system

Abstract

We provide the system. [Solution] A speech recognition means that converts the user's voice input into text data, A biometric information collection method that acquires heart rate, body temperature, and exercise level data from a wearable device, A data integration means that integrates text data converted by a speech recognition means and biometric data acquired by a biometric information collection means, A generative AI advice method that generates individual medical advice based on integrated data, A user interface means for notifying the user of the generated medical advice, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] An object of the present invention is to provide a system that effectively supports a user's overall medical care. Conventionally, it has been difficult for many users to obtain appropriate medical advice quickly and accurately for individual health problems. In particular, there have been problems such as the difficulty of self-judgment-based health management, the lack of medical advice suitable for individual symptoms, and the difficulty of immediate access to medical institutions.

Means for Solving the Problems

[0005] The present invention is a system that solves the above-mentioned problems by the following means: A voice recognition means converts the user's voice input into text data, and a biometric information collection means acquires biometric data such as heart rate, body temperature, and exercise level from a wearable device. Furthermore, a data integration means integrates the voice-text data and biometric data, and a generation AI advice means generates personalized medical advice based on the integrated data. Finally, a user interface means notifies the user of the generated advice, enabling the user to easily manage their health at home.

[0006] "Voice recognition means" refers to a means of converting a user's voice input into text data.

[0007] "Methods for collecting biometric information" refer to methods for acquiring biometric data such as heart rate, body temperature, and exercise level from wearable devices.

[0008] "Data integration means" refers to means for integrating text data converted by speech recognition means with biometric data acquired by biometric information collection means.

[0009] "Generative AI advice means" refers to a method of generating individual medical advice based on integrated data.

[0010] "User interface means" refers to means of notifying the user of the generated medical advice. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4]This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0029] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] The system of the present invention includes voice recognition means, biometric information collection means, data integration means, generation AI advice means, and user interface means. These means work together to effectively support the user's overall medical care.

[0033] First, the user launches the application and uses voice input to initiate a medical consultation. For example, they might say, "Recently, I've been getting headaches at night. What should I do?" The device captures this voice data and sends it to the server in real time. The server then uses speech recognition to convert this voice data into text.

[0034] Next, biometric data such as heart rate, body temperature, and activity level are acquired from the wearable device worn by the user using a biometric data collection system. The device periodically collects this data and sends it to the server.

[0035] The server receives voice text data and biometric data and integrates them using a data integration mechanism. The integrated data is formatted as time-series data, and features such as heart rate variability, sleep patterns, and exercise intensity are extracted.

[0036] Subsequently, the integrated data is analyzed by a generating AI advice system. Based on the analysis results, the AI ​​generates personalized medical advice. For example, if a user's heart rate increases at night, stress or lack of sleep may be the cause of their headaches. Based on this analysis, specific advice is generated such as, "Your headache may be due to stress. We recommend stretching to relax or going to bed early."

[0037] Finally, the generated medical advice is communicated to the user through the user interface. The device displays and plays this generated advice in text or audio format, allowing the user to review the advice and take specific actions.

[0038] Specific example

[0039] For example, if a user makes the following statement as a medical consultation:

[0040] User: "Lately, I've been getting headaches at night. What should I do?"

[0041] 1. Speech recognition means:

[0042] User: Makes a comment to the application.

[0043] Terminal: Captures audio data and sends it to the server.

[0044] Server: Converts audio data into text data.

[0045] 2. Means of collecting biometric information:

[0046] User: Puts on a wearable device.

[0047] Terminal: Acquires data on heart rate, body temperature, and exercise level, and sends it to the server.

[0048] Server: Receives biometric data.

[0049] 3. Data integration means:

[0050] Server: Integrates voice-text data and biometric data.

[0051] Server: Extracts features such as heart rate variability, sleep patterns, and exercise intensity.

[0052] 4. Generative AI advice methods:

[0053] Server: Analyzes data and generates personalized medical advice.

[0054] Server: For example, it generates specific advice such as, "This headache may be caused by stress. We recommend stretching to relax or going to bed early."

[0055] 5. User interface means:

[0056] Server: Sends the generated advice to the terminal.

[0057] Device: Notifies users of advice via text or voice.

[0058] User: Review the notified advice and take the necessary actions.

[0059] Thus, the present invention is a system that enables users to easily manage their health at home by analyzing the user's statements and biometric information and providing personalized medical advice.

[0060] The following describes the processing flow.

[0061] Step 1:

[0062] User: Launches the application and says, "I've been getting headaches at night lately. What should I do?" to begin the medical consultation.

[0063] Step 2:

[0064] Terminal: Captures the user's voice and sends the audio data to the server in real time.

[0065] Step 3:

[0066] Server: Uses speech recognition to convert transmitted speech data into text data.

[0067] Step 4:

[0068] User: Wears a wearable device (e.g., a smartwatch) at all times.

[0069] Step 5:

[0070] Terminal: Periodically acquires heart rate, body temperature, and activity level data from a wearable device and transmits this biometric data to a server.

[0071] Step 6:

[0072] Server: Receives biometric data sent from terminals and integrates it with text data sent from speech recognition devices.

[0073] Step 7:

[0074] Server: Using data integration means, it combines voice / text data and biometric data to extract features necessary for analyzing health status (heart rate variability, sleep patterns, exercise intensity, etc.).

[0075] Step 8:

[0076] Server: The AI-generated advice system generates personalized medical advice based on integrated data. For example, if stress or lack of sleep is likely the cause of the headache, it will generate specific advice such as, "Your headache may be caused by stress. We recommend stretching to relax or going to bed early."

[0077] Step 9:

[0078] Server: Formats the generated medical advice into natural language and sends it to the terminal.

[0079] Step 10:

[0080] Terminal: Notifies the user of medical advice sent from the server via text or voice.

[0081] Step 11:

[0082] User: Review the notified advice and take action based on it. For example, take measures such as doing stretches to relax or going to bed early.

[0083] (Example 1)

[0084] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0085] Conventional medical support systems have struggled to effectively combine and analyze user voice input and biometric information to provide appropriate medical advice. Furthermore, real-time data integration and the generation of personalized advice using generative AI models have been insufficient, preventing users from quickly obtaining specific advice about their health status. This could lead to users being unable to take appropriate action and potentially neglecting their health management.

[0086] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0087] In this invention, the server includes means for capturing voice input, means for transmitting voice data to the server, means for converting voice data into text data, means for collecting biometric data from a wearable device, means for transmitting biometric data to the server, means for integrating voice-text data and biometric data, means for extracting features from the integrated data, means for analyzing the data using a generative AI model to generate personalized advice, and means for notifying the user of the advice. This makes it possible to integrate the user's voice input and biometric information, analyze it in real time, and quickly provide personalized medical advice.

[0088] "Means for capturing voice input" refers to devices or systems that acquire the voice spoken by a user as digital data.

[0089] "Means for sending audio data to a server" refers to devices or systems for transferring captured audio data to a server via a communication network.

[0090] "Means of converting audio data into text data" refers to software or algorithms that analyze audio data and convert it into text information.

[0091] "Means of collecting biometric data from wearable devices" refers to devices that are attached to the user's body to measure biometric data such as heart rate, body temperature, and exercise level.

[0092] "Means for transmitting biometric data to a server" refers to devices or systems for transferring biometric data acquired from wearable devices to a server via a communication network.

[0093] "Means for integrating voice-text data and biometric data" refers to software or algorithms that combine voice data and biometric data chronologically and format them into a single dataset that can be analyzed.

[0094] "Methods for extracting features from integrated data" refers to software or algorithms used to extract meaningful information, such as specific patterns or variations, from integrated data.

[0095] "Means of analyzing and generating personalized advice using generative AI models" refers to software or systems that use machine learning and natural language processing technologies to analyze integrated data and generate personalized advice based on the results.

[0096] "Means of notifying users of advice" refers to devices or systems that transmit generated advice to users as text messages or audio.

[0097] The present invention comprises a voice recognition means, a biometric information collection means, a data integration means, a generation AI advice means, and a user interface means, which work together to effectively support the user's overall medical care.

[0098] System Configuration

[0099] The components of the system of the present invention are as follows.

[0100] 1. Voice recognition means: Captures the voice spoken by the user and acquires it as digital data. This means uses the microphone of a smartphone or tablet.

[0101] 2. Means of collecting biometric information: Use a wearable device worn by the user (e.g., a smartwatch) to measure biometric data such as heart rate, body temperature, and activity level.

[0102] 3. Data Integration Method: A software process that receives voice data and biometric data from the server and integrates them in a time-series format. The data is formatted using the Python Pandas library.

[0103] 4. Generative AI Advice Method: Based on integrated data, the data is analyzed using a generative AI model (e.g., OpenAI's GPT-4) to generate personalized medical advice.

[0104] 5. User Interface Means: Means for notifying the user of the generated advice. This includes screen displays on smartphones and voice notifications using speech synthesis APIs.

[0105] System operation

[0106] This system provides users with prompt and appropriate medical advice through the following specific actions.

[0107] Acquiring and transmitting voice input

[0108] A user uses their smartphone and says, "I've been getting headaches at night lately. What should I do?"

[0109] The device captures this audio and sends it to the server in real time as audio data. This is done using an HTTP POST request.

[0110] Converting audio data to text

[0111] The server receives the audio data and uses the Google Cloud Speech-to-Text API to convert the audio data into text data.

[0112] Collection and transmission of biometric data

[0113] While the user is wearing the wearable device, data such as heart rate, body temperature, and activity level are measured periodically.

[0114] The device sends this biometric data to the server. An HTTP POST request is again used to send the data.

[0115] Data integration and analysis

[0116] The server integrates the voice-text data and biometric data and formats it as time-series data. The Python Pandas library is used for this process.

[0117] Subsequently, features such as heart rate variability, sleep patterns, and exercise intensity are extracted from the integrated data.

[0118] Generating advice using generative AI

[0119] The server analyzes the generated AI model (GPT-4) as a prompt message and generates personalized medical advice.

[0120] For example, it can generate specific advice such as, "The user's heart rate increases at night, suggesting that stress or lack of sleep may be causing the headache. We recommend stretching to relax and going to bed earlier."

[0121] Advice notification

[0122] The server sends generated medical advice to the device, and the device notifies the user of this advice via text or voice.

[0123] Users can review the advice provided and take specific actions.

[0124] Specific example

[0125] For example, if a user says the following:

[0126] User: "Lately, I've been getting headaches at night. What should I do?"

[0127] Example of a prompt

[0128] The following prompt will be generated:

[0129] "Use the user's voice input and biometric data to generate personalized medical advice. Inputs are as follows: Voice input: 'I've been getting headaches at night recently. What should I do?' Heart rate data: 70-90 bpm, Body temperature data: 36.5-37.0 degrees Celsius, Activity data: 5000-7000 steps."

[0130] Thus, the system of the present invention can integrate the user's voice input and biometric information from multiple perspectives, enabling it to provide rapid and highly accurate medical advice.

[0131] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0132] Step 1:

[0133] The user performs voice input.

[0134] User: Launches a medical consultation application on their smartphone and says, "I've been getting headaches at night lately. What should I do?"

[0135] Input: User voice input

[0136] Output: Captured audio data

[0137] Step 2:

[0138] Capture and send audio data.

[0139] Device: The system captures the user's voice using the smartphone's microphone and encodes it as digital audio data. This audio data is then sent to the server using an HTTP POST request.

[0140] Input: Captured audio data

[0141] Output: Audio data sent to the server

[0142] Step 3:

[0143] Convert audio data to text data

[0144] Server: Sends the received audio data to the Google Cloud Speech-to-Text API to convert the audio data into text data.

[0145] Specific operation: Speech data is sent to the Google Cloud Speech-to-Text API and text data is received as a response.

[0146] Input: Audio data

[0147] Output: Text data

[0148] Step 4:

[0149] Collect and transmit biometric data

[0150] User: Wears wearable devices such as Apple Watch or Fitbit while going about their daily life.

[0151] Device: The wearable device measures biometric data such as heart rate, body temperature, and activity level, and transmits this data to a smartphone via Bluetooth or Wi-Fi. The smartphone periodically collects this data and sends it to a server via HTTP POST requests.

[0152] Input: Biometric data (heart rate, body temperature, activity level)

[0153] Output: Biometric data sent to the server

[0154] Step 5:

[0155] Data integration

[0156] Server: Receives voice / text data and biometric data, and integrates them using data integration tools. The Python Pandas library is used to format this data as time-series data.

[0157] Specific operation: Use a Pandas DataFrame to combine speech-text data and biometric data to create a single unified dataset.

[0158] Input: Voice text data, biometric data

[0159] Output: Integrated data

[0160] Step 6:

[0161] Feature extraction

[0162] Server: Extracts features such as heart rate variability, sleep patterns, and exercise intensity from integrated data. Performs data analysis using Python libraries such as NumPy and SciPy.

[0163] Specific operations: Extract features from integrated data and analyze, for example, the standard deviation of heart rate or sleep patterns.

[0164] Input: Integrated Data

[0165] Output: Feature data

[0166] Step 7:

[0167] Generating advice using generative AI

[0168] Server: Uses a generative AI model (e.g., GPT-4) to analyze integrated data and features and generate personalized medical advice.

[0169] Specific operation: Input prompt text into the generation AI, and generate medical advice based on the analysis.

[0170] Input: Feature data, integrated data

[0171] Output: Individual medical advice

[0172] Step 8:

[0173] Advice notification

[0174] Server: Sends generated medical advice to the terminal.

[0175] Terminal: Displays received advice to the user as a text message and, if necessary, plays it back as audio using a speech synthesis API.

[0176] Input: Individual medical advice

[0177] Output: Advice notified to the user

[0178] Specific example:

[0179] For example, if a user says, "I've been getting headaches at night lately. What should I do?", the following specific actions will be performed at each processing step.

[0180] 1. The user's voice is captured.

[0181] 2. The audio is sent to the server.

[0182] 3. The audio data is converted to text.

[0183] 4. Biometric data is collected from the wearable device and sent to the server.

[0184] 5. Voice-text data and biometric data are integrated.

[0185] 6. Features are extracted from the integrated data.

[0186] 7. The generative AI model performs the analysis and generates advice such as, "This may be a headache caused by stress."

[0187] 8. The generated advice is notified to the user.

[0188] (Application Example 1)

[0189] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0190] In modern society, it is important to offer personalized meal plans based on individual health conditions and lifestyles, but effective systems for achieving this are not yet widespread. Food delivery services, in particular, are required to provide meal plans suitable for health management. To meet these needs, a new system is needed that uses ubiquitous devices to understand the user's health condition and provide meal suggestions based on that information.

[0191] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0192] In this invention, the server includes voice recognition means, biometric information collection means, data integration means, generation AI advice means, user interface means, and means having the function of suggesting an appropriate meal menu based on biometric data. This makes it possible to automatically generate and suggest a personalized meal menu by combining and analyzing the user's voice input and biometric information.

[0193] "Speech recognition means" refers to a device or software that has the function of converting speech uttered by a user into text data.

[0194] "Biometric information collection means" refers to devices and software that have the function of acquiring biometric data such as heart rate, body temperature, and exercise level from wearable devices.

[0195] "Data integration means" refers to a device or software that has the function of integrating voice text data and biometric data and formatting it as time-series data.

[0196] A "generating AI advice system" is an artificial intelligence system that has the function of analyzing integrated data and generating individual advice.

[0197] A "user interface means" is a device or software that notifies the user of the generated advice and is capable of displaying and playing it in text or audio.

[0198] "Means having the function of suggesting appropriate meal menus based on biometric data" refers to devices or software that have the function of analyzing the user's biometric data and suggesting the optimal meal menu based on the results.

[0199] The present invention analyzes a user's voice input and biometric information collected from a wearable device to propose a personalized meal menu. This system includes voice recognition means, biometric information collection means, data integration means, generation AI advice means, user interface means, and means having the function of proposing an appropriate meal menu based on biometric data.

[0200] First, the user launches a smartphone application and inputs information about their dietary preferences and health status via voice. For example, they might say, "I've gained weight recently. Please tell me about low-calorie meals." The device captures this voice data and sends it to the server in real time. A voice recognition system converts this voice data into text data.

[0201] Next, the biometric information collection means acquires biometric data such as heart rate, body temperature, and exercise level from the wearable device. The terminal periodically collects this data and sends it to the server. The server receives the text data converted by the speech recognition means and the biometric data acquired by the biometric information collection means, and integrates them using the data integration means. The integrated data is formatted as time-series data, and features such as heart rate variability, sleep patterns, and exercise intensity are extracted.

[0202] Subsequently, the integrated data is analyzed by a generated AI advice system. Based on the analysis results, the AI ​​generates personalized meal menus. For example, if the user's heart rate and exercise data are high, light snacks or nutritionally balanced meals that take calorie consumption into consideration will be suggested. Based on these analysis results, specific advice such as "You're looking for a low-calorie meal. We recommend a salad and grilled fish" is generated.

[0203] Finally, the generated meal menu advice is communicated to the user through the user interface. The device displays and plays this advice in text or audio format, allowing the user to review the advice and take specific actions.

[0204] Hardware and software used

[0205] Speech recognition method: The speech_recognition library is used to convert speech to text.

[0206] Biometric information collection method: Data on heart rate, body temperature, and exercise level are acquired from wearable devices.

[0207] Data integration method: The integrated data is formatted as time-series data, and features are extracted.

[0208] AI-generated advice method: Using OpenAI APIs (such as ChatGPT), the system analyzes integrated data and generates personalized meal menu advice.

[0209] User interface means: Text data is used to provide voice notifications using the pyttsx3 library.

[0210] For example, if a user says, "I've gained weight recently. Could you tell me about a low-calorie diet?":

[0211] Example of a prompt:

[0212] Message from a user: I've been gaining weight recently. Could you please suggest a low-calorie diet?

[0213] Biometric information:

[0214] Heart rate: 75 bpm

[0215] Body temperature: 36.5℃

[0216] Exercise level: 50

[0217] Based on this user's health status, please suggest a low-calorie meal plan.

[0218] In this way, the system of the present invention proposes a personalized meal menu based on the user's biometric data and voice input, thereby supporting the user's health management.

[0219] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0220] Step 1:

[0221] User voice input

[0222] The user launches a smartphone application and inputs information about their dietary preferences and health status via voice. For example, they might say, "I've gained weight recently. Please tell me about a low-calorie diet." The input voice data is captured by the device and sent to the server in real time.

[0223] Input: User's voice data

[0224] Output: Audio data sent to the server

[0225] Step 2:

[0226] Speech recognition

[0227] The server uses speech recognition to convert the received audio data into text data. Specifically, it uses the speech_recognition library to convert speech to text.

[0228] Input: Audio data

[0229] Output: Text data

[0230] Step 3:

[0231] Collection of biometric information

[0232] The biometric data collection system acquires biometric data such as heart rate, body temperature, and activity level from the user's wearable device. The device periodically collects this data and sends it to the server.

[0233] Input: Biometric data acquired from wearable devices

[0234] Output: Biometric data sent to the server

[0235] Step 4:

[0236] Data integration and formatting

[0237] The server's data integration mechanism integrates text data converted by the speech recognition mechanism with biometric data acquired by the biometric information collection mechanism. The integrated data is formatted as time-series data, and features such as heart rate variability, sleep patterns, and exercise intensity are extracted.

[0238] Input: Text data, biometric data

[0239] Output: Formatted time-series data and extracted features

[0240] Step 5:

[0241] AI-generated advice

[0242] The AI-generated advice system analyzes integrated data and generates personalized meal plans. Specifically, it uses the OpenAI API to perform analysis and generation based on prompt messages. For example, it suggests the optimal meal plan based on a user request such as "I'm looking for a low-calorie meal."

[0243] Input: Formatted time-series data, prompt text

[0244] Output: Personalized meal plan advice

[0245] Step 6:

[0246] User notifications

[0247] The generated meal menu advice is communicated to the user through a user interface. The device displays and plays this advice as text or audio, allowing the user to review the advice and take specific actions. Specifically, the pyttsx3 library is used to provide audio notifications of text data.

[0248] Input: Meal menu advice

[0249] Output: Notification to the user

[0250] Through the above processing steps, users can receive personalized meal menu suggestions based on their biometric data and voice input.

[0251] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0252] The system of the present invention includes voice recognition means, biometric information collection means, data integration means, generative AI advice means, user interface means, and emotion engine. These means work together to effectively support the user's overall medical and emotional state.

[0253] First, the user launches the application and uses voice input to begin a medical consultation. For example, they might say, "Recently, I've been getting headaches at night. What should I do?" The device captures this voice data and sends it to the server in real time. The server converts this voice data into text data using speech recognition technology. In addition, the voice data is analyzed by an emotion engine to recognize the user's emotional state.

[0254] Next, biometric data such as heart rate, body temperature, and activity level are acquired from the wearable device worn by the user using a biometric data collection system. The device periodically collects this data and sends it to the server.

[0255] The server receives data from the speech recognition means and emotion engine, as well as biometric data from the biometric information collection means, and integrates it using the data integration means. The integrated data is formatted as time-series data, and features such as speech text data, emotional state data, heart rate variability, sleep patterns, and exercise intensity are extracted.

[0256] Subsequently, the integrated data is analyzed by a generating AI advice system. Based on the analysis results, the AI ​​generates personalized medical advice. For example, if a user's heart rate increases at night and their emotional state indicates stress, stress and lack of sleep are likely causes of their headaches. Based on these analysis results, specific advice is generated, such as, "Your headache may be caused by stress. We recommend stretching to relax or going to bed early."

[0257] Finally, the generated medical advice is communicated to the user through the user interface. The device displays and plays this generated advice in text or audio format, allowing the user to review the advice and take specific actions.

[0258] Specific example

[0259] For example, if a user makes the following statement as a medical consultation:

[0260] User: "Lately, I've been getting headaches at night. What should I do?"

[0261] 1. Speech recognition means and emotion engine:

[0262] User: Makes a comment to the application.

[0263] Terminal: Captures audio data and sends it to the server.

[0264] Server: Converts audio data into text data.

[0265] Server: The emotion engine analyzes the user's emotional state from voice data.

[0266] 2. Means of collecting biometric information:

[0267] User: Puts on a wearable device.

[0268] Terminal: Acquires data on heart rate, body temperature, and exercise level, and sends it to the server.

[0269] Server: Receives biometric data.

[0270] 3. Data integration means:

[0271] Server: Integrates voice-text data, emotional state data, and biometric data.

[0272] Server: Extracts feature quantities such as fluctuations in heart rate, sleep patterns, and exercise intensity.

[0273] 4. AI advice generation means:

[0274] Server: Analyzes the data and generates individual medical advice.

[0275] Server: Generates specific advice such as "There may be stress-induced headaches. We recommend stretches to relax and going to bed earlier."

[0276] 5. User interface means:

[0277] Server: Sends the generated advice to the terminal.

[0278] Terminal: Notifies the user of the advice in text or voice.

[0279] User: Checks the notified advice and takes appropriate actions.

[0280] In this way, the present invention is a system that enables users to easily manage their health at home by providing individual medical advice based on the user's speech, biological information, and further the user's emotional state.

[0281] The following describes the processing flow.

[0282] Step 1:

[0283] User: Launches the application and makes a statement "Recently, I get headaches at night. What should I do?" to start a medical consultation.

[0284] Step 2:

[0285] Terminal: Captures the user's voice and sends the voice data to the server in real time.

[0286] Step 3:

[0287] Server: Convert the transmitted voice data into text data by means of voice recognition.

[0288] Step 4:

[0289] Server: Send the converted text data to the emotion engine and analyze the user's emotional state.

[0290] Step 5:

[0291] User: Always wear a wearable device (e.g., a smartwatch).

[0292] Step 6:

[0293] Terminal: Regularly obtain data on heart rate, body temperature, and amount of exercise from the wearable device and send the biometric data to the server.

[0294] Step 7:

[0295] Server: Receive the biometric data sent from the terminal and integrate it with the text data sent from the voice recognition means and the emotional state data from the emotion engine.

[0296] Step 8:

[0297] Server: Combine the voice text data, emotional state data, and biometric data by means of data integration means, and extract feature quantities (such as fluctuations in heart rate, sleep patterns, exercise intensity, etc.) necessary for analyzing the health state.

[0298] Step 9:

[0299] Server: Based on the integrated data, generate individual medical advice through the generative AI advice means. For example, if the heart rate increases at night and the emotional state indicates stress, it is determined that stress and lack of sleep are likely to be the causes of headache. Based on this, specific advice such as "There may be stress-induced headaches. We recommend stretches to relax and going to bed earlier" is generated.

[0300] Step 10:

[0301] Server: Format the generated medical advice in natural language and send it to the terminal.

[0302] Step 11:

[0303] Terminal: Notify the user of the medical advice sent from the server in text or voice.

[0304] Step 12:

[0305] User: Confirm the notified advice and take actions based on it. For example, perform stretches to relax or take measures such as going to bed earlier.

[0306] (Example 2)

[0307] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart device 14 is referred to as the "terminal".

[0308] In modern society, there is a demand for a system that enables users to easily and efficiently manage their health at home. However, conventional systems have been unable to comprehensively analyze users' voice inputs, biometric information, and emotional states and provide appropriate medical advice. Therefore, it has been difficult for users to appropriately manage their health status.

[0309] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes voice recognition means, biometric information collection means, data integration means, generation AI advice means, user interface means, and emotion analysis means. This makes it possible to comprehensively analyze the user's voice input, biometric information, and emotional state and provide individualized medical advice.

[0310] "Voice recognition means" refers to a device or software that has the function of acquiring a user's voice input as digital data and converting it into text data.

[0311] "Biometric information collection means" refers to a device or system that has the function of acquiring biometric data such as heart rate, body temperature, and exercise level from a wearable device worn by a user.

[0312] "Data integration means" refers to a device or software that has the function of combining voice text data, emotional state data, and biometric information data and treating them as a single integrated data set.

[0313] "Generating AI advice means" refers to a device or software that uses artificial intelligence technology to analyze integrated data and generate personalized medical advice based on the user's health condition.

[0314] "User interface means" refers to a device or software that has the function of notifying the user of the generated advice and displaying / playing it in text or audio.

[0315] "Emotional analysis means" refers to a device or software that has the function of analyzing the emotional state from the user's voice data.

[0316] The system of the present invention integrates and analyzes the user's voice input, biometric information, and emotional state to provide health management and medical advice, thereby enabling the user to efficiently manage their health.

[0317] Hardware and software

[0318] The following hardware and software will be used to implement this system.

[0319] Hardware:

[0320] Device (e.g., smartphone, tablet)

[0321] server

[0322] Wearable devices (e.g., smartwatches, fitness trackers)

[0323] software:

[0324] Speech recognition method (e.g. Google Cloud Speech-to-Text)

[0325] Methods for collecting biometric information (e.g., health apps, apps specifically for wearable devices)

[0326] Data integration methods (e.g., Apache Kafka, Apache Spark)

[0327] Generative AI advice methods (e.g., OpenAI GPT-4)

[0328] Emotion analysis method (e.g. Microsoft Azure Emotion API)

[0329] User interface means (e.g., React Native-based mobile apps)

[0330] System Processing Description

[0331] When a user initiates a medical consultation, they launch the application and use voice input. For example, they might say, "Recently, I've been getting headaches at night. What should I do?" The device captures this voice data and sends it to the server in real time.

[0332] The server uses speech recognition to convert voice data into text data. Simultaneously, it uses emotion analysis to analyze the user's emotional state from the voice data. For example, it might detect a high stress level.

[0333] Next, biometric data such as heart rate, body temperature, and activity level are acquired from the wearable device worn by the user using a biometric data collection system. The device periodically collects this data and sends it to the server.

[0334] The server receives voice text data, emotional state data, and biometric data, and integrates them using data integration tools. The integrated data is formatted as time-series data, and features such as heart rate variability, sleep patterns, and exercise intensity are extracted.

[0335] Subsequently, the integrated data is analyzed by a generation AI advice system. Based on the analysis results, the AI ​​creates personalized medical advice. For example, if a user's heart rate increases at night and their emotional state indicates stress, the analysis might conclude that stress and lack of sleep are likely causes of their headaches. Based on these results, specific advice is generated, such as, "Your headache may be due to stress. We recommend stretching to relax or going to bed early."

[0336] Finally, the generated medical advice is communicated to the user through a user interface. The device displays and plays the generated advice in text or audio format, allowing the user to review the advice and take specific actions.

[0337] Specific example

[0338] For example, if a user says the following:

[0339] User: "Lately, I've been getting headaches at night. What should I do?"

[0340] 1. Acquisition of voice input:

[0341] User: Makes a comment to the application.

[0342] Terminal: Captures audio data and sends it to the server.

[0343] 2. Speech recognition and sentiment analysis:

[0344] Server: Converts audio data into text data.

[0345] Server: Analyzes the user's emotional state from voice data using emotion analysis tools.

[0346] 3. Collection of biometric data:

[0347] User: Puts on a wearable device.

[0348] Terminal: Acquires data such as heart rate, body temperature, and exercise level, and sends it to the server.

[0349] 4. Data Integration:

[0350] Server: Integrates voice-text data, emotional state data, and biometric data.

[0351] 5. AI advice generation:

[0352] Server: Analyzes data and generates personalized medical advice.

[0353] 6. Notification of advice:

[0354] Server: Sends the generated advice to the terminal.

[0355] Device: Notifies users of advice via text or voice.

[0356] Thus, the present invention is a system that enables users to easily manage their health at home by comprehensively analyzing the user's voice input, biometric information, and emotional state, and providing personalized medical advice.

[0357] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0358] Step 1: Capture voice input

[0359] The user launches the application and uses voice input to ask a medical question. For example, they might say, "I've been getting headaches at night lately. What should I do?"

[0360] The device uses its built-in microphone to capture the user's speech in real time and generate audio data.

[0361] Input: User voice input

[0362] Output: Captured audio data

[0363] Step 2: Convert audio to text

[0364] The device sends the captured audio data to the server.

[0365] The server uses speech recognition technology (e.g., Google Cloud Speech-to-Text) to convert speech data into text data.

[0366] Input: Audio data

[0367] Output: Text data

[0368] Step 3: Analysis of emotional state

[0369] The server uses emotion analysis tools (e.g., Microsoft Azure Emotion API) to analyze the user's emotional state from the audio data. For example, it can identify a state of stress.

[0370] This process analyzes the tone, pitch, and speed of the voice to classify the emotional state.

[0371] Input: Audio data

[0372] Output: Emotional state data

[0373] Step 4: Biometric Data Collection

[0374] Users wear wearable devices that can measure heart rate, body temperature, and activity level.

[0375] The device collects biometric data in real time from wearable devices and transmits that data to a server. The data includes heart rate, body temperature, and activity level.

[0376] Input: Biometric data from wearable devices

[0377] Output: Biometric data sent to the server

[0378] Step 5: Data Integration and Feature Extraction

[0379] The server integrates voice-text data, emotional state data, and biometric data. Here, it formats the data as a time series.

[0380] The server uses data integration tools (e.g., Apache Spark) to extract features such as heart rate variability, sleep patterns, and exercise intensity from the integrated data.

[0381] Input: Voice text data, emotional state data, biometric data

[0382] Output: Integrated data and extracted features

[0383] Step 6: Generating AI advice

[0384] The server uses a generation AI advice tool (e.g., OpenAI GPT-4) to analyze the integrated data and generate personalized medical advice based on the user's health status.

[0385] Based on the analysis results, specific advice is generated, such as, "Your headache may be caused by stress. We recommend stretching to relax or going to bed early."

[0386] Input: Integrated data and features

[0387] Output: Generated medical advice

[0388] Step 7: Notification of advice

[0389] The server sends the generated medical advice to the terminal via the user interface.

[0390] The device will notify the user of advice via text or voice.

[0391] Input: Generated medical advice

[0392] Output: Advice notified to the user

[0393] (Application Example 2)

[0394] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0395] Conventional systems rely solely on voice recognition and biometric information when users receive personalized health counseling in a virtual store, making it difficult to provide detailed advice that takes into account the user's emotional state. This invention aims to provide comprehensive health advice that also considers the user's mental state by utilizing emotion analysis methods.

[0396] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0397] In this invention, the server includes voice recognition means, biometric information collection means, data integration means, generative AI advice means, and emotion analysis means. This makes it possible to convert the user's voice input into text data and integrate and analyze biometric information and emotional state. By providing the user with optimal health advice through the generative AI advice means, personalized counseling within the virtual store becomes possible.

[0398] "Voice recognition means" refers to a device or software that has the function of converting a user's voice input into text data.

[0399] "Means of collecting biometric information" refers to a device or software that has the function of collecting biometric data such as a user's heart rate, body temperature, and exercise level using a wearable device or the like.

[0400] "Data integration means" refers to a device or software for integrating data obtained from speech recognition means, emotion analysis means, and biometric information collection means, and for extracting feature quantities.

[0401] "Generating AI advice means" refers to a device or software that has the function of analyzing integrated data and generating appropriate advice for the user.

[0402] "User interface means" refers to a device or software that notifies the user of generated advice and provides an interface for the user to take action accordingly.

[0403] "Emotional analysis means" refers to a device or software that has the function of analyzing a user's emotional state from their voice data or biometric information.

[0404] "Counseling methods within a virtual store" refers to a device or software that has the function of providing health counseling to users within a virtual store.

[0405] The system that realizes this application example includes voice recognition means, biometric information collection means, data integration means, generative AI advice means, user interface means, emotion analysis means, and counseling means within a virtual store. Each means of this system functions as follows:

[0406] Explanation of the program's processing

[0407] This system uses a smartphone or head-mounted display (HMD) as the user interface to transmit voice input and biometric information to a server. The data arriving at the server is processed as follows:

[0408] 1. Speech recognition means

[0409] Capture user voice input using a smartphone or HMD.

[0410] Audio data is sent to the server in real time.

[0411] The server uses speech recognition technology (e.g., Google Speech-to-Text API) to convert the audio data into text data.

[0412] 2. Emotion analysis method

[0413] The server analyzes text and audio data to determine the user's emotional state (e.g., IBM Watson Tone Analyzer).

[0414] 3. Means of collecting biological information

[0415] Wearable devices (e.g., smartwatches) are used to periodically acquire biometric data such as heart rate, body temperature, and activity level, and send it to a server.

[0416] 4. Data Integration Means

[0417] The server integrates voice-text data, sentiment analysis data, and biometric information.

[0418] The integrated data is formatted into a time-series format, and features (e.g., heart rate variability, sleep patterns, exercise intensity) are extracted.

[0419] 5. Generative AI advice methods

[0420] Based on integrated data, an AI model (e.g., GPT-4) is used for analysis to generate appropriate health advice for the user.

[0421] The generated advice includes specific action plans tailored to the user's situation.

[0422] 6. User Interface Means

[0423] The generated advice is notified via text or voice through your smartphone or HMD.

[0424] Users review the advice provided and take the necessary actions.

[0425] Specific example

[0426] The following is a concrete example of a user receiving health counseling within a virtual store.

[0427] 1. User: "Lately, my knees hurt when I run. What should I do?"

[0428] 2. Speech recognition system (Google Speech-to-Text): Converts user speech into text.

[0429] 3. Sentiment Analysis (IBM Watson Tone Analyzer): Analyzes whether the user is experiencing anxiety or stress.

[0430] 4. Biometric data collection (smartwatch): Collects heart rate and exercise data and sends it to a server.

[0431] 5. Data Integration: Integrate collected data (voice-to-text, sentiment analysis, biometric information) and extract features.

[0432] 6. Generated AI Advice (GPT-4): Analyzes the data and generates advice such as, "Knee pain may be due to excessive strain. Take a break from exercise for a few days and try icing if necessary."

[0433] 7. User Interface: Generated advice is notified to the user via text or voice.

[0434] In this way, this system combines speech recognition, emotion analysis, biometric data collection, data integration, and generative AI advice to realize personalized health counseling within a virtual store.

[0435] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0436] Step 1:

[0437] Voice input capture and transmission

[0438] The user uses a smartphone or head-mounted display to provide voice input. The voice data is captured by the device and sent to the server in real time. In this step, the input is the user's voice, and the output is the voice data sent to the server.

[0439] Step 2:

[0440] Text conversion using speech recognition

[0441] The audio data sent to the server is converted into text data by a speech recognition tool (e.g., Google Speech-to-Text API). The input for this step is audio data, and the output is text data.

[0442] Step 3:

[0443] Analysis of emotional states

[0444] The server uses sentiment analysis tools (e.g., IBM Watson Tone Analyzer) to analyze the user's emotional state from text and audio data. The input for this step is text and audio data, and the output is the result of the emotional state analysis.

[0445] Step 4:

[0446] Collection of biometric information

[0447] The user wears a wearable device (e.g., a smartwatch), through which biometric information (heart rate, body temperature, activity level, etc.) is periodically acquired by the terminal. This biometric information is then transmitted from the terminal to a server. In this step, the input is the biometric information acquired from the wearable device, and the output is the biometric information transmitted to the server.

[0448] Step 5:

[0449] Data integration

[0450] The server integrates all data obtained from speech recognition, emotion analysis, and biometric data collection. The integrated data is formatted as time-series data, and features (heart rate variability, sleep patterns, exercise intensity, etc.) are extracted. The input for this step is text data, the results of emotional state analysis, and biometric data, and the output is the formatted integrated data.

[0451] Step 6:

[0452] AI-generated advice

[0453] The server analyzes the integrated data using an AI-generated advice tool (e.g., GPT-4) to generate optimal health advice for the user. The generated advice includes specific action plans. The input for this step is the integrated data, and the output is the generated health advice.

[0454] Step 7:

[0455] Advice notification to users

[0456] The generated advice is communicated to the user via text or voice through their device. The user reviews the provided advice and takes the necessary actions. The input for this step is the generated health advice, and the output is the advice communicated to the user.

[0457] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0458] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0459] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0460] [Second Embodiment]

[0461] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0462] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0463] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0464] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0465] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0466] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0467] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0468] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0469] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0470] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0471] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0472] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0473] The system of the present invention includes voice recognition means, biometric information collection means, data integration means, generation AI advice means, and user interface means. These means work together to effectively support the user's overall medical care.

[0474] First, the user launches the application and uses voice input to initiate a medical consultation. For example, they might say, "Recently, I've been getting headaches at night. What should I do?" The device captures this voice data and sends it to the server in real time. The server then uses speech recognition to convert this voice data into text.

[0475] Next, biometric data such as heart rate, body temperature, and activity level are acquired from the wearable device worn by the user using a biometric data collection system. The device periodically collects this data and sends it to the server.

[0476] The server receives voice text data and biometric data and integrates them using a data integration mechanism. The integrated data is formatted as time-series data, and features such as heart rate variability, sleep patterns, and exercise intensity are extracted.

[0477] Subsequently, the integrated data is analyzed by a generating AI advice system. Based on the analysis results, the AI ​​generates personalized medical advice. For example, if a user's heart rate increases at night, stress or lack of sleep may be the cause of their headaches. Based on this analysis, specific advice is generated such as, "Your headache may be due to stress. We recommend stretching to relax or going to bed early."

[0478] Finally, the generated medical advice is communicated to the user through the user interface. The device displays and plays this generated advice in text or audio format, allowing the user to review the advice and take specific actions.

[0479] Specific example

[0480] For example, if a user makes the following statement as a medical consultation:

[0481] User: "Lately, I've been getting headaches at night. What should I do?"

[0482] 1. Speech recognition means:

[0483] User: Makes a comment to the application.

[0484] Terminal: Captures audio data and sends it to the server.

[0485] Server: Converts audio data into text data.

[0486] 2. Means of collecting biometric information:

[0487] User: Puts on a wearable device.

[0488] Terminal: Acquires data on heart rate, body temperature, and exercise level, and sends it to the server.

[0489] Server: Receives biometric data.

[0490] 3. Data integration means:

[0491] Server: Integrates voice-text data and biometric data.

[0492] Server: Extracts features such as heart rate variability, sleep patterns, and exercise intensity.

[0493] 4. Generative AI advice methods:

[0494] Server: Analyzes data and generates personalized medical advice.

[0495] Server: For example, it generates specific advice such as, "This headache may be caused by stress. We recommend stretching to relax or going to bed early."

[0496] 5. User interface means:

[0497] Server: Sends the generated advice to the terminal.

[0498] Device: Notifies users of advice via text or voice.

[0499] User: Review the notified advice and take the necessary actions.

[0500] Thus, the present invention is a system that enables users to easily manage their health at home by analyzing the user's statements and biometric information and providing personalized medical advice.

[0501] The following describes the processing flow.

[0502] Step 1:

[0503] User: Launches the application and says, "I've been getting headaches at night lately. What should I do?" to begin the medical consultation.

[0504] Step 2:

[0505] Terminal: Captures the user's voice and sends the audio data to the server in real time.

[0506] Step 3:

[0507] Server: Uses speech recognition to convert transmitted speech data into text data.

[0508] Step 4:

[0509] User: Wears a wearable device (e.g., a smartwatch) at all times.

[0510] Step 5:

[0511] Terminal: Periodically acquires heart rate, body temperature, and activity level data from a wearable device and transmits this biometric data to a server.

[0512] Step 6:

[0513] Server: Receives biometric data sent from terminals and integrates it with text data sent from speech recognition devices.

[0514] Step 7:

[0515] Server: Using data integration means, it combines voice / text data and biometric data to extract features necessary for analyzing health status (heart rate variability, sleep patterns, exercise intensity, etc.).

[0516] Step 8:

[0517] Server: The AI-generated advice system generates personalized medical advice based on integrated data. For example, if stress or lack of sleep is likely the cause of the headache, it will generate specific advice such as, "Your headache may be caused by stress. We recommend stretching to relax or going to bed early."

[0518] Step 9:

[0519] Server: Formats the generated medical advice into natural language and sends it to the terminal.

[0520] Step 10:

[0521] Terminal: Notifies the user of medical advice sent from the server via text or voice.

[0522] Step 11:

[0523] User: Review the notified advice and take action based on it. For example, take measures such as doing stretches to relax or going to bed early.

[0524] (Example 1)

[0525] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0526] Conventional medical support systems have struggled to effectively combine and analyze user voice input and biometric information to provide appropriate medical advice. Furthermore, real-time data integration and the generation of personalized advice using generative AI models have been insufficient, preventing users from quickly obtaining specific advice about their health status. This could lead to users being unable to take appropriate action and potentially neglecting their health management.

[0527] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0528] In this invention, the server includes means for capturing voice input, means for transmitting voice data to the server, means for converting voice data into text data, means for collecting biometric data from a wearable device, means for transmitting biometric data to the server, means for integrating voice-text data and biometric data, means for extracting features from the integrated data, means for analyzing the data using a generative AI model to generate personalized advice, and means for notifying the user of the advice. This makes it possible to integrate the user's voice input and biometric information, analyze it in real time, and quickly provide personalized medical advice.

[0529] "Means for capturing voice input" refers to devices or systems that acquire the voice spoken by a user as digital data.

[0530] "Means for sending audio data to a server" refers to devices or systems for transferring captured audio data to a server via a communication network.

[0531] "Means of converting audio data into text data" refers to software or algorithms that analyze audio data and convert it into text information.

[0532] "Means of collecting biometric data from wearable devices" refers to devices that are attached to the user's body to measure biometric data such as heart rate, body temperature, and exercise level.

[0533] "Means for transmitting biometric data to a server" refers to devices or systems for transferring biometric data acquired from wearable devices to a server via a communication network.

[0534] "Means for integrating voice-text data and biometric data" refers to software or algorithms that combine voice data and biometric data chronologically and format them into a single dataset that can be analyzed.

[0535] "Methods for extracting features from integrated data" refers to software or algorithms used to extract meaningful information, such as specific patterns or variations, from integrated data.

[0536] "Means of analyzing and generating personalized advice using generative AI models" refers to software or systems that use machine learning and natural language processing technologies to analyze integrated data and generate personalized advice based on the results.

[0537] "Means of notifying users of advice" refers to devices or systems that transmit generated advice to users as text messages or audio.

[0538] The present invention comprises a voice recognition means, a biometric information collection means, a data integration means, a generation AI advice means, and a user interface means, which work together to effectively support the user's overall medical care.

[0539] System Configuration

[0540] The components of the system of the present invention are as follows.

[0541] 1. Voice recognition means: Captures the voice spoken by the user and acquires it as digital data. This means uses the microphone of a smartphone or tablet.

[0542] 2. Means of collecting biometric information: Use a wearable device worn by the user (e.g., a smartwatch) to measure biometric data such as heart rate, body temperature, and activity level.

[0543] 3. Data Integration Method: A software process that receives voice data and biometric data from the server and integrates them in a time-series format. The data is formatted using the Python Pandas library.

[0544] 4. Generative AI Advice Method: Based on integrated data, the data is analyzed using a generative AI model (e.g., OpenAI's GPT-4) to generate personalized medical advice.

[0545] 5. User Interface Means: Means for notifying the user of the generated advice. This includes screen displays on smartphones and voice notifications using speech synthesis APIs.

[0546] System operation

[0547] This system provides users with prompt and appropriate medical advice through the following specific actions.

[0548] Acquiring and transmitting voice input

[0549] A user uses their smartphone and says, "I've been getting headaches at night lately. What should I do?"

[0550] The device captures this audio and sends it to the server in real time as audio data. This is done using an HTTP POST request.

[0551] Converting audio data to text

[0552] The server receives the audio data and uses the Google Cloud Speech-to-Text API to convert the audio data into text data.

[0553] Collection and transmission of biometric data

[0554] While the user is wearing the wearable device, data such as heart rate, body temperature, and activity level are measured periodically.

[0555] The device sends this biometric data to the server. An HTTP POST request is again used to send the data.

[0556] Data integration and analysis

[0557] The server integrates the voice-text data and biometric data and formats it as time-series data. The Python Pandas library is used for this process.

[0558] Subsequently, features such as heart rate variability, sleep patterns, and exercise intensity are extracted from the integrated data.

[0559] Generating advice using generative AI

[0560] The server analyzes the generated AI model (GPT-4) as a prompt message and generates personalized medical advice.

[0561] For example, it can generate specific advice such as, "The user's heart rate increases at night, suggesting that stress or lack of sleep may be causing the headache. We recommend stretching to relax and going to bed earlier."

[0562] Advice notification

[0563] The server sends generated medical advice to the device, and the device notifies the user of this advice via text or voice.

[0564] Users can review the advice provided and take specific actions.

[0565] Specific example

[0566] For example, if a user says the following:

[0567] User: "Lately, I've been getting headaches at night. What should I do?"

[0568] Example of a prompt

[0569] The following prompt will be generated:

[0570] "Use the user's voice input and biometric data to generate personalized medical advice. Inputs are as follows: Voice input: 'I've been getting headaches at night recently. What should I do?' Heart rate data: 70-90 bpm, Body temperature data: 36.5-37.0 degrees Celsius, Activity data: 5000-7000 steps."

[0571] Thus, the system of the present invention can integrate the user's voice input and biometric information from multiple perspectives, enabling it to provide rapid and highly accurate medical advice.

[0572] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0573] Step 1:

[0574] The user performs voice input.

[0575] User: Launches a medical consultation application on their smartphone and says, "I've been getting headaches at night lately. What should I do?"

[0576] Input: User voice input

[0577] Output: Captured audio data

[0578] Step 2:

[0579] Capture and send audio data.

[0580] Device: The system captures the user's voice using the smartphone's microphone and encodes it as digital audio data. This audio data is then sent to the server using an HTTP POST request.

[0581] Input: Captured audio data

[0582] Output: Audio data sent to the server

[0583] Step 3:

[0584] Convert audio data to text data

[0585] Server: Sends the received audio data to the Google Cloud Speech-to-Text API to convert the audio data into text data.

[0586] Specific operation: Speech data is sent to the Google Cloud Speech-to-Text API and text data is received as a response.

[0587] Input: Audio data

[0588] Output: Text data

[0589] Step 4:

[0590] Collect and transmit biometric data

[0591] User: Wears wearable devices such as Apple Watch or Fitbit while going about their daily life.

[0592] Device: The wearable device measures biometric data such as heart rate, body temperature, and activity level, and transmits this data to a smartphone via Bluetooth or Wi-Fi. The smartphone periodically collects this data and sends it to a server via HTTP POST requests.

[0593] Input: Biometric data (heart rate, body temperature, activity level)

[0594] Output: Biometric data sent to the server

[0595] Step 5:

[0596] Data integration

[0597] Server: Receives voice / text data and biometric data, and integrates them using data integration tools. The Python Pandas library is used to format this data as time-series data.

[0598] Specific operation: Use a Pandas DataFrame to combine speech-text data and biometric data to create a single unified dataset.

[0599] Input: Voice text data, biometric data

[0600] Output: Integrated data

[0601] Step 6:

[0602] Feature extraction

[0603] Server: Extracts features such as heart rate variability, sleep patterns, and exercise intensity from integrated data. Performs data analysis using Python libraries such as NumPy and SciPy.

[0604] Specific operations: Extract features from integrated data and analyze, for example, the standard deviation of heart rate or sleep patterns.

[0605] Input: Integrated Data

[0606] Output: Feature data

[0607] Step 7:

[0608] Generating advice using generative AI

[0609] Server: Uses a generative AI model (e.g., GPT-4) to analyze integrated data and features and generate personalized medical advice.

[0610] Specific operation: Input prompt text into the generation AI, and generate medical advice based on the analysis.

[0611] Input: Feature data, integrated data

[0612] Output: Individual medical advice

[0613] Step 8:

[0614] Advice notification

[0615] Server: Sends generated medical advice to the terminal.

[0616] Terminal: Displays received advice to the user as a text message and, if necessary, plays it back as audio using a speech synthesis API.

[0617] Input: Individual medical advice

[0618] Output: Advice notified to the user

[0619] Specific example:

[0620] For example, if a user says, "I've been getting headaches at night lately. What should I do?", the following specific actions will be performed at each processing step.

[0621] 1. The user's voice is captured.

[0622] 2. The audio is sent to the server.

[0623] 3. The audio data is converted to text.

[0624] 4. Biometric data is collected from the wearable device and sent to the server.

[0625] 5. Voice-text data and biometric data are integrated.

[0626] 6. Features are extracted from the integrated data.

[0627] 7. The generative AI model performs the analysis and generates advice such as, "This may be a headache caused by stress."

[0628] 8. The generated advice is notified to the user.

[0629] (Application Example 1)

[0630] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0631] In modern society, it is important to offer personalized meal plans based on individual health conditions and lifestyles, but effective systems for achieving this are not yet widespread. Food delivery services, in particular, are required to provide meal plans suitable for health management. To meet these needs, a new system is needed that uses ubiquitous devices to understand the user's health condition and provide meal suggestions based on that information.

[0632] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0633] In this invention, the server includes voice recognition means, biometric information collection means, data integration means, generation AI advice means, user interface means, and means having the function of suggesting an appropriate meal menu based on biometric data. This makes it possible to automatically generate and suggest a personalized meal menu by combining and analyzing the user's voice input and biometric information.

[0634] "Speech recognition means" refers to a device or software that has the function of converting speech uttered by a user into text data.

[0635] "Biometric information collection means" refers to devices and software that have the function of acquiring biometric data such as heart rate, body temperature, and exercise level from wearable devices.

[0636] "Data integration means" refers to a device or software that has the function of integrating voice text data and biometric data and formatting it as time-series data.

[0637] A "generating AI advice system" is an artificial intelligence system that has the function of analyzing integrated data and generating individual advice.

[0638] A "user interface means" is a device or software that notifies the user of the generated advice and is capable of displaying and playing it in text or audio.

[0639] "Means having the function of suggesting appropriate meal menus based on biometric data" refers to devices or software that have the function of analyzing the user's biometric data and suggesting the optimal meal menu based on the results.

[0640] The present invention analyzes a user's voice input and biometric information collected from a wearable device to propose a personalized meal menu. This system includes voice recognition means, biometric information collection means, data integration means, generation AI advice means, user interface means, and means having the function of proposing an appropriate meal menu based on biometric data.

[0641] First, the user launches a smartphone application and inputs information about their dietary preferences and health status via voice. For example, they might say, "I've gained weight recently. Please tell me about low-calorie meals." The device captures this voice data and sends it to the server in real time. A voice recognition system converts this voice data into text data.

[0642] Next, the biometric information collection means acquires biometric data such as heart rate, body temperature, and exercise level from the wearable device. The terminal periodically collects this data and sends it to the server. The server receives the text data converted by the speech recognition means and the biometric data acquired by the biometric information collection means, and integrates them using the data integration means. The integrated data is formatted as time-series data, and features such as heart rate variability, sleep patterns, and exercise intensity are extracted.

[0643] Subsequently, the integrated data is analyzed by a generated AI advice system. Based on the analysis results, the AI ​​generates personalized meal menus. For example, if the user's heart rate and exercise data are high, light snacks or nutritionally balanced meals that take calorie consumption into consideration will be suggested. Based on these analysis results, specific advice such as "You're looking for a low-calorie meal. We recommend a salad and grilled fish" is generated.

[0644] Finally, the generated meal menu advice is communicated to the user through the user interface. The device displays and plays this advice in text or audio format, allowing the user to review the advice and take specific actions.

[0645] Hardware and software used

[0646] Speech recognition method: The speech_recognition library is used to convert speech to text.

[0647] Biometric information collection method: Data on heart rate, body temperature, and exercise level are acquired from wearable devices.

[0648] Data integration method: The integrated data is formatted as time-series data, and features are extracted.

[0649] AI-generated advice method: Using OpenAI APIs (such as ChatGPT), the system analyzes integrated data and generates personalized meal menu advice.

[0650] User interface means: Text data is used to provide voice notifications using the pyttsx3 library.

[0651] For example, if a user says, "I've gained weight recently. Could you tell me about a low-calorie diet?":

[0652] Example of a prompt:

[0653] Message from a user: I've been gaining weight recently. Could you please suggest a low-calorie diet?

[0654] Biometric information:

[0655] Heart rate: 75 bpm

[0656] Body temperature: 36.5℃

[0657] Exercise level: 50

[0658] Based on this user's health status, please suggest a low-calorie meal plan.

[0659] In this way, the system of the present invention proposes a personalized meal menu based on the user's biometric data and voice input, thereby supporting the user's health management.

[0660] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0661] Step 1:

[0662] User voice input

[0663] The user launches a smartphone application and inputs information about their dietary preferences and health status via voice. For example, they might say, "I've gained weight recently. Please tell me about a low-calorie diet." The input voice data is captured by the device and sent to the server in real time.

[0664] Input: User's voice data

[0665] Output: Audio data sent to the server

[0666] Step 2:

[0667] Speech recognition

[0668] The server uses speech recognition to convert the received audio data into text data. Specifically, it uses the speech_recognition library to convert speech to text.

[0669] Input: Audio data

[0670] Output: Text data

[0671] Step 3:

[0672] Collection of biometric information

[0673] The biometric data collection system acquires biometric data such as heart rate, body temperature, and activity level from the user's wearable device. The device periodically collects this data and sends it to the server.

[0674] Input: Biometric data acquired from wearable devices

[0675] Output: Biometric data sent to the server

[0676] Step 4:

[0677] Data integration and formatting

[0678] The server's data integration mechanism integrates text data converted by the speech recognition mechanism with biometric data acquired by the biometric information collection mechanism. The integrated data is formatted as time-series data, and features such as heart rate variability, sleep patterns, and exercise intensity are extracted.

[0679] Input: Text data, biometric data

[0680] Output: Formatted time-series data and extracted features

[0681] Step 5:

[0682] AI-generated advice

[0683] The AI-generated advice system analyzes integrated data and generates personalized meal plans. Specifically, it uses the OpenAI API to perform analysis and generation based on prompt messages. For example, it suggests the optimal meal plan based on a user request such as "I'm looking for a low-calorie meal."

[0684] Input: Formatted time-series data, prompt text

[0685] Output: Personalized meal plan advice

[0686] Step 6:

[0687] User notifications

[0688] The generated meal menu advice is communicated to the user through a user interface. The device displays and plays this advice as text or audio, allowing the user to review the advice and take specific actions. Specifically, the pyttsx3 library is used to provide audio notifications of text data.

[0689] Input: Meal menu advice

[0690] Output: Notification to the user

[0691] Through the above processing steps, users can receive personalized meal menu suggestions based on their biometric data and voice input.

[0692] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0693] The system of the present invention includes voice recognition means, biometric information collection means, data integration means, generative AI advice means, user interface means, and emotion engine. These means work together to effectively support the user's overall medical and emotional state.

[0694] First, the user launches the application and uses voice input to begin a medical consultation. For example, they might say, "Recently, I've been getting headaches at night. What should I do?" The device captures this voice data and sends it to the server in real time. The server converts this voice data into text data using speech recognition technology. In addition, the voice data is analyzed by an emotion engine to recognize the user's emotional state.

[0695] Next, biometric data such as heart rate, body temperature, and activity level are acquired from the wearable device worn by the user using a biometric data collection system. The device periodically collects this data and sends it to the server.

[0696] The server receives data from the speech recognition means and emotion engine, as well as biometric data from the biometric information collection means, and integrates it using the data integration means. The integrated data is formatted as time-series data, and features such as speech text data, emotional state data, heart rate variability, sleep patterns, and exercise intensity are extracted.

[0697] Subsequently, the integrated data is analyzed by a generating AI advice system. Based on the analysis results, the AI ​​generates personalized medical advice. For example, if a user's heart rate increases at night and their emotional state indicates stress, stress and lack of sleep are likely causes of their headaches. Based on these analysis results, specific advice is generated, such as, "Your headache may be caused by stress. We recommend stretching to relax or going to bed early."

[0698] Finally, the generated medical advice is communicated to the user through the user interface. The device displays and plays this generated advice in text or audio format, allowing the user to review the advice and take specific actions.

[0699] Specific example

[0700] For example, if a user makes the following statement as a medical consultation:

[0701] User: "Lately, I've been getting headaches at night. What should I do?"

[0702] 1. Speech recognition means and emotion engine:

[0703] User: Makes a comment to the application.

[0704] Terminal: Captures audio data and sends it to the server.

[0705] Server: Converts audio data into text data.

[0706] Server: The emotion engine analyzes the user's emotional state from voice data.

[0707] 2. Means of collecting biometric information:

[0708] User: Puts on a wearable device.

[0709] Terminal: Acquires data on heart rate, body temperature, and exercise level, and sends it to the server.

[0710] Server: Receives biometric data.

[0711] 3. Data integration means:

[0712] Server: Integrates voice-text data, emotional state data, and biometric data.

[0713] Server: Extracts features such as heart rate variability, sleep patterns, and exercise intensity.

[0714] 4. Generative AI advice methods:

[0715] Server: Analyzes data and generates personalized medical advice.

[0716] Server: For example, it generates specific advice such as, "This headache may be caused by stress. We recommend stretching to relax or going to bed early."

[0717] 5. User interface means:

[0718] Server: Sends the generated advice to the terminal.

[0719] Device: Notifies users of advice via text or voice.

[0720] User: Review the notified advice and take appropriate action.

[0721] Thus, the present invention is a system that enables users to easily manage their health at home by providing personalized medical advice based on the user's statements, biometric information, and emotional state.

[0722] The following describes the processing flow.

[0723] Step 1:

[0724] User: Launches the application and says, "I've been getting headaches at night lately. What should I do?" to begin the medical consultation.

[0725] Step 2:

[0726] Terminal: Captures the user's voice and sends the audio data to the server in real time.

[0727] Step 3:

[0728] Server: Uses speech recognition to convert transmitted speech data into text data.

[0729] Step 4:

[0730] Server: Sends the converted text data to the emotion engine to analyze the user's emotional state.

[0731] Step 5:

[0732] User: Wears a wearable device (e.g., a smartwatch) at all times.

[0733] Step 6:

[0734] Terminal: Periodically acquires heart rate, body temperature, and activity level data from a wearable device and transmits this biometric data to a server.

[0735] Step 7:

[0736] Server: Receives biometric data sent from terminals and integrates it with text data sent from speech recognition devices and emotional state data from the emotion engine.

[0737] Step 8:

[0738] Server: Using data integration means, it combines voice / text data, emotional state data, and biometric data to extract features necessary for analyzing health status (heart rate variability, sleep patterns, exercise intensity, etc.).

[0739] Step 9:

[0740] Server: The AI-generated advice system generates personalized medical advice based on integrated data. For example, if heart rate increases at night and emotional state indicates stress, it determines that stress and lack of sleep are likely causes of headaches. Based on this, it generates specific advice such as, "Your headache may be caused by stress. We recommend stretching to relax and going to bed early."

[0741] Step 10:

[0742] Server: Formats the generated medical advice into natural language and sends it to the terminal.

[0743] Step 11:

[0744] Terminal: Notifies the user of medical advice sent from the server via text or voice.

[0745] Step 12:

[0746] User: Review the notified advice and take action based on it. For example, take measures such as doing stretches to relax or going to bed early.

[0747] (Example 2)

[0748] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0749] In modern society, there is a need for systems that allow users to easily and efficiently manage their health at home. However, conventional systems have been unable to comprehensively analyze users' voice input, biometric information, and emotional state to provide appropriate medical advice. As a result, it has been difficult for users to properly manage their own health.

[0750] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes voice recognition means, biometric information collection means, data integration means, generation AI advice means, user interface means, and emotion analysis means. This makes it possible to comprehensively analyze the user's voice input, biometric information, and emotional state and provide individualized medical advice.

[0751] "Voice recognition means" refers to a device or software that has the function of acquiring a user's voice input as digital data and converting it into text data.

[0752] "Biometric information collection means" refers to a device or system that has the function of acquiring biometric data such as heart rate, body temperature, and exercise level from a wearable device worn by a user.

[0753] "Data integration means" refers to a device or software that has the function of combining voice text data, emotional state data, and biometric information data and treating them as a single integrated data set.

[0754] "Generating AI advice means" refers to a device or software that uses artificial intelligence technology to analyze integrated data and generate personalized medical advice based on the user's health condition.

[0755] "User interface means" refers to a device or software that has the function of notifying the user of the generated advice and displaying / playing it in text or audio.

[0756] "Emotional analysis means" refers to a device or software that has the function of analyzing the emotional state from the user's voice data.

[0757] The system of the present invention integrates and analyzes the user's voice input, biometric information, and emotional state to provide health management and medical advice, thereby enabling the user to efficiently manage their health.

[0758] Hardware and software

[0759] The following hardware and software will be used to implement this system.

[0760] Hardware:

[0761] Device (e.g., smartphone, tablet)

[0762] server

[0763] Wearable devices (e.g., smartwatches, fitness trackers)

[0764] software:

[0765] Speech recognition method (e.g. Google Cloud Speech-to-Text)

[0766] Methods for collecting biometric information (e.g., health apps, apps specifically for wearable devices)

[0767] Data integration methods (e.g., Apache Kafka, Apache Spark)

[0768] Generative AI advice methods (e.g., OpenAI GPT-4)

[0769] Emotion analysis method (e.g. Microsoft Azure Emotion API)

[0770] User interface means (e.g., React Native-based mobile apps)

[0771] System Processing Description

[0772] When a user initiates a medical consultation, they launch the application and use voice input. For example, they might say, "Recently, I've been getting headaches at night. What should I do?" The device captures this voice data and sends it to the server in real time.

[0773] The server uses speech recognition to convert voice data into text data. Simultaneously, it uses emotion analysis to analyze the user's emotional state from the voice data. For example, it might detect a high stress level.

[0774] Next, biometric data such as heart rate, body temperature, and activity level are acquired from the wearable device worn by the user using a biometric data collection system. The device periodically collects this data and sends it to the server.

[0775] The server receives voice text data, emotional state data, and biometric data, and integrates them using data integration tools. The integrated data is formatted as time-series data, and features such as heart rate variability, sleep patterns, and exercise intensity are extracted.

[0776] Subsequently, the integrated data is analyzed by a generation AI advice system. Based on the analysis results, the AI ​​creates personalized medical advice. For example, if a user's heart rate increases at night and their emotional state indicates stress, the analysis might conclude that stress and lack of sleep are likely causes of their headaches. Based on these results, specific advice is generated, such as, "Your headache may be due to stress. We recommend stretching to relax or going to bed early."

[0777] Finally, the generated medical advice is communicated to the user through a user interface. The device displays and plays the generated advice in text or audio format, allowing the user to review the advice and take specific actions.

[0778] Specific example

[0779] For example, if a user says the following:

[0780] User: "Lately, I've been getting headaches at night. What should I do?"

[0781] 1. Acquisition of voice input:

[0782] User: Makes a comment to the application.

[0783] Terminal: Captures audio data and sends it to the server.

[0784] 2. Speech recognition and sentiment analysis:

[0785] Server: Converts audio data into text data.

[0786] Server: Analyzes the user's emotional state from voice data using emotion analysis tools.

[0787] 3. Collection of biometric data:

[0788] User: Puts on a wearable device.

[0789] Terminal: Acquires data such as heart rate, body temperature, and exercise level, and sends it to the server.

[0790] 4. Data Integration:

[0791] Server: Integrates voice-text data, emotional state data, and biometric data.

[0792] 5. AI advice generation:

[0793] Server: Analyzes data and generates personalized medical advice.

[0794] 6. Notification of advice:

[0795] Server: Sends the generated advice to the terminal.

[0796] Device: Notifies users of advice via text or voice.

[0797] Thus, the present invention is a system that enables users to easily manage their health at home by comprehensively analyzing the user's voice input, biometric information, and emotional state, and providing personalized medical advice.

[0798] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0799] Step 1: Capture voice input

[0800] The user launches the application and uses voice input to ask a medical question. For example, they might say, "I've been getting headaches at night lately. What should I do?"

[0801] The device uses its built-in microphone to capture the user's speech in real time and generate audio data.

[0802] Input: User voice input

[0803] Output: Captured audio data

[0804] Step 2: Convert audio to text

[0805] The device sends the captured audio data to the server.

[0806] The server uses speech recognition technology (e.g., Google Cloud Speech-to-Text) to convert speech data into text data.

[0807] Input: Audio data

[0808] Output: Text data

[0809] Step 3: Analysis of emotional state

[0810] The server uses emotion analysis tools (e.g., Microsoft Azure Emotion API) to analyze the user's emotional state from the audio data. For example, it can identify a state of stress.

[0811] This process analyzes the tone, pitch, and speed of the voice to classify the emotional state.

[0812] Input: Audio data

[0813] Output: Emotional state data

[0814] Step 4: Biometric Data Collection

[0815] Users wear wearable devices that can measure heart rate, body temperature, and activity level.

[0816] The device collects biometric data in real time from wearable devices and transmits that data to a server. The data includes heart rate, body temperature, and activity level.

[0817] Input: Biometric data from wearable devices

[0818] Output: Biometric data sent to the server

[0819] Step 5: Data Integration and Feature Extraction

[0820] The server integrates voice-text data, emotional state data, and biometric data. Here, it formats the data as a time series.

[0821] The server uses data integration tools (e.g., Apache Spark) to extract features such as heart rate variability, sleep patterns, and exercise intensity from the integrated data.

[0822] Input: Voice text data, emotional state data, biometric data

[0823] Output: Integrated data and extracted features

[0824] Step 6: Generating AI advice

[0825] The server uses a generation AI advice tool (e.g., OpenAI GPT-4) to analyze the integrated data and generate personalized medical advice based on the user's health status.

[0826] Based on the analysis results, specific advice is generated, such as, "Your headache may be caused by stress. We recommend stretching to relax or going to bed early."

[0827] Input: Integrated data and features

[0828] Output: Generated medical advice

[0829] Step 7: Notification of advice

[0830] The server sends the generated medical advice to the terminal via the user interface.

[0831] The device will notify the user of advice via text or voice.

[0832] Input: Generated medical advice

[0833] Output: Advice notified to the user

[0834] (Application Example 2)

[0835] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0836] Conventional systems rely solely on voice recognition and biometric information when users receive personalized health counseling in a virtual store, making it difficult to provide detailed advice that takes into account the user's emotional state. This invention aims to provide comprehensive health advice that also considers the user's mental state by utilizing emotion analysis methods.

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

[0838] In this invention, the server includes voice recognition means, biometric information collection means, data integration means, generative AI advice means, and emotion analysis means. This makes it possible to convert the user's voice input into text data and integrate and analyze biometric information and emotional state. By providing the user with optimal health advice through the generative AI advice means, personalized counseling within the virtual store becomes possible.

[0839] "Voice recognition means" refers to a device or software that has the function of converting a user's voice input into text data.

[0840] "Means of collecting biometric information" refers to a device or software that has the function of collecting biometric data such as a user's heart rate, body temperature, and exercise level using a wearable device or the like.

[0841] "Data integration means" refers to a device or software for integrating data obtained from speech recognition means, emotion analysis means, and biometric information collection means, and for extracting feature quantities.

[0842] "Generating AI advice means" refers to a device or software that has the function of analyzing integrated data and generating appropriate advice for the user.

[0843] "User interface means" refers to a device or software that notifies the user of generated advice and provides an interface for the user to take action accordingly.

[0844] "Emotional analysis means" refers to a device or software that has the function of analyzing a user's emotional state from their voice data or biometric information.

[0845] "Counseling methods within a virtual store" refers to a device or software that has the function of providing health counseling to users within a virtual store.

[0846] The system that realizes this application example includes voice recognition means, biometric information collection means, data integration means, generative AI advice means, user interface means, emotion analysis means, and counseling means within a virtual store. Each means of this system functions as follows:

[0847] Explanation of the program's processing

[0848] This system uses a smartphone or head-mounted display (HMD) as the user interface to transmit voice input and biometric information to a server. The data arriving at the server is processed as follows:

[0849] 1. Speech recognition means

[0850] Capture user voice input using a smartphone or HMD.

[0851] Audio data is sent to the server in real time.

[0852] The server uses speech recognition technology (e.g., Google Speech-to-Text API) to convert the audio data into text data.

[0853] 2. Emotion analysis method

[0854] The server analyzes text and audio data to determine the user's emotional state (e.g., IBM Watson Tone Analyzer).

[0855] 3. Means of collecting biological information

[0856] Wearable devices (e.g., smartwatches) are used to periodically acquire biometric data such as heart rate, body temperature, and activity level, and send it to a server.

[0857] 4. Data Integration Means

[0858] The server integrates voice-text data, sentiment analysis data, and biometric information.

[0859] The integrated data is formatted into a time-series format, and features (e.g., heart rate variability, sleep patterns, exercise intensity) are extracted.

[0860] 5. Generative AI advice methods

[0861] Based on integrated data, an AI model (e.g., GPT-4) is used for analysis to generate appropriate health advice for the user.

[0862] The generated advice includes specific action plans tailored to the user's situation.

[0863] 6. User Interface Means

[0864] The generated advice is notified via text or voice through your smartphone or HMD.

[0865] Users review the advice provided and take the necessary actions.

[0866] Specific example

[0867] The following is a concrete example of a user receiving health counseling within a virtual store.

[0868] 1. User: "Lately, my knees hurt when I run. What should I do?"

[0869] 2. Speech recognition system (Google Speech-to-Text): Converts user speech into text.

[0870] 3. Sentiment Analysis (IBM Watson Tone Analyzer): Analyzes whether the user is experiencing anxiety or stress.

[0871] 4. Biometric data collection (smartwatch): Collects heart rate and exercise data and sends it to a server.

[0872] 5. Data Integration: Integrate collected data (voice-to-text, sentiment analysis, biometric information) and extract features.

[0873] 6. Generated AI Advice (GPT-4): Analyzes the data and generates advice such as, "Knee pain may be due to excessive strain. Take a break from exercise for a few days and try icing if necessary."

[0874] 7. User Interface: Generated advice is notified to the user via text or voice.

[0875] In this way, this system combines speech recognition, emotion analysis, biometric data collection, data integration, and generative AI advice to realize personalized health counseling within a virtual store.

[0876] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0877] Step 1:

[0878] Voice input capture and transmission

[0879] The user uses a smartphone or head-mounted display to provide voice input. The voice data is captured by the device and sent to the server in real time. In this step, the input is the user's voice, and the output is the voice data sent to the server.

[0880] Step 2:

[0881] Text conversion using speech recognition

[0882] The audio data sent to the server is converted into text data by a speech recognition tool (e.g., Google Speech-to-Text API). The input for this step is audio data, and the output is text data.

[0883] Step 3:

[0884] Analysis of emotional states

[0885] The server uses sentiment analysis tools (e.g., IBM Watson Tone Analyzer) to analyze the user's emotional state from text and audio data. The input for this step is text and audio data, and the output is the result of the emotional state analysis.

[0886] Step 4:

[0887] Collection of biometric information

[0888] The user wears a wearable device (e.g., a smartwatch), through which biometric information (heart rate, body temperature, activity level, etc.) is periodically acquired by the terminal. This biometric information is then transmitted from the terminal to a server. In this step, the input is the biometric information acquired from the wearable device, and the output is the biometric information transmitted to the server.

[0889] Step 5:

[0890] Data integration

[0891] The server integrates all data obtained from speech recognition, emotion analysis, and biometric data collection. The integrated data is formatted as time-series data, and features (heart rate variability, sleep patterns, exercise intensity, etc.) are extracted. The input for this step is text data, the results of emotional state analysis, and biometric data, and the output is the formatted integrated data.

[0892] Step 6:

[0893] AI-generated advice

[0894] The server analyzes the integrated data using an AI-generated advice tool (e.g., GPT-4) to generate optimal health advice for the user. The generated advice includes specific action plans. The input for this step is the integrated data, and the output is the generated health advice.

[0895] Step 7:

[0896] Advice notification to users

[0897] The generated advice is communicated to the user via text or voice through their device. The user reviews the provided advice and takes the necessary actions. The input for this step is the generated health advice, and the output is the advice communicated to the user.

[0898] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0899] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0900] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0901] [Third Embodiment]

[0902] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0903] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0904] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0905] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0906] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0907] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0908] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0909] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0910] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0911] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0912] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0913] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0914] The system of the present invention includes voice recognition means, biometric information collection means, data integration means, generation AI advice means, and user interface means. These means work together to effectively support the user's overall medical care.

[0915] First, the user launches the application and uses voice input to initiate a medical consultation. For example, they might say, "Recently, I've been getting headaches at night. What should I do?" The device captures this voice data and sends it to the server in real time. The server then uses speech recognition to convert this voice data into text.

[0916] Next, biometric data such as heart rate, body temperature, and activity level are acquired from the wearable device worn by the user using a biometric data collection system. The device periodically collects this data and sends it to the server.

[0917] The server receives voice text data and biometric data and integrates them using a data integration mechanism. The integrated data is formatted as time-series data, and features such as heart rate variability, sleep patterns, and exercise intensity are extracted.

[0918] Subsequently, the integrated data is analyzed by a generating AI advice system. Based on the analysis results, the AI ​​generates personalized medical advice. For example, if a user's heart rate increases at night, stress or lack of sleep may be the cause of their headaches. Based on this analysis, specific advice is generated such as, "Your headache may be due to stress. We recommend stretching to relax or going to bed early."

[0919] Finally, the generated medical advice is communicated to the user through the user interface. The device displays and plays this generated advice in text or audio format, allowing the user to review the advice and take specific actions.

[0920] Specific example

[0921] For example, if a user makes the following statement as a medical consultation:

[0922] User: "Lately, I've been getting headaches at night. What should I do?"

[0923] 1. Speech recognition means:

[0924] User: Makes a comment to the application.

[0925] Terminal: Captures audio data and sends it to the server.

[0926] Server: Converts audio data into text data.

[0927] 2. Means of collecting biometric information:

[0928] User: Puts on a wearable device.

[0929] Terminal: Acquires data on heart rate, body temperature, and exercise level, and sends it to the server.

[0930] Server: Receives biometric data.

[0931] 3. Data integration means:

[0932] Server: Integrates voice-text data and biometric data.

[0933] Server: Extracts features such as heart rate variability, sleep patterns, and exercise intensity.

[0934] 4. Generative AI advice methods:

[0935] Server: Analyzes data and generates personalized medical advice.

[0936] Server: For example, it generates specific advice such as, "This headache may be caused by stress. We recommend stretching to relax or going to bed early."

[0937] 5. User interface means:

[0938] Server: Sends the generated advice to the terminal.

[0939] Device: Notifies users of advice via text or voice.

[0940] User: Review the notified advice and take the necessary actions.

[0941] Thus, the present invention is a system that enables users to easily manage their health at home by analyzing the user's statements and biometric information and providing personalized medical advice.

[0942] The following describes the processing flow.

[0943] Step 1:

[0944] User: Launches the application and says, "I've been getting headaches at night lately. What should I do?" to begin the medical consultation.

[0945] Step 2:

[0946] Terminal: Captures the user's voice and sends the audio data to the server in real time.

[0947] Step 3:

[0948] Server: Uses speech recognition to convert transmitted speech data into text data.

[0949] Step 4:

[0950] User: Wears a wearable device (e.g., a smartwatch) at all times.

[0951] Step 5:

[0952] Terminal: Periodically acquires heart rate, body temperature, and activity level data from a wearable device and transmits this biometric data to a server.

[0953] Step 6:

[0954] Server: Receives biometric data sent from terminals and integrates it with text data sent from speech recognition devices.

[0955] Step 7:

[0956] Server: Using data integration means, it combines voice / text data and biometric data to extract features necessary for analyzing health status (heart rate variability, sleep patterns, exercise intensity, etc.).

[0957] Step 8:

[0958] Server: The AI-generated advice system generates personalized medical advice based on integrated data. For example, if stress or lack of sleep is likely the cause of the headache, it will generate specific advice such as, "Your headache may be caused by stress. We recommend stretching to relax or going to bed early."

[0959] Step 9:

[0960] Server: Formats the generated medical advice into natural language and sends it to the terminal.

[0961] Step 10:

[0962] Terminal: Notifies the user of medical advice sent from the server via text or voice.

[0963] Step 11:

[0964] User: Review the notified advice and take action based on it. For example, take measures such as doing stretches to relax or going to bed early.

[0965] (Example 1)

[0966] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0967] Conventional medical support systems have struggled to effectively combine and analyze user voice input and biometric information to provide appropriate medical advice. Furthermore, real-time data integration and the generation of personalized advice using generative AI models have been insufficient, preventing users from quickly obtaining specific advice about their health status. This could lead to users being unable to take appropriate action and potentially neglecting their health management.

[0968] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0969] In this invention, the server includes means for capturing voice input, means for transmitting voice data to the server, means for converting voice data into text data, means for collecting biometric data from a wearable device, means for transmitting biometric data to the server, means for integrating voice-text data and biometric data, means for extracting features from the integrated data, means for analyzing the data using a generative AI model to generate personalized advice, and means for notifying the user of the advice. This makes it possible to integrate the user's voice input and biometric information, analyze it in real time, and quickly provide personalized medical advice.

[0970] "Means for capturing voice input" refers to devices or systems that acquire the voice spoken by a user as digital data.

[0971] "Means for sending audio data to a server" refers to devices or systems for transferring captured audio data to a server via a communication network.

[0972] "Means of converting audio data into text data" refers to software or algorithms that analyze audio data and convert it into text information.

[0973] "Means of collecting biometric data from wearable devices" refers to devices that are attached to the user's body to measure biometric data such as heart rate, body temperature, and exercise level.

[0974] "Means for transmitting biometric data to a server" refers to devices or systems for transferring biometric data acquired from wearable devices to a server via a communication network.

[0975] "Means for integrating voice-text data and biometric data" refers to software or algorithms that combine voice data and biometric data chronologically and format them into a single dataset that can be analyzed.

[0976] "Methods for extracting features from integrated data" refers to software or algorithms used to extract meaningful information, such as specific patterns or variations, from integrated data.

[0977] "Means of analyzing and generating personalized advice using generative AI models" refers to software or systems that use machine learning and natural language processing technologies to analyze integrated data and generate personalized advice based on the results.

[0978] "Means of notifying users of advice" refers to devices or systems that transmit generated advice to users as text messages or audio.

[0979] The present invention comprises a voice recognition means, a biometric information collection means, a data integration means, a generation AI advice means, and a user interface means, which work together to effectively support the user's overall medical care.

[0980] System Configuration

[0981] The components of the system of the present invention are as follows.

[0982] 1. Voice recognition means: Captures the voice spoken by the user and acquires it as digital data. This means uses the microphone of a smartphone or tablet.

[0983] 2. Means of collecting biometric information: Use a wearable device worn by the user (e.g., a smartwatch) to measure biometric data such as heart rate, body temperature, and activity level.

[0984] 3. Data Integration Method: A software process that receives voice data and biometric data from the server and integrates them in a time-series format. The data is formatted using the Python Pandas library.

[0985] 4. Generative AI Advice Method: Based on integrated data, the data is analyzed using a generative AI model (e.g., OpenAI's GPT-4) to generate personalized medical advice.

[0986] 5. User Interface Means: Means for notifying the user of the generated advice. This includes screen displays on smartphones and voice notifications using speech synthesis APIs.

[0987] System operation

[0988] This system provides users with prompt and appropriate medical advice through the following specific actions.

[0989] Acquiring and transmitting voice input

[0990] A user uses their smartphone and says, "I've been getting headaches at night lately. What should I do?"

[0991] The device captures this audio and sends it to the server in real time as audio data. This is done using an HTTP POST request.

[0992] Converting audio data to text

[0993] The server receives the audio data and uses the Google Cloud Speech-to-Text API to convert the audio data into text data.

[0994] Collection and transmission of biometric data

[0995] While the user is wearing the wearable device, data such as heart rate, body temperature, and activity level are measured periodically.

[0996] The device sends this biometric data to the server. An HTTP POST request is again used to send the data.

[0997] Data integration and analysis

[0998] The server integrates the voice-text data and biometric data and formats it as time-series data. The Python Pandas library is used for this process.

[0999] Subsequently, features such as heart rate variability, sleep patterns, and exercise intensity are extracted from the integrated data.

[1000] Generating advice using generative AI

[1001] The server analyzes the generated AI model (GPT-4) as a prompt message and generates personalized medical advice.

[1002] For example, it can generate specific advice such as, "The user's heart rate increases at night, suggesting that stress or lack of sleep may be causing the headache. We recommend stretching to relax and going to bed earlier."

[1003] Advice notification

[1004] The server sends generated medical advice to the device, and the device notifies the user of this advice via text or voice.

[1005] Users can review the advice provided and take specific actions.

[1006] Specific example

[1007] For example, if a user says the following:

[1008] User: "Lately, I've been getting headaches at night. What should I do?"

[1009] Example of a prompt

[1010] The following prompt will be generated:

[1011] "Use the user's voice input and biometric data to generate personalized medical advice. Inputs are as follows: Voice input: 'I've been getting headaches at night recently. What should I do?' Heart rate data: 70-90 bpm, Body temperature data: 36.5-37.0 degrees Celsius, Activity data: 5000-7000 steps."

[1012] Thus, the system of the present invention can integrate the user's voice input and biometric information from multiple perspectives, enabling it to provide rapid and highly accurate medical advice.

[1013] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1014] Step 1:

[1015] The user performs voice input.

[1016] User: Launches a medical consultation application on their smartphone and says, "I've been getting headaches at night lately. What should I do?"

[1017] Input: User voice input

[1018] Output: Captured audio data

[1019] Step 2:

[1020] Capture and send audio data.

[1021] Device: The system captures the user's voice using the smartphone's microphone and encodes it as digital audio data. This audio data is then sent to the server using an HTTP POST request.

[1022] Input: Captured audio data

[1023] Output: Audio data sent to the server

[1024] Step 3:

[1025] Convert audio data to text data

[1026] Server: Sends the received audio data to the Google Cloud Speech-to-Text API to convert the audio data into text data.

[1027] Specific operation: Speech data is sent to the Google Cloud Speech-to-Text API and text data is received as a response.

[1028] Input: Audio data

[1029] Output: Text data

[1030] Step 4:

[1031] Collect and transmit biometric data

[1032] User: Wears wearable devices such as Apple Watch or Fitbit while going about their daily life.

[1033] Device: The wearable device measures biometric data such as heart rate, body temperature, and activity level, and transmits this data to a smartphone via Bluetooth or Wi-Fi. The smartphone periodically collects this data and sends it to a server via HTTP POST requests.

[1034] Input: Biometric data (heart rate, body temperature, activity level)

[1035] Output: Biometric data sent to the server

[1036] Step 5:

[1037] Data integration

[1038] Server: Receives voice / text data and biometric data, and integrates them using data integration tools. The Python Pandas library is used to format this data as time-series data.

[1039] Specific operation: Use a Pandas DataFrame to combine speech-text data and biometric data to create a single unified dataset.

[1040] Input: Voice text data, biometric data

[1041] Output: Integrated data

[1042] Step 6:

[1043] Feature extraction

[1044] Server: Extracts features such as heart rate variability, sleep patterns, and exercise intensity from integrated data. Performs data analysis using Python libraries such as NumPy and SciPy.

[1045] Specific operations: Extract features from integrated data and analyze, for example, the standard deviation of heart rate or sleep patterns.

[1046] Input: Integrated Data

[1047] Output: Feature data

[1048] Step 7:

[1049] Generating advice using generative AI

[1050] Server: Uses a generative AI model (e.g., GPT-4) to analyze integrated data and features and generate personalized medical advice.

[1051] Specific operation: Input prompt text into the generation AI, and generate medical advice based on the analysis.

[1052] Input: Feature data, integrated data

[1053] Output: Individual medical advice

[1054] Step 8:

[1055] Advice notification

[1056] Server: Sends generated medical advice to the terminal.

[1057] Terminal: Displays received advice to the user as a text message and, if necessary, plays it back as audio using a speech synthesis API.

[1058] Input: Individual medical advice

[1059] Output: Advice notified to the user

[1060] Specific example:

[1061] For example, if a user says, "I've been getting headaches at night lately. What should I do?", the following specific actions will be performed at each processing step.

[1062] 1. The user's voice is captured.

[1063] 2. The audio is sent to the server.

[1064] 3. The audio data is converted to text.

[1065] 4. Biometric data is collected from the wearable device and sent to the server.

[1066] 5. Voice-text data and biometric data are integrated.

[1067] 6. Features are extracted from the integrated data.

[1068] 7. The generative AI model performs the analysis and generates advice such as, "This may be a headache caused by stress."

[1069] 8. The generated advice is notified to the user.

[1070] (Application Example 1)

[1071] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1072] In modern society, it is important to offer personalized meal plans based on individual health conditions and lifestyles, but effective systems for achieving this are not yet widespread. Food delivery services, in particular, are required to provide meal plans suitable for health management. To meet these needs, a new system is needed that uses ubiquitous devices to understand the user's health condition and provide meal suggestions based on that information.

[1073] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1074] In this invention, the server includes voice recognition means, biometric information collection means, data integration means, generation AI advice means, user interface means, and means having the function of suggesting an appropriate meal menu based on biometric data. This makes it possible to automatically generate and suggest a personalized meal menu by combining and analyzing the user's voice input and biometric information.

[1075] "Speech recognition means" refers to a device or software that has the function of converting speech uttered by a user into text data.

[1076] "Biometric information collection means" refers to devices and software that have the function of acquiring biometric data such as heart rate, body temperature, and exercise level from wearable devices.

[1077] "Data integration means" refers to a device or software that has the function of integrating voice text data and biometric data and formatting it as time-series data.

[1078] A "generating AI advice system" is an artificial intelligence system that has the function of analyzing integrated data and generating individual advice.

[1079] A "user interface means" is a device or software that notifies the user of the generated advice and is capable of displaying and playing it in text or audio.

[1080] "Means having the function of suggesting appropriate meal menus based on biometric data" refers to devices or software that have the function of analyzing the user's biometric data and suggesting the optimal meal menu based on the results.

[1081] The present invention analyzes a user's voice input and biometric information collected from a wearable device to propose a personalized meal menu. This system includes voice recognition means, biometric information collection means, data integration means, generation AI advice means, user interface means, and means having the function of proposing an appropriate meal menu based on biometric data.

[1082] First, the user launches a smartphone application and inputs information about their dietary preferences and health status via voice. For example, they might say, "I've gained weight recently. Please tell me about low-calorie meals." The device captures this voice data and sends it to the server in real time. A voice recognition system converts this voice data into text data.

[1083] Next, the biometric information collection means acquires biometric data such as heart rate, body temperature, and exercise level from the wearable device. The terminal periodically collects this data and sends it to the server. The server receives the text data converted by the speech recognition means and the biometric data acquired by the biometric information collection means, and integrates them using the data integration means. The integrated data is formatted as time-series data, and features such as heart rate variability, sleep patterns, and exercise intensity are extracted.

[1084] Subsequently, the integrated data is analyzed by a generated AI advice system. Based on the analysis results, the AI ​​generates personalized meal menus. For example, if the user's heart rate and exercise data are high, light snacks or nutritionally balanced meals that take calorie consumption into consideration will be suggested. Based on these analysis results, specific advice such as "You're looking for a low-calorie meal. We recommend a salad and grilled fish" is generated.

[1085] Finally, the generated meal menu advice is communicated to the user through the user interface. The device displays and plays this advice in text or audio format, allowing the user to review the advice and take specific actions.

[1086] Hardware and software used

[1087] Speech recognition method: The speech_recognition library is used to convert speech to text.

[1088] Biometric information collection method: Data on heart rate, body temperature, and exercise level are acquired from wearable devices.

[1089] Data integration method: The integrated data is formatted as time-series data, and features are extracted.

[1090] AI-generated advice method: Using OpenAI APIs (such as ChatGPT), the system analyzes integrated data and generates personalized meal menu advice.

[1091] User interface means: Text data is used to provide voice notifications using the pyttsx3 library.

[1092] For example, if a user says, "I've gained weight recently. Could you tell me about a low-calorie diet?":

[1093] Example of a prompt:

[1094] Message from a user: I've been gaining weight recently. Could you please suggest a low-calorie diet?

[1095] Biometric information:

[1096] Heart rate: 75 bpm

[1097] Body temperature: 36.5℃

[1098] Exercise level: 50

[1099] Based on this user's health status, please suggest a low-calorie meal plan.

[1100] In this way, the system of the present invention proposes a personalized meal menu based on the user's biometric data and voice input, thereby supporting the user's health management.

[1101] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1102] Step 1:

[1103] User voice input

[1104] The user launches a smartphone application and inputs information about their dietary preferences and health status via voice. For example, they might say, "I've gained weight recently. Please tell me about a low-calorie diet." The input voice data is captured by the device and sent to the server in real time.

[1105] Input: User's voice data

[1106] Output: Audio data sent to the server

[1107] Step 2:

[1108] Speech recognition

[1109] The server uses speech recognition to convert the received audio data into text data. Specifically, it uses the speech_recognition library to convert speech to text.

[1110] Input: Audio data

[1111] Output: Text data

[1112] Step 3:

[1113] Collection of biometric information

[1114] The biometric data collection system acquires biometric data such as heart rate, body temperature, and activity level from the user's wearable device. The device periodically collects this data and sends it to the server.

[1115] Input: Biometric data acquired from wearable devices

[1116] Output: Biometric data sent to the server

[1117] Step 4:

[1118] Data integration and formatting

[1119] The server's data integration mechanism integrates text data converted by the speech recognition mechanism with biometric data acquired by the biometric information collection mechanism. The integrated data is formatted as time-series data, and features such as heart rate variability, sleep patterns, and exercise intensity are extracted.

[1120] Input: Text data, biometric data

[1121] Output: Formatted time-series data and extracted features

[1122] Step 5:

[1123] AI-generated advice

[1124] The AI-generated advice system analyzes integrated data and generates personalized meal plans. Specifically, it uses the OpenAI API to perform analysis and generation based on prompt messages. For example, it suggests the optimal meal plan based on a user request such as "I'm looking for a low-calorie meal."

[1125] Input: Formatted time-series data, prompt text

[1126] Output: Personalized meal plan advice

[1127] Step 6:

[1128] User notifications

[1129] The generated meal menu advice is communicated to the user through a user interface. The device displays and plays this advice as text or audio, allowing the user to review the advice and take specific actions. Specifically, the pyttsx3 library is used to provide audio notifications of text data.

[1130] Input: Meal menu advice

[1131] Output: Notification to the user

[1132] Through the above processing steps, users can receive personalized meal menu suggestions based on their biometric data and voice input.

[1133] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1134] The system of the present invention includes voice recognition means, biometric information collection means, data integration means, generative AI advice means, user interface means, and emotion engine. These means work together to effectively support the user's overall medical and emotional state.

[1135] First, the user launches the application and uses voice input to begin a medical consultation. For example, they might say, "Recently, I've been getting headaches at night. What should I do?" The device captures this voice data and sends it to the server in real time. The server converts this voice data into text data using speech recognition technology. In addition, the voice data is analyzed by an emotion engine to recognize the user's emotional state.

[1136] Next, biometric data such as heart rate, body temperature, and activity level are acquired from the wearable device worn by the user using a biometric data collection system. The device periodically collects this data and sends it to the server.

[1137] The server receives data from the speech recognition means and emotion engine, as well as biometric data from the biometric information collection means, and integrates it using the data integration means. The integrated data is formatted as time-series data, and features such as speech text data, emotional state data, heart rate variability, sleep patterns, and exercise intensity are extracted.

[1138] Subsequently, the integrated data is analyzed by a generating AI advice system. Based on the analysis results, the AI ​​generates personalized medical advice. For example, if a user's heart rate increases at night and their emotional state indicates stress, stress and lack of sleep are likely causes of their headaches. Based on these analysis results, specific advice is generated, such as, "Your headache may be caused by stress. We recommend stretching to relax or going to bed early."

[1139] Finally, the generated medical advice is communicated to the user through the user interface. The device displays and plays this generated advice in text or audio format, allowing the user to review the advice and take specific actions.

[1140] Specific example

[1141] For example, if a user makes the following statement as a medical consultation:

[1142] User: "Lately, I've been getting headaches at night. What should I do?"

[1143] 1. Speech recognition means and emotion engine:

[1144] User: Makes a comment to the application.

[1145] Terminal: Captures audio data and sends it to the server.

[1146] Server: Converts audio data into text data.

[1147] Server: The emotion engine analyzes the user's emotional state from voice data.

[1148] 2. Means of collecting biometric information:

[1149] User: Puts on a wearable device.

[1150] Terminal: Acquires data on heart rate, body temperature, and exercise level, and sends it to the server.

[1151] Server: Receives biometric data.

[1152] 3. Data integration means:

[1153] Server: Integrates voice-text data, emotional state data, and biometric data.

[1154] Server: Extracts features such as heart rate variability, sleep patterns, and exercise intensity.

[1155] 4. Generative AI advice methods:

[1156] Server: Analyzes data and generates personalized medical advice.

[1157] Server: For example, it generates specific advice such as, "This headache may be caused by stress. We recommend stretching to relax or going to bed early."

[1158] 5. User interface means:

[1159] Server: Sends the generated advice to the terminal.

[1160] Device: Notifies users of advice via text or voice.

[1161] User: Review the notified advice and take appropriate action.

[1162] Thus, the present invention is a system that enables users to easily manage their health at home by providing personalized medical advice based on the user's statements, biometric information, and emotional state.

[1163] The following describes the processing flow.

[1164] Step 1:

[1165] User: Launches the application and says, "I've been getting headaches at night lately. What should I do?" to begin the medical consultation.

[1166] Step 2:

[1167] Terminal: Captures the user's voice and sends the audio data to the server in real time.

[1168] Step 3:

[1169] Server: Uses speech recognition to convert transmitted speech data into text data.

[1170] Step 4:

[1171] Server: Sends the converted text data to the emotion engine to analyze the user's emotional state.

[1172] Step 5:

[1173] User: Wears a wearable device (e.g., a smartwatch) at all times.

[1174] Step 6:

[1175] Terminal: Periodically acquires heart rate, body temperature, and activity level data from a wearable device and transmits this biometric data to a server.

[1176] Step 7:

[1177] Server: Receives biometric data sent from terminals and integrates it with text data sent from speech recognition devices and emotional state data from the emotion engine.

[1178] Step 8:

[1179] Server: Using data integration means, it combines voice / text data, emotional state data, and biometric data to extract features necessary for analyzing health status (heart rate variability, sleep patterns, exercise intensity, etc.).

[1180] Step 9:

[1181] Server: The AI-generated advice system generates personalized medical advice based on integrated data. For example, if heart rate increases at night and emotional state indicates stress, it determines that stress and lack of sleep are likely causes of headaches. Based on this, it generates specific advice such as, "Your headache may be caused by stress. We recommend stretching to relax and going to bed early."

[1182] Step 10:

[1183] Server: Formats the generated medical advice into natural language and sends it to the terminal.

[1184] Step 11:

[1185] Terminal: Notifies the user of medical advice sent from the server via text or voice.

[1186] Step 12:

[1187] User: Review the notified advice and take action based on it. For example, take measures such as doing stretches to relax or going to bed early.

[1188] (Example 2)

[1189] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1190] In modern society, there is a need for systems that allow users to easily and efficiently manage their health at home. However, conventional systems have been unable to comprehensively analyze users' voice input, biometric information, and emotional state to provide appropriate medical advice. As a result, it has been difficult for users to properly manage their own health.

[1191] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes voice recognition means, biometric information collection means, data integration means, generation AI advice means, user interface means, and emotion analysis means. This makes it possible to comprehensively analyze the user's voice input, biometric information, and emotional state and provide individualized medical advice.

[1192] "Voice recognition means" refers to a device or software that has the function of acquiring a user's voice input as digital data and converting it into text data.

[1193] "Biometric information collection means" refers to a device or system that has the function of acquiring biometric data such as heart rate, body temperature, and exercise level from a wearable device worn by a user.

[1194] "Data integration means" refers to a device or software that has the function of combining voice text data, emotional state data, and biometric information data and treating them as a single integrated data set.

[1195] "Generating AI advice means" refers to a device or software that uses artificial intelligence technology to analyze integrated data and generate personalized medical advice based on the user's health condition.

[1196] "User interface means" refers to a device or software that has the function of notifying the user of the generated advice and displaying / playing it in text or audio.

[1197] "Emotional analysis means" refers to a device or software that has the function of analyzing the emotional state from the user's voice data.

[1198] The system of the present invention integrates and analyzes the user's voice input, biometric information, and emotional state to provide health management and medical advice, thereby enabling the user to efficiently manage their health.

[1199] Hardware and software

[1200] The following hardware and software will be used to implement this system.

[1201] Hardware:

[1202] Device (e.g., smartphone, tablet)

[1203] server

[1204] Wearable devices (e.g., smartwatches, fitness trackers)

[1205] software:

[1206] Speech recognition method (e.g. Google Cloud Speech-to-Text)

[1207] Methods for collecting biometric information (e.g., health apps, apps specifically for wearable devices)

[1208] Data integration methods (e.g., Apache Kafka, Apache Spark)

[1209] Generative AI advice methods (e.g., OpenAI GPT-4)

[1210] Emotion analysis method (e.g. Microsoft Azure Emotion API)

[1211] User interface means (e.g., React Native-based mobile apps)

[1212] System Processing Description

[1213] When a user initiates a medical consultation, they launch the application and use voice input. For example, they might say, "Recently, I've been getting headaches at night. What should I do?" The device captures this voice data and sends it to the server in real time.

[1214] The server uses speech recognition to convert voice data into text data. Simultaneously, it uses emotion analysis to analyze the user's emotional state from the voice data. For example, it might detect a high stress level.

[1215] Next, biometric data such as heart rate, body temperature, and activity level are acquired from the wearable device worn by the user using a biometric data collection system. The device periodically collects this data and sends it to the server.

[1216] The server receives voice text data, emotional state data, and biometric data, and integrates them using data integration tools. The integrated data is formatted as time-series data, and features such as heart rate variability, sleep patterns, and exercise intensity are extracted.

[1217] Subsequently, the integrated data is analyzed by a generation AI advice system. Based on the analysis results, the AI ​​creates personalized medical advice. For example, if a user's heart rate increases at night and their emotional state indicates stress, the analysis might conclude that stress and lack of sleep are likely causes of their headaches. Based on these results, specific advice is generated, such as, "Your headache may be due to stress. We recommend stretching to relax or going to bed early."

[1218] Finally, the generated medical advice is communicated to the user through a user interface. The device displays and plays the generated advice in text or audio format, allowing the user to review the advice and take specific actions.

[1219] Specific example

[1220] For example, if a user says the following:

[1221] User: "Lately, I've been getting headaches at night. What should I do?"

[1222] 1. Acquisition of voice input:

[1223] User: Makes a comment to the application.

[1224] Terminal: Captures audio data and sends it to the server.

[1225] 2. Speech recognition and sentiment analysis:

[1226] Server: Converts audio data into text data.

[1227] Server: Analyzes the user's emotional state from voice data using emotion analysis tools.

[1228] 3. Collection of biometric data:

[1229] User: Puts on a wearable device.

[1230] Terminal: Acquires data such as heart rate, body temperature, and exercise level, and sends it to the server.

[1231] 4. Data Integration:

[1232] Server: Integrates voice-text data, emotional state data, and biometric data.

[1233] 5. AI advice generation:

[1234] Server: Analyzes data and generates personalized medical advice.

[1235] 6. Notification of advice:

[1236] Server: Sends the generated advice to the terminal.

[1237] Device: Notifies users of advice via text or voice.

[1238] Thus, the present invention is a system that enables users to easily manage their health at home by comprehensively analyzing the user's voice input, biometric information, and emotional state, and providing personalized medical advice.

[1239] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1240] Step 1: Capture voice input

[1241] The user launches the application and uses voice input to ask a medical question. For example, they might say, "I've been getting headaches at night lately. What should I do?"

[1242] The device uses its built-in microphone to capture the user's speech in real time and generate audio data.

[1243] Input: User voice input

[1244] Output: Captured audio data

[1245] Step 2: Convert audio to text

[1246] The device sends the captured audio data to the server.

[1247] The server uses speech recognition technology (e.g., Google Cloud Speech-to-Text) to convert speech data into text data.

[1248] Input: Audio data

[1249] Output: Text data

[1250] Step 3: Analysis of emotional state

[1251] The server uses emotion analysis tools (e.g., Microsoft Azure Emotion API) to analyze the user's emotional state from the audio data. For example, it can identify a state of stress.

[1252] This process analyzes the tone, pitch, and speed of the voice to classify the emotional state.

[1253] Input: Audio data

[1254] Output: Emotional state data

[1255] Step 4: Biometric Data Collection

[1256] Users wear wearable devices that can measure heart rate, body temperature, and activity level.

[1257] The device collects biometric data in real time from wearable devices and transmits that data to a server. The data includes heart rate, body temperature, and activity level.

[1258] Input: Biometric data from wearable devices

[1259] Output: Biometric data sent to the server

[1260] Step 5: Data Integration and Feature Extraction

[1261] The server integrates voice-text data, emotional state data, and biometric data. Here, it formats the data as a time series.

[1262] The server uses data integration tools (e.g., Apache Spark) to extract features such as heart rate variability, sleep patterns, and exercise intensity from the integrated data.

[1263] Input: Voice text data, emotional state data, biometric data

[1264] Output: Integrated data and extracted features

[1265] Step 6: Generating AI advice

[1266] The server uses a generation AI advice tool (e.g., OpenAI GPT-4) to analyze the integrated data and generate personalized medical advice based on the user's health status.

[1267] Based on the analysis results, specific advice is generated, such as, "Your headache may be caused by stress. We recommend stretching to relax or going to bed early."

[1268] Input: Integrated data and features

[1269] Output: Generated medical advice

[1270] Step 7: Notification of advice

[1271] The server sends the generated medical advice to the terminal via the user interface.

[1272] The device will notify the user of advice via text or voice.

[1273] Input: Generated medical advice

[1274] Output: Advice notified to the user

[1275] (Application Example 2)

[1276] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1277] Conventional systems rely solely on voice recognition and biometric information when users receive personalized health counseling in a virtual store, making it difficult to provide detailed advice that takes into account the user's emotional state. This invention aims to provide comprehensive health advice that also considers the user's mental state by utilizing emotion analysis methods.

[1278] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1279] In this invention, the server includes voice recognition means, biometric information collection means, data integration means, generative AI advice means, and emotion analysis means. This makes it possible to convert the user's voice input into text data and integrate and analyze biometric information and emotional state. By providing the user with optimal health advice through the generative AI advice means, personalized counseling within the virtual store becomes possible.

[1280] "Voice recognition means" refers to a device or software that has the function of converting a user's voice input into text data.

[1281] "Means of collecting biometric information" refers to a device or software that has the function of collecting biometric data such as a user's heart rate, body temperature, and exercise level using a wearable device or the like.

[1282] "Data integration means" refers to a device or software for integrating data obtained from speech recognition means, emotion analysis means, and biometric information collection means, and for extracting feature quantities.

[1283] "Generating AI advice means" refers to a device or software that has the function of analyzing integrated data and generating appropriate advice for the user.

[1284] "User interface means" refers to a device or software that notifies the user of generated advice and provides an interface for the user to take action accordingly.

[1285] "Emotional analysis means" refers to a device or software that has the function of analyzing a user's emotional state from their voice data or biometric information.

[1286] "Counseling methods within a virtual store" refers to a device or software that has the function of providing health counseling to users within a virtual store.

[1287] The system that realizes this application example includes voice recognition means, biometric information collection means, data integration means, generative AI advice means, user interface means, emotion analysis means, and counseling means within a virtual store. Each means of this system functions as follows:

[1288] Explanation of the program's processing

[1289] This system uses a smartphone or head-mounted display (HMD) as the user interface to transmit voice input and biometric information to a server. The data arriving at the server is processed as follows:

[1290] 1. Speech recognition means

[1291] Capture user voice input using a smartphone or HMD.

[1292] Audio data is sent to the server in real time.

[1293] The server uses speech recognition technology (e.g., Google Speech-to-Text API) to convert the audio data into text data.

[1294] 2. Emotion analysis method

[1295] The server analyzes text and audio data to determine the user's emotional state (e.g., IBM Watson Tone Analyzer).

[1296] 3. Means of collecting biological information

[1297] Wearable devices (e.g., smartwatches) are used to periodically acquire biometric data such as heart rate, body temperature, and activity level, and send it to a server.

[1298] 4. Data Integration Means

[1299] The server integrates voice-text data, sentiment analysis data, and biometric information.

[1300] The integrated data is formatted into a time-series format, and features (e.g., heart rate variability, sleep patterns, exercise intensity) are extracted.

[1301] 5. Generative AI advice methods

[1302] Based on integrated data, an AI model (e.g., GPT-4) is used for analysis to generate appropriate health advice for the user.

[1303] The generated advice includes specific action plans tailored to the user's situation.

[1304] 6. User Interface Means

[1305] The generated advice is notified via text or voice through your smartphone or HMD.

[1306] Users review the advice provided and take the necessary actions.

[1307] Specific example

[1308] The following is a concrete example of a user receiving health counseling within a virtual store.

[1309] 1. User: "Lately, my knees hurt when I run. What should I do?"

[1310] 2. Speech recognition system (Google Speech-to-Text): Converts user speech into text.

[1311] 3. Sentiment Analysis (IBM Watson Tone Analyzer): Analyzes whether the user is experiencing anxiety or stress.

[1312] 4. Biometric data collection (smartwatch): Collects heart rate and exercise data and sends it to a server.

[1313] 5. Data Integration: Integrate collected data (voice-to-text, sentiment analysis, biometric information) and extract features.

[1314] 6. Generated AI Advice (GPT-4): Analyzes the data and generates advice such as, "Knee pain may be due to excessive strain. Take a break from exercise for a few days and try icing if necessary."

[1315] 7. User Interface: Generated advice is notified to the user via text or voice.

[1316] In this way, this system combines speech recognition, emotion analysis, biometric data collection, data integration, and generative AI advice to realize personalized health counseling within a virtual store.

[1317] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1318] Step 1:

[1319] Voice input capture and transmission

[1320] The user uses a smartphone or head-mounted display to provide voice input. The voice data is captured by the device and sent to the server in real time. In this step, the input is the user's voice, and the output is the voice data sent to the server.

[1321] Step 2:

[1322] Text conversion using speech recognition

[1323] The audio data sent to the server is converted into text data by a speech recognition tool (e.g., Google Speech-to-Text API). The input for this step is audio data, and the output is text data.

[1324] Step 3:

[1325] Analysis of emotional states

[1326] The server uses sentiment analysis tools (e.g., IBM Watson Tone Analyzer) to analyze the user's emotional state from text and audio data. The input for this step is text and audio data, and the output is the result of the emotional state analysis.

[1327] Step 4:

[1328] Collection of biometric information

[1329] The user wears a wearable device (e.g., a smartwatch), through which biometric information (heart rate, body temperature, activity level, etc.) is periodically acquired by the terminal. This biometric information is then transmitted from the terminal to a server. In this step, the input is the biometric information acquired from the wearable device, and the output is the biometric information transmitted to the server.

[1330] Step 5:

[1331] Data integration

[1332] The server integrates all data obtained from speech recognition, emotion analysis, and biometric data collection. The integrated data is formatted as time-series data, and features (heart rate variability, sleep patterns, exercise intensity, etc.) are extracted. The input for this step is text data, the results of emotional state analysis, and biometric data, and the output is the formatted integrated data.

[1333] Step 6:

[1334] AI-generated advice

[1335] The server analyzes the integrated data using an AI-generated advice tool (e.g., GPT-4) to generate optimal health advice for the user. The generated advice includes specific action plans. The input for this step is the integrated data, and the output is the generated health advice.

[1336] Step 7:

[1337] Advice notification to users

[1338] The generated advice is communicated to the user via text or voice through their device. The user reviews the provided advice and takes the necessary actions. The input for this step is the generated health advice, and the output is the advice communicated to the user.

[1339] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1340] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1341] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1342] [Fourth Embodiment]

[1343] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1344] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1345] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1346] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1347] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1348] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1349] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1350] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1351] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1352] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1353] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1354] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1355] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1356] The system of the present invention includes voice recognition means, biometric information collection means, data integration means, generation AI advice means, and user interface means. These means work together to effectively support the user's overall medical care.

[1357] First, the user launches the application and uses voice input to initiate a medical consultation. For example, they might say, "Recently, I've been getting headaches at night. What should I do?" The device captures this voice data and sends it to the server in real time. The server then uses speech recognition to convert this voice data into text.

[1358] Next, biometric data such as heart rate, body temperature, and activity level are acquired from the wearable device worn by the user using a biometric data collection system. The device periodically collects this data and sends it to the server.

[1359] The server receives voice text data and biometric data and integrates them using a data integration mechanism. The integrated data is formatted as time-series data, and features such as heart rate variability, sleep patterns, and exercise intensity are extracted.

[1360] Subsequently, the integrated data is analyzed by a generating AI advice system. Based on the analysis results, the AI ​​generates personalized medical advice. For example, if a user's heart rate increases at night, stress or lack of sleep may be the cause of their headaches. Based on this analysis, specific advice is generated such as, "Your headache may be due to stress. We recommend stretching to relax or going to bed early."

[1361] Finally, the generated medical advice is communicated to the user through the user interface. The device displays and plays this generated advice in text or audio format, allowing the user to review the advice and take specific actions.

[1362] Specific example

[1363] For example, if a user makes the following statement as a medical consultation:

[1364] User: "Lately, I've been getting headaches at night. What should I do?"

[1365] 1. Speech recognition means:

[1366] User: Makes a comment to the application.

[1367] Terminal: Captures audio data and sends it to the server.

[1368] Server: Converts audio data into text data.

[1369] 2. Means of collecting biometric information:

[1370] User: Puts on a wearable device.

[1371] Terminal: Acquires data on heart rate, body temperature, and exercise level, and sends it to the server.

[1372] Server: Receives biometric data.

[1373] 3. Data integration means:

[1374] Server: Integrates voice-text data and biometric data.

[1375] Server: Extracts features such as heart rate variability, sleep patterns, and exercise intensity.

[1376] 4. Generative AI advice methods:

[1377] Server: Analyzes data and generates personalized medical advice.

[1378] Server: For example, it generates specific advice such as, "This headache may be caused by stress. We recommend stretching to relax or going to bed early."

[1379] 5. User interface means:

[1380] Server: Sends the generated advice to the terminal.

[1381] Device: Notifies users of advice via text or voice.

[1382] User: Review the notified advice and take the necessary actions.

[1383] Thus, the present invention is a system that enables users to easily manage their health at home by analyzing the user's statements and biometric information and providing personalized medical advice.

[1384] The following describes the processing flow.

[1385] Step 1:

[1386] User: Launches the application and says, "I've been getting headaches at night lately. What should I do?" to begin the medical consultation.

[1387] Step 2:

[1388] Terminal: Captures the user's voice and sends the audio data to the server in real time.

[1389] Step 3:

[1390] Server: Uses speech recognition to convert transmitted speech data into text data.

[1391] Step 4:

[1392] User: Wears a wearable device (e.g., a smartwatch) at all times.

[1393] Step 5:

[1394] Terminal: Periodically acquires heart rate, body temperature, and activity level data from a wearable device and transmits this biometric data to a server.

[1395] Step 6:

[1396] Server: Receives biometric data sent from terminals and integrates it with text data sent from speech recognition devices.

[1397] Step 7:

[1398] Server: Using data integration means, it combines voice / text data and biometric data to extract features necessary for analyzing health status (heart rate variability, sleep patterns, exercise intensity, etc.).

[1399] Step 8:

[1400] Server: The AI-generated advice system generates personalized medical advice based on integrated data. For example, if stress or lack of sleep is likely the cause of the headache, it will generate specific advice such as, "Your headache may be caused by stress. We recommend stretching to relax or going to bed early."

[1401] Step 9:

[1402] Server: Formats the generated medical advice into natural language and sends it to the terminal.

[1403] Step 10:

[1404] Terminal: Notifies the user of medical advice sent from the server via text or voice.

[1405] Step 11:

[1406] User: Review the notified advice and take action based on it. For example, take measures such as doing stretches to relax or going to bed early.

[1407] (Example 1)

[1408] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1409] Conventional medical support systems have struggled to effectively combine and analyze user voice input and biometric information to provide appropriate medical advice. Furthermore, real-time data integration and the generation of personalized advice using generative AI models have been insufficient, preventing users from quickly obtaining specific advice about their health status. This could lead to users being unable to take appropriate action and potentially neglecting their health management.

[1410] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1411] In this invention, the server includes means for capturing voice input, means for transmitting voice data to the server, means for converting voice data into text data, means for collecting biometric data from a wearable device, means for transmitting biometric data to the server, means for integrating voice-text data and biometric data, means for extracting features from the integrated data, means for analyzing the data using a generative AI model to generate personalized advice, and means for notifying the user of the advice. This makes it possible to integrate the user's voice input and biometric information, analyze it in real time, and quickly provide personalized medical advice.

[1412] "Means for capturing voice input" refers to devices or systems that acquire the voice spoken by a user as digital data.

[1413] "Means for sending audio data to a server" refers to devices or systems for transferring captured audio data to a server via a communication network.

[1414] "Means of converting audio data into text data" refers to software or algorithms that analyze audio data and convert it into text information.

[1415] "Means of collecting biometric data from wearable devices" refers to devices that are attached to the user's body to measure biometric data such as heart rate, body temperature, and exercise level.

[1416] "Means for transmitting biometric data to a server" refers to devices or systems for transferring biometric data acquired from wearable devices to a server via a communication network.

[1417] "Means for integrating voice-text data and biometric data" refers to software or algorithms that combine voice data and biometric data chronologically and format them into a single dataset that can be analyzed.

[1418] "Methods for extracting features from integrated data" refers to software or algorithms used to extract meaningful information, such as specific patterns or variations, from integrated data.

[1419] "Means of analyzing and generating personalized advice using generative AI models" refers to software or systems that use machine learning and natural language processing technologies to analyze integrated data and generate personalized advice based on the results.

[1420] "Means of notifying users of advice" refers to devices or systems that transmit generated advice to users as text messages or audio.

[1421] The present invention comprises a voice recognition means, a biometric information collection means, a data integration means, a generation AI advice means, and a user interface means, which work together to effectively support the user's overall medical care.

[1422] System Configuration

[1423] The components of the system of the present invention are as follows.

[1424] 1. Voice recognition means: Captures the voice spoken by the user and acquires it as digital data. This means uses the microphone of a smartphone or tablet.

[1425] 2. Means of collecting biometric information: Use a wearable device worn by the user (e.g., a smartwatch) to measure biometric data such as heart rate, body temperature, and activity level.

[1426] 3. Data Integration Method: A software process that receives voice data and biometric data from the server and integrates them in a time-series format. The data is formatted using the Python Pandas library.

[1427] 4. Generative AI Advice Method: Based on integrated data, the data is analyzed using a generative AI model (e.g., OpenAI's GPT-4) to generate personalized medical advice.

[1428] 5. User Interface Means: Means for notifying the user of the generated advice. This includes screen displays on smartphones and voice notifications using speech synthesis APIs.

[1429] System operation

[1430] This system provides users with prompt and appropriate medical advice through the following specific actions.

[1431] Acquiring and transmitting voice input

[1432] A user uses their smartphone and says, "I've been getting headaches at night lately. What should I do?"

[1433] The device captures this audio and sends it to the server in real time as audio data. This is done using an HTTP POST request.

[1434] Converting audio data to text

[1435] The server receives the audio data and uses the Google Cloud Speech-to-Text API to convert the audio data into text data.

[1436] Collection and transmission of biometric data

[1437] While the user is wearing the wearable device, data such as heart rate, body temperature, and activity level are measured periodically.

[1438] The device sends this biometric data to the server. An HTTP POST request is again used to send the data.

[1439] Data integration and analysis

[1440] The server integrates the voice-text data and biometric data and formats it as time-series data. The Python Pandas library is used for this process.

[1441] Subsequently, features such as heart rate variability, sleep patterns, and exercise intensity are extracted from the integrated data.

[1442] Generating advice using generative AI

[1443] The server analyzes the generated AI model (GPT-4) as a prompt message and generates personalized medical advice.

[1444] For example, it can generate specific advice such as, "The user's heart rate increases at night, suggesting that stress or lack of sleep may be causing the headache. We recommend stretching to relax and going to bed earlier."

[1445] Advice notification

[1446] The server sends generated medical advice to the device, and the device notifies the user of this advice via text or voice.

[1447] Users can review the advice provided and take specific actions.

[1448] Specific example

[1449] For example, if a user says the following:

[1450] User: "Lately, I've been getting headaches at night. What should I do?"

[1451] Example of a prompt

[1452] The following prompt will be generated:

[1453] "Use the user's voice input and biometric data to generate personalized medical advice. Inputs are as follows: Voice input: 'I've been getting headaches at night recently. What should I do?' Heart rate data: 70-90 bpm, Body temperature data: 36.5-37.0 degrees Celsius, Activity data: 5000-7000 steps."

[1454] Thus, the system of the present invention can integrate the user's voice input and biometric information from multiple perspectives, enabling it to provide rapid and highly accurate medical advice.

[1455] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1456] Step 1:

[1457] The user performs voice input.

[1458] User: Launches a medical consultation application on their smartphone and says, "I've been getting headaches at night lately. What should I do?"

[1459] Input: User voice input

[1460] Output: Captured audio data

[1461] Step 2:

[1462] Capture and send audio data.

[1463] Device: The system captures the user's voice using the smartphone's microphone and encodes it as digital audio data. This audio data is then sent to the server using an HTTP POST request.

[1464] Input: Captured audio data

[1465] Output: Audio data sent to the server

[1466] Step 3:

[1467] Convert audio data to text data

[1468] Server: Sends the received audio data to the Google Cloud Speech-to-Text API to convert the audio data into text data.

[1469] Specific operation: Speech data is sent to the Google Cloud Speech-to-Text API and text data is received as a response.

[1470] Input: Audio data

[1471] Output: Text data

[1472] Step 4:

[1473] Collect and transmit biometric data

[1474] User: Wears wearable devices such as Apple Watch or Fitbit while going about their daily life.

[1475] Device: The wearable device measures biometric data such as heart rate, body temperature, and activity level, and transmits this data to a smartphone via Bluetooth or Wi-Fi. The smartphone periodically collects this data and sends it to a server via HTTP POST requests.

[1476] Input: Biometric data (heart rate, body temperature, activity level)

[1477] Output: Biometric data sent to the server

[1478] Step 5:

[1479] Data integration

[1480] Server: Receives voice / text data and biometric data, and integrates them using data integration tools. The Python Pandas library is used to format this data as time-series data.

[1481] Specific operation: Use a Pandas DataFrame to combine speech-text data and biometric data to create a single unified dataset.

[1482] Input: Voice text data, biometric data

[1483] Output: Integrated data

[1484] Step 6:

[1485] Feature extraction

[1486] Server: Extracts features such as heart rate variability, sleep patterns, and exercise intensity from integrated data. Performs data analysis using Python libraries such as NumPy and SciPy.

[1487] Specific operations: Extract features from integrated data and analyze, for example, the standard deviation of heart rate or sleep patterns.

[1488] Input: Integrated Data

[1489] Output: Feature data

[1490] Step 7:

[1491] Generating advice using generative AI

[1492] Server: Uses a generative AI model (e.g., GPT-4) to analyze integrated data and features and generate personalized medical advice.

[1493] Specific operation: Input prompt text into the generation AI, and generate medical advice based on the analysis.

[1494] Input: Feature data, integrated data

[1495] Output: Individual medical advice

[1496] Step 8:

[1497] Advice notification

[1498] Server: Sends generated medical advice to the terminal.

[1499] Terminal: Displays received advice to the user as a text message and, if necessary, plays it back as audio using a speech synthesis API.

[1500] Input: Individual medical advice

[1501] Output: Advice notified to the user

[1502] Specific example:

[1503] For example, if a user says, "I've been getting headaches at night lately. What should I do?", the following specific actions will be performed at each processing step.

[1504] 1. The user's voice is captured.

[1505] 2. The audio is sent to the server.

[1506] 3. The audio data is converted to text.

[1507] 4. Biometric data is collected from the wearable device and sent to the server.

[1508] 5. Voice-text data and biometric data are integrated.

[1509] 6. Features are extracted from the integrated data.

[1510] 7. The generative AI model performs the analysis and generates advice such as, "This may be a headache caused by stress."

[1511] 8. The generated advice is notified to the user.

[1512] (Application Example 1)

[1513] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1514] In modern society, it is important to offer personalized meal plans based on individual health conditions and lifestyles, but effective systems for achieving this are not yet widespread. Food delivery services, in particular, are required to provide meal plans suitable for health management. To meet these needs, a new system is needed that uses ubiquitous devices to understand the user's health condition and provide meal suggestions based on that information.

[1515] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1516] In this invention, the server includes voice recognition means, biometric information collection means, data integration means, generation AI advice means, user interface means, and means having the function of suggesting an appropriate meal menu based on biometric data. This makes it possible to automatically generate and suggest a personalized meal menu by combining and analyzing the user's voice input and biometric information.

[1517] "Speech recognition means" refers to a device or software that has the function of converting speech uttered by a user into text data.

[1518] "Biometric information collection means" refers to devices and software that have the function of acquiring biometric data such as heart rate, body temperature, and exercise level from wearable devices.

[1519] "Data integration means" refers to a device or software that has the function of integrating voice text data and biometric data and formatting it as time-series data.

[1520] A "generating AI advice system" is an artificial intelligence system that has the function of analyzing integrated data and generating individual advice.

[1521] A "user interface means" is a device or software that notifies the user of the generated advice and is capable of displaying and playing it in text or audio.

[1522] "Means having the function of suggesting appropriate meal menus based on biometric data" refers to devices or software that have the function of analyzing the user's biometric data and suggesting the optimal meal menu based on the results.

[1523] The present invention analyzes a user's voice input and biometric information collected from a wearable device to propose a personalized meal menu. This system includes voice recognition means, biometric information collection means, data integration means, generation AI advice means, user interface means, and means having the function of proposing an appropriate meal menu based on biometric data.

[1524] First, the user launches a smartphone application and inputs information about their dietary preferences and health status via voice. For example, they might say, "I've gained weight recently. Please tell me about low-calorie meals." The device captures this voice data and sends it to the server in real time. A voice recognition system converts this voice data into text data.

[1525] Next, the biometric information collection means acquires biometric data such as heart rate, body temperature, and exercise level from the wearable device. The terminal periodically collects this data and sends it to the server. The server receives the text data converted by the speech recognition means and the biometric data acquired by the biometric information collection means, and integrates them using the data integration means. The integrated data is formatted as time-series data, and features such as heart rate variability, sleep patterns, and exercise intensity are extracted.

[1526] Subsequently, the integrated data is analyzed by a generated AI advice system. Based on the analysis results, the AI ​​generates personalized meal menus. For example, if the user's heart rate and exercise data are high, light snacks or nutritionally balanced meals that take calorie consumption into consideration will be suggested. Based on these analysis results, specific advice such as "You're looking for a low-calorie meal. We recommend a salad and grilled fish" is generated.

[1527] Finally, the generated meal menu advice is communicated to the user through the user interface. The device displays and plays this advice in text or audio format, allowing the user to review the advice and take specific actions.

[1528] Hardware and software used

[1529] Speech recognition method: The speech_recognition library is used to convert speech to text.

[1530] Biometric information collection method: Data on heart rate, body temperature, and exercise level are acquired from wearable devices.

[1531] Data integration method: The integrated data is formatted as time-series data, and features are extracted.

[1532] AI-generated advice method: Using OpenAI APIs (such as ChatGPT), the system analyzes integrated data and generates personalized meal menu advice.

[1533] User interface means: Text data is used to provide voice notifications using the pyttsx3 library.

[1534] For example, if a user says, "I've gained weight recently. Could you tell me about a low-calorie diet?":

[1535] Example of a prompt:

[1536] Message from a user: I've been gaining weight recently. Could you please suggest a low-calorie diet?

[1537] Biometric information:

[1538] Heart rate: 75 bpm

[1539] Body temperature: 36.5℃

[1540] Exercise level: 50

[1541] Based on this user's health status, please suggest a low-calorie meal plan.

[1542] In this way, the system of the present invention proposes a personalized meal menu based on the user's biometric data and voice input, thereby supporting the user's health management.

[1543] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1544] Step 1:

[1545] User voice input

[1546] The user launches a smartphone application and inputs information about their dietary preferences and health status via voice. For example, they might say, "I've gained weight recently. Please tell me about a low-calorie diet." The input voice data is captured by the device and sent to the server in real time.

[1547] Input: User's voice data

[1548] Output: Audio data sent to the server

[1549] Step 2:

[1550] Speech recognition

[1551] The server uses speech recognition to convert the received audio data into text data. Specifically, it uses the speech_recognition library to convert speech to text.

[1552] Input: Audio data

[1553] Output: Text data

[1554] Step 3:

[1555] Collection of biometric information

[1556] The biometric data collection system acquires biometric data such as heart rate, body temperature, and activity level from the user's wearable device. The device periodically collects this data and sends it to the server.

[1557] Input: Biometric data acquired from wearable devices

[1558] Output: Biometric data sent to the server

[1559] Step 4:

[1560] Data integration and formatting

[1561] The server's data integration mechanism integrates text data converted by the speech recognition mechanism with biometric data acquired by the biometric information collection mechanism. The integrated data is formatted as time-series data, and features such as heart rate variability, sleep patterns, and exercise intensity are extracted.

[1562] Input: Text data, biometric data

[1563] Output: Formatted time-series data and extracted features

[1564] Step 5:

[1565] AI-generated advice

[1566] The AI-generated advice system analyzes integrated data and generates personalized meal plans. Specifically, it uses the OpenAI API to perform analysis and generation based on prompt messages. For example, it suggests the optimal meal plan based on a user request such as "I'm looking for a low-calorie meal."

[1567] Input: Formatted time-series data, prompt text

[1568] Output: Personalized meal plan advice

[1569] Step 6:

[1570] User notifications

[1571] The generated meal menu advice is communicated to the user through a user interface. The device displays and plays this advice as text or audio, allowing the user to review the advice and take specific actions. Specifically, the pyttsx3 library is used to provide audio notifications of text data.

[1572] Input: Meal menu advice

[1573] Output: Notification to the user

[1574] Through the above processing steps, users can receive personalized meal menu suggestions based on their biometric data and voice input.

[1575] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1576] The system of the present invention includes voice recognition means, biometric information collection means, data integration means, generative AI advice means, user interface means, and emotion engine. These means work together to effectively support the user's overall medical and emotional state.

[1577] First, the user launches the application and uses voice input to begin a medical consultation. For example, they might say, "Recently, I've been getting headaches at night. What should I do?" The device captures this voice data and sends it to the server in real time. The server converts this voice data into text data using speech recognition technology. In addition, the voice data is analyzed by an emotion engine to recognize the user's emotional state.

[1578] Next, biometric data such as heart rate, body temperature, and activity level are acquired from the wearable device worn by the user using a biometric data collection system. The device periodically collects this data and sends it to the server.

[1579] The server receives data from the speech recognition means and emotion engine, as well as biometric data from the biometric information collection means, and integrates it using the data integration means. The integrated data is formatted as time-series data, and features such as speech text data, emotional state data, heart rate variability, sleep patterns, and exercise intensity are extracted.

[1580] Subsequently, the integrated data is analyzed by a generating AI advice system. Based on the analysis results, the AI ​​generates personalized medical advice. For example, if a user's heart rate increases at night and their emotional state indicates stress, stress and lack of sleep are likely causes of their headaches. Based on these analysis results, specific advice is generated, such as, "Your headache may be caused by stress. We recommend stretching to relax or going to bed early."

[1581] Finally, the generated medical advice is communicated to the user through the user interface. The device displays and plays this generated advice in text or audio format, allowing the user to review the advice and take specific actions.

[1582] Specific example

[1583] For example, if a user makes the following statement as a medical consultation:

[1584] User: "Lately, I've been getting headaches at night. What should I do?"

[1585] 1. Speech recognition means and emotion engine:

[1586] User: Makes a comment to the application.

[1587] Terminal: Captures audio data and sends it to the server.

[1588] Server: Converts audio data into text data.

[1589] Server: The emotion engine analyzes the user's emotional state from voice data.

[1590] 2. Means of collecting biometric information:

[1591] User: Puts on a wearable device.

[1592] Terminal: Acquires data on heart rate, body temperature, and exercise level, and sends it to the server.

[1593] Server: Receives biometric data.

[1594] 3. Data integration means:

[1595] Server: Integrates voice-text data, emotional state data, and biometric data.

[1596] Server: Extracts features such as heart rate variability, sleep patterns, and exercise intensity.

[1597] 4. Generative AI advice methods:

[1598] Server: Analyzes data and generates personalized medical advice.

[1599] Server: For example, it generates specific advice such as, "This headache may be caused by stress. We recommend stretching to relax or going to bed early."

[1600] 5. User interface means:

[1601] Server: Sends the generated advice to the terminal.

[1602] Device: Notifies users of advice via text or voice.

[1603] User: Review the notified advice and take appropriate action.

[1604] Thus, the present invention is a system that enables users to easily manage their health at home by providing personalized medical advice based on the user's statements, biometric information, and emotional state.

[1605] The following describes the processing flow.

[1606] Step 1:

[1607] User: Launches the application and says, "I've been getting headaches at night lately. What should I do?" to begin the medical consultation.

[1608] Step 2:

[1609] Terminal: Captures the user's voice and sends the audio data to the server in real time.

[1610] Step 3:

[1611] Server: Uses speech recognition to convert transmitted speech data into text data.

[1612] Step 4:

[1613] Server: Sends the converted text data to the emotion engine to analyze the user's emotional state.

[1614] Step 5:

[1615] User: Wears a wearable device (e.g., a smartwatch) at all times.

[1616] Step 6:

[1617] Terminal: Periodically acquires heart rate, body temperature, and activity level data from a wearable device and transmits this biometric data to a server.

[1618] Step 7:

[1619] Server: Receives biometric data sent from terminals and integrates it with text data sent from speech recognition devices and emotional state data from the emotion engine.

[1620] Step 8:

[1621] Server: Using data integration means, it combines voice / text data, emotional state data, and biometric data to extract features necessary for analyzing health status (heart rate variability, sleep patterns, exercise intensity, etc.).

[1622] Step 9:

[1623] Server: The AI-generated advice system generates personalized medical advice based on integrated data. For example, if heart rate increases at night and emotional state indicates stress, it determines that stress and lack of sleep are likely causes of headaches. Based on this, it generates specific advice such as, "Your headache may be caused by stress. We recommend stretching to relax and going to bed early."

[1624] Step 10:

[1625] Server: Formats the generated medical advice into natural language and sends it to the terminal.

[1626] Step 11:

[1627] Terminal: Notifies the user of medical advice sent from the server via text or voice.

[1628] Step 12:

[1629] User: Review the notified advice and take action based on it. For example, take measures such as doing stretches to relax or going to bed early.

[1630] (Example 2)

[1631] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1632] In modern society, there is a need for systems that allow users to easily and efficiently manage their health at home. However, conventional systems have been unable to comprehensively analyze users' voice input, biometric information, and emotional state to provide appropriate medical advice. As a result, it has been difficult for users to properly manage their own health.

[1633] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes voice recognition means, biometric information collection means, data integration means, generation AI advice means, user interface means, and emotion analysis means. This makes it possible to comprehensively analyze the user's voice input, biometric information, and emotional state and provide individualized medical advice.

[1634] "Voice recognition means" refers to a device or software that has the function of acquiring a user's voice input as digital data and converting it into text data.

[1635] "Biometric information collection means" refers to a device or system that has the function of acquiring biometric data such as heart rate, body temperature, and exercise level from a wearable device worn by a user.

[1636] "Data integration means" refers to a device or software that has the function of combining voice text data, emotional state data, and biometric information data and treating them as a single integrated data set.

[1637] "Generating AI advice means" refers to a device or software that uses artificial intelligence technology to analyze integrated data and generate personalized medical advice based on the user's health condition.

[1638] "User interface means" refers to a device or software that has the function of notifying the user of the generated advice and displaying / playing it in text or audio.

[1639] "Emotional analysis means" refers to a device or software that has the function of analyzing the emotional state from the user's voice data.

[1640] The system of the present invention integrates and analyzes the user's voice input, biometric information, and emotional state to provide health management and medical advice, thereby enabling the user to efficiently manage their health.

[1641] Hardware and software

[1642] The following hardware and software will be used to implement this system.

[1643] Hardware:

[1644] Device (e.g., smartphone, tablet)

[1645] server

[1646] Wearable devices (e.g., smartwatches, fitness trackers)

[1647] software:

[1648] Speech recognition method (e.g. Google Cloud Speech-to-Text)

[1649] Methods for collecting biometric information (e.g., health apps, apps specifically for wearable devices)

[1650] Data integration methods (e.g., Apache Kafka, Apache Spark)

[1651] Generative AI advice methods (e.g., OpenAI GPT-4)

[1652] Emotion analysis method (e.g. Microsoft Azure Emotion API)

[1653] User interface means (e.g., React Native-based mobile apps)

[1654] System Processing Description

[1655] When a user initiates a medical consultation, they launch the application and use voice input. For example, they might say, "Recently, I've been getting headaches at night. What should I do?" The device captures this voice data and sends it to the server in real time.

[1656] The server uses speech recognition to convert voice data into text data. Simultaneously, it uses emotion analysis to analyze the user's emotional state from the voice data. For example, it might detect a high stress level.

[1657] Next, biometric data such as heart rate, body temperature, and activity level are acquired from the wearable device worn by the user using a biometric data collection system. The device periodically collects this data and sends it to the server.

[1658] The server receives voice text data, emotional state data, and biometric data, and integrates them using data integration tools. The integrated data is formatted as time-series data, and features such as heart rate variability, sleep patterns, and exercise intensity are extracted.

[1659] Subsequently, the integrated data is analyzed by a generation AI advice system. Based on the analysis results, the AI ​​creates personalized medical advice. For example, if a user's heart rate increases at night and their emotional state indicates stress, the analysis might conclude that stress and lack of sleep are likely causes of their headaches. Based on these results, specific advice is generated, such as, "Your headache may be due to stress. We recommend stretching to relax or going to bed early."

[1660] Finally, the generated medical advice is communicated to the user through a user interface. The device displays and plays the generated advice in text or audio format, allowing the user to review the advice and take specific actions.

[1661] Specific example

[1662] For example, if a user says the following:

[1663] User: "Lately, I've been getting headaches at night. What should I do?"

[1664] 1. Acquisition of voice input:

[1665] User: Makes a comment to the application.

[1666] Terminal: Captures audio data and sends it to the server.

[1667] 2. Speech recognition and sentiment analysis:

[1668] Server: Converts audio data into text data.

[1669] Server: Analyzes the user's emotional state from voice data using emotion analysis tools.

[1670] 3. Collection of biometric data:

[1671] User: Puts on a wearable device.

[1672] Terminal: Acquires data such as heart rate, body temperature, and exercise level, and sends it to the server.

[1673] 4. Data Integration:

[1674] Server: Integrates voice-text data, emotional state data, and biometric data.

[1675] 5. AI advice generation:

[1676] Server: Analyzes data and generates personalized medical advice.

[1677] 6. Notification of advice:

[1678] Server: Sends the generated advice to the terminal.

[1679] Device: Notifies users of advice via text or voice.

[1680] Thus, the present invention is a system that enables users to easily manage their health at home by comprehensively analyzing the user's voice input, biometric information, and emotional state, and providing personalized medical advice.

[1681] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1682] Step 1: Capture voice input

[1683] The user launches the application and uses voice input to ask a medical question. For example, they might say, "I've been getting headaches at night lately. What should I do?"

[1684] The device uses its built-in microphone to capture the user's speech in real time and generate audio data.

[1685] Input: User voice input

[1686] Output: Captured audio data

[1687] Step 2: Convert audio to text

[1688] The device sends the captured audio data to the server.

[1689] The server uses speech recognition technology (e.g., Google Cloud Speech-to-Text) to convert speech data into text data.

[1690] Input: Audio data

[1691] Output: Text data

[1692] Step 3: Analysis of emotional state

[1693] The server uses emotion analysis tools (e.g., Microsoft Azure Emotion API) to analyze the user's emotional state from the audio data. For example, it can identify a state of stress.

[1694] This process analyzes the tone, pitch, and speed of the voice to classify the emotional state.

[1695] Input: Audio data

[1696] Output: Emotional state data

[1697] Step 4: Biometric Data Collection

[1698] Users wear wearable devices that can measure heart rate, body temperature, and activity level.

[1699] The device collects biometric data in real time from wearable devices and transmits that data to a server. The data includes heart rate, body temperature, and activity level.

[1700] Input: Biometric data from wearable devices

[1701] Output: Biometric data sent to the server

[1702] Step 5: Data Integration and Feature Extraction

[1703] The server integrates voice-text data, emotional state data, and biometric data. Here, it formats the data as a time series.

[1704] The server uses data integration tools (e.g., Apache Spark) to extract features such as heart rate variability, sleep patterns, and exercise intensity from the integrated data.

[1705] Input: Voice text data, emotional state data, biometric data

[1706] Output: Integrated data and extracted features

[1707] Step 6: Generating AI advice

[1708] The server uses a generation AI advice tool (e.g., OpenAI GPT-4) to analyze the integrated data and generate personalized medical advice based on the user's health status.

[1709] Based on the analysis results, specific advice is generated, such as, "Your headache may be caused by stress. We recommend stretching to relax or going to bed early."

[1710] Input: Integrated data and features

[1711] Output: Generated medical advice

[1712] Step 7: Notification of advice

[1713] The server sends the generated medical advice to the terminal via the user interface.

[1714] The device will notify the user of advice via text or voice.

[1715] Input: Generated medical advice

[1716] Output: Advice notified to the user

[1717] (Application Example 2)

[1718] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1719] Conventional systems rely solely on voice recognition and biometric information when users receive personalized health counseling in a virtual store, making it difficult to provide detailed advice that takes into account the user's emotional state. This invention aims to provide comprehensive health advice that also considers the user's mental state by utilizing emotion analysis methods.

[1720] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1721] In this invention, the server includes voice recognition means, biometric information collection means, data integration means, generative AI advice means, and emotion analysis means. This makes it possible to convert the user's voice input into text data and integrate and analyze biometric information and emotional state. By providing the user with optimal health advice through the generative AI advice means, personalized counseling within the virtual store becomes possible.

[1722] "Voice recognition means" refers to a device or software that has the function of converting a user's voice input into text data.

[1723] "Means of collecting biometric information" refers to a device or software that has the function of collecting biometric data such as a user's heart rate, body temperature, and exercise level using a wearable device or the like.

[1724] "Data integration means" refers to a device or software for integrating data obtained from speech recognition means, emotion analysis means, and biometric information collection means, and for extracting feature quantities.

[1725] "Generating AI advice means" refers to a device or software that has the function of analyzing integrated data and generating appropriate advice for the user.

[1726] "User interface means" refers to a device or software that notifies the user of generated advice and provides an interface for the user to take action accordingly.

[1727] "Emotional analysis means" refers to a device or software that has the function of analyzing a user's emotional state from their voice data or biometric information.

[1728] "Counseling methods within a virtual store" refers to a device or software that has the function of providing health counseling to users within a virtual store.

[1729] The system that realizes this application example includes voice recognition means, biometric information collection means, data integration means, generative AI advice means, user interface means, emotion analysis means, and counseling means within a virtual store. Each means of this system functions as follows:

[1730] Explanation of the program's processing

[1731] This system uses a smartphone or head-mounted display (HMD) as the user interface to transmit voice input and biometric information to a server. The data arriving at the server is processed as follows:

[1732] 1. Speech recognition means

[1733] Capture user voice input using a smartphone or HMD.

[1734] Audio data is sent to the server in real time.

[1735] The server uses speech recognition technology (e.g., Google Speech-to-Text API) to convert the audio data into text data.

[1736] 2. Emotion analysis method

[1737] The server analyzes text and audio data to determine the user's emotional state (e.g., IBM Watson Tone Analyzer).

[1738] 3. Means of collecting biological information

[1739] Wearable devices (e.g., smartwatches) are used to periodically acquire biometric data such as heart rate, body temperature, and activity level, and send it to a server.

[1740] 4. Data Integration Means

[1741] The server integrates voice-text data, sentiment analysis data, and biometric information.

[1742] The integrated data is formatted into a time-series format, and features (e.g., heart rate variability, sleep patterns, exercise intensity) are extracted.

[1743] 5. Generative AI advice methods

[1744] Based on integrated data, an AI model (e.g., GPT-4) is used for analysis to generate appropriate health advice for the user.

[1745] The generated advice includes specific action plans tailored to the user's situation.

[1746] 6. User Interface Means

[1747] The generated advice is notified via text or voice through your smartphone or HMD.

[1748] Users review the advice provided and take the necessary actions.

[1749] Specific example

[1750] The following is a concrete example of a user receiving health counseling within a virtual store.

[1751] 1. User: "Lately, my knees hurt when I run. What should I do?"

[1752] 2. Speech recognition system (Google Speech-to-Text): Converts user speech into text.

[1753] 3. Sentiment Analysis (IBM Watson Tone Analyzer): Analyzes whether the user is experiencing anxiety or stress.

[1754] 4. Biometric data collection (smartwatch): Collects heart rate and exercise data and sends it to a server.

[1755] 5. Data Integration: Integrate collected data (voice-to-text, sentiment analysis, biometric information) and extract features.

[1756] 6. Generated AI Advice (GPT-4): Analyzes the data and generates advice such as, "Knee pain may be due to excessive strain. Take a break from exercise for a few days and try icing if necessary."

[1757] 7. User Interface: Generated advice is notified to the user via text or voice.

[1758] In this way, this system combines speech recognition, emotion analysis, biometric data collection, data integration, and generative AI advice to realize personalized health counseling within a virtual store.

[1759] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1760] Step 1:

[1761] Voice input capture and transmission

[1762] The user uses a smartphone or head-mounted display to provide voice input. The voice data is captured by the device and sent to the server in real time. In this step, the input is the user's voice, and the output is the voice data sent to the server.

[1763] Step 2:

[1764] Text conversion using speech recognition

[1765] The audio data sent to the server is converted into text data by a speech recognition tool (e.g., Google Speech-to-Text API). The input for this step is audio data, and the output is text data.

[1766] Step 3:

[1767] Analysis of emotional states

[1768] The server uses sentiment analysis tools (e.g., IBM Watson Tone Analyzer) to analyze the user's emotional state from text and audio data. The input for this step is text and audio data, and the output is the result of the emotional state analysis.

[1769] Step 4:

[1770] Collection of biometric information

[1771] The user wears a wearable device (e.g., a smartwatch), through which biometric information (heart rate, body temperature, activity level, etc.) is periodically acquired by the terminal. This biometric information is then transmitted from the terminal to a server. In this step, the input is the biometric information acquired from the wearable device, and the output is the biometric information transmitted to the server.

[1772] Step 5:

[1773] Data integration

[1774] The server integrates all data obtained from speech recognition, emotion analysis, and biometric data collection. The integrated data is formatted as time-series data, and features (heart rate variability, sleep patterns, exercise intensity, etc.) are extracted. The input for this step is text data, the results of emotional state analysis, and biometric data, and the output is the formatted integrated data.

[1775] Step 6:

[1776] AI-generated advice

[1777] The server analyzes the integrated data using an AI-generated advice tool (e.g., GPT-4) to generate optimal health advice for the user. The generated advice includes specific action plans. The input for this step is the integrated data, and the output is the generated health advice.

[1778] Step 7:

[1779] Advice notification to users

[1780] The generated advice is communicated to the user via text or voice through their device. The user reviews the provided advice and takes the necessary actions. The input for this step is the generated health advice, and the output is the advice communicated to the user.

[1781] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1782] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1783] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1784] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1785] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1786] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1787] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1788] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1789] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1790] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1791] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1792] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1793] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1794] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1795] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1796] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1797] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1798] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1799] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1800] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1801] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1802] The following is further disclosed regarding the embodiments described above.

[1803] (Claim 1)

[1804] Voice recognition means and

[1805] Means for collecting biometric information,

[1806] Data integration means,

[1807] Generating AI advice methods,

[1808] User interface means,

[1809] A system that includes this.

[1810] (Claim 2)

[1811] The system according to claim 1, wherein the speech recognition means has the function of converting the user's voice input into text data.

[1812] (Claim 3)

[1813] The system according to claim 1, wherein the biometric information collection means has the function of acquiring data on heart rate, body temperature, and exercise level from a wearable device.

[1814] "Example 1"

[1815] (Claim 1)

[1816] A means of capturing voice input,

[1817] A means of sending audio data to a server,

[1818] A means of converting audio data into text data,

[1819] A means of collecting biometric data from wearable devices,

[1820] A means of transmitting biometric data to a server,

[1821] A means of integrating voice text data and biometric data,

[1822] A method for extracting features from integrated data,

[1823] A means of analyzing and generating personalized advice using a generative AI model,

[1824] A means of notifying users of advice,

[1825] A system that includes this.

[1826] (Claim 2)

[1827] The system according to claim 1, wherein the means for capturing the voice input has a function to transmit the user's voice input to a server in real time.

[1828] (Claim 3)

[1829] The system according to claim 1, wherein the means for collecting biometric data from the wearable device has the function of acquiring data on heart rate, body temperature, and exercise level.

[1830] "Application Example 1"

[1831] (Claim 1)

[1832] Voice recognition means and

[1833] Means for collecting biometric information,

[1834] Data integration means,

[1835] Generating AI advice methods,

[1836] User interface means,

[1837] A means having the function of suggesting an appropriate meal menu based on biometric data,

[1838] A system that includes this.

[1839] (Claim 2)

[1840] The system according to claim 1, wherein the speech recognition means has the function of converting the user's voice input into text data.

[1841] (Claim 3)

[1842] The system according to claim 1, wherein the biometric information collection means has the function of acquiring data on heart rate, body temperature, and exercise level from a wearable device.

[1843] "Example 2 of combining an emotion engine"

[1844] (Claim 1)

[1845] Voice recognition means and

[1846] Means for collecting biometric information,

[1847] Data integration means,

[1848] Generating AI advice methods,

[1849] User interface means,

[1850] Emotion analysis methods,

[1851] A system that includes this.

[1852] (Claim 2)

[1853] The system according to claim 1, wherein the speech recognition means has the function of converting the user's voice input into text data.

[1854] (Claim 3)

[1855] The system according to claim 1, wherein the biometric information collection means has the function of acquiring data on heart rate, body temperature, and exercise level from a wearable device.

[1856] (Claim 4)

[1857] The system according to claim 1, wherein the emotion analysis means has the function of analyzing the user's emotional state from voice data.

[1858] (Claim 5)

[1859] The system according to claim 1, wherein the data integration means has the function of integrating speech text data, emotional state data, and biometric information data to extract features.

[1860] (Claim 6)

[1861] The system according to claim 1, wherein the generating AI advice means has the function of analyzing integrated data and generating individual medical advice based on the user's health condition.

[1862] "Application example 2 when combining with an emotional engine"

[1863] (Claim 1)

[1864] Voice recognition means and

[1865] Means for collecting biometric information,

[1866] Data integration means,

[1867] Generating AI advice methods,

[1868] User interface means,

[1869] Emotion analysis methods,

[1870] Counseling methods within virtual stores,

[1871] A system that includes this.

[1872] (Claim 2)

[1873] The system according to claim 1, wherein the speech recognition means has the function of converting the user's voice input into text data.

[1874] (Claim 3)

[1875] The system according to claim 1, wherein the biometric information collection means has the function of acquiring data on heart rate, body temperature, and exercise level from a wearable device. [Explanation of Symbols]

[1876] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] A speech recognition method that converts user voice input into text data, A biometric information collection method that acquires heart rate, body temperature, and exercise level data from a wearable device, A data integration means that integrates text data converted by a speech recognition means and biometric data acquired by a biometric information collection means, A generative AI advice method that generates individual medical advice based on integrated data, A user interface means for notifying the user of the generated medical advice, A system that includes this.

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