step 2:

[0501] The headset transmits the EEG data to the device. The headset transmits the captured EEG data to the user's device (e.g., smartphone or tablet) via Bluetooth or Wi-Fi. The input is the captured EEG data, and the output is the EEG data transmitted to the device.

[0502] Step 3:

[0503] The terminal transmits the received EEG data to the server. The terminal encrypts the received EEG data and transmits it to the server via the Internet. The input is the EEG data transmitted to the terminal, and the output is the encrypted EEG data transmitted to the server.

[0504] Step 4:

[0505] The server passes the EEG data to the generative AI. The server calls an API to pass the received EEG data to the generative AI. The input is the encrypted EEG data sent to the server, and the output is the EEG data passed to the generative AI.

[0506] Step 5:

[0507] The generative AI analyzes the EEG data to identify the user's thoughts and emotions. The generative AI uses an emotion engine to analyze the EEG data to identify the emotions and thoughts the user is feeling. The input is the EEG data passed to the generative AI, and the output is the identified emotions and thoughts of the user.

[0508] Step 6:

[0509] The generative AI generates advice based on the analysis results. The generative AI generates appropriate advice for the user based on the analysis results. For example, if it is recognized that the user is feeling stressed, it generates advice to encourage relaxation. The input is the identified user's emotions and thoughts, and the output is the generated advice.

[0510] Step 7:

[0511] The server sends the generated advice to the terminal. The server sends the advice received from the generative AI to the user's terminal. The input is the generated advice, and the output is the advice sent to the terminal.

[0512] Step 8:

[0513] The terminal displays the advice to the user. The terminal notifies the user of the received advice. For example, the smartphone's notification function can be used to display a message such as "We recommend that you take a deep breath and take some time to relax." The input is the advice sent to the terminal, and the output is the advice displayed to the user.

[0514] (Application example 2)

[0515] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0516] In modern society, it is important to understand a user's thoughts and emotions in real time and provide appropriate advice accordingly. However, with conventional systems, it was difficult to accurately recognize a user's emotional state and suggest specific actions based on it. In addition, there was no system that provided actions to reduce security risks according to the user's emotional state. This made it difficult to improve the quality of users' lives.

[0517] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0518] In this invention, the server includes a means for constantly collecting data from a wearable device, a means for inputting the collected data to the generative AI, a means for the generative AI to provide help for the user's problems and advice to enrich daily life, a means for inputting data from a device that reads the user's thoughts and emotions to the generative AI, a means for the generative AI to recognize the user's emotions and provide advice according to the emotions, and a means for suggesting actions to reduce security risks according to the user's emotional state. This makes it possible to accurately recognize the user's emotional state and suggest specific actions based on it.

[0519] A "wearable device" is an electronic device that can be worn by a user and is used to collect data continuously.

[0520] "Generative AI" is an artificial intelligence system that analyzes collected data and generates appropriate advice and suggestions for users.

[0521] A "device that reads a user's thoughts and emotions" is a device that reads brainwaves and other bio-signals to detect the user's thoughts and emotional state.

[0522] The "emotion engine" is a part of generative AI that recognizes the user's emotions and provides appropriate advice based on those emotions.

[0523] "Actions to reduce security risks" are specific actions or measures suggested to minimize security risks depending on the user's emotional state.

[0524] A system for implementing this invention includes a wearable device, a device for reading a user's thoughts and emotions, a generative AI, an emotion engine, and a function for suggesting actions to reduce security risks. Specific embodiments of each element are described below.

[0525] 1. Wearable Devices

[0526] A wearable device is an electronic device that can be worn by the user and is used to constantly collect data. Specifically, it collects biometric information such as heart rate, body temperature, and activity level. This data is sent to a smartphone via Bluetooth or Wi-Fi.

[0527] 2. Devices that read your thoughts and emotions

[0528] A device that reads a user's thoughts and emotions is a device that reads brainwaves and other biosignals to detect the user's thoughts and emotional state. Specifically, a headset that reads brainwaves (e.g., Emotiv Insight) is used. This device collects the user's brainwave data in real time and transmits it to a smartphone.

[0529] 3. Generative AI and Emotion Engines

[0530] Generative AI is an artificial intelligence system that analyzes collected data and generates appropriate advice and suggestions for users. The emotion engine is part of generative AI and is a function for recognizing user emotions and providing appropriate advice based on those emotions. Specifically, we use Keras (a deep learning library) to build an emotion recognition model and Scikit-learn (a data preprocessing library) to preprocess the data.

[0531] 4. Feature that suggests actions to reduce security risks

[0532] The action suggestions to reduce security risks are specific actions or measures suggested to minimize security risks depending on the user's emotional state. For example, if the user is feeling stressed, it will provide advice to promote relaxation, and if the user is feeling anxious, it will provide advice to give reassurance.

[0533] Examples

[0534] If a user feels stressed at work, the app offers advice such as, "Take a deep breath to relax." If a user feels anxious at night, the app offers advice such as, "Listen to your favorite music to feel more at ease."

[0535] Examples of prompt statements

[0536] "If users are feeling stressed, design your application to provide them with advice that encourages relaxation."

[0537] In this way, it is possible to accurately recognize the user's emotional state and suggest specific actions based on it.

[0538] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0539] Step 1:

[0540] Wearable devices collect biometric information from users, such as heart rate, body temperature, and activity level, in real time, and transmit this data to a smartphone via Bluetooth or Wi-Fi.

[0541] Input: User's biometric information

[0542] Output: Biometric data sent to a smartphone

[0543] Step 2:

[0544] The device that reads the user's thoughts and emotions collects the user's brainwave data. Specifically, a brainwave reading headset is used to obtain the user's brainwave data in real time. The device also transmits the data to a smartphone via Bluetooth or Wi-Fi.

[0545] Input: User's brainwave data

[0546] Output: EEG data transmitted to a smartphone

[0547] Step 3:

[0548] We preprocess the biometric data and EEG data collected by the smartphone. Specifically, we use Scikit-learn's StandardScaler to standardize the data. This unifies the scale of the data, making it easier to analyze with generative AI.

[0549] Input: Biometric data, EEG data

[0550] Output: Standardized data

[0551] Step 4:

[0552] The smartphone inputs the preprocessed data into the generative AI, which then uses an emotion recognition model built using Keras to analyze the user's emotions. Specifically, the data is fed into a deep learning model to predict the user's emotional state.

[0553] Input: Standardized data

[0554] Output: The user's emotional state.

[0555] Step 5:

[0556] The generative AI uses an emotion engine to generate advice based on the user's emotions. For example, if the user is feeling stressed, it will provide advice to encourage relaxation.

[0557] Input: The user's emotional state

[0558] Output: Advice based on emotions

[0559] Step 6:

[0560] The smartphone notifies the user of the generated advice, specifically by providing it via push notification or voice assistant.

[0561] Input: Emotion-based advice

[0562] Output:Notification to the user

[0563] Step 7:

[0564] Depending on the user's emotional state, it will suggest actions to reduce security risks. For example, if the user is feeling anxious, it will suggest specific actions and measures to make them feel safe.

[0565] Input: The user's emotional state

[0566] Output: Suggested actions to reduce security risks

[0567] The above are the specific process steps for carrying out the present invention.

[0568] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0569] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0570] Another example of generative AI is Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0571] In the above embodiment, an example was given in which the specific process was performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0572] [Second embodiment]

[0573] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0574] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0575] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[0576] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0577] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0578] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0579] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0580] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0581] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure.

[0582] 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.

[0583] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0584] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0585] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0586] "Example 1"

[0587] The present invention is a system that constantly collects biometric and behavioral data of a user using a wearable device and inputs the data into a generative AI. Specifically, the wearable device collects healthcare-related data such as heart rate, blood pressure, and number of steps, and transmits the data to the generative AI. The generative AI analyzes the received data and provides advice on the user's health condition and lifestyle habits.

[0588] "Example 2"

[0589] In addition, in another aspect of the present invention, a system is provided that inputs data from a device that reads a user's thoughts into a generative AI. Specifically, a device such as a headset that reads brainwaves is used to read the user's thoughts and transmit the data to the generative AI. The generative AI analyzes the received data and provides advice corresponding to the user's thoughts and feelings.

[0590] The process flow of each embodiment will be described below.

[0591] "Example 1"

[0592] Step 1: The wearable device constantly collects the user's biometric and behavioral data. Step 2: The collected data is sent from the wearable device to the generative AI.

[0593] Step 3: The generative AI analyzes the received data and provides advice on the user's health and lifestyle.

[0594] "Example 2"

[0595] Step 1: A mind-reading device (e.g. a brainwave-reading headset) reads the user's thoughts.

[0596] Step 2: The read data is sent from the device to the generative AI.

[0597] Step 3: The generative AI analyzes the received data and provides advice that corresponds to the user's thoughts and feelings.

[0598] Example 1

[0599] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[0600] Conventional wearable devices can collect biometric and behavioral data of users, but they lack a system that can effectively analyze the data and provide specific advice to users. Another issue is that it is difficult to analyze the collected data in real time and provide quick feedback to users.

[0601] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0602] In this invention, the server includes a means for constantly collecting data by the wearable device, a means for transmitting the collected data to the server, a means for transmitting the data received by the server to the generative AI, a means for the generative AI to analyze the received data, a means for the generative AI to generate advice on the user's health condition and lifestyle habits based on the analysis results, a means for the server to transmit the generated advice to the user's terminal, and a means for the user to check the advice on the terminal. This allows the user to understand their own health condition in real time and quickly receive appropriate advice.

[0603] A "wearable device" is a device that is worn by the user to constantly collect biometric information such as heart rate, blood pressure, and number of steps taken, as well as behavioral data.

[0604] The "server" is a computer system that acts as an intermediary to receive data sent from the wearable device and pass it on to the generative AI.

[0605] "Generative AI" is an artificial intelligence system that analyzes data received from a server and generates advice regarding the user's health and lifestyle habits.

[0606] "Means of continuous data collection" refers to the function of a wearable device to continuously collect the user's biometric information and behavioral data.

[0607] "Data transmission means" is a function that transmits data collected by the wearable device to a server.

[0608] "Data receiving means" is a function by which the server receives data transmitted from the wearable device.

[0609] "Data analysis means" refers to the function by which the generative AI analyzes the data it receives from the server.

[0610] The "advice generation means" is a function in which the generative AI generates advice regarding the user's health condition and lifestyle habits based on the analysis results.

[0611] "Advice sending means" refers to a function whereby the server sends advice generated by the generative AI to the user's device.

[0612] "Advice confirmation means" refers to a function that allows a user to confirm advice provided by the generative AI on their device.

[0613] This invention is a system that uses a wearable device to constantly collect a user's biometric and behavioral data and inputs that data into a generative AI. Specifically, the wearable device collects healthcare-related data such as heart rate, blood pressure, and number of steps, and transmits that data to the generative AI. The generative AI analyzes the received data and provides advice on the user's health condition and lifestyle habits.

[0614] Hardware and Software Used

[0615] Wearable devices: Devices that collect health-related data such as heart rate, blood pressure, and number of steps (e.g., Apple Watch, Fitbit).

[0616] Server: Receives data sent from the wearable device and acts as a relay to pass it on to the generative AI.

[0617] Generative AI: An AI system that analyzes the data it receives and provides advice to users about their health and lifestyle (e.g., OpenAI’s GPT-4).

[0618] Program Processing Procedure

[0619] 1. Data collection:

[0620] The user puts on the wearable device.

[0621] Wearable devices constantly collect the user's biometric information, such as heart rate, blood pressure, and number of steps, as well as behavioral data.

[0622] 2. Data transmission:

[0623] The wearable device transmits the collected data to a server.

[0624] The server sends the received data to the generative AI.

[0625] 3. Data Analysis:

[0626] The generative AI analyzes the data received from the server.

[0627] Based on the analysis results, the generative AI generates advice regarding the user's health condition and lifestyle habits.

[0628] 4. Advice provided:

[0629] The advice generated by the generative AI is sent to the server.

[0630] The server sends the advice to the user's device (such as a smartphone or PC).

[0631] The user checks the advice on the device.

[0632] Examples

[0633] Example 1: When the user is wearing an Apple Watch

[0634] The user wears the Apple Watch.

[0635] The Apple Watch collects the user's heart rate and steps.

[0636] The data collected by Apple Watch is sent to the user's iPhone.

[0637] The iPhone uploads the data to the server.

[0638] The server sends the data to the generative AI (GPT-4).

[0639] Generative AI analyzes the data and generates advice such as, "Your heart rate has been high recently. To reduce stress, try spending more time relaxing."

[0640] The server sends the advice to the user's iPhone.

[0641] The user checks the advice on their iPhone.

[0642] Examples of prompt statements

[0643] User heart rate data: [80, 85, 90, 95, 100]

[0644] User step count data: [1000, 2000, 3000, 4000, 5000]

[0645] Analyze this data and provide advice regarding the user's health status.

[0646] In this way, users can constantly monitor their own health status through their wearable devices and receive appropriate advice.

[0647] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0648] Step 1:

[0649] Data collection

[0650] The user puts on the wearable device.

[0651] Wearable devices constantly collect the user's biometric information, such as heart rate, blood pressure, and number of steps, as well as behavioral data.

[0652] Input: User biometric information (heart rate, blood pressure, number of steps, etc.).

[0653] Output: Collected biometric data.

[0654] What it does: For example, your Apple Watch measures your heart rate every minute and counts your steps in real time.

[0655] Step 2:

[0656] Data transmission

[0657] The wearable device transmits the data it collects to the user's smartphone.

[0658] The smartphone uploads the data to the server.

[0659] Input: Collected biometric data.

[0660] Output: Biometric data sent to the server.

[0661] Specific operations: For example, Apple Watch sends collected data via Bluetooth or Wi-Fi to the user's iPhone, which then uploads the data to a server.

[0662] Step 3:

[0663] Data Reception

[0664] The server receives the data transmitted from the wearable device.

[0665] Input: Biometric data sent from your smartphone.

[0666] Output: Biometric data stored on the server.

[0667] Specific operation: The server receives the data in real time and stores it in a database.

[0668] Step 4:

[0669] 4. Data Analysis

[0670] The server sends the received data to the generative AI.

[0671] The generative AI analyzes the data received from the server.

[0672] Input: Biometric data stored on the server.

[0673] Output: Analysis results (information about the user's health and lifestyle).

[0674] How it works: Generative AI analyzes heart rate and step count data to detect outliers and trends.

[0675] Step 5:

[0676] Advice Generation

[0677] Based on the analysis results, the generative AI generates advice regarding the user's health condition and lifestyle habits.

[0678] Input: The analysis results.

[0679] Output: Advice (e.g. "Your heart rate has been high lately. Try spending more time relaxing to reduce stress").

[0680] Specific behavior: The generative AI generates specific advice for the user based on the analysis results.

[0681] Step 6:

[0682] Send Advice

[0683] The advice generated by the generative AI is sent to the server.

[0684] The server sends the advice to the user's device (such as a smartphone or PC).

[0685] Input: The generated advice.

[0686] Output: Advice sent to the user's terminal.

[0687] Specific operation: The server sends the advice received from the generative AI to the user's smartphone via push notification.

[0688] Step 7:

[0689] Advice confirmation

[0690] The user checks the advice on the device.

[0691] Input: The advice sent to the user's terminal.

[0692] Output: The advice the user confirmed.

[0693] Specific action: The user taps on the notification on their smartphone to view the advice.

[0694] (Application example 1)

[0695] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[0696] Conventional healthcare systems using wearable devices were limited to collecting users' biometric and behavioral data, and monitoring their health status and providing advice. However, these systems did not adequately address specific behavioral suggestions based on the user's health status or improving convenience in daily life. In particular, there was a lack of suggestions for meal menus optimal for the user's health status and integration with meal delivery services, resulting in insufficient specific support for users to live a healthy lifestyle.

[0697] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means. In this invention, the server includes a means for constantly collecting data by the wearable device, a means for inputting the collected data to the generative AI, a means for the generative AI to provide help for the user's problems and advice for enriching daily life, a means for proposing an optimal meal menu based on the user's biometric information and behavioral data, and a means for ordering the proposed meal menu from a food delivery service. This enables specific meal suggestions based on the user's health condition to be linked with a meal delivery service.

[0698] A "wearable device" is a device that is worn on the user's body and constantly collects biometric information such as heart rate, blood pressure, and number of steps taken, as well as behavioral data.

[0699] "Means of continuous data collection" refers to a function that uses a wearable device to continuously obtain a user's biometric information and behavioral data.

[0700] "Generative AI" is an artificial intelligence system that analyzes collected data and generates advice and suggestions based on the user's health and lifestyle habits.

[0701] "Means of input" refers to the function of sending data collected from a wearable device to the generative AI.

[0702] "Means of providing advice" refers to a function that provides users with advice regarding their health status and lifestyle habits based on data analyzed by the generative AI.

[0703] The "means of suggesting meal menus" refers to a function in which generative AI analyzes the user's biometric information and behavioral data, and suggests to the user the meal menu that is optimal for their health condition.

[0704] The "means for ordering from a food delivery service" is a function for automatically ordering the suggested meal menu from a food delivery service.

[0705] The system for implementing this invention is configured by combining a wearable device, generative AI, and a food delivery service API. A specific embodiment is shown below.

[0706] System Configuration

[0707] Hardware:

[0708] Wearable device: A device that is worn on the user's body and constantly collects biometric and behavioral data such as heart rate, blood pressure, and number of steps taken. Examples include smart watches and fitness trackers.

[0709] Server: A computer system for running generative AI, analyzing data and proposing meal menus.

[0710] User device: A device operated by a user, such as a smartphone or tablet, on which a food delivery service application is installed.

[0711] software:

[0712] Generative AI: An artificial intelligence system that analyzes collected data and generates advice and meal menus based on the user's health condition and lifestyle habits.

[0713] Food Delivery Service API: An interface for ordering suggested food menus.

[0714] Data collection and analysis

[0715] Wearable devices constantly collect biometric information such as the user's heart rate, blood pressure, number of steps, etc. This data is sent to a server via the user's terminal.

[0716] The server inputs the received data into the generative AI, which then analyzes the data and evaluates the user's health condition. Based on the analysis results, the generative AI then suggests the optimal meal menu for the user.

[0717] Food menu suggestions and ordering

[0718] Generative AI can generate meal plans based on a user's health status. For example, if a user's heart rate is high, it can suggest low-calorie, high-protein meals.

[0719] The user device receives the meal menu suggestions from the generative AI and places the order via the food delivery service's API, allowing users to easily order meals that are optimal for their health.

[0720] Examples

[0721] For example, when a user wears a smartwatch, data such as heart rate and number of steps are automatically collected. This data is sent to a server, and the generative AI provides advice such as "Based on your current health condition, we recommend a low-calorie, high-protein meal." The user's device then automatically orders the suggested meal through a food delivery service.

[0722] Examples of prompt statements

[0723] User heart rate: 75

[0724] User's blood pressure: 120 / 80

[0725] User steps: 10000

[0726] Based on this data, suggest the best meal menu for your users.

[0727] In this way, it becomes possible to provide specific meal suggestions based on the user's health condition and link them to a meal delivery service.

[0728] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0729] Step 1:

[0730] Collecting data

[0731] A user wears a wearable device and constantly collects biometric information such as heart rate, blood pressure, and number of steps. This data is sent to the user's terminal such as a smartphone or tablet. The input is the biometric data from the wearable device, and the output is the biometric data sent to the terminal.

[0732] Step 2:

[0733] Sending Data

[0734] The terminal transmits the collected biometric data to the server. The input is the biometric data stored in the terminal, and the output is the biometric data transmitted to the server. In concrete terms, the terminal transmits data to the server using an HTTP request.

[0735] Step 3:

[0736] Analyzing data

[0737] The server inputs the received biometric data into the generative AI. The generative AI analyzes this data and evaluates the user's health condition. The input is the biometric data sent to the server, and the output is the analysis results regarding the user's health condition. In concrete terms, the generative AI analyzes the data and detects abnormalities in heart rate and blood pressure.

[0738] Step 4:

[0739] Meal menu suggestions

[0740] The generative AI proposes the optimal meal menu for the user based on the analysis results. The input is the analysis result on the user's health condition, and the output is the proposed meal menu. In concrete terms, the generative AI generates a prompt sentence and proposes a meal menu suitable for the user.

[0741] Step 5:

[0742] Meal menu notification

[0743] The server notifies the user's device of the meal menu proposed by the generative AI. The input is the proposed meal menu, and the output is the meal menu notified to the user's device. In concrete terms, the server sends the menu to the device using push notifications.

[0744] Step 6:

[0745] Ordering food from the menu

[0746] The user's device orders the proposed meal menu via the API of the food delivery service. The input is the notified meal menu, and the output is the order data sent to the food delivery service. In concrete terms, the device sends the order using an API request.

[0747] Step 7:

[0748] Review Order

[0749] The food delivery service receives the order and sends a confirmation to the user's terminal. The input is the order data sent to the food delivery service, and the output is the order confirmation sent to the user's terminal. In particular, the food delivery service processes the order and sends a confirmation to the terminal.

[0750] In this way, it becomes possible to provide specific meal suggestions based on the user's health condition and link them to a meal delivery service.

[0751] Example 2

[0752] Next, a description will be given of Example 2 of the embodiment 2. 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".

[0753] Conventional systems have had difficulty analyzing a user's thoughts and feelings in real time and providing appropriate advice based on them. In addition, there was a lack of means to efficiently process data collected from wearable devices and provide immediate feedback to users. This made it difficult to provide specific advice to improve users' problems and daily lives.

[0754] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0755] In this invention, the server includes a means for constantly collecting data by a wearable device worn by the user, a means for transmitting the collected data to the terminal, a means for the terminal to transmit the collected data to the server, a means for inputting the data collected by the server to the generative AI model, a means for the generative AI model to analyze the user's thoughts and emotions, a means for the generative AI model to generate advice based on the analysis results, a means for transmitting the generated advice to the terminal, and a means for the terminal to display the advice to the user. This makes it possible to provide appropriate advice based on the user's thoughts and emotions in real time.

[0756] A "wearable device" is an electronic device that can be worn by a user and is used to collect biometric and behavioral data of the user.

[0757] A "terminal" is an electronic device that receives and processes data transmitted from a wearable device, and includes smartphones, personal computers, etc.

[0758] The "server" is a central processing unit that receives data sent from the terminal and inputs it into the generative AI model.

[0759] A "generative AI model" is an artificial intelligence algorithm that analyzes input data and generates advice based on the user's thoughts and feelings.

[0760] "Means for continuous data collection" refers to a function that enables a wearable device to continuously collect the user's biometric and behavioral data.

[0761] "Means for transmitting data to a terminal" refers to a function for transmitting data collected by a wearable device to a terminal via wireless communication or the like.

[0762] The "means for transmitting data to a server" is a function for transmitting data received by a terminal to a server.

[0763] "Means for inputting data into a generative AI model" refers to a function for inputting data received by the server into a generative AI model.

[0764] "Means of analyzing the user's thoughts and emotions" refers to the function that enables the generative AI model to analyze input data and understand the user's thoughts and emotions.

[0765] "Means for generating advice" refers to the function that enables the generative AI model to generate advice for the user based on the analysis results.

[0766] The "means for transmitting advice to a terminal" is a function for the server to transmit the generated advice to the terminal.

[0767] The "means for displaying advice to a user" refers to a function for visually or audibly displaying the advice received by the terminal to the user.

[0768] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0769] This invention is a system that analyzes a user's thoughts and feelings in real time and provides appropriate advice based on the results. This system is implemented using a wearable device, a terminal, a server, and a generative AI model.

[0770] Hardware and Software Used

[0771] 1. Wearable Devices

[0772] The user wears a wearable device (e.g., an EEG headset) that reads the user's brainwaves, and the device collects the user's brainwave data in real time.

[0773] 2. Terminal

[0774] A terminal (e.g., a smartphone or a personal computer) receives the brainwave data transmitted from the wearable device. A dedicated application for processing the brainwave data is installed on the terminal.

[0775] 3. Server

[0776] The server receives the EEG data sent from the device and inputs it into the generative AI model. The server also pre-processes the data and manages the analysis results.

[0777] 4. Generative AI Models

[0778] A generative AI model (e.g., GPT-4) analyzes the brainwave data input from the server to understand the user's thoughts and emotions. Based on the analysis results, it generates advice for the user.

[0779] Data processing and calculation

[0780] 1. Data Collection

[0781] When a user wears the wearable device, it collects brainwave data and transmits it to a terminal via Bluetooth.

[0782] 2. Data Receipt and Transmission

[0783] The terminal receives the brainwave data sent from the wearable device and transmits the data to a server using a dedicated application.

[0784] 3. Data Analysis

[0785] The server inputs the brainwave data sent from the device into a generative AI model, which then analyzes the brainwave data to understand the user's thoughts and emotions.

[0786] 4. Advice Generation

[0787] The generative AI model generates advice for the user based on the analysis results. For example, if the user is feeling stressed, it generates advice such as "Try taking a deep breath to relax."

[0788] 5. Sending and Displaying Advice

[0789] The server transmits the generated advice to the terminal, which displays the received advice to the user.

[0790] Examples

[0791] For example, consider a case where a user feels stressed at work. The user puts on a wearable device and brainwave data is collected. The device sends this data to a server, which inputs the data into a generative AI model. The generative AI model analyzes that the user is feeling stressed and generates advice such as "Try taking a deep breath to relax." This advice is sent to the device and displayed to the user.

[0792] Example prompt:

[0793] “What advice should we offer our users if they are stressed?”

[0794] The system allows users to receive appropriate advice in real time based on their thoughts and feelings.

[0795] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0796] Program processing flow

[0797] Step 1: User puts on headset

[0798] A user wears a wearable device (e.g., an EEG headset) on their head to read their brainwaves, and the device collects the user's brainwave data in real time.

[0799] Input: User's brainwaves

[0800] Output: EEG data

[0801] Step 2: The headset collects brainwave data

[0802] The headset collects the user's brainwave data and transmits it to the device via Bluetooth.

[0803] Input: User's brainwaves

[0804] Output: EEG data sent to the terminal

[0805] Step 3: The device receives the EEG data

[0806] A terminal (e.g., a smartphone or a personal computer) receives the brainwave data transmitted from the headset. A dedicated application for processing the brainwave data is installed on the terminal.

[0807] Input: EEG data transmitted from the headset

[0808] Output: EEG data stored on the device

[0809] Step 4: The device transmits the EEG data to the server.

[0810] The device then transmits the received brainwave data to a server, where it is pre-processed for input into a generative AI model.

[0811] Input: EEG data stored on the device

[0812] Output: EEG data sent to the server

[0813] Step 5: The server inputs the EEG data into the generative AI model

[0814] The server receives the brainwave data sent from the device and inputs it into the generative AI model, which has an algorithm for analyzing this data.

[0815] Input: EEG data sent to the server

[0816] Output: EEG data input into a generative AI model

[0817] Step 6: The generative AI model analyzes the EEG data

[0818] The generative AI model analyzes the input brainwave data to understand the user's thoughts and emotions. For example, if the user is feeling stressed, it will extract that information.

[0819] Input: EEG data input to the generative AI model

[0820] Output: Analysis results (user's thoughts and feelings)

[0821] Step 7: The generative AI model generates advice

[0822] The generative AI model generates advice for the user based on the analysis results, such as "Try taking a deep breath to relax."

[0823] Input: Analysis results (user's thoughts and feelings)

[0824] Output: Generated advice

[0825] Step 8: The server sends the generated advice to the device

[0826] The server transmits the advice generated by the generative AI model to the terminal, and the advice is provided to the user in an appropriate format.

[0827] Input: Generated advice

[0828] Output: Advice sent to terminal

[0829] Step 9: The device displays the advice to the user

[0830] The terminal receives the advice sent from the server and displays it to the user, who can then confirm and act on the advice.

[0831] Input: Advice sent to terminal

[0832] Output: Advice displayed to the user

[0833] In this way, users can receive relevant advice in real time based on their own thoughts and feelings.

[0834] (Application example 2)

[0835] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server", and the smart glasses 214 will be referred to as a "terminal".

[0836] Conventional systems using wearable devices and generative AI can provide help for users' problems and advice to enrich their daily lives, but they cannot read the user's brainwaves, detect anxiety or tension in real time, and provide appropriate security advice. This makes it difficult to respond immediately when a user feels anxious, and there is an issue of remaining concerns about security.

[0837] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0838] In this invention, the server includes a means for constantly collecting data from a wearable device, a means for inputting the collected data to a generative AI, a means for the generative AI to provide help for a user's troubles and advice for enriching daily life, a means for inputting data from a device that reads the user's brainwaves to the generative AI, and a means for detecting the user's anxiety or tension and providing appropriate security advice. This makes it possible to detect the situation in real time when the user feels anxious and provide appropriate security advice.

[0839] A "wearable device" is an electronic device that can be worn by the user and has the ability to collect data continuously.

[0840] "Generative AI" is an artificial intelligence system that analyzes collected data and provides users with help and advice for their problems.

[0841] A "brainwave reading device" is a device that detects the user's brainwaves and collects the data.

[0842] The "means for detecting anxiety or tension" refers to a function that analyzes the user's brainwave data and detects the anxiety or tension felt by the user in real time.

[0843] "Security advice" is advice provided to users on appropriate actions and precautions when they feel anxious or tense.

[0844] The following system configuration will be described as an embodiment of the present invention.

[0845] System Configuration

[0846] The system consists of a wearable device, a device that reads brainwaves, a generative AI, and a server. The wearable device can be worn by the user and has the ability to constantly collect data. The device that reads brainwaves is a device that detects the user's brainwaves and collects that data. The generative AI is an artificial intelligence system that analyzes the collected data and offers help and advice for the user's problems. The server is responsible for receiving data from these devices and inputting it into the generative AI.

[0847] Hardware and Software

[0848] Hardware:

[0849] Wearable devices (e.g. smart glasses)

[0850] Brainwave Sensor

[0851] server

[0852] software:

[0853] Python

[0854] Keras (deep learning library)

[0855] Scikit-learn (data preprocessing library)

[0856] Requests (HTTP request library)

[0857] Data processing and calculation

[0858] 1. Data collection:

[0859] Wearable devices and brainwave sensors constantly collect the user's biometric and brainwave data.

[0860] The collected data is sent to a server.

[0861] 2. Data preprocessing:

[0862] The server uses Scikit-learn to standardize the data it receives.

[0863] The standardized data is input into the generative AI.

[0864] 3. Data analysis and advice generation:

[0865] The generative AI uses Keras to analyze data and detect the user's concerns, anxieties, and tensions.

[0866] Based on the detected information, appropriate security advice is generated.

[0867] 4. Advice provided:

[0868] The generated advice is provided to the user in real time via a wearable device.

[0869] Examples

[0870] If a user feels anxious while walking down a dark street at night, the EEG sensor will detect that anxiety, and the generative AI will analyze the data and provide advice such as, "Pay attention to your surroundings. Suspicious activity has been detected nearby."

[0871] Examples of prompt statements

[0872] "Please develop an application that analyzes the surrounding situation and provides appropriate security advice when the user feels uneasy. The system reads brainwave data, transmits it to a generative AI, and receives advice."

[0873] The above is an embodiment of the present invention.

[0874] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0875] Step 1:

[0876] A user wears a wearable device and an EEG sensor. The wearable device constantly collects the user's biometric data (e.g., heart rate and body temperature). The EEG sensor collects the user's EEG data in real time. These data are sent from the wearable device and the EEG sensor to a server.

[0877] Input: User's biometric data, brainwave data

[0878] Output: Biometric data and EEG data sent to the server

[0879] Step 2:

[0880] The server standardizes the received biometric data and EEG data using Scikit-learn's StandardScaler. The standardized data is then input to the generative AI.

[0881] Input: Biometric data and EEG data sent to the server

[0882] Output: Standardized biometric data, EEG data

[0883] Step 3:

[0884] The server sends the standardized data to the generative AI, which uses Keras to analyze the data and detects the user's concerns, anxieties, and tensions. Based on the analysis results, it generates appropriate security advice.

[0885] Input: Standardized biometric data, EEG data

[0886] Output: Analysis results by generative AI, security advice

[0887] Step 4:

[0888] The security advice generated by the generative AI is sent to the wearable device via a server. The wearable device displays the advice to the user in real time. For example, a message such as "Pay attention to your surroundings. Suspicious activity has been detected nearby" is displayed.

[0889] Input: Analysis results by generative AI, security advice

[0890] Output: The security advice displayed to the user

[0891] Step 5:

[0892] The user acts according to the security advice displayed on the wearable device, for example by checking the surroundings and moving to a safe place.

[0893] Input: Security advice displayed to the user

[0894] Output: User actions

[0895] The above are the specific processing steps of this system.

[0896] In addition, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0897] "Example 1"

[0898] As one embodiment of the present invention, a system is provided that includes a means for constantly collecting data by a wearable device, a means for inputting the collected data to a generative AI, a means for the generative AI to provide help for a user's troubles and advice to enrich daily life, and an emotion engine in which the generative AI recognizes the user's emotions. Specifically, the wearable device constantly collects biometric information and behavioral data of the user and transmits the data to the generative AI. The generative AI analyzes the received data and provides advice on the user's health condition and lifestyle habits. Furthermore, the generative AI uses the emotion engine to recognize the user's emotions and provides advice according to the emotions. For example, if it recognizes that the user is feeling stressed, it provides advice to encourage relaxation.

[0899] "Example 2"

[0900] As another embodiment of the present invention, a system is provided that includes a means for a wearable device to collect healthcare-related data and an emotion engine for a generative AI to recognize a user's emotions. Specifically, the wearable device constantly collects the user's biometric and behavioral data, particularly healthcare-related data, and transmits the data to the generative AI. The generative AI analyzes the received data and provides advice regarding the user's health condition and lifestyle habits. Furthermore, the generative AI uses the emotion engine to recognize the user's emotions and provides advice according to the emotions. For example, if it recognizes that the user is feeling stressed, it provides advice to encourage relaxation.

[0901] "Example 5"

[0902] As yet another embodiment of the present invention, a system is provided that includes a means for inputting data from a device that reads a user's thoughts into a generative AI, and an emotion engine that allows the generative AI to recognize the user's emotions. Specifically, a device that reads the user's thoughts (e.g., a headset that reads brainwaves) reads the user's thoughts and transmits the data to the generative AI. The generative AI analyzes the received data and provides advice corresponding to the user's thoughts and emotions. Furthermore, the generative AI uses the emotion engine to recognize the user's emotions and provides advice according to the emotions. For example, if it recognizes that the user is feeling stressed, it provides advice encouraging relaxation.

[0903] The process flow of each embodiment will be described below.

[0904] "Example 1"

[0905] Step 1: The wearable device constantly collects the user's biometric and behavioral data. Step 2: The wearable device sends the collected data to the generative AI.

[0906] Step 3: The generative AI analyzes the received data.

[0907] Step 4: Generative AI provides advice on the user's health and lifestyle.

[0908] Step 5: The generative AI uses the emotion engine to recognize the user's emotions.

[0909] Step 6: Generative AI provides advice based on emotions.

[0910] "Example 2"

[0911] Step 1: Wearable devices constantly collect biometric and behavioral data from users, especially health-related data.

[0912] Step 2: The wearable device sends the data it collects to the generative AI.

[0913] Step 3: The generative AI analyzes the received data.

[0914] Step 4: Generative AI provides advice on the user's health and lifestyle.

[0915] Step 5: The generative AI uses the emotion engine to recognize the user's emotions.

[0916] Step 6: Generative AI provides advice based on emotions.

[0917] "Example 5"

[0918] Step 1: A mind-reading device (e.g. a brainwave-reading headset) reads the user's thoughts.

[0919] Step 2: The device that reads the user's thoughts sends the read data to the generative AI

[0920] do.

[0921] Step 3: The generative AI analyzes the received data.

[0922] Step 4: Generative AI provides advice that responds to the user's thoughts and feelings.

[0923] Step 5: The generative AI uses the emotion engine to recognize the user's emotions.

[0924] Step 6: Generative AI provides advice based on emotions.

[0925] Example 1

[0926] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[0927] Conventional health management systems using wearable devices often do not analyze collected data or provide feedback to users in real time, making it difficult to provide appropriate advice based on the user's health condition and emotions. In addition, they lacked the ability to recognize the user's emotions and provide advice accordingly, making it difficult to improve the user's quality of life.

[0928] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0929] In this invention, the server includes a means for receiving and storing data, a means for analyzing the data using a generative AI model, and a means for recognizing the user's emotions using an emotion engine, which makes it possible to analyze the collected data in real time and provide appropriate advice according to the user's health condition and emotions.

[0930] A "wearable device" is a device that can be worn by the user and constantly collects biometric information such as heart rate, blood pressure, and number of steps taken, as well as behavioral data.

[0931] A "terminal" is a device that receives data sent from a wearable device and transmits it to a server, and examples of this include smartphones and tablets.

[0932] The "server" is a central processing unit that receives data sent from the terminals, stores it, analyzes it, and generates advice.

[0933] A "generative AI model" is an artificial intelligence model that analyzes collected data, identifies patterns related to the user's health and lifestyle habits, and generates appropriate advice.

[0934] An "emotion engine" is an algorithm or software that recognizes a user's emotions based on their biometric and behavioral data.

[0935] A "database" is a storage device for storing data received by a server.

[0936] "Advice" refers to instructions or suggestions that the generative AI model provides to the user based on the results of analysis and emotion recognition.

[0937] "Notification" is a function that notifies the user of advice that the terminal has received from the server.

[0938] This invention is a system that combines a wearable device, a terminal, a server, and a generative AI model to provide advice based on the user's health condition and emotions.

[0939] Hardware and Software Used

[0940] Wearable Devices

[0941] The user wears a wearable device that constantly collects biometric and behavioral data, such as heart rate, blood pressure, and number of steps taken. Specific examples of such devices include common smart watches and fitness trackers.

[0942] Terminal

[0943] The wearable device transmits the collected data to a terminal (such as a smartphone or tablet). The terminal receives the data and transmits it to a server. An application for receiving and transmitting data is installed on the terminal.

[0944] server

[0945] The server receives the data sent from the device and stores it in a database. The server analyzes the data using a generative AI model to identify patterns related to the user's health and lifestyle. It also uses an emotion engine to recognize the user's emotions.

[0946] Generative AI Models

[0947] The generative AI model analyzes the data stored on the server and generates advice on the user's health condition and lifestyle habits. The generative AI model predicts the user's health condition based on past data and provides appropriate advice.

[0948] Emotion Engine

[0949] An emotion engine is an algorithm or software that recognizes a user's emotions based on their biometric and behavioral data. For example, it can determine whether a user is feeling stressed based on fluctuations in their heart rate or blood pressure.

[0950] Examples

[0951] Health monitoring

[0952] Suppose a user is wearing a smartwatch. The smartwatch constantly collects heart rate and step counts and sends them to the device. The device then sends the received data to a server. The server uses a generative AI model to analyze the data and advises the user, "Your step count is low today, so you should walk a little more."

[0953] Emotion Recognition and Advice

[0954] Suppose a user is wearing a fitness tracker. The fitness tracker constantly collects heart rate and blood pressure and sends them to the device. The device then sends the received data to the server. The generative AI model analyzes the data and recognizes that the user is feeling stressed. The generative AI model advises, "Take a deep breath and relax."

[0955] Examples of prompt statements

[0956] "If the user's heart rate is higher than normal, provide them with advice on how to relax."

[0957] By combining a wearable device with generative AI, the system can provide appropriate advice based on the user's health condition and emotions.

[0958] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0959] Step 1:

[0960] A user wears a wearable device. The wearable device constantly collects biometric and behavioral data, such as heart rate, blood pressure, and number of steps. Specifically, the wearable device uses sensors to measure these data and stores them in its internal memory. The input is the user's biometric information, and the output is the collected data.

[0961] Step 2:

[0962] The wearable device transmits the collected data to the terminal. Specifically, the data is transferred to the terminal using wireless communication technologies such as Bluetooth and Wi-Fi. The input is the data stored in the wearable device, and the output is the data transmitted to the terminal.

[0963] Step 3:

[0964] The terminal sends the received data to the server. Specifically, the terminal uploads data to the server using an Internet connection. The input is the data stored on the terminal, and the output is the data sent to the server.

[0965] Step 4:

[0966] The server receives data sent from the terminal and stores it in a database. Specifically, the server converts the data into a format that is easy to analyze and stores it in the database. The input is the data sent to the server, and the output is the data stored in the database.

[0967] Step 5:

[0968] The server analyzes the stored data using a generative AI model. Specifically, the generative AI model identifies patterns related to the user's health and lifestyle based on past data. The input is the data stored in the database, and the output is the analysis results.

[0969] Step 6:

[0970] The server uses an emotion engine to recognize the user's emotions. Specifically, the emotion engine analyzes fluctuations in heart rate and blood pressure to determine whether the user is feeling stressed. The input is the analysis result, and the output is the result of emotion recognition.

[0971] Step 7:

[0972] The server generates advice for the user based on the analysis results and emotion recognition results. In concrete terms, the generative AI model generates advice such as "Take a deep breath and relax." The input is the analysis results and emotion recognition results, and the output is the generated advice.

[0973] Step 8:

[0974] The server sends the generated advice to the terminal. In particular, the server uses an Internet connection to send the advice to the terminal. The input is the generated advice, and the output is the advice sent to the terminal.

[0975] Step 9:

[0976] The terminal notifies the user of the received advice. In concrete terms, the terminal uses a notification function to display the advice to the user. The input is the advice sent to the terminal, and the output is the advice notified to the user.

[0977] (Application example 1)

[0978] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[0979] In modern society, it is important for each user to properly manage their own health condition and lifestyle habits. However, with conventional systems, it has been difficult to collect a user's biometric and behavioral data in real time and provide appropriate advice based on that data. Furthermore, there have been no systems that recognize a user's emotions and provide advice based on those emotions. This has resulted in users being unable to take appropriate measures based on their own health condition and emotions, which could lead to a decline in their quality of life.

[0980] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0981] In this invention, the server includes a means for constantly collecting data by the wearable device, a means for inputting the collected data to the generative AI, a means for the generative AI to provide help for the user's problems and advice for enriching daily life, a means for the generative AI to recognize the user's emotions and provide advice according to the emotions, and a means for the generative AI to provide advice on the user's health condition and lifestyle habits. This makes it possible to collect the user's biometric information and behavioral data in real time and provide appropriate advice on the health condition and lifestyle habits based on the collected data. In addition, by recognizing the user's emotions and providing advice according to the emotions, the quality of the user's life can be improved.

[0982] A "wearable device" is an electronic device that is worn to constantly collect the user's biometric and behavioral data.

[0983] "Generative AI" is an artificial intelligence system that analyzes collected data and provides appropriate advice to users.

[0984] "Means of continuous data collection" refers to a function that uses a wearable device to continuously collect a user's biometric information and behavioral data.

[0985] "Means of input" is a function for sending collected data to the generative AI.

[0986] "Means of providing advice" refers to the function by which the generative AI provides appropriate advice and suggestions to the user based on the results of analysis.

[0987] "Means of recognizing emotions" refers to the function whereby the generative AI analyzes the user's emotional state and provides advice accordingly.

[0988] "Means of providing advice regarding health status and lifestyle habits" refers to a function in which generative AI analyzes a user's health and behavioral data and provides advice regarding health management and improving lifestyle habits.

[0989] A system for carrying out the present invention includes a wearable device, a generative AI, an emotion engine, and a communication means for linking these components. The specific configuration and operation of the system are described below.

[0990] System Configuration

[0991] 1. Wearable devices:

[0992] Constantly collect user biometric information (heart rate, blood pressure, number of steps, etc.) and behavioral data.

[0993] Example: Fitbit, Apple Watch, etc.

[0994] 2. Generative AI:

[0995] The collected data is analysed to generate advice on the user's health and lifestyle habits.

[0996] Examples: OpenAI GPT-3, Google Cloud AI, etc.

[0997] 3. Emotion Engine:

[0998] Recognize user emotions and provide advice accordingly.

[0999] Examples: Affectiva, IBM Watson Tone Analyzer, etc.

[1000] 4. Means of communication:

[1001] Network communication to send data from wearable devices to the generative AI and emotion engine.

[1002] Examples: Bluetooth, Wi-Fi, cellular, etc.

[1003] System Operation

[1004] 1. Data collection:

[1005] Wearable devices collect the user's biometric information, such as heart rate, blood pressure, and number of steps, in real time.

[1006] The collected data is sent to the generative AI at regular intervals.

[1007] 2. Data Analysis:

[1008] The generative AI analyzes the biometric and behavioral data it receives and generates advice about the user's health and lifestyle habits.

[1009] The analysis results are displayed on the user's smartphone app.

[1010] 3. Emotion recognition:

[1011] The emotion engine recognizes the user's emotions based on data sent from the generative AI.

[1012] Generate and provide emotion-based advice to users.

[1013] Examples

[1014] Health Tip: "Your heart rate is high. Take a deep breath and relax."

[1015] Emotional Advice: "You seem to be stressed. Try meditating for five minutes to help you relax."

[1016] Examples of prompt statements

[1017] "Generate advice for when a user's heart rate is high and they are feeling stressed."

[1018] In this way, it is possible to provide appropriate advice in real time based on the user's biometric information and emotional state. This system allows users to take appropriate measures according to their own health condition and emotions, improving their quality of life.

[1019] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1020] Step 1:

[1021] A wearable device collects the user's biometric information (heart rate, blood pressure, number of steps, etc.) in real time. The input is the user's biometric information, and the output is the collected data. In concrete terms, the wearable device's sensors collect data from the user's body and store it in its internal memory.

[1022] Step 2:

[1023] The wearable device transmits the collected data to a smartphone. The input is the biometric information stored in the wearable device, and the output is the data transferred to the smartphone. Specifically, the data is transmitted to the smartphone using Bluetooth or Wi-Fi.

[1024] Step 3:

[1025] The smartphone sends the received data to the generative AI. The input is the biometric information transferred to the smartphone, and the output is the data sent to the generative AI. In concrete terms, the smartphone application sends data to the generative AI server via the internet.

[1026] Step 4:

[1027] The generative AI analyzes the data it receives and generates advice about the user's health condition and lifestyle habits. The input is the biometric information sent to the generative AI, and the output is the generated advice. In concrete terms, the generative AI analyzes the data and generates advice based on the user's health condition and lifestyle habits.

[1028] Step 5:

[1029] The generative AI sends the analysis results to the emotion engine, which recognizes the user's emotions. The input is the analysis results of the generative AI, and the output is the user's emotions recognized by the emotion engine. In concrete terms, the emotion engine identifies the user's emotions based on the analysis results.

[1030] Step 6:

[1031] The emotion engine generates advice according to the user's emotion. The input is the user's emotion recognized by the emotion engine, and the output is advice according to the emotion. In concrete terms, the emotion engine generates appropriate advice based on the user's emotional state.

[1032] Step 7:

[1033] The advice generated by the generative AI and emotion engine is sent to the smartphone. The input is the generated advice, and the output is the advice transferred to the smartphone. In concrete terms, the generative AI and emotion engine send advice to the smartphone via the Internet.

[1034] Step 8:

[1035] The smartphone displays the received advice to the user. The input is the advice transferred to the smartphone, and the output is the advice displayed to the user. In concrete terms, the smartphone application displays the advice on the screen and the user confirms it.

[1036] Example 2

[1037] Next, a description will be given of Example 2 of the embodiment 2. 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".

[1038] Conventional systems have had difficulty analyzing a user's biometric information and thoughts in real time and providing appropriate advice. In addition, there was a lack of means to accurately recognize a user's emotions and thoughts and provide advice based on them. This resulted in insufficient help for users' problems and advice to enrich their daily lives.

[1039] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for constantly collecting biometric information of the user, a means for inputting the collected biometric information to the generative AI, a means for the generative AI to analyze the user's emotions and thoughts and provide help for the user's troubles and advice for enriching the user's daily life, a means for the generative AI to generate appropriate advice for the user based on the analysis result and transmit the advice to the terminal, and a means for the terminal to display the advice to the user. This makes it possible to analyze the user's biometric information and thoughts in real time and provide appropriate advice.

[1040] "User" means an individual or organization that uses the system.

[1041] "Biometric information" is data obtained from the user's body, and includes heart rate, brain waves, body temperature, and the like.

[1042] A "wearable device" is an electronic device that can be worn by a user and is equipped with sensors for collecting biometric information.

[1043] "Generative AI" is an artificial intelligence system that analyzes collected data and generates appropriate advice for users.

[1044] An "emotion engine" is a part of generative AI and is a module that contains algorithms for analyzing user emotions.

[1045] A "terminal" is an electronic device used by a user, and includes a smartphone, tablet, etc.

[1046] "Advice" refers to advice or suggestions that the generative AI provides to the user based on the results of its analysis.

[1047] A "thought-reading device" is a device for capturing data related to a user's thoughts, such as brainwaves.

[1048] This invention is a system that analyzes a user's biological information and thoughts in real time and provides appropriate advice. The system includes a wearable device, a terminal, a server, and a generative AI.

[1049] The user wears a wearable device, such as a headset, that reads brainwaves. The device captures the user's brainwaves and other biometric information and transmits it to the user's device (e.g., a smartphone or tablet) via Bluetooth or Wi-Fi. The device then transmits the received biometric information to a server via the Internet. The data is encrypted before being transmitted, ensuring security.

[1050] The server passes the received biometric information to a generative AI, which is hosted within the server or resides on the cloud. The generative AI uses an emotion engine to analyze the user's brainwave patterns to identify the emotions and thoughts the user is feeling. Based on the analysis results, the generative AI generates appropriate advice for the user. For example, if the user is recognized as feeling stressed, the generative AI generates advice to encourage relaxation.

[1051] The server sends the advice received from the generative AI to the user's device. The device notifies the user of the advice received. For example, the smartphone's notification function can be used to display a message such as "We recommend that you take a deep breath and take some time to relax."

[1052] As a concrete example, consider a situation where a user is wearing a headset that reads brainwaves. The headset captures the stress the user feels while working in real time. The server receives this data and sends it to a generative AI. The generative AI analyzes the data and determines that the user is at a high stress level. As a result, the generative AI provides advice such as, "I recommend that you take a moment to take a deep breath and relax."

[1053] Examples of prompt sentences to be input to a generative AI model include the following:

[1054] "Analyze the user's brainwave data to identify the emotions the user is feeling. If the user is feeling stressed, provide advice to promote relaxation."

[1055] By using this prompt, the generative AI can generate appropriate advice based on the user's brainwave data.

[1056] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1057] Step 1:

[1058] A user wears a headset that reads brainwaves. The user puts the headset on their head to capture brainwaves and turns on the device. The headset starts capturing the user's brainwave data in real time. The input is the user's brainwaves and the output is the captured brainwave data.

[1059] Step 2:

[1060] The headset transmits the EEG data to the device. The headset transmits the captured EEG data to the user's device (e.g., smartphone or tablet) via Bluetooth or Wi-Fi. The input is the captured EEG data, and the output is the EEG data transmitted to the device.

[1061] Step 3:

[1062] The terminal transmits the received EEG data to the server. The terminal encrypts the received EEG data and transmits it to the server via the Internet. The input is the EEG data transmitted to the terminal, and the output is the encrypted EEG data transmitted to the server.

[1063] Step 4:

[1064] The server passes the EEG data to the generative AI. The server calls an API to pass the received EEG data to the generative AI. The input is the encrypted EEG data sent to the server, and the output is the EEG data passed to the generative AI.

[1065] Step 5:

[1066] The generative AI analyzes the EEG data to identify the user's thoughts and emotions. The generative AI uses an emotion engine to analyze the EEG data to identify the emotions and thoughts the user is feeling. The input is the EEG data passed to the generative AI, and the output is the identified emotions and thoughts of the user.

[1067] Step 6:

[1068] The generative AI generates advice based on the analysis results. The generative AI generates appropriate advice for the user based on the analysis results. For example, if it is recognized that the user is feeling stressed, it generates advice to encourage relaxation. The input is the identified user's emotions and thoughts, and the output is the generated advice.

[1069] Step 7:

[1070] The server sends the generated advice to the terminal. The server sends the advice received from the generative AI to the user's terminal. The input is the generated advice, and the output is the advice sent to the terminal.

[1071] Step 8:

[1072] The terminal displays the advice to the user. The terminal notifies the user of the received advice. For example, the smartphone's notification function can be used to display a message such as "We recommend that you take a deep breath and take some time to relax." The input is the advice sent to the terminal, and the output is the advice displayed to the user.

[1073] (Application example 2)

[1074] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server", and the smart glasses 214 will be referred to as a "terminal".

[1075] In modern society, it is important to understand a user's thoughts and emotions in real time and provide appropriate advice accordingly. However, with conventional systems, it was difficult to accurately recognize a user's emotional state and suggest specific actions based on it. In addition, there was no system that provided actions to reduce security risks according to the user's emotional state. This made it difficult to improve the quality of users' lives.

[1076] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1077] In this invention, the server includes a means for constantly collecting data from a wearable device, a means for inputting the collected data to the generative AI, a means for the generative AI to provide help for the user's problems and advice to enrich daily life, a means for inputting data from a device that reads the user's thoughts and emotions to the generative AI, a means for the generative AI to recognize the user's emotions and provide advice according to the emotions, and a means for suggesting actions to reduce security risks according to the user's emotional state. This makes it possible to accurately recognize the user's emotional state and suggest specific actions based on it.

[1078] A "wearable device" is an electronic device that can be worn by a user and is used to collect data continuously.

[1079] "Generative AI" is an artificial intelligence system that analyzes collected data and generates appropriate advice and suggestions for users.

[1080] A "device that reads a user's thoughts and emotions" is a device that reads brainwaves and other bio-signals to detect the user's thoughts and emotional state.

[1081] The "emotion engine" is a part of generative AI that recognizes the user's emotions and provides appropriate advice based on those emotions.

[1082] "Actions to reduce security risks" are specific actions or measures suggested to minimize security risks depending on the user's emotional state.

[1083] A system for implementing this invention includes a wearable device, a device for reading a user's thoughts and emotions, a generative AI, an emotion engine, and a function for suggesting actions to reduce security risks. Specific embodiments of each element are described below.

[1084] 1. Wearable Devices

[1085] A wearable device is an electronic device that can be worn by the user and is used to constantly collect data. Specifically, it collects biometric information such as heart rate, body temperature, and activity level. This data is sent to a smartphone via Bluetooth or Wi-Fi.

[1086] 2. Devices that read your thoughts and emotions

[1087] A device that reads a user's thoughts and emotions is a device that reads brainwaves and other biosignals to detect the user's thoughts and emotional state. Specifically, a headset that reads brainwaves (e.g., Emotiv Insight) is used. This device collects the user's brainwave data in real time and transmits it to a smartphone.

[1088] 3. Generative AI and Emotion Engines

[1089] Generative AI is an artificial intelligence system that analyzes collected data and generates appropriate advice and suggestions for users. The emotion engine is part of generative AI and is a function for recognizing user emotions and providing appropriate advice based on those emotions. Specifically, we use Keras (a deep learning library) to build an emotion recognition model and Scikit-learn (a data preprocessing library) to preprocess the data.

[1090] 4. Feature that suggests actions to reduce security risks

[1091] The action suggestions to reduce security risks are specific actions or measures suggested to minimize security risks depending on the user's emotional state. For example, if the user is feeling stressed, it will provide advice to promote relaxation, and if the user is feeling anxious, it will provide advice to give reassurance.

[1092] Examples

[1093] If a user feels stressed at work, the app offers advice such as, "Take a deep breath to relax." If a user feels anxious at night, the app offers advice such as, "Listen to your favorite music to feel more at ease."

[1094] Examples of prompt statements

[1095] "If users are feeling stressed, design your application to provide them with advice that encourages relaxation."

[1096] In this way, it is possible to accurately recognize the user's emotional state and suggest specific actions based on it.

[1097] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1098] Step 1:

[1099] Wearable devices collect biometric information from users, such as heart rate, body temperature, and activity level, in real time, and transmit this data to a smartphone via Bluetooth or Wi-Fi.

[1100] Input: User's biometric information

[1101] Output: Biometric data sent to a smartphone

[1102] Step 2:

[1103] The device that reads the user's thoughts and emotions collects the user's brainwave data. Specifically, a brainwave reading headset is used to obtain the user's brainwave data in real time. The device also transmits the data to a smartphone via Bluetooth or Wi-Fi.

[1104] Input: User's brainwave data

[1105] Output: EEG data transmitted to a smartphone

[1106] Step 3:

[1107] We preprocess the biometric data and EEG data collected by the smartphone. Specifically, we use Scikit-learn's StandardScaler to standardize the data. This unifies the scale of the data, making it easier to analyze with generative AI.

[1108] Input: Biometric data, EEG data

[1109] Output: Standardized data

[1110] Step 4:

[1111] The smartphone inputs the preprocessed data into the generative AI, which then uses an emotion recognition model built using Keras to analyze the user's emotions. Specifically, the data is fed into a deep learning model to predict the user's emotional state.

[1112] Input: Standardized data

[1113] Output: The user's emotional state.

[1114] Step 5:

[1115] The generative AI uses an emotion engine to generate advice based on the user's emotions. For example, if the user is feeling stressed, it will provide advice to encourage relaxation.

[1116] Input: The user's emotional state

[1117] Output: Advice based on emotions

[1118] Step 6:

[1119] The smartphone notifies the user of the generated advice, specifically by providing it via push notification or voice assistant.

[1120] Input: Emotion-based advice

[1121] Output:Notification to the user

[1122] Step 7:

[1123] Depending on the user's emotional state, it will suggest actions to reduce security risks. For example, if the user is feeling anxious, it will suggest specific actions and measures to make them feel safe.

[1124] Input: The user's emotional state

[1125] Output: Suggested actions to reduce security risks

[1126] The above are the specific process steps for carrying out the present invention.

[1127] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1128] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1129] Another example of generative AI is Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1130] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1131] [Third embodiment]

[1132] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1133] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[1135] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1136] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[1137] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[1138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[1139] Fig. 6 shows an example of main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1140] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1142] In the headset type terminal 314, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1143] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1144] "Example 1"

[1145] The present invention is a system that constantly collects biometric and behavioral data of a user using a wearable device and inputs the data into a generative AI. Specifically, the wearable device collects healthcare-related data such as heart rate, blood pressure, and number of steps, and transmits the data to the generative AI. The generative AI analyzes the received data and provides advice on the user's health condition and lifestyle habits.

[1146] "Example 2"

[1147] In addition, in another aspect of the present invention, a system is provided that inputs data from a device that reads a user's thoughts into a generative AI. Specifically, a device such as a headset that reads brainwaves is used to read the user's thoughts and transmit the data to the generative AI. The generative AI analyzes the received data and provides advice corresponding to the user's thoughts and feelings.

[1148] The process flow of each embodiment will be described below.

[1149] "Example 1"

[1150] Step 1: The wearable device constantly collects the user's biometric and behavioral data. Step 2: The collected data is sent from the wearable device to the generative AI.

[1151] Step 3: The generative AI analyzes the received data and provides advice on the user's health and lifestyle.

[1152] "Example 2"

[1153] Step 1: A mind-reading device (e.g. a brainwave-reading headset) reads the user's thoughts.

[1154] Step 2: The read data is sent from the device to the generative AI.

[1155] Step 3: The generative AI analyzes the received data and provides advice that corresponds to the user's thoughts and feelings.

[1156] Example 1

[1157] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal".

[1158] Conventional wearable devices can collect biometric and behavioral data of users, but they lack a system that can effectively analyze the data and provide specific advice to users. Another issue is that it is difficult to analyze the collected data in real time and provide quick feedback to users.

[1159] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1160] In this invention, the server includes a means for constantly collecting data by the wearable device, a means for transmitting the collected data to the server, a means for transmitting the data received by the server to the generative AI, a means for the generative AI to analyze the received data, a means for the generative AI to generate advice on the user's health condition and lifestyle habits based on the analysis results, a means for the server to transmit the generated advice to the user's terminal, and a means for the user to check the advice on the terminal. This allows the user to understand their own health condition in real time and quickly receive appropriate advice.

[1161] A "wearable device" is a device that is worn by the user to constantly collect biometric information such as heart rate, blood pressure, and number of steps taken, as well as behavioral data.

[1162] The "server" is a computer system that acts as an intermediary to receive data sent from the wearable device and pass it on to the generative AI.

[1163] "Generative AI" is an artificial intelligence system that analyzes data received from a server and generates advice regarding the user's health and lifestyle habits.

[1164] "Means of continuous data collection" refers to the function of a wearable device to continuously collect the user's biometric information and behavioral data.

[1165] "Data transmission means" is a function that transmits data collected by the wearable device to a server.

[1166] "Data receiving means" is a function by which the server receives data transmitted from the wearable device.

[1167] "Data analysis means" refers to the function by which the generative AI analyzes the data it receives from the server.

[1168] The "advice generation means" is a function in which the generative AI generates advice regarding the user's health condition and lifestyle habits based on the analysis results.

[1169] "Advice sending means" refers to a function whereby the server sends advice generated by the generative AI to the user's device.

[1170] "Advice confirmation means" refers to a function that allows a user to confirm advice provided by the generative AI on their device.

[1171] This invention is a system that uses a wearable device to constantly collect a user's biometric and behavioral data and inputs that data into a generative AI. Specifically, the wearable device collects healthcare-related data such as heart rate, blood pressure, and number of steps, and transmits that data to the generative AI. The generative AI analyzes the received data and provides advice on the user's health condition and lifestyle habits.

[1172] Hardware and Software Used

[1173] Wearable devices: Devices that collect health-related data such as heart rate, blood pressure, and number of steps (e.g., Apple Watch, Fitbit).

[1174] Server: Receives data sent from the wearable device and acts as a relay to pass it on to the generative AI.

[1175] Generative AI: An AI system that analyzes the data it receives and provides advice to users about their health and lifestyle (e.g., OpenAI’s GPT-4).

[1176] Program Processing Procedure

[1177] 1. Data collection:

[1178] The user puts on the wearable device.

[1179] Wearable devices constantly collect the user's biometric information, such as heart rate, blood pressure, and number of steps, as well as behavioral data.

[1180] 2. Data transmission:

[1181] The wearable device transmits the collected data to a server.

[1182] The server sends the received data to the generative AI.

[1183] 3. Data Analysis:

[1184] The generative AI analyzes the data received from the server.

[1185] Based on the analysis results, the generative AI generates advice regarding the user's health condition and lifestyle habits.

[1186] 4. Advice provided:

[1187] The advice generated by the generative AI is sent to the server.

[1188] The server sends the advice to the user's device (such as a smartphone or PC).

[1189] The user checks the advice on the device.

[1190] Examples

[1191] Example 1: When the user is wearing an Apple Watch

[1192] The user wears the Apple Watch.

[1193] The Apple Watch collects the user's heart rate and steps.

[1194] The data collected by Apple Watch is sent to the user's iPhone.

[1195] The iPhone uploads the data to the server.

[1196] The server sends the data to the generative AI (GPT-4).

[1197] Generative AI analyzes the data and generates advice such as, "Your heart rate has been high recently. To reduce stress, try spending more time relaxing."

[1198] The server sends the advice to the user's iPhone.

[1199] The user checks the advice on their iPhone.

[1200] Examples of prompt statements

[1201] User heart rate data: [80, 85, 90, 95, 100]

[1202] User step count data: [1000, 2000, 3000, 4000, 5000]

[1203] Analyze this data and provide advice regarding the user's health status.

[1204] In this way, users can constantly monitor their own health status through their wearable devices and receive appropriate advice.

[1205] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1206] Step 1:

[1207] Data collection

[1208] The user puts on the wearable device.

[1209] Wearable devices constantly collect the user's biometric information, such as heart rate, blood pressure, and number of steps, as well as behavioral data.

[1210] Input: User biometric information (heart rate, blood pressure, number of steps, etc.).

[1211] Output: Collected biometric data.

[1212] What it does: For example, your Apple Watch measures your heart rate every minute and counts your steps in real time.

[1213] Step 2:

[1214] Data transmission

[1215] The wearable device transmits the data it collects to the user's smartphone.

[1216] The smartphone uploads the data to the server.

[1217] Input: Collected biometric data.

[1218] Output: Biometric data sent to the server.

[1219] Specific operations: For example, Apple Watch sends collected data via Bluetooth or Wi-Fi to the user's iPhone, which then uploads the data to a server.

[1220] Step 3:

[1221] Data Reception

[1222] The server receives the data transmitted from the wearable device.

[1223] Input: Biometric data sent from your smartphone.

[1224] Output: Biometric data stored on the server.

[1225] Specific operation: The server receives the data in real time and stores it in a database.

[1226] Step 4:

[1227] 4. Data Analysis

[1228] The server sends the received data to the generative AI.

[1229] The generative AI analyzes the data received from the server.

[1230] Input: Biometric data stored on the server.

[1231] Output: Analysis results (information about the user's health and lifestyle).

[1232] How it works: Generative AI analyzes heart rate and step count data to detect outliers and trends.

[1233] Step 5:

[1234] Advice Generation

[1235] Based on the analysis results, the generative AI generates advice regarding the user's health condition and lifestyle habits.

[1236] Input: The analysis results.

[1237] Output: Advice (e.g. "Your heart rate has been high lately. Try spending more time relaxing to reduce stress").

[1238] Specific behavior: The generative AI generates specific advice for the user based on the analysis results.

[1239] Step 6:

[1240] Send Advice

[1241] The advice generated by the generative AI is sent to the server.

[1242] The server sends the advice to the user's device (such as a smartphone or PC).

[1243] Input: The generated advice.

[1244] Output: Advice sent to the user's terminal.

[1245] Specific operation: The server sends the advice received from the generative AI to the user's smartphone via push notification.

[1246] Step 7:

[1247] Advice confirmation

[1248] The user checks the advice on the device.

[1249] Input: The advice sent to the user's terminal.

[1250] Output: The advice the user confirmed.

[1251] Specific action: The user taps on the notification on their smartphone to view the advice.

[1252] (Application example 1)

[1253] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server", and the headset type terminal 314 will be referred to as a "terminal".

[1254] Conventional healthcare systems using wearable devices were limited to collecting users' biometric and behavioral data, and monitoring their health status and providing advice. However, these systems did not adequately address specific behavioral suggestions based on the user's health status or improving convenience in daily life. In particular, there was a lack of suggestions for meal menus optimal for the user's health status and integration with meal delivery services, resulting in insufficient specific support for users to live a healthy lifestyle.

[1255] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means. In this invention, the server includes a means for constantly collecting data by the wearable device, a means for inputting the collected data to the generative AI, a means for the generative AI to provide help for the user's problems and advice for enriching daily life, a means for proposing an optimal meal menu based on the user's biometric information and behavioral data, and a means for ordering the proposed meal menu from a food delivery service. This enables specific meal suggestions based on the user's health condition to be linked with a meal delivery service.

[1256] A "wearable device" is a device that is worn on the user's body and constantly collects biometric information such as heart rate, blood pressure, and number of steps taken, as well as behavioral data.

[1257] "Means of continuous data collection" refers to a function that uses a wearable device to continuously obtain a user's biometric information and behavioral data.

[1258] "Generative AI" is an artificial intelligence system that analyzes collected data and generates advice and suggestions based on the user's health and lifestyle habits.

[1259] "Means of input" refers to the function of sending data collected from a wearable device to the generative AI.

[1260] "Means of providing advice" refers to a function that provides users with advice regarding their health status and lifestyle habits based on data analyzed by the generative AI.

[1261] The "means of suggesting meal menus" refers to a function in which generative AI analyzes the user's biometric information and behavioral data, and suggests to the user the meal menu that is optimal for their health condition.

[1262] The "means for ordering from a food delivery service" is a function for automatically ordering the suggested meal menu from a food delivery service.

[1263] The system for implementing this invention is configured by combining a wearable device, generative AI, and a food delivery service API. A specific embodiment is shown below.

[1264] System Configuration

[1265] Hardware:

[1266] Wearable device: A device that is worn on the user's body and constantly collects biometric and behavioral data such as heart rate, blood pressure, and number of steps taken. Examples include smart watches and fitness trackers.

[1267] Server: A computer system for running generative AI, analyzing data and proposing meal menus.

[1268] User device: A device operated by a user, such as a smartphone or tablet, on which a food delivery service application is installed.

[1269] software:

[1270] Generative AI: An artificial intelligence system that analyzes collected data and generates advice and meal menus based on the user's health condition and lifestyle habits.

[1271] Food Delivery Service API: An interface for ordering suggested food menus.

[1272] Data collection and analysis

[1273] Wearable devices constantly collect biometric information such as the user's heart rate, blood pressure, number of steps, etc. This data is sent to a server via the user's terminal.

[1274] The server inputs the received data into the generative AI, which then analyzes the data and evaluates the user's health condition. Based on the analysis results, the generative AI then suggests the optimal meal menu for the user.

[1275] Food menu suggestions and ordering

[1276] Generative AI can generate meal plans based on a user's health status. For example, if a user's heart rate is high, it can suggest low-calorie, high-protein meals.

[1277] The user device receives the meal menu suggestions from the generative AI and places the order via the food delivery service's API, allowing users to easily order meals that are optimal for their health.

[1278] Examples

[1279] For example, when a user wears a smartwatch, data such as heart rate and number of steps are automatically collected. This data is sent to a server, and the generative AI provides advice such as "Based on your current health condition, we recommend a low-calorie, high-protein meal." The user's device then automatically orders the suggested meal through a food delivery service.

[1280] Examples of prompt statements

[1281] User heart rate: 75

[1282] User's blood pressure: 120 / 80

[1283] User steps: 10000

[1284] Based on this data, suggest the best meal menu for your users.

[1285] In this way, it becomes possible to provide specific meal suggestions based on the user's health condition and link them to a meal delivery service.

[1286] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1287] Step 1:

[1288] Collecting data

[1289] A user wears a wearable device and constantly collects biometric information such as heart rate, blood pressure, and number of steps. This data is sent to the user's terminal such as a smartphone or tablet. The input is the biometric data from the wearable device, and the output is the biometric data sent to the terminal.

[1290] Step 2:

[1291] Sending Data

[1292] The terminal transmits the collected biometric data to the server. The input is the biometric data stored in the terminal, and the output is the biometric data transmitted to the server. In concrete terms, the terminal transmits data to the server using an HTTP request.

[1293] Step 3:

[1294] Analyzing data

[1295] The server inputs the received biometric data into the generative AI. The generative AI analyzes this data and evaluates the user's health condition. The input is the biometric data sent to the server, and the output is the analysis results regarding the user's health condition. In concrete terms, the generative AI analyzes the data and detects abnormalities in heart rate and blood pressure.

[1296] Step 4:

[1297] Meal menu suggestions

[1298] The generative AI proposes the optimal meal menu for the user based on the analysis results. The input is the analysis result on the user's health condition, and the output is the proposed meal menu. In concrete terms, the generative AI generates a prompt sentence and proposes a meal menu suitable for the user.

[1299] Step 5:

[1300] Meal menu notification

[1301] The server notifies the user's device of the meal menu proposed by the generative AI. The input is the proposed meal menu, and the output is the meal menu notified to the user's device. In concrete terms, the server sends the menu to the device using push notifications.

[1302] Step 6:

[1303] Ordering food from the menu

[1304] The user's device orders the proposed meal menu via the API of the food delivery service. The input is the notified meal menu, and the output is the order data sent to the food delivery service. In concrete terms, the device sends the order using an API request.

[1305] Step 7:

[1306] Review Order

[1307] The food delivery service receives the order and sends a confirmation to the user's terminal. The input is the order data sent to the food delivery service, and the output is the order confirmation sent to the user's terminal. In particular, the food delivery service processes the order and sends a confirmation to the terminal.

[1308] In this way, it becomes possible to provide specific meal suggestions based on the user's health condition and link them to a meal delivery service.

[1309] Example 2

[1310] Next, a second embodiment of the second form will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal".

[1311] Conventional systems have had difficulty analyzing a user's thoughts and feelings in real time and providing appropriate advice based on them. In addition, there was a lack of means to efficiently process data collected from wearable devices and provide immediate feedback to users. This made it difficult to provide specific advice to improve users' problems and daily lives.

[1312] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1313] In this invention, the server includes a means for constantly collecting data by a wearable device worn by the user, a means for transmitting the collected data to the terminal, a means for the terminal to transmit the collected data to the server, a means for inputting the data collected by the server to the generative AI model, a means for the generative AI model to analyze the user's thoughts and emotions, a means for the generative AI model to generate advice based on the analysis results, a means for transmitting the generated advice to the terminal, and a means for the terminal to display the advice to the user. This makes it possible to provide appropriate advice based on the user's thoughts and emotions in real time.

[1314] A "wearable device" is an electronic device that can be worn by a user and is used to collect biometric and behavioral data of the user.

[1315] A "terminal" is an electronic device that receives and processes data transmitted from a wearable device, and includes smartphones, personal computers, etc.

[1316] The "server" is a central processing unit that receives data sent from the terminal and inputs it into the generative AI model.

[1317] A "generative AI model" is an artificial intelligence algorithm that analyzes input data and generates advice based on the user's thoughts and feelings.

[1318] "Means for continuous data collection" refers to a function that enables a wearable device to continuously collect the user's biometric and behavioral data.

[1319] "Means for transmitting data to a terminal" refers to a function for transmitting data collected by a wearable device to a terminal via wireless communication or the like.

[1320] The "means for transmitting data to a server" is a function for transmitting data received by a terminal to a server.

[1321] "Means for inputting data into a generative AI model" refers to a function for inputting data received by the server into a generative AI model.

[1322] "Means of analyzing the user's thoughts and emotions" refers to the function that enables the generative AI model to analyze input data and understand the user's thoughts and emotions.

[1323] "Means for generating advice" refers to the function that enables the generative AI model to generate advice for the user based on the analysis results.

[1324] The "means for transmitting advice to a terminal" is a function for the server to transmit the generated advice to the terminal.

[1325] The "means for displaying advice to a user" refers to a function for visually or audibly displaying the advice received by the terminal to the user.

[1326] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[1327] This invention is a system that analyzes a user's thoughts and feelings in real time and provides appropriate advice based on the results. This system is implemented using a wearable device, a terminal, a server, and a generative AI model.

[1328] Hardware and Software Used

[1329] 1. Wearable Devices

[1330] The user wears a wearable device (e.g., an EEG headset) that reads the user's brainwaves, and the device collects the user's brainwave data in real time.

[1331] 2. Terminal

[1332] A terminal (e.g., a smartphone or a personal computer) receives the brainwave data transmitted from the wearable device. A dedicated application for processing the brainwave data is installed on the terminal.

[1333] 3. Server

[1334] The server receives the EEG data sent from the device and inputs it into the generative AI model. The server also pre-processes the data and manages the analysis results.

[1335] 4. Generative AI Models

[1336] A generative AI model (e.g., GPT-4) analyzes the brainwave data input from the server to understand the user's thoughts and emotions. Based on the analysis results, it generates advice for the user.

[1337] Data processing and calculation

[1338] 1. Data Collection

[1339] When a user wears the wearable device, it collects brainwave data and transmits it to a terminal via Bluetooth.

[1340] 2. Data Receipt and Transmission

[1341] The terminal receives the brainwave data sent from the wearable device and transmits the data to a server using a dedicated application.

[1342] 3. Data Analysis

[1343] The server inputs the brainwave data sent from the device into a generative AI model, which then analyzes the brainwave data to understand the user's thoughts and emotions.

[1344] 4. Advice Generation

[1345] The generative AI model generates advice for the user based on the analysis results. For example, if the user is feeling stressed, it generates advice such as "Try taking a deep breath to relax."

[1346] 5. Sending and Displaying Advice

[1347] The server transmits the generated advice to the terminal, which displays the received advice to the user.

[1348] Examples

[1349] For example, consider a case where a user feels stressed at work. The user puts on a wearable device and brainwave data is collected. The device sends this data to a server, which inputs the data into a generative AI model. The generative AI model analyzes that the user is feeling stressed and generates advice such as "Try taking a deep breath to relax." This advice is sent to the device and displayed to the user.

[1350] Example prompt:

[1351] “What advice should we offer our users if they are stressed?”

[1352] The system allows users to receive appropriate advice in real time based on their thoughts and feelings.

[1353] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1354] Program processing flow

[1355] Step 1: User puts on headset

[1356] A user wears a wearable device (e.g., an EEG headset) on their head to read their brainwaves, and the device collects the user's brainwave data in real time.

[1357] Input: User's brainwaves

[1358] Output: EEG data

[1359] Step 2: The headset collects brainwave data

[1360] The headset collects the user's brainwave data and transmits it to the device via Bluetooth.

[1361] Input: User's brainwaves

[1362] Output: EEG data sent to the terminal

[1363] Step 3: The device receives the EEG data

[1364] A terminal (e.g., a smartphone or a personal computer) receives the brainwave data transmitted from the headset. A dedicated application for processing the brainwave data is installed on the terminal.

[1365] Input: EEG data transmitted from the headset

[1366] Output: EEG data stored on the device

[1367] Step 4: The device transmits the EEG data to the server.

[1368] The device then transmits the received brainwave data to a server, where it is pre-processed for input into a generative AI model.

[1369] Input: EEG data stored on the device

[1370] Output: EEG data sent to the server

[1371] Step 5: The server inputs the EEG data into the generative AI model

[1372] The server receives the brainwave data sent from the device and inputs it into the generative AI model, which has an algorithm for analyzing this data.

[1373] Input: EEG data sent to the server

[1374] Output: EEG data input into a generative AI model

[1375] Step 6: The generative AI model analyzes the EEG data

[1376] The generative AI model analyzes the input brainwave data to understand the user's thoughts and emotions. For example, if the user is feeling stressed, it will extract that information.

[1377] Input: EEG data input to the generative AI model

[1378] Output: Analysis results (user's thoughts and feelings)

[1379] Step 7: The generative AI model generates advice

[1380] The generative AI model generates advice for the user based on the analysis results, such as "Try taking a deep breath to relax."

[1381] Input: Analysis results (user's thoughts and feelings)

[1382] Output: Generated advice

[1383] Step 8: The server sends the generated advice to the device

[1384] The server transmits the advice generated by the generative AI model to the terminal, and the advice is provided to the user in an appropriate format.

[1385] Input: Generated advice

[1386] Output: Advice sent to terminal

[1387] Step 9: The device displays the advice to the user

[1388] The terminal receives the advice sent from the server and displays it to the user, who can then confirm and act on the advice.

[1389] Input: Advice sent to terminal

[1390] Output: Advice displayed to the user

[1391] In this way, users can receive relevant advice in real time based on their own thoughts and feelings.

[1392] (Application example 2)

[1393] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server", and the headset type terminal 314 will be referred to as a "terminal".

[1394] Conventional systems using wearable devices and generative AI can provide help for users' problems and advice to enrich their daily lives, but they cannot read the user's brainwaves, detect anxiety or tension in real time, and provide appropriate security advice. This makes it difficult to respond immediately when a user feels anxious, and there is an issue of remaining concerns about security.

[1395] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1396] In this invention, the server includes a means for constantly collecting data from a wearable device, a means for inputting the collected data to a generative AI, a means for the generative AI to provide help for a user's troubles and advice for enriching daily life, a means for inputting data from a device that reads the user's brainwaves to the generative AI, and a means for detecting the user's anxiety or tension and providing appropriate security advice. This makes it possible to detect the situation in real time when the user feels anxious and provide appropriate security advice.

[1397] A "wearable device" is an electronic device that can be worn by the user and has the ability to collect data continuously.

[1398] "Generative AI" is an artificial intelligence system that analyzes collected data and provides users with help and advice for their problems.

[1399] A "brainwave reading device" is a device that detects the user's brainwaves and collects the data.

[1400] The "means for detecting anxiety or tension" refers to a function that analyzes the user's brainwave data and detects the anxiety or tension felt by the user in real time.

[1401] "Security advice" is advice provided to users on appropriate actions and precautions when they feel anxious or tense.

[1402] The following system configuration will be described as an embodiment of the present invention.

[1403] System Configuration

[1404] The system consists of a wearable device, a device that reads brainwaves, a generative AI, and a server. The wearable device can be worn by the user and has the ability to constantly collect data. The device that reads brainwaves is a device that detects the user's brainwaves and collects that data. The generative AI is an artificial intelligence system that analyzes the collected data and offers help and advice for the user's problems. The server is responsible for receiving data from these devices and inputting it into the generative AI.

[1405] Hardware and Software

[1406] Hardware:

[1407] Wearable devices (e.g. smart glasses)

[1408] Brainwave Sensor

[1409] server

[1410] software:

[1411] Python

[1412] Keras (deep learning library)

[1413] Scikit-learn (data preprocessing library)

[1414] Requests (HTTP request library)

[1415] Data processing and calculation

[1416] 1. Data collection:

[1417] Wearable devices and brainwave sensors constantly collect the user's biometric and brainwave data.

[1418] The collected data is sent to a server.

[1419] 2. Data preprocessing:

[1420] The server uses Scikit-learn to standardize the data it receives.

[1421] The standardized data is input into the generative AI.

[1422] 3. Data analysis and advice generation:

[1423] The generative AI uses Keras to analyze data and detect the user's concerns, anxieties, and tensions.

[1424] Based on the detected information, appropriate security advice is generated.

[1425] 4. Advice provided:

[1426] The generated advice is provided to the user in real time via a wearable device.

[1427] Examples

[1428] If a user feels anxious while walking down a dark street at night, the EEG sensor will detect that anxiety, and the generative AI will analyze the data and provide advice such as, "Pay attention to your surroundings. Suspicious activity has been detected nearby."

[1429] Examples of prompt statements

[1430] "Please develop an application that analyzes the surrounding situation and provides appropriate security advice when the user feels uneasy. The system reads brainwave data, transmits it to a generative AI, and receives advice."

[1431] The above is an embodiment of the present invention.

[1432] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1433] Step 1:

[1434] A user wears a wearable device and an EEG sensor. The wearable device constantly collects the user's biometric data (e.g., heart rate and body temperature). The EEG sensor collects the user's EEG data in real time. These data are sent from the wearable device and the EEG sensor to a server.

[1435] Input: User's biometric data, brainwave data

[1436] Output: Biometric data and EEG data sent to the server

[1437] Step 2:

[1438] The server standardizes the received biometric data and EEG data using Scikit-learn's StandardScaler. The standardized data is then input to the generative AI.

[1439] Input: Biometric data and EEG data sent to the server

[1440] Output: Standardized biometric data, EEG data

[1441] Step 3:

[1442] The server sends the standardized data to the generative AI, which uses Keras to analyze the data and detects the user's concerns, anxieties, and tensions. Based on the analysis results, it generates appropriate security advice.

[1443] Input: Standardized biometric data, EEG data

[1444] Output: Analysis results by generative AI, security advice

[1445] Step 4:

[1446] The security advice generated by the generative AI is sent to the wearable device via a server. The wearable device displays the advice to the user in real time. For example, a message such as "Pay attention to your surroundings. Suspicious activity has been detected nearby" is displayed.

[1447] Input: Analysis results by generative AI, security advice

[1448] Output: The security advice displayed to the user

[1449] Step 5:

[1450] The user acts according to the security advice displayed on the wearable device, for example by checking the surroundings and moving to a safe place.

[1451] Input: Security advice displayed to the user

[1452] Output: User actions

[1453] The above are the specific processing steps of this system.

[1454] In addition, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1455] "Example 1"

[1456] As one embodiment of the present invention, a system is provided that includes a means for constantly collecting data by a wearable device, a means for inputting the collected data to a generative AI, a means for the generative AI to provide help for a user's troubles and advice to enrich daily life, and an emotion engine in which the generative AI recognizes the user's emotions. Specifically, the wearable device constantly collects biometric information and behavioral data of the user and transmits the data to the generative AI. The generative AI analyzes the received data and provides advice on the user's health condition and lifestyle habits. Furthermore, the generative AI uses the emotion engine to recognize the user's emotions and provides advice according to the emotions. For example, if it recognizes that the user is feeling stressed, it provides advice to encourage relaxation.

[1457] "Example 2"

[1458] As another embodiment of the present invention, a system is provided that includes a means for a wearable device to collect healthcare-related data and an emotion engine for a generative AI to recognize a user's emotions. Specifically, the wearable device constantly collects the user's biometric and behavioral data, particularly healthcare-related data, and transmits the data to the generative AI. The generative AI analyzes the received data and provides advice regarding the user's health condition and lifestyle habits. Furthermore, the generative AI uses the emotion engine to recognize the user's emotions and provides advice according to the emotions. For example, if it recognizes that the user is feeling stressed, it provides advice to encourage relaxation.

[1459] "Example 5"

[1460] As yet another embodiment of the present invention, a system is provided that includes a means for inputting data from a device that reads a user's thoughts into a generative AI, and an emotion engine that allows the generative AI to recognize the user's emotions. Specifically, a device that reads the user's thoughts (e.g., a headset that reads brainwaves) reads the user's thoughts and transmits the data to the generative AI. The generative AI analyzes the received data and provides advice corresponding to the user's thoughts and emotions. Furthermore, the generative AI uses the emotion engine to recognize the user's emotions and provides advice according to the emotions. For example, if it recognizes that the user is feeling stressed, it provides advice encouraging relaxation.

[1461] The process flow of each embodiment will be described below.

[1462] "Example 1"

[1463] Step 1: The wearable device constantly collects the user's biometric and behavioral data. Step 2: The wearable device sends the collected data to the generative AI.

[1464] Step 3: The generative AI analyzes the received data.

[1465] Step 4: Generative AI provides advice on the user's health and lifestyle.

[1466] Step 5: The generative AI uses the emotion engine to recognize the user's emotions.

[1467] Step 6: Generative AI provides advice based on emotions.

[1468] "Example 2"

[1469] Step 1: Wearable devices constantly collect biometric and behavioral data from users, especially health-related data.

[1470] Step 2: The wearable device sends the data it collects to the generative AI.

[1471] Step 3: The generative AI analyzes the received data.

[1472] Step 4: Generative AI provides advice on the user's health and lifestyle.

[1473] Step 5: The generative AI uses the emotion engine to recognize the user's emotions.

[1474] Step 6: Generative AI provides advice based on emotions.

[1475] "Example 5"

[1476] Step 1: A mind-reading device (e.g. a brainwave-reading headset) reads the user's thoughts.

[1477] Step 2: The device that reads the user's thoughts sends the data it has read to the generative AI.

[1478] Step 3: The generative AI analyzes the received data.

[1479] Step 4: Generative AI provides advice that responds to the user's thoughts and feelings.

[1480] Step 5: The generative AI uses the emotion engine to recognize the user's emotions.

[1481] Step 6: Generative AI provides advice based on emotions.

[1482] Example 1

[1483] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal".

[1484] Conventional health management systems using wearable devices often do not analyze collected data or provide feedback to users in real time, making it difficult to provide appropriate advice based on the user's health condition and emotions. In addition, they lacked the ability to recognize the user's emotions and provide advice based on them, making it difficult to improve the user's quality of life.

[1485] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1486] In this invention, the server includes a means for receiving and storing data, a means for analyzing the data using a generative AI model, and a means for recognizing the user's emotions using an emotion engine, which makes it possible to analyze the collected data in real time and provide appropriate advice according to the user's health condition and emotions.

[1487] A "wearable device" is a device that can be worn by the user and constantly collects biometric information such as heart rate, blood pressure, and number of steps taken, as well as behavioral data.

[1488] A "terminal" is a device that receives data sent from a wearable device and transmits it to a server, and examples of this include smartphones and tablets.

[1489] The "server" is a central processing unit that receives data sent from the terminals, stores it, analyzes it, and generates advice.

[1490] A "generative AI model" is an artificial intelligence model that analyzes collected data, identifies patterns related to the user's health and lifestyle habits, and generates appropriate advice.

[1491] An "emotion engine" is an algorithm or software that recognizes a user's emotions based on their biometric and behavioral data.

[1492] A "database" is a storage device for storing data received by a server.

[1493] "Advice" refers to instructions or suggestions that the generative AI model provides to the user based on the results of analysis and emotion recognition.

[1494] "Notification" is a function that notifies the user of advice that the terminal has received from the server.

[1495] This invention is a system that combines a wearable device, a terminal, a server, and a generative AI model to provide advice based on the user's health condition and emotions.

[1496] Hardware and Software Used

[1497] Wearable Devices

[1498] The user wears a wearable device that constantly collects biometric and behavioral data, such as heart rate, blood pressure, and number of steps taken. Specific examples of such devices include common smart watches and fitness trackers.

[1499] Terminal

[1500] The wearable device transmits the collected data to a terminal (such as a smartphone or tablet). The terminal receives the data and transmits it to a server. An application for receiving and transmitting data is installed on the terminal.

[1501] server

[1502] The server receives the data sent from the device and stores it in a database. The server analyzes the data using a generative AI model to identify patterns related to the user's health and lifestyle. It also uses an emotion engine to recognize the user's emotions.

[1503] Generative AI Models

[1504] The generative AI model analyzes the data stored on the server and generates advice on the user's health condition and lifestyle habits. The generative AI model predicts the user's health condition based on past data and provides appropriate advice.

[1505] Emotion Engine

[1506] An emotion engine is an algorithm or software that recognizes a user's emotions based on their biometric and behavioral data. For example, it can determine whether the user is feeling stressed based on fluctuations in their heart rate or blood pressure.

[1507] Examples

[1508] Health monitoring

[1509] Suppose a user is wearing a smartwatch. The smartwatch constantly collects heart rate and step counts and sends them to the device. The device then sends the received data to a server. The server uses a generative AI model to analyze the data and advises the user, "Your step count is low today, so you should walk a little more."

[1510] Emotion Recognition and Advice

[1511] Suppose a user is wearing a fitness tracker. The fitness tracker constantly collects heart rate and blood pressure and sends them to the device. The device then sends the received data to the server. The generative AI model analyzes the data and recognizes that the user is feeling stressed. The generative AI model advises, "Take a deep breath and relax."

[1512] Examples of prompt statements

[1513] "If the user's heart rate is higher than normal, provide them with advice on how to relax."

[1514] By combining a wearable device with generative AI, the system can provide appropriate advice based on the user's health condition and emotions.

[1515] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1516] Step 1:

[1517] A user wears a wearable device. The wearable device constantly collects biometric and behavioral data, such as heart rate, blood pressure, and number of steps. Specifically, the wearable device uses sensors to measure these data and stores them in its internal memory. The input is the user's biometric information, and the output is the collected data.

[1518] Step 2:

[1519] The wearable device transmits the collected data to the terminal. Specifically, the data is transferred to the terminal using wireless communication technologies such as Bluetooth and Wi-Fi. The input is the data stored in the wearable device, and the output is the data transmitted to the terminal.

[1520] Step 3:

[1521] The terminal sends the received data to the server. Specifically, the terminal uploads data to the server using an Internet connection. The input is the data stored on the terminal, and the output is the data sent to the server.

[1522] Step 4:

[1523] The server receives data sent from the terminal and stores it in a database. Specifically, the server converts the data into a format that is easy to analyze and stores it in the database. The input is the data sent to the server, and the output is the data stored in the database.

[1524] Step 5:

[1525] The server analyzes the stored data using a generative AI model. Specifically, the generative AI model identifies patterns related to the user's health and lifestyle based on past data. The input is the data stored in the database, and the output is the analysis results.

[1526] Step 6:

[1527] The server uses an emotion engine to recognize the user's emotions. Specifically, the emotion engine analyzes fluctuations in heart rate and blood pressure to determine whether the user is feeling stressed. The input is the analysis result, and the output is the result of emotion recognition.

[1528] Step 7:

[1529] The server generates advice for the user based on the analysis results and emotion recognition results. In concrete terms, the generative AI model generates advice such as "Take a deep breath and relax." The input is the analysis results and emotion recognition results, and the output is the generated advice.

[1530] Step 8:

[1531] The server sends the generated advice to the terminal. In particular, the server uses an Internet connection to send the advice to the terminal. The input is the generated advice, and the output is the advice sent to the terminal.

[1532] Step 9:

[1533] The terminal notifies the user of the received advice. In concrete terms, the terminal uses a notification function to display the advice to the user. The input is the advice sent to the terminal, and the output is the advice notified to the user.

[1534] (Application example 1)

[1535] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server", and the headset type terminal 314 will be referred to as a "terminal".

[1536] In modern society, it is important for each user to properly manage their own health condition and lifestyle habits. However, with conventional systems, it has been difficult to collect a user's biometric and behavioral data in real time and provide appropriate advice based on that data. Furthermore, there have been no systems that recognize a user's emotions and provide advice based on those emotions. This has resulted in users being unable to take appropriate measures based on their own health condition and emotions, which could lead to a decline in their quality of life.

[1537] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1538] In this invention, the server includes a means for constantly collecting data by the wearable device, a means for inputting the collected data to the generative AI, a means for the generative AI to provide help for the user's problems and advice for enriching daily life, a means for the generative AI to recognize the user's emotions and provide advice according to the emotions, and a means for the generative AI to provide advice on the user's health condition and lifestyle habits. This makes it possible to collect the user's biometric information and behavioral data in real time and provide appropriate advice on the health condition and lifestyle habits based on the collected data. In addition, by recognizing the user's emotions and providing advice according to the emotions, the quality of the user's life can be improved.

[1539] A "wearable device" is an electronic device that is worn to constantly collect the user's biometric and behavioral data.

[1540] "Generative AI" is an artificial intelligence system that analyzes collected data and provides appropriate advice to users.

[1541] "Means of continuous data collection" refers to a function that uses a wearable device to continuously collect a user's biometric information and behavioral data.

[1542] "Means of input" is a function for sending collected data to the generative AI.

[1543] "Means of providing advice" refers to the function by which the generative AI provides appropriate advice and suggestions to the user based on the results of analysis.

[1544] "Means of recognizing emotions" refers to the function whereby the generative AI analyzes the user's emotional state and provides advice accordingly.

[1545] "Means of providing advice regarding health status and lifestyle habits" refers to a function in which generative AI analyzes a user's health and behavioral data and provides advice regarding health management and improving lifestyle habits.

[1546] A system for carrying out the present invention includes a wearable device, a generative AI, an emotion engine, and a communication means for linking these components. The specific configuration and operation of the system are described below.

[1547] System Configuration

[1548] 1. Wearable devices:

[1549] Constantly collect user biometric information (heart rate, blood pressure, number of steps, etc.) and behavioral data.

[1550] Example: Fitbit, Apple Watch, etc.

[1551] 2. Generative AI:

[1552] The collected data is analysed to generate advice on the user's health and lifestyle habits.

[1553] Examples: OpenAI GPT-3, Google Cloud AI, etc.

[1554] 3. Emotion Engine:

[1555] Recognize user emotions and provide advice accordingly.

[1556] Examples: Affectiva, IBM Watson Tone Analyzer, etc.

[1557] 4. Means of communication:

[1558] Network communication to send data from wearable devices to the generative AI and emotion engine.

[1559] Examples: Bluetooth, Wi-Fi, cellular, etc.

[1560] System Operation

[1561] 1. Data collection:

[1562] Wearable devices collect the user's biometric information, such as heart rate, blood pressure, and number of steps, in real time.

[1563] The collected data is sent to the generative AI at regular intervals.

[1564] 2. Data Analysis:

[1565] The generative AI analyzes the biometric and behavioral data it receives and generates advice about the user's health and lifestyle habits.

[1566] The analysis results are displayed on the user's smartphone app.

[1567] 3. Emotion recognition:

[1568] The emotion engine recognizes the user's emotions based on data sent from the generative AI.

[1569] Generate and provide emotion-based advice to users.

[1570] Examples

[1571] Health Tip: "Your heart rate is high. Take a deep breath and relax."

[1572] Emotional Advice: "You seem to be stressed. Try meditating for five minutes to help you relax."

[1573] Examples of prompt statements

[1574] "Generate advice for when a user's heart rate is high and they are feeling stressed."

[1575] In this way, it is possible to provide appropriate advice in real time based on the user's biometric information and emotional state. This system allows users to take appropriate measures according to their own health condition and emotions, improving their quality of life.

[1576] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1577] Step 1:

[1578] A wearable device collects the user's biometric information (heart rate, blood pressure, number of steps, etc.) in real time. The input is the user's biometric information, and the output is the collected data. In concrete terms, the wearable device's sensors collect data from the user's body and store it in its internal memory.

[1579] Step 2:

[1580] The wearable device transmits the collected data to a smartphone. The input is the biometric information stored in the wearable device, and the output is the data transferred to the smartphone. Specifically, the data is transmitted to the smartphone using Bluetooth or Wi-Fi.

[1581] Step 3:

[1582] The smartphone sends the received data to the generative AI. The input is the biometric information transferred to the smartphone, and the output is the data sent to the generative AI. In concrete terms, the smartphone application sends data to the generative AI server via the internet.

[1583] Step 4:

[1584] The generative AI analyzes the data it receives and generates advice about the user's health condition and lifestyle habits. The input is the biometric information sent to the generative AI, and the output is the generated advice. In concrete terms, the generative AI analyzes the data and generates advice based on the user's health condition and lifestyle habits.

[1585] Step 5:

[1586] The generative AI sends the analysis results to the emotion engine, which recognizes the user's emotions. The input is the analysis results of the generative AI, and the output is the user's emotions recognized by the emotion engine. In concrete terms, the emotion engine identifies the user's emotions based on the analysis results.

[1587] Step 6:

[1588] The emotion engine generates advice according to the user's emotion. The input is the user's emotion recognized by the emotion engine, and the output is advice according to the emotion. In concrete terms, the emotion engine generates appropriate advice based on the user's emotional state.

[1589] Step 7:

[1590] The advice generated by the generative AI and emotion engine is sent to the smartphone. The input is the generated advice, and the output is the advice transferred to the smartphone. In concrete terms, the generative AI and emotion engine send advice to the smartphone via the Internet.

[1591] Step 8:

[1592] The smartphone displays the received advice to the user. The input is the advice transferred to the smartphone, and the output is the advice displayed to the user. In concrete terms, the smartphone application displays the advice on the screen and the user confirms it.

[1593] Example 2

[1594] Next, a second embodiment of the second form will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal".

[1595] Conventional systems have had difficulty analyzing a user's biometric information and thoughts in real time and providing appropriate advice. In addition, there was a lack of means to accurately recognize a user's emotions and thoughts and provide advice based on them. This resulted in insufficient help for users' problems and advice to enrich their daily lives.

[1596] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for constantly collecting biometric information of the user, a means for inputting the collected biometric information to the generative AI, a means for the generative AI to analyze the user's emotions and thoughts and provide help for the user's troubles and advice for enriching the user's daily life, a means for the generative AI to generate appropriate advice for the user based on the analysis result and transmit the advice to the terminal, and a means for the terminal to display the advice to the user. This makes it possible to analyze the user's biometric information and thoughts in real time and provide appropriate advice.

[1597] "User" means an individual or organization that uses the system.

[1598] "Biometric information" is data obtained from the user's body, and includes heart rate, brain waves, body temperature, and the like.

[1599] A "wearable device" is an electronic device that can be worn by a user and is equipped with sensors for collecting biometric information.

[1600] "Generative AI" is an artificial intelligence system that analyzes collected data and generates appropriate advice for users.

[1601] An "emotion engine" is a part of generative AI and is a module that contains algorithms for analyzing user emotions.

[1602] A "terminal" is an electronic device used by a user, and includes a smartphone, tablet, etc.

[1603] "Advice" refers to advice or suggestions that the generative AI provides to the user based on the results of its analysis.

[1604] A "thought-reading device" is a device for capturing data related to a user's thoughts, such as brainwaves.

[1605] This invention is a system that analyzes a user's biometric information and thoughts in real time and provides appropriate advice. The system includes a wearable device, a terminal, a server, and a generative AI.

[1606] The user wears a wearable device, such as a headset, that reads brainwaves. The device captures the user's brainwaves and other biometric information and transmits it to the user's device (e.g., a smartphone or tablet) via Bluetooth or Wi-Fi. The device then transmits the received biometric information to a server via the Internet. The data is encrypted before being transmitted, ensuring security.

[1607] The server passes the received biometric information to a generative AI, which is hosted within the server or resides on the cloud. The generative AI uses an emotion engine to analyze the user's brainwave patterns to identify the emotions and thoughts the user is feeling. Based on the analysis results, the generative AI generates appropriate advice for the user. For example, if the user is recognized as feeling stressed, the generative AI generates advice to encourage relaxation.

[1608] The server sends the advice received from the generative AI to the user's device. The device notifies the user of the advice received. For example, the smartphone's notification function can be used to display a message such as "We recommend that you take a deep breath and take some time to relax."

[1609] As a concrete example, consider a situation where a user is wearing a headset that reads brainwaves. The headset captures the stress the user feels while working in real time. The server receives this data and sends it to a generative AI. The generative AI analyzes the data and determines that the user is at a high stress level. As a result, the generative AI provides advice such as, "I recommend that you take a moment to take a deep breath and relax."

[1610] Examples of prompt sentences to be input to a generative AI model include the following:

[1611] "Analyze the user's brainwave data to identify the emotions the user is feeling. If the user is feeling stressed, provide advice to promote relaxation."

[1612] By using this prompt, the generative AI can generate appropriate advice based on the user's brainwave data.

[1613] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1614] Step 1:

[1615] A user wears a headset that reads brainwaves. The user puts the headset on their head to capture brainwaves and turns on the device. The headset starts capturing the user's brainwave data in real time. The input is the user's brainwaves and the output is the captured brainwave data.

[1616] Step 2:

[1617] The headset transmits the EEG data to the device. The headset transmits the captured EEG data to the user's device (e.g., smartphone or tablet) via Bluetooth or Wi-Fi. The input is the captured EEG data, and the output is the EEG data transmitted to the device.

[1618] Step 3:

[1619] The terminal transmits the received EEG data to the server. The terminal encrypts the received EEG data and transmits it to the server via the Internet. The input is the EEG data transmitted to the terminal, and the output is the encrypted EEG data transmitted to the server.

[1620] Step 4:

[1621] The server passes the EEG data to the generative AI. The server calls an API to pass the received EEG data to the generative AI. The input is the encrypted EEG data sent to the server, and the output is the EEG data passed to the generative AI.

[1622] Step 5:

[1623] The generative AI analyzes the EEG data to identify the user's thoughts and emotions. The generative AI uses an emotion engine to analyze the EEG data to identify the emotions and thoughts the user is feeling. The input is the EEG data passed to the generative AI, and the output is the identified emotions and thoughts of the user.

[1624] Step 6:

[1625] The generative AI generates advice based on the analysis results. The generative AI generates appropriate advice for the user based on the analysis results. For example, if it is recognized that the user is feeling stressed, it generates advice to encourage relaxation. The input is the identified user's emotions and thoughts, and the output is the generated advice.

[1626] Step 7:

[1627] The server sends the generated advice to the terminal. The server sends the advice received from the generative AI to the user's terminal. The input is the generated advice, and the output is the advice sent to the terminal.

[1628] Step 8:

[1629] The terminal displays the advice to the user. The terminal notifies the user of the received advice. For example, the smartphone's notification function can be used to display a message such as "We recommend that you take a deep breath and take some time to relax." The input is the advice sent to the terminal, and the output is the advice displayed to the user.

[1630] (Application example 2)

[1631] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server", and the headset type terminal 314 will be referred to as a "terminal".

[1632] In modern society, it is important to understand a user's thoughts and emotions in real time and provide appropriate advice accordingly. However, with conventional systems, it was difficult to accurately recognize a user's emotional state and suggest specific actions based on it. In addition, there was no system that provided actions to reduce security risks according to the user's emotional state. This made it difficult to improve the quality of users' lives.

[1633] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1634] In this invention, the server includes a means for constantly collecting data from a wearable device, a means for inputting the collected data to the generative AI, a means for the generative AI to provide help for the user's problems and advice to enrich daily life, a means for inputting data from a device that reads the user's thoughts and emotions to the generative AI, a means for the generative AI to recognize the user's emotions and provide advice according to the emotions, and a means for suggesting actions to reduce security risks according to the user's emotional state. This makes it possible to accurately recognize the user's emotional state and suggest specific actions based on it.

[1635] A "wearable device" is an electronic device that can be worn by a user and is used to collect data continuously.

[1636] "Generative AI" is an artificial intelligence system that analyzes collected data and generates appropriate advice and suggestions for users.

[1637] A "device that reads a user's thoughts and emotions" is a device that reads brainwaves and other bio-signals to detect the user's thoughts and emotional state.

[1638] The "emotion engine" is a part of generative AI that recognizes the user's emotions and provides appropriate advice based on those emotions.

[1639] "Actions to reduce security risks" are specific actions or measures suggested to minimize security risks depending on the user's emotional state.

[1640] A system for implementing this invention includes a wearable device, a device for reading a user's thoughts and emotions, a generative AI, an emotion engine, and a function for suggesting actions to reduce security risks. Specific embodiments of each element are described below.

[1641] 1. Wearable Devices

[1642] A wearable device is an electronic device that can be worn by the user and is used to constantly collect data. Specifically, it collects biometric information such as heart rate, body temperature, and activity level. This data is sent to a smartphone via Bluetooth or Wi-Fi.

[1643] 2. Devices that read your thoughts and emotions

[1644] A device that reads a user's thoughts and emotions is a device that reads brainwaves and other biosignals to detect the user's thoughts and emotional state. Specifically, a headset that reads brainwaves (e.g., Emotiv Insight) is used. This device collects the user's brainwave data in real time and transmits it to a smartphone.

[1645] 3. Generative AI and Emotion Engines

[1646] Generative AI is an artificial intelligence system that analyzes collected data and generates appropriate advice and suggestions for users. The emotion engine is part of generative AI and is a function for recognizing user emotions and providing appropriate advice based on those emotions. Specifically, we use Keras (a deep learning library) to build an emotion recognition model and Scikit-learn (a data preprocessing library) to preprocess the data.

[1647] 4. Feature that suggests actions to reduce security risks

[1648] The action suggestions to reduce security risks are specific actions or measures suggested to minimize security risks depending on the user's emotional state. For example, if the user is feeling stressed, it will provide advice to promote relaxation, and if the user is feeling anxious, it will provide advice to give reassurance.

[1649] Examples

[1650] If a user feels stressed at work, the app offers advice such as, "Take a deep breath to relax." If a user feels anxious at night, the app offers advice such as, "Listen to your favorite music to feel more at ease."

[1651] Examples of prompt statements

[1652] "If users are feeling stressed, design your application to provide them with advice that encourages relaxation."

[1653] In this way, it is possible to accurately recognize the user's emotional state and suggest specific actions based on it.

[1654] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1655] Step 1:

[1656] Wearable devices collect biometric information from users, such as heart rate, body temperature, and activity level, in real time, and transmit this data to a smartphone via Bluetooth or Wi-Fi.

[1657] Input: User's biometric information

[1658] Output: Biometric data sent to a smartphone

[1659] Step 2:

[1660] The device that reads the user's thoughts and emotions collects the user's brainwave data. Specifically, a brainwave reading headset is used to obtain the user's brainwave data in real time. The device also transmits the data to a smartphone via Bluetooth or Wi-Fi.

[1661] Input: User's brainwave data

[1662] Output: EEG data transmitted to a smartphone

[1663] Step 3:

[1664] We preprocess the biometric data and EEG data collected by the smartphone. Specifically, we use Scikit-learn's StandardScaler to standardize the data. This unifies the scale of the data, making it easier to analyze with generative AI.

[1665] Input: Biometric data, EEG data

[1666] Output: Standardized data

[1667] Step 4:

[1668] The smartphone inputs the preprocessed data into the generative AI, which then uses an emotion recognition model built using Keras to analyze the user's emotions. Specifically, the data is fed into a deep learning model to predict the user's emotional state.

[1669] Input: Standardized data

[1670] Output: The user's emotional state.

[1671] Step 5:

[1672] The generative AI uses an emotion engine to generate advice based on the user's emotions. For example, if the user is feeling stressed, it will provide advice to encourage relaxation.

[1673] Input: The user's emotional state

[1674] Output: Advice based on emotions

[1675] Step 6:

[1676] The smartphone notifies the user of the generated advice, specifically by providing it via push notification or voice assistant.

[1677] Input: Emotion-based advice

[1678] Output:Notification to the user

[1679] Step 7:

[1680] Depending on the user's emotional state, it will suggest actions to reduce security risks. For example, if the user is feeling anxious, it will suggest specific actions and measures to make them feel safe.

[1681] Input: The user's emotional state

[1682] Output: Suggested actions to reduce security risks

[1683] The above are the specific process steps for carrying out the present invention.

[1684] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1685] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1686] Another example of generative AI is Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1687] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1688] [Fourth embodiment]

[1689] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1690] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1691] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[1692] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1693] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[1694] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[1695] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[1696] The control target 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, legs, etc. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, legs, etc. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1697] Fig. 8 shows an example of main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1698] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1699] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1700] In the robot 414, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1701] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1702] "Example 1"

[1703] The present invention is a system that constantly collects biometric and behavioral data of a user using a wearable device and inputs the data into a generative AI. Specifically, the wearable device collects healthcare-related data such as heart rate, blood pressure, and number of steps, and transmits the data to the generative AI. The generative AI analyzes the received data and provides advice on the user's health condition and lifestyle habits.

[1704] "Example 2"

[1705] In addition, in another aspect of the present invention, a system is provided that inputs data from a device that reads a user's thoughts into a generative AI. Specifically, a device such as a headset that reads brainwaves is used to read the user's thoughts and transmit the data to the generative AI. The generative AI analyzes the received data and provides advice corresponding to the user's thoughts and feelings.

[1706] The process flow of each embodiment will be described below.

[1707] "Example 1"

[1708] Step 1: The wearable device constantly collects the user's biometric and behavioral data. Step 2: The collected data is sent from the wearable device to the generative AI.

[1709] Step 3: The generative AI analyzes the received data and provides advice on the user's health and lifestyle.

[1710] "Example 2"

[1711] Step 1: A mind-reading device (e.g. a brainwave-reading headset) reads the user's thoughts.

[1712] Step 2: The read data is sent from the device to the generative AI.

[1713] Step 3: The generative AI analyzes the received data and provides advice that corresponds to the user's thoughts and feelings.

[1714] Example 1

[1715] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal".

[1716] Conventional wearable devices can collect biometric and behavioral data of users, but they lack a system that can effectively analyze the data and provide specific advice to users. Another issue is that it is difficult to analyze the collected data in real time and provide quick feedback to users.

[1717] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1718] In this invention, the server includes a means for constantly collecting data by the wearable device, a means for transmitting the collected data to the server, a means for transmitting the data received by the server to the generative AI, a means for the generative AI to analyze the received data, a means for the generative AI to generate advice on the user's health condition and lifestyle habits based on the analysis results, a means for the server to transmit the generated advice to the user's terminal, and a means for the user to check the advice on the terminal. This allows the user to understand their own health condition in real time and quickly receive appropriate advice.

[1719] A "wearable device" is a device that is worn by the user to constantly collect biometric information such as heart rate, blood pressure, and number of steps taken, as well as behavioral data.

[1720] The "server" is a computer system that acts as an intermediary to receive data sent from the wearable device and pass it on to the generative AI.

[1721] "Generative AI" is an artificial intelligence system that analyzes data received from a server and generates advice regarding the user's health and lifestyle habits.

[1722] "Means of continuous data collection" refers to the function of a wearable device to continuously collect the user's biometric information and behavioral data.

[1723] "Data transmission means" is a function that transmits data collected by the wearable device to a server.

[1724] "Data receiving means" is a function by which the server receives data transmitted from the wearable device.

[1725] "Data analysis means" refers to the function by which the generative AI analyzes the data it receives from the server.

[1726] The "advice generation means" is a function in which the generative AI generates advice regarding the user's health condition and lifestyle habits based on the analysis results.

[1727] "Advice sending means" refers to a function whereby the server sends advice generated by the generative AI to the user's device.

[1728] "Advice confirmation means" refers to a function that allows a user to confirm advice provided by the generative AI on their device.

[1729] This invention is a system that uses a wearable device to constantly collect a user's biometric and behavioral data and inputs that data into a generative AI. Specifically, the wearable device collects healthcare-related data such as heart rate, blood pressure, and number of steps, and transmits that data to the generative AI. The generative AI analyzes the received data and provides advice on the user's health condition and lifestyle habits.

[1730] Hardware and Software Used

[1731] Wearable devices: Devices that collect health-related data such as heart rate, blood pressure, and number of steps (e.g., Apple Watch, Fitbit).

[1732] Server: Receives data sent from the wearable device and acts as a relay to pass it on to the generative AI.

[1733] Generative AI: An AI system that analyzes the data it receives and provides advice to users about their health and lifestyle (e.g., OpenAI’s GPT-4).

[1734] Program Processing Procedure

[1735] 1. Data collection:

[1736] The user puts on the wearable device.

[1737] Wearable devices constantly collect the user's biometric information, such as heart rate, blood pressure, and number of steps, as well as behavioral data.

[1738] 2. Data transmission:

[1739] The wearable device transmits the collected data to a server.

[1740] The server sends the received data to the generative AI.

[1741] 3. Data Analysis:

[1742] The generative AI analyzes the data received from the server.

[1743] Based on the analysis results, the generative AI generates advice regarding the user's health condition and lifestyle habits.

[1744] 4. Advice provided:

[1745] The advice generated by the generative AI is sent to the server.

[1746] The server sends the advice to the user's device (such as a smartphone or PC).

[1747] The user checks the advice on the device.

[1748] Examples

[1749] Example 1: When the user is wearing an Apple Watch

[1750] The user wears the Apple Watch.

[1751] The Apple Watch collects the user's heart rate and steps.

[1752] The data collected by Apple Watch is sent to the user's iPhone.

[1753] The iPhone uploads the data to the server.

[1754] The server sends the data to the generative AI (GPT-4).

[1755] Generative AI analyzes the data and generates advice such as, "Your heart rate has been high recently. To reduce stress, try spending more time relaxing."

[1756] The server sends the advice to the user's iPhone.

[1757] The user checks the advice on their iPhone.

[1758] Examples of prompt statements

[1759] User heart rate data: [80, 85, 90, 95, 100]

[1760] User step count data: [1000, 2000, 3000, 4000, 5000]

[1761] Analyze this data and provide advice regarding the user's health status.

[1762] In this way, users can constantly monitor their own health status through their wearable devices and receive appropriate advice.

[1763] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1764] Step 1:

[1765] Data collection

[1766] The user puts on the wearable device.

[1767] Wearable devices constantly collect the user's biometric information, such as heart rate, blood pressure, and number of steps, as well as behavioral data.

[1768] Input: User biometric information (heart rate, blood pressure, number of steps, etc.).

[1769] Output: Collected biometric data.

[1770] What it does: For example, your Apple Watch measures your heart rate every minute and counts your steps in real time.

[1771] Step 2:

[1772] Data transmission

[1773] The wearable device transmits the data it collects to the user's smartphone.

[1774] The smartphone uploads the data to the server.

[1775] Input: Collected biometric data.

[1776] Output: Biometric data sent to the server.

[1777] Specific operations: For example, Apple Watch sends collected data via Bluetooth or Wi-Fi to the user's iPhone, which then uploads the data to a server.

[1778] Step 3:

[1779] Data Reception

[1780] The server receives the data transmitted from the wearable device.

[1781] Input: Biometric data sent from your smartphone.

[1782] Output: Biometric data stored on the server.

[1783] Specific operation: The server receives the data in real time and stores it in a database.

[1784] Step 4:

[1785] 4. Data Analysis

[1786] The server sends the received data to the generative AI.

[1787] The generative AI analyzes the data received from the server.

[1788] Input: Biometric data stored on the server.

[1789] Output: Analysis results (information about the user's health and lifestyle).

[1790] How it works: Generative AI analyzes heart rate and step count data to detect outliers and trends.

[1791] Step 5:

[1792] Advice Generation

[1793] Based on the analysis results, the generative AI generates advice regarding the user's health condition and lifestyle habits.

[1794] Input: The analysis results.

[1795] Output: Advice (e.g. "Your heart rate has been high lately. Try spending more time relaxing to reduce stress").

[1796] Specific behavior: The generative AI generates specific advice for the user based on the analysis results.

[1797] Step 6:

[1798] Send Advice

[1799] The advice generated by the generative AI is sent to the server.

[1800] The server sends the advice to the user's device (such as a smartphone or PC).

[1801] Input: The generated advice.

[1802] Output: Advice sent to the user's terminal.

[1803] Specific operation: The server sends the advice received from the generative AI to the user's smartphone via push notification.

[1804] Step 7:

[1805] Advice confirmation

[1806] The user checks the advice on the device.

[1807] Input: The advice sent to the user's terminal.

[1808] Output: The advice the user confirmed.

[1809] Specific action: The user taps on the notification on their smartphone to view the advice.

[1810] (Application example 1)

[1811] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal".

[1812] Conventional healthcare systems using wearable devices were limited to collecting users' biometric and behavioral data, and monitoring their health status and providing advice. However, these systems did not adequately address specific behavioral suggestions based on the user's health status or improving convenience in daily life. In particular, there was a lack of suggestions for meal menus optimal for the user's health status and integration with meal delivery services, resulting in insufficient specific support for users to live a healthy lifestyle.

[1813] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means. In this invention, the server includes a means for constantly collecting data by the wearable device, a means for inputting the collected data to the generative AI, a means for the generative AI to provide help for the user's problems and advice for enriching daily life, a means for proposing an optimal meal menu based on the user's biometric information and behavioral data, and a means for ordering the proposed meal menu from a food delivery service. This enables specific meal suggestions based on the user's health condition to be linked with a meal delivery service.

[1814] A "wearable device" is a device that is worn on the user's body and constantly collects biometric information such as heart rate, blood pressure, and number of steps taken, as well as behavioral data.

[1815] "Means of continuous data collection" refers to a function that uses a wearable device to continuously obtain a user's biometric information and behavioral data.

[1816] "Generative AI" is an artificial intelligence system that analyzes collected data and generates advice and suggestions based on the user's health and lifestyle habits.

[1817] "Means of input" refers to the function of sending data collected from a wearable device to the generative AI.

[1818] "Means of providing advice" refers to a function that provides users with advice regarding their health status and lifestyle habits based on data analyzed by the generative AI.

[1819] The "means of suggesting meal menus" refers to a function in which generative AI analyzes the user's biometric information and behavioral data, and suggests to the user the meal menu that is optimal for their health condition.

[1820] The "means for ordering from a food delivery service" is a function for automatically ordering the suggested meal menu from a food delivery service.

[1821] The system for implementing this invention is configured by combining a wearable device, generative AI, and a food delivery service API. A specific embodiment is shown below.

[1822] System Configuration

[1823] Hardware:

[1824] Wearable device: A device that is worn on the user's body and constantly collects biometric and behavioral data such as heart rate, blood pressure, and number of steps taken. Examples include smart watches and fitness trackers.

[1825] Server: A computer system for running generative AI, analyzing data and proposing meal menus.

[1826] User device: A device operated by a user, such as a smartphone or tablet, on which a food delivery service application is installed.

[1827] software:

[1828] Generative AI: An artificial intelligence system that analyzes collected data and generates advice and meal menus based on the user's health condition and lifestyle habits.

[1829] Food Delivery Service API: An interface for ordering suggested food menus.

[1830] Data collection and analysis

[1831] Wearable devices constantly collect biometric information such as the user's heart rate, blood pressure, number of steps, etc. This data is sent to a server via the user's terminal.

[1832] The server inputs the received data into the generative AI, which then analyzes the data and evaluates the user's health condition. Based on the analysis results, the generative AI then suggests the optimal meal menu for the user.

[1833] Food menu suggestions and ordering

[1834] Generative AI can generate meal plans based on a user's health status. For example, if a user's heart rate is high, it can suggest low-calorie, high-protein meals.

[1835] The user device receives the meal menu suggestions from the generative AI and places the order via the food delivery service's API, allowing users to easily order meals that are optimal for their health.

[1836] Examples

[1837] For example, when a user wears a smartwatch, data such as heart rate and number of steps are automatically collected. This data is sent to a server, and the generative AI provides advice such as "Based on your current health condition, we recommend a low-calorie, high-protein meal." The user's device then automatically orders the suggested meal through a food delivery service.

[1838] Examples of prompt statements

[1839] User heart rate: 75

[1840] User's blood pressure: 120 / 80

[1841] User steps: 10000

[1842] Based on this data, suggest the best meal menu for your users.

[1843] In this way, it becomes possible to provide specific meal suggestions based on the user's health condition and link them to a meal delivery service.

[1844] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1845] Step 1:

[1846] Collecting data

[1847] A user wears a wearable device and constantly collects biometric information such as heart rate, blood pressure, and number of steps. This data is sent to the user's terminal such as a smartphone or tablet. The input is the biometric data from the wearable device, and the output is the biometric data sent to the terminal.

[1848] Step 2:

[1849] Sending Data

[1850] The terminal transmits the collected biometric data to the server. The input is the biometric data stored in the terminal, and the output is the biometric data transmitted to the server. In concrete terms, the terminal transmits data to the server using an HTTP request.

[1851] Step 3:

[1852] Analyzing data

[1853] The server inputs the received biometric data into the generative AI. The generative AI analyzes this data and evaluates the user's health condition. The input is the biometric data sent to the server, and the output is the analysis results regarding the user's health condition. In concrete terms, the generative AI analyzes the data and detects abnormalities in heart rate and blood pressure.

[1854] Step 4:

[1855] Meal menu suggestions

[1856] The generative AI proposes the optimal meal menu for the user based on the analysis results. The input is the analysis result on the user's health condition, and the output is the proposed meal menu. In concrete terms, the generative AI generates a prompt sentence and proposes a meal menu suitable for the user.

[1857] Step 5:

[1858] Meal menu notification

[1859] The server notifies the user's device of the meal menu proposed by the generative AI. The input is the proposed meal menu, and the output is the meal menu notified to the user's device. In concrete terms, the server sends the menu to the device using push notifications.

[1860] Step 6:

[1861] Ordering food from the menu

[1862] The user's device orders the proposed meal menu via the API of the food delivery service. The input is the notified meal menu, and the output is the order data sent to the food delivery service. In concrete terms, the device sends the order using an API request.

[1863] Step 7:

[1864] Review Order

[1865] The food delivery service receives the order and sends a confirmation to the user's terminal. The input is the order data sent to the food delivery service, and the output is the order confirmation sent to the user's terminal. In particular, the food delivery service processes the order and sends a confirmation to the terminal.

[1866] In this way, it becomes possible to provide specific meal suggestions based on the user's health condition and link them to a meal delivery service.

[1867] Example 2

[1868] Next, a second embodiment of the second embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal".

[1869] Conventional systems have had difficulty analyzing a user's thoughts and feelings in real time and providing appropriate advice based on them. In addition, there was a lack of means to efficiently process data collected from wearable devices and provide immediate feedback to users. This made it difficult to provide specific advice to improve users' problems and daily lives.

[1870] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1871] In this invention, the server includes a means for constantly collecting data by a wearable device worn by the user, a means for transmitting the collected data to the terminal, a means for the terminal to transmit the collected data to the server, a means for inputting the data collected by the server to the generative AI model, a means for the generative AI model to analyze the user's thoughts and emotions, a means for the generative AI model to generate advice based on the analysis results, a means for transmitting the generated advice to the terminal, and a means for the terminal to display the advice to the user. This makes it possible to provide appropriate advice based on the user's thoughts and emotions in real time.

[1872] A "wearable device" is an electronic device that can be worn by a user and is used to collect biometric and behavioral data of the user.

[1873] A "terminal" is an electronic device that receives and processes data transmitted from a wearable device, and includes smartphones, personal computers, etc.

[1874] The "server" is a central processing unit that receives data sent from the terminal and inputs it into the generative AI model.

[1875] A "generative AI model" is an artificial intelligence algorithm that analyzes input data and generates advice based on the user's thoughts and feelings.

[1876] "Means for continuous data collection" refers to a function that enables a wearable device to continuously collect the user's biometric and behavioral data.

[1877] "Means for transmitting data to a terminal" refers to a function for transmitting data collected by a wearable device to a terminal via wireless communication or the like.

[1878] The "means for transmitting data to a server" is a function for transmitting data received by a terminal to a server.

[1879] "Means for inputting data into a generative AI model" refers to a function for inputting data received by the server into a generative AI model.

[1880] "Means of analyzing the user's thoughts and emotions" refers to the function that enables the generative AI model to analyze input data and understand the user's thoughts and emotions.

[1881] "Means for generating advice" refers to the function that enables the generative AI model to generate advice for the user based on the analysis results.

[1882] The "means for transmitting advice to a terminal" is a function for the server to transmit the generated advice to the terminal.

[1883] The "means for displaying advice to a user" refers to a function for visually or audibly displaying the advice received by the terminal to the user.

[1884] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[1885] This invention is a system that analyzes a user's thoughts and feelings in real time and provides appropriate advice based on the results. This system is implemented using a wearable device, a terminal, a server, and a generative AI model.

[1886] Hardware and Software Used

[1887] 1. Wearable Devices

[1888] The user wears a wearable device (e.g., an EEG headset) that reads the user's brainwaves, and the device collects the user's brainwave data in real time.

[1889] 2. Terminal

[1890] A terminal (e.g., a smartphone or a personal computer) receives the brainwave data transmitted from the wearable device. A dedicated application for processing the brainwave data is installed on the terminal.

[1891] 3. Server

[1892] The server receives the EEG data sent from the device and inputs it into the generative AI model. The server also pre-processes the data and manages the analysis results.

[1893] 4. Generative AI Models

[1894] A generative AI model (e.g., GPT-4) analyzes the brainwave data input from the server to understand the user's thoughts and emotions. Based on the analysis results, it generates advice for the user.

[1895] Data processing and calculation

[1896] 1. Data Collection

[1897] When a user wears the wearable device, it collects brainwave data and transmits it to a terminal via Bluetooth.

[1898] 2. Data Receipt and Transmission

[1899] The terminal receives the brainwave data sent from the wearable device and transmits the data to a server using a dedicated application.

[1900] 3. Data Analysis

[1901] The server inputs the brainwave data sent from the device into a generative AI model, which then analyzes the brainwave data to understand the user's thoughts and emotions.

[1902] 4. Advice Generation

[1903] The generative AI model generates advice for the user based on the analysis results. For example, if the user is feeling stressed, it generates advice such as "Try taking a deep breath to relax."

[1904] 5. Sending and Displaying Advice

[1905] The server transmits the generated advice to the terminal, which displays the received advice to the user.

[1906] Examples

[1907] For example, consider a case where a user feels stressed at work. The user puts on a wearable device and brainwave data is collected. The device sends this data to a server, which inputs the data into a generative AI model. The generative AI model analyzes that the user is feeling stressed and generates advice such as "Try taking a deep breath to relax." This advice is sent to the device and displayed to the user.

[1908] Example prompt:

[1909] “What advice should we offer our users if they are stressed?”

[1910] The system allows users to receive appropriate advice in real time based on their thoughts and feelings.

[1911] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1912] Program processing flow

[1913] Step 1: User puts on headset

[1914] A user wears a wearable device (e.g., an EEG headset) on their head to read their brainwaves, and the device collects the user's brainwave data in real time.

[1915] Input: User's brainwaves

[1916] Output: EEG data

[1917] Step 2: The headset collects brainwave data

[1918] The headset collects the user's brainwave data and transmits it to the device via Bluetooth.

[1919] Input: User's brainwaves

[1920] Output: EEG data sent to the terminal

[1921] Step 3: The device receives the EEG data

[1922] A terminal (e.g., a smartphone or a personal computer) receives the brainwave data transmitted from the headset. A dedicated application for processing the brainwave data is installed on the terminal.

[1923] Input: EEG data transmitted from the headset

[1924] Output: EEG data stored on the device

[1925] Step 4: The device transmits the EEG data to the server.

[1926] The device then transmits the received brainwave data to a server, where it is pre-processed for input into a generative AI model.

[1927] Input: EEG data stored on the device

[1928] Output: EEG data sent to the server

[1929] Step 5: The server inputs the EEG data into the generative AI model

[1930] The server receives the brainwave data sent from the device and inputs it into the generative AI model, which has an algorithm for analyzing this data.

[1931] Input: EEG data sent to the server

[1932] Output: EEG data input into a generative AI model

[1933] Step 6: The generative AI model analyzes the EEG data

[1934] The generative AI model analyzes the input brainwave data to understand the user's thoughts and emotions. For example, if the user is feeling stressed, it will extract that information.

[1935] Input: EEG data input to the generative AI model

[1936] Output: Analysis results (user's thoughts and feelings)

[1937] Step 7: The generative AI model generates advice

[1938] The generative AI model generates advice for the user based on the analysis results, such as "Try taking a deep breath to relax."

[1939] Input: Analysis results (user's thoughts and feelings)

[1940] Output: Generated advice

[1941] Step 8: The server sends the generated advice to the device

[1942] The server transmits the advice generated by the generative AI model to the terminal, and the advice is provided to the user in an appropriate format.

[1943] Input: Generated advice

[1944] Output: Advice sent to terminal

[1945] Step 9: The device displays the advice to the user

[1946] The terminal receives the advice sent from the server and displays it to the user, who can then confirm and act on the advice.

[1947] Input: Advice sent to terminal

[1948] Output: Advice displayed to the user

[1949] In this way, users can receive relevant advice in real time based on their own thoughts and feelings.

[1950] (Application example 2)

[1951] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal".

[1952] Conventional systems using wearable devices and generative AI can provide help for users' problems and advice to enrich their daily lives, but they cannot read the user's brainwaves, detect anxiety or tension in real time, and provide appropriate security advice. This makes it difficult to respond immediately when a user feels anxious, and there is an issue of remaining concerns about security.

[1953] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1954] In this invention, the server includes a means for constantly collecting data from a wearable device, a means for inputting the collected data to a generative AI, a means for the generative AI to provide help for a user's troubles and advice for enriching daily life, a means for inputting data from a device that reads the user's brainwaves to the generative AI, and a means for detecting the user's anxiety or tension and providing appropriate security advice. This makes it possible to detect the situation in real time when the user feels anxious and provide appropriate security advice.

[1955] A "wearable device" is an electronic device that can be worn by the user and has the ability to collect data continuously.

[1956] "Generative AI" is an artificial intelligence system that analyzes collected data and provides users with help and advice for their problems.

[1957] A "brainwave reading device" is a device that detects the user's brainwaves and collects the data.

[1958] The "means for detecting anxiety or tension" refers to a function that analyzes the user's brainwave data and detects the anxiety or tension felt by the user in real time.

[1959] "Security advice" is advice provided to users on appropriate actions and precautions when they feel anxious or tense.

[1960] The following system configuration will be described as an embodiment of the present invention.

[1961] System Configuration

[1962] The system consists of a wearable device, a device that reads brainwaves, a generative AI, and a server. The wearable device can be worn by the user and has the ability to constantly collect data. The device that reads brainwaves is a device that detects the user's brainwaves and collects that data. The generative AI is an artificial intelligence system that analyzes the collected data and offers help and advice for the user's problems. The server is responsible for receiving data from these devices and inputting it into the generative AI.

[1963] Hardware and Software

[1964] Hardware:

[1965] Wearable devices (e.g. smart glasses)

[1966] Brainwave Sensor

[1967] server

[1968] software:

[1969] Python

[1970] Keras (deep learning library)

[1971] Scikit-learn (data preprocessing library)

[1972] Requests (HTTP request library)

[1973] Data processing and calculation

[1974] 1. Data collection:

[1975] Wearable devices and brainwave sensors constantly collect the user's biometric and brainwave data.

[1976] The collected data is sent to a server.

[1977] 2. Data preprocessing:

[1978] The server uses Scikit-learn to standardize the data it receives.

[1979] The standardized data is input into the generative AI.

[1980] 3. Data analysis and advice generation:

[1981] The generative AI uses Keras to analyze data and detect the user's concerns, anxieties, and tensions.

[1982] Based on the detected information, appropriate security advice is generated.

[1983] 4. Advice provided:

[1984] The generated advice is provided to the user in real time via a wearable device.

[1985] Examples

[1986] If a user feels anxious while walking down a dark street at night, the EEG sensor will detect that anxiety, and the generative AI will analyze the data and provide advice such as, "Pay attention to your surroundings. Suspicious activity has been detected nearby."

[1987] Examples of prompt statements

[1988] "Please develop an application that analyzes the surrounding situation and provides appropriate security advice when the user feels uneasy. The system reads brainwave data, transmits it to a generative AI, and receives advice."

[1989] The above is an embodiment of the present invention.

[1990] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1991] Step 1:

[1992] A user wears a wearable device and an EEG sensor. The wearable device constantly collects the user's biometric data (e.g., heart rate and body temperature). The EEG sensor collects the user's EEG data in real time. These data are sent from the wearable device and the EEG sensor to a server.

[1993] Input: User's biometric data, brainwave data

[1994] Output: Biometric data and EEG data sent to the server

[1995] Step 2:

[1996] The server standardizes the received biometric data and EEG data using Scikit-learn's StandardScaler. The standardized data is then input to the generative AI.

[1997] Input: Biometric data and EEG data sent to the server

[1998] Output: Standardized biometric data, EEG data

[1999] Step 3:

[2000] The server sends the standardized data to the generative AI, which uses Keras to analyze the data and detects the user's concerns, anxieties, and tensions. Based on the analysis results, it generates appropriate security advice.

[2001] Input: Standardized biometric data, EEG data

[2002] Output: Analysis results by generative AI, security advice

[2003] Step 4:

[2004] The security advice generated by the generative AI is sent to the wearable device via a server. The wearable device displays the advice to the user in real time. For example, a message such as "Pay attention to your surroundings. Suspicious activity has been detected nearby" is displayed.

[2005] Input: Analysis results by generative AI, security advice

[2006] Output: The security advice displayed to the user

[2007] Step 5:

[2008] The user acts according to the security advice displayed on the wearable device, for example by checking the surroundings and moving to a safe place.

[2009] Input: Security advice displayed to the user

[2010] Output: User actions

[2011] The above are the specific processing steps of this system.

[2012] In addition, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[2013] "Example 1"

[2014] As one embodiment of the present invention, a system is provided that includes a means for constantly collecting data by a wearable device, a means for inputting the collected data to a generative AI, a means for the generative AI to provide help for a user's troubles and advice to enrich daily life, and an emotion engine in which the generative AI recognizes the user's emotions. Specifically, the wearable device constantly collects biometric information and behavioral data of the user and transmits the data to the generative AI. The generative AI analyzes the received data and provides advice on the user's health condition and lifestyle habits. Furthermore, the generative AI uses the emotion engine to recognize the user's emotions and provides advice according to the emotions. For example, if it recognizes that the user is feeling stressed, it provides advice to encourage relaxation.

[2015] "Example 2"

[2016] As another embodiment of the present invention, a system is provided that includes a means for a wearable device to collect healthcare-related data and an emotion engine for a generative AI to recognize a user's emotions. Specifically, the wearable device constantly collects the user's biometric and behavioral data, particularly healthcare-related data, and transmits the data to the generative AI. The generative AI analyzes the received data and provides advice regarding the user's health condition and lifestyle habits. Furthermore, the generative AI uses the emotion engine to recognize the user's emotions and provides advice according to the emotions. For example, if it recognizes that the user is feeling stressed, it provides advice to encourage relaxation.

[2017] "Example 5"

[2018] As yet another embodiment of the present invention, a system is provided that includes a means for inputting data from a device that reads a user's thoughts into a generative AI, and an emotion engine that allows the generative AI to recognize the user's emotions. Specifically, a device that reads the user's thoughts (e.g., a headset that reads brainwaves) reads the user's thoughts and transmits the data to the generative AI. The generative AI analyzes the received data and provides advice corresponding to the user's thoughts and emotions. Furthermore, the generative AI uses the emotion engine to recognize the user's emotions and provides advice according to the emotions. For example, if it recognizes that the user is feeling stressed, it provides advice encouraging relaxation.

[2019] The process flow of each embodiment will be described below.

[2020] "Example 1"

[2021] Step 1: The wearable device constantly collects the user's biometric and behavioral data. Step 2: The wearable device sends the collected data to the generative AI.

[2022] Step 3: The generative AI analyzes the received data.

[2023] Step 4: Generative AI provides advice on the user's health and lifestyle.

[2024] Step 5: The generative AI uses the emotion engine to recognize the user's emotions.

[2025] Step 6: Generative AI provides advice based on emotions.

[2026] "Example 2"

[2027] Step 1: Wearable devices constantly collect biometric and behavioral data from users, especially health-related data.

[2028] Step 2: The wearable device sends the data it collects to the generative AI.

[2029] Step 3: The generative AI analyzes the received data.

[2030] Step 4: Generative AI provides advice on the user's health and lifestyle.

[2031] Step 5: The generative AI uses the emotion engine to recognize the user's emotions.

[2032] Step 6: Generative AI provides advice based on emotions.

[2033] "Example 5"

[2034] Step 1: A mind-reading device (e.g. a brainwave-reading headset) reads the user's thoughts.

[2035] Step 2: The device that reads the user's thoughts sends the data it has read to the generative AI.

[2036] Step 3: The generative AI analyzes the received data.

[2037] Step 4: Generative AI provides advice that responds to the user's thoughts and feelings.

[2038] Step 5: The generative AI uses the emotion engine to recognize the user's emotions.

[2039] Step 6: Generative AI provides advice based on emotions.

[2040] Example 1

[2041] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal".

[2042] Conventional health management systems using wearable devices often do not analyze collected data or provide feedback to users in real time, making it difficult to provide appropriate advice based on the user's health condition and emotions. In addition, they lacked the ability to recognize the user's emotions and provide advice based on them, making it difficult to improve the user's quality of life.

[2043] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[2044] In this invention, the server includes a means for receiving and storing data, a means for analyzing the data using a generative AI model, and a means for recognizing the user's emotions using an emotion engine, which makes it possible to analyze the collected data in real time and provide appropriate advice according to the user's health condition and emotions.

[2045] A "wearable device" is a device that can be worn by the user and constantly collects biometric information such as heart rate, blood pressure, and number of steps taken, as well as behavioral data.

[2046] A "terminal" is a device that receives data sent from a wearable device and transmits it to a server, and examples of this include smartphones and tablets.

[2047] The "server" is a central processing unit that receives data sent from the terminals, stores it, analyzes it, and generates advice.

[2048] A "generative AI model" is an artificial intelligence model that analyzes collected data, identifies patterns related to the user's health and lifestyle habits, and generates appropriate advice.

[2049] An "emotion engine" is an algorithm or software that recognizes a user's emotions based on their biometric and behavioral data.

[2050] A "database" is a storage device for storing data received by a server.

[2051] "Advice" refers to instructions or suggestions that the generative AI model provides to the user based on the results of analysis and emotion recognition.

[2052] "Notification" is a function that notifies the user of advice that the terminal has received from the server.

[2053] This invention is a system that combines a wearable device, a terminal, a server, and a generative AI model to provide advice based on the user's health condition and emotions.

[2054] Hardware and Software Used

[2055] Wearable Devices

[2056] The user wears a wearable device that constantly collects biometric and behavioral data, such as heart rate, blood pressure, and number of steps taken. Specific examples of such devices include common smart watches and fitness trackers.

[2057] Terminal

[2058] The wearable device transmits the collected data to a terminal (such as a smartphone or tablet). The terminal receives the data and transmits it to a server. An application for receiving and transmitting data is installed on the terminal.

[2059] server

[2060] The server receives the data sent from the device and stores it in a database. The server analyzes the data using a generative AI model to identify patterns related to the user's health and lifestyle. It also uses an emotion engine to recognize the user's emotions.

[2061] Generative AI Models

[2062] The generative AI model analyzes the data stored on the server and generates advice on the user's health condition and lifestyle habits. The generative AI model predicts the user's health condition based on past data and provides appropriate advice.

[2063] Emotion Engine

[2064] An emotion engine is an algorithm or software that recognizes a user's emotions based on their biometric and behavioral data. For example, it can determine whether the user is feeling stressed based on fluctuations in their heart rate or blood pressure.

[2065] Examples

[2066] Health monitoring

[2067] Suppose a user is wearing a smartwatch. The smartwatch constantly collects heart rate and step counts and sends them to the device. The device then sends the received data to a server. The server uses a generative AI model to analyze the data and advises the user, "Your step count is low today, so you should walk a little more."

[2068] Emotion Recognition and Advice

[2069] Suppose a user is wearing a fitness tracker. The fitness tracker constantly collects heart rate and blood pressure and sends them to the device. The device then sends the received data to the server. The generative AI model analyzes the data and recognizes that the user is feeling stressed. The generative AI model advises, "Take a deep breath and relax."

[2070] Examples of prompt statements

[2071] "If the user's heart rate is higher than normal, provide them with advice on how to relax."

[2072] By combining a wearable device with generative AI, the system can provide appropriate advice based on the user's health condition and emotions.

[2073] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2074] Step 1:

[2075] A user wears a wearable device. The wearable device constantly collects biometric and behavioral data, such as heart rate, blood pressure, and number of steps. Specifically, the wearable device uses sensors to measure these data and stores them in its internal memory. The input is the user's biometric information, and the output is the collected data.

[2076] Step 2:

[2077] The wearable device transmits the collected data to the terminal. Specifically, the data is transferred to the terminal using wireless communication technologies such as Bluetooth and Wi-Fi. The input is the data stored in the wearable device, and the output is the data transmitted to the terminal.

[2078] Step 3:

[2079] The terminal sends the received data to the server. Specifically, the terminal uploads data to the server using an Internet connection. The input is the data stored on the terminal, and the output is the data sent to the server.

[2080] Step 4:

[2081] The server receives data sent from the terminal and stores it in a database. Specifically, the server converts the data into a format that is easy to analyze and stores it in the database. The input is the data sent to the server, and the output is the data stored in the database.

[2082] Step 5:

[2083] The server analyzes the stored data using a generative AI model. Specifically, the generative AI model identifies patterns related to the user's health and lifestyle based on past data. The input is the data stored in the database, and the output is the analysis results.

[2084] Step 6:

[2085] The server uses an emotion engine to recognize the user's emotions. Specifically, the emotion engine analyzes fluctuations in heart rate and blood pressure to determine whether the user is feeling stressed. The input is the analysis result, and the output is the result of emotion recognition.

[2086] Step 7:

[2087] The server generates advice for the user based on the analysis results and emotion recognition results. In concrete terms, the generative AI model generates advice such as "Take a deep breath and relax." The input is the analysis results and emotion recognition results, and the output is the generated advice.

[2088] Step 8:

[2089] The server sends the generated advice to the terminal. In particular, the server uses an Internet connection to send the advice to the terminal. The input is the generated advice, and the output is the advice sent to the terminal.

[2090] Step 9:

[2091] The terminal notifies the user of the received advice. In concrete terms, the terminal uses a notification function to display the advice to the user. The input is the advice sent to the terminal, and the output is the advice notified to the user.

[2092] (Application example 1)

[2093] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal".

[2094] In modern society, it is important for each user to properly manage their own health condition and lifestyle habits. However, with conventional systems, it has been difficult to collect a user's biometric and behavioral data in real time and provide appropriate advice based on that data. Furthermore, there have been no systems that recognize a user's emotions and provide advice based on those emotions. This has resulted in users being unable to take appropriate measures based on their own health condition and emotions, which could lead to a decline in their quality of life.

[2095] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[2096] In this invention, the server includes a means for constantly collecting data by the wearable device, a means for inputting the collected data to the generative AI, a means for the generative AI to provide help for the user's problems and advice for enriching daily life, a means for the generative AI to recognize the user's emotions and provide advice according to the emotions, and a means for the generative AI to provide advice on the user's health condition and lifestyle habits. This makes it possible to collect the user's biometric information and behavioral data in real time and provide appropriate advice on the health condition and lifestyle habits based on the collected data. In addition, by recognizing the user's emotions and providing advice according to the emotions, the quality of the user's life can be improved.

[2097] A "wearable device" is an electronic device that is worn to constantly collect the user's biometric and behavioral data.

[2098] "Generative AI" is an artificial intelligence system that analyzes collected data and provides appropriate advice to users.

[2099] "Means of continuous data collection" refers to a function that uses a wearable device to continuously collect a user's biometric information and behavioral data.

[2100] "Means of input" is a function for sending collected data to the generative AI.

[2101] "Means of providing advice" refers to the function by which the generative AI provides appropriate advice and suggestions to the user based on the results of analysis.

[2102] "Means of recognizing emotions" refers to the function whereby the generative AI analyzes the user's emotional state and provides advice accordingly.

[2103] "Means of providing advice regarding health status and lifestyle habits" refers to a function in which generative AI analyzes a user's health and behavioral data and provides advice regarding health management and improving lifestyle habits.

[2104] A system for carrying out the present invention includes a wearable device, a generative AI, an emotion engine, and a communication means for linking these components. The specific configuration and operation of the system are described belo...

Claims

1. A means for receiving data collected by a wearable device worn by a user; A means for analyzing the data using a generating AI, evaluating the health condition of the user, and proposing an optimal meal menu to the user; means for notifying a terminal of the user of the meal menu; A means of ordering meal menus from food delivery services; A system including:

2. The system of claim 1 , wherein the wearable device collects biometric information of the user.

3. The system of claim 1 , wherein the means for receiving data further receives data from a device that reads the user's thoughts.

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