System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting body information of a user; means for transmitting the collected body information of the user to a server; means for storing and managing the collected body information of the user in the server; means for diagnosing a mental state using an artificial intelligence model based on the collected body information of the user; means for generating a positive suggestion based on a diagnosis result; means for converting the generated positive suggestion into a voice format; and means for transmitting the voice data to a terminal of the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In modern society, the number of people suffering from mental illness is steadily increasing, making recovery difficult. Mental illness not only reduces an individual's happiness, but also causes significant losses to companies and society as a whole. This has led to a need for early detection and prevention of mental illness. However, conventional methods have limited means for monitoring mental state in real time and intervening quickly. To solve this problem, a system is needed that naturally monitors the user's mental state in their daily life and provides appropriate advice at the right time. [Means for solving the problem]

[0005] To solve this problem, the present invention provides the following means: a system including means for collecting a user's physical information, means for transmitting the collected user's physical information to a server, means for storing and managing the collected user's physical information in the server, means for diagnosing the mental state of the user using an artificial intelligence model based on the collected user's physical information, means for generating positive suggestions based on the diagnosis results, means for converting the generated positive suggestions into audio format, means for transmitting the audio data to the user's terminal, and means for the terminal to play back the audio data and present it to the user. This system makes it possible to naturally monitor the user's mental state in their daily lives and provide appropriate advice at the appropriate time.

[0006] "User's physical information" refers to data collected by a wearable device that indicates the user's physical condition, such as heart rate, number of steps, sleep time, body temperature, sweating, brain waves, muscle movements, and facial expressions.

[0007] The "means of collection" refers to a system for obtaining the user's physical information using wearable terminals or sensor devices.

[0008] The "means of transmission" refers to the mechanism by which collected user physical information is transferred to a server via the Internet, wireless communication, etc.

[0009] "Means for storage and management" refers to the ability to appropriately store data collected on the server in a database or the like, and to search, update, and delete the data as necessary.

[0010] An "artificial intelligence model" is a model that uses machine learning and deep learning algorithms to analyze data and diagnose a user's mental state.

[0011] The "means for diagnosing mental state" is a mechanism that uses an artificial intelligence model to analyze the user's physical information collected and evaluate the user's current mental state.

[0012] "Positive suggestions" are content that generates advice and behavioral instructions to maintain mental health based on the user's mental state.

[0013] The "means for generation" is a mechanism for creating appropriate positive suggestions based on the results of a mental state diagnosis.

[0014] "Means for converting into audio format" refers to technology for converting the generated positive suggestions into audio data, generally text-to-speech (TTS) technology.

[0015] The "transmitting means" refers to a system that transmits the generated voice data to the user's terminal, and uses the Internet or wireless communication.

[0016] "Means for playing and presenting" refers to a function for playing audio data on the user's terminal and presenting it to the user in an audible form. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 2, in the data processing device 12, a specific process 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" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process 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.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] MODE FOR CARRYING OUT THE INVENTION

[0039] This system collects a user's physical information, analyzes and diagnoses the user's mental state using artificial intelligence (AI), and provides appropriate positive suggestions in the form of voice, as needed. This system is composed of a wearable device, a communication means, a server, an AI model, a generative AI, voice conversion technology, and a user device.

[0040] Terminal

[0041] First, the user puts on a wearable device. This device is equipped with a heart rate sensor, pedometer, body temperature sensor, sleep tracker, sweat sensor, brain wave sensor, camera, etc., and collects physical information from the user's daily life in real time. The device has the function of periodically sending the collected data to a server.

[0042] server

[0043] The server receives the user's physical information sent from the device and stores it in a database. The stored data is then analyzed using an AI model. The AI ​​model uses machine learning and deep learning algorithms to diagnose the user's mental state based on data such as the user's heart rate, number of steps, sleep time, body temperature, sweating, brain waves, and facial expressions.

[0044] Once the AI ​​model has completed its mental state diagnosis, the generative AI then generates positive suggestions for the user based on the diagnosis. For example, if the user's heart rate is high and they are under stress, the generative AI will suggest "take a short break."

[0045] The generated suggestions are converted into audio format. The server generates the suggestions as audio files and sends them to the user's device. The audio files are generated using Text-to-Speech (TTS) technology.

[0046] User

[0047] The user's device receives and plays audio files from the server. The user listens to the audio suggestions provided by the device and takes action as necessary. In this way, users can manage their mental state in real time in their daily lives and receive positive advice at the right time.

[0048] Specific examples

[0049] For example, if a user begins to show signs of overwork after a day at work, the wearable device will detect an increase in heart rate, a decrease in steps, and lack of sleep. This data is immediately sent to a server, where an AI model analyzes it and determines that the user is in a state of "fatigue." Based on this diagnosis, the generative AI generates a positive suggestion, such as "We recommend that you take a short break." This suggestion is sent to the user's device as an audio file, which the device plays to notify the user. The user can then take a break and prevent overwork.

[0050] This system will enable the early detection and prevention of mental illness, helping to maintain the mental health of individuals, and is also expected to improve productivity for companies and society as a whole.

[0051] The processing flow will be explained below.

[0052] Step 1:

[0053] The device collects the user's physical information. The wearable device is equipped with a heart rate sensor, pedometer, body temperature sensor, sleep tracker, sweat sensor, brainwave sensor, camera, etc., and obtains data from these sensors in real time.

[0054] Step 2:

[0055] The device transmits the collected data to a server via the internet or wireless communication, either periodically or in real time as needed.

[0056] Step 3:

[0057] The server receives the data sent from the terminal via the Internet or wireless communication and temporarily stores the received data.

[0058] Step 4:

[0059] The server stores the received data in a database, where data for each user is recorded and managed so that it can be accessed later as needed.

[0060] Step 5:

[0061] The server preprocesses the stored data, scaling and normalizing it to convert it into a format that can be input to an AI model.

[0062] Step 6:

[0063] The server inputs the preprocessed data into an AI model that uses machine learning and deep learning algorithms to analyze the user's heart rate, steps, sleep time, body temperature, sweating, brain waves, facial expressions, and other data to diagnose the user's mental state.

[0064] Step 7:

[0065] The server uses generative AI to generate positive suggestions based on the diagnosis results. For example, if fatigue is detected, the server will make specific suggestions such as "Take a short break."

[0066] Step 8:

[0067] The server converts the generated suggestions into audio format using Text-to-Speech (TTS) technology, creating a generated audio file.

[0068] Step 9:

[0069] The server sends the audio file to the user's device, which is then transferred to the user's device via the Internet or wireless communication.

[0070] Step 10:

[0071] The device receives the audio file sent from the server and stores it in the device's internal memory.

[0072] Step 11:

[0073] The device will play the audio file to notify the user, and the audio will be played through the device's speaker so the user can hear the suggestion.

[0074] Step 12:

[0075] The user follows the audio suggestions and takes appropriate actions, such as taking a break or taking a deep breath, to improve their mental state.

[0076] Example 1

[0077] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0078] In modern society, many people are prone to high levels of stress and anxiety, but few systems exist that can adequately monitor their mental state and provide effective countermeasures in real time. Even when such systems exist, they face the challenge of accurately acquiring a user's physical information and providing prompt and appropriate advice based on that information. Furthermore, there are currently insufficient technological means to provide effective mental care while protecting the user's privacy.

[0079] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0080] In this invention, the server includes means for transmitting the user's physical information to a central computer, means for storing and managing the collected user's physical information in the central computer, means for diagnosing the mental state using an artificial intelligence model based on the collected user's physical information, voice synthesis means for converting the generated positive suggestions into voice format, means for transmitting the generated positive suggestions as voice data to the user's terminal, and means for the terminal to play back and present the voice data to the user. This enables real-time mental care based on the user's physical information, and can effectively support stress relief and maintenance of mental health.

[0081] "User's physical information" refers to information about the user's physical condition, including data such as heart rate, number of steps, sleep time, body temperature, sweating, brain waves, and facial expressions.

[0082] A "central computer" is a computer system such as a server or database that stores, manages, and analyzes a user's physical information.

[0083] An "artificial intelligence model" is a software model that uses algorithms such as machine learning and deep learning to analyze a user's physical information and diagnose their mental state.

[0084] A "generative model" is an algorithm or software that generates positive suggestions for users based on the diagnostic results analyzed by artificial intelligence.

[0085] "Speech synthesis means" means means, including text-to-speech (TTS) technology, for converting textual positive suggestions into speech form.

[0086] A "terminal" is an electronic device used by a user, such as a smartphone or computer, that has the function of receiving and playing back audio data sent from a server.

[0087] This invention is a system that collects a user's physical information, analyzes and diagnoses the user's mental state using artificial intelligence (AI), and provides positive suggestions in the form of voice. This system is composed of a wearable device, a communication means, a central computer, an AI model, a generative AI model, a voice synthesis means, and a user device.

[0088] Terminal

[0089] The user wears a wearable device equipped with a heart rate sensor, pedometer, body temperature sensor, sleep tracker, sweat sensor, brain wave sensor, camera, etc., to collect real-time physical information from the user's daily life. The device has the function of periodically transmitting the collected data to a central computer.

[0090] Server (central computer)

[0091] The central computer receives the user's physical information sent from the device and stores it in a database. The stored data is analyzed using an AI model. The AI ​​model uses machine learning and deep learning algorithms to diagnose the user's mental state from data such as the user's heart rate, number of steps, sleep time, body temperature, sweating, brain waves, and facial expressions.

[0092] For example, if a user's heart rate is higher than normal and their step count is decreasing, the AI ​​model will diagnose the user's mental state as "stressed." Based on this diagnosis, the generative AI model will generate positive suggestions such as "Take a short break."

[0093] The generated suggestions are converted into an audio file by a speech synthesizer. The server then sends the audio file to the user's device. Text-to-speech (TTS) technology is used to generate the audio file.

[0094] User

[0095] The user's device has the ability to receive and play audio files from the server. The user listens to the audio suggestions presented by the device and takes action as necessary. For example, if the user receives a suggestion to "take a short break," the user can actually take a break and refresh their mind and body.

[0096] This system allows users to manage their mental state in real time in their daily lives and receive positive advice at the appropriate time. This will enable early detection and prevention of mental illness, and help maintain individual mental health. It is also expected to improve productivity in companies and society as a whole.

[0097] Specific examples

[0098] For example, if a user begins to show signs of fatigue after a day at work, the wearable device will detect an increase in heart rate, a decrease in steps, and lack of sleep. This data is immediately sent to a central computer, where an AI model analyzes it and determines that the user is in a "fatigue" state. Based on this diagnosis, the generative AI model generates a positive suggestion, such as "We recommend that you take a short break." This suggestion is sent to the user's device as an audio file, which the device plays to notify the user. The user can then take a break and prevent overwork.

[0099] Prompt Sentence Examples

[0100] Here are some example prompts to input to a generative AI model:

[0101] You have detected that the user's heart rate is higher than normal and their step count is decreasing. Create an appropriate suggestion for this user, such as "Take a short break."

[0102] Using this prompt, the generative AI model generates positive suggestions that are appropriate for the user's current state.

[0103] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0104] Step 1:

[0105] The device collects the user's physical information in real time.

[0106] How it works: The wearable device is equipped with a heart rate sensor, pedometer, body temperature sensor, sleep tracker, sweat sensor, brainwave sensor, and camera, which collect and store data internally.

[0107] Input: User's physical information

[0108] Output: Internally stored physical information data

[0109] Step 2:

[0110] The terminals transmit the collected data to a central computer.

[0111] Specific operation: The terminal uses Bluetooth or Wi-Fi to send all collected data to a central computer at regular intervals or based on specified conditions (e.g., when a certain amount of data is reached).

[0112] Input: Internally stored physical information data

[0113] Output: Data sent to the central computer

[0114] Step 3:

[0115] The server stores and manages the received data.

[0116] Specific operation: When the central computer receives data sent from the terminal, it organizes each sensor data by category and stores it in a database. The database is a NoSQL type, which enables high-speed reading and writing.

[0117] Input: Data sent from the terminal

[0118] Output: Data stored in the database

[0119] Step 4:

[0120] The server analyzes the stored data using an AI model.

[0121] Specific operation: Various sensor data is acquired from the database and input into the AI ​​model for analysis. The AI ​​model uses machine learning and deep learning algorithms to diagnose the user's mental state. For example, if the heart rate is high and the number of steps is low, it will be diagnosed as "under stress."

[0122] Input: Various sensor data stored in the database

[0123] Output: Diagnosis of the user's mental state

[0124] Step 5:

[0125] A generative AI model generates positive suggestions based on the diagnostic results.

[0126] Specific operation: Using the diagnostic results analyzed by the AI ​​model as input, the generative AI model uses prompt sentences to generate appropriate positive suggestions, such as "Your heart rate is high and you are under stress. Take a short break."

[0127] Input: Diagnosis of the user's mental state

[0128] Output: Generated positive suggestions (in text format)

[0129] Step 6:

[0130] The generated suggestions are converted into audio format.

[0131] How it works: The text-based suggestions output by the generative AI model are converted into audio using text-to-speech (TTS) technology.

[0132] Input: Generated positive suggestions (in text format)

[0133] Output: Audio file

[0134] Step 7:

[0135] The server sends the generated audio file to the user's terminal.

[0136] How it works: Once the audio file is generated, a central computer sends it over the internet to the user's smartphone or tablet.

[0137] Input: Audio file

[0138] Output: Audio file sent to the user's device

[0139] Step 8:

[0140] The terminal plays the audio file and presents it to the user.

[0141] Specific behavior: When the user's device receives the audio file, a notification will be displayed, and when the user opens the app, the audio file will be played. The user can decide what to do based on the audio suggestions.

[0142] Input: Audio file sent to the user's device

[0143] Output: Played audio suggestions

[0144] Step 9:

[0145] The user responds to the voice suggestions from the device and takes action.

[0146] Specific behavior: The user hears the suggestion to "take a short break" and actually takes a break, which is expected to bring the user's heart rate back to a normal range and relieve stress.

[0147] Input: Played speech suggestions

[0148] Output: User action (e.g., taking a break)

[0149] (Application example 1)

[0150] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0151] In modern society, real-time monitoring of a user's mental state and early detection of emergencies are important. However, conventional systems have had difficulty in quickly assessing a user's mental state and proposing appropriate security measures. To solve this problem, a system is needed that can accurately diagnose a user's mental state using their physical information and provide prompt advice on countermeasures in emergencies.

[0152] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0153] In this invention, the server includes means for collecting user physical information, means for transmitting the collected user physical information to the server, means for storing and managing the collected user physical information in the server, means for diagnosing the mental state of the user using an artificial intelligence model based on the collected user physical information, means for generating a warning and security measure proposals based on the diagnosis results, means for converting the generated warning and security measure proposals into audio format, means for transmitting the generated warning and security measure proposals as audio data to the user's terminal, and means for the terminal to play the audio data and present it to the user. This makes it possible to monitor the user's mental state in real time, and when an abnormality is detected, to quickly issue a warning and propose appropriate security measures.

[0154] "User's physical information" refers to biometric data such as an individual's heart rate, number of steps, sleep time, body temperature, sweat, brain waves, muscle movements, and facial expressions.

[0155] "Means for collection" refers to a device or system that collects a user's physical information using sensors in a wearable terminal or smart device.

[0156] "Server" refers to a central processing unit for storing, managing, and analyzing collected user physical information.

[0157] "Means for storing and managing" refers to a system that stores a user's physical information in a database and accesses and updates it as needed.

[0158] An "artificial intelligence model" refers to an algorithm that uses machine learning and deep learning techniques to analyze and diagnose a user's mental state from collected data.

[0159] "Mental state" refers to the user's current psychological and emotional state.

[0160] "Diagnostic means" refers to a system that uses an artificial intelligence model to analyze collected data and determine the user's mental state.

[0161] "Warning and suggested security measures" refers to warnings and recommendations for safety measures issued to users based on the diagnostic results.

[0162] "Generating means" refers to a system or algorithm that generates warnings and security recommendations based on diagnostic results.

[0163] "Means for converting to audio format" refers to technology that converts the generated warnings and security recommendations into audio data (e.g., text-to-speech technology).

[0164] "Audio Data" means digital audio files containing warnings and security suggestions in converted audio format.

[0165] "Terminal" refers to a device held by a user (e.g., a smartphone or smart glasses).

[0166] "Means of presentation" refers to the function of the user's device to play audio data and notify the user.

[0167] 1. Data collection and transmission

[0168] Users wear wearable devices or smart devices equipped with heart rate sensors, pedometers, body temperature sensors, sweat sensors, and brain wave sensors. These sensors collect the user's physical information in real time. The collected data is sent to a server via Bluetooth or Wi-Fi.

[0169] 2. Data storage and management

[0170] The server stores and manages the received user's physical information in a database. The database is built using a database management system such as AWS RDS or MySQL, allowing for efficient storage, search, and analysis of large amounts of data.

[0171] 3. Diagnosis of mental conditions

[0172] The user's physical information stored on the server is analyzed using an artificial intelligence (AI) model, which uses deep learning frameworks such as TensorFlow and PyTorch, to diagnose the user's mental state from data such as the user's heart rate, body temperature, sweating, and brain waves.

[0173] 4. Generate warnings and security action suggestions

[0174] Based on the diagnosis results, a generative AI (such as OpenAI's GPT-3) is used to generate warnings and security recommendations for the user. The recommendations are customized to suit the situation, requiring appropriate attention. The recommendations are then converted into audio data using text-to-speech (TTS) technology. The Google Cloud Text-to-Speech API is used as the TTS technology.

[0175] 5. Sending and playing audio data

[0176] The generated voice data is sent from the server to the user's device (such as a smartphone or smart glasses). The device then plays the received voice data and notifies the user. This allows the user to receive warnings and suggestions for security measures at the appropriate time.

[0177] Specific examples

[0178] If a user begins to show signs of stress, the wearable device will detect an increase in heart rate and changes in body temperature. This data is immediately sent to a server, where an AI model analyzes it and diagnoses the user as being in a "high stress state." Based on this diagnosis, the generative AI generates a suggestion such as "Take a break now and try a relaxation technique." This suggestion is sent to the user's device as an audio file, which plays it and notifies the user.

[0179] Example prompt sentence:

[0180] The user is in a high mental state. What are the next security measures that should be taken?

[0181] Hardware and Software Examples

[0182] Wearable device: Equipped with heart rate sensor, body temperature sensor, sweat sensor, and brain wave sensor

[0183] Communication means: Bluetooth, Wi-Fi

[0184] Server: AWS EC2, Database (AWS RDS, MySQL)

[0185] AI models: TensorFlow, PyTorch

[0186] Generative AI: OpenAI GPT-3

[0187] TTS technology: Google Cloud Text-to-Speech API

[0188] User devices: smartphones, smart glasses

[0189] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0190] Step 1: Data collection and transmission

[0191] The user wears a wearable device. This device collects physical information such as heart rate, body temperature, sweating, and brain waves in real time. The collected data is sent to a device (such as a smartphone) via Bluetooth or Wi-Fi. The input is sensor data of physical information, and the output is data sent to the device. Pre-processing (e.g., data format conversion, compression, etc.) is performed within the device.

[0192] Step 2: Send data to the server

[0193] The physical information collected by the device is packetized and sent to a server via an internet connection. The input is the sensor data sent from the device, and the output is the data stored on the server. When the server receives the data, it checks its integrity and stores it in a database.

[0194] Step 3: Data storage and management

[0195] The server stores the received data in a database (e.g., AWS RDS, MySQL). The input is the sensor data sent to the server, and the output is well-formatted data stored in the database. Data processing here includes data validation and indexing as time-series data.

[0196] Step 4: Diagnose your mental condition

[0197] The stored data is analyzed by an AI model (such as TensorFlow or PyTorch). The input is physical information obtained from a database, and the output is a diagnosis of the user's mental state. The AI ​​model extracts various features (heart rate fluctuations, changes in body temperature, etc.) based on the collected data and predicts the user's mental state (e.g., stress level). Specific operations include inputting data into the model, preprocessing, and outputting the predicted results.

[0198] Step 5: Generate warnings and security recommendations

[0199] Based on the diagnosis results, a generative AI (e.g., OpenAI GPT-3) is used to generate a warning for the user and suggest security measures. The input is the mental state diagnosis result, and the output is a text-based warning and suggested measures. The generative AI generates the suggestions based on the diagnosis results, creating appropriate wording and specific actions in text format.

[0200] Step 6: Convert to audio format

[0201] The generated text suggestions are converted into audio data using TTS (such as Google Cloud Text-to-Speech API) technology. The input is the text suggestions from the generative AI, and the output is a digital audio file. Specifically, this involves sending text to the TTS API and receiving it as an audio file (e.g., in MP3 format).

[0202] Step 7: Sending audio data to the user device

[0203] The server sends the generated voice data to the user's terminal. The input is a voice data file, and the output is the transmission of the voice data to the user's terminal. The server packetizes the voice file and sends it to the user's terminal via the Internet.

[0204] Step 8: Playing back audio data and notifying users

[0205] The user's device plays the received audio data and notifies the user. The input is the transmitted audio data, and the output is an audio notification that reaches the user's ears. This includes the specific actions of the device's media player playing the audio file and informing the user of the suggestion.

[0206] The above steps realize a system that allows users to receive warnings and suggestions for security measures at appropriate times.

[0207] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0208] MODE FOR CARRYING OUT THE INVENTION

[0209] This invention is a system that collects a user's physical and emotional information, analyzes and diagnoses the user's mental state using artificial intelligence (AI), and provides appropriate positive suggestions in the form of voice, as needed. This system is composed of a wearable device, a communication means, a server, an AI model, a generative AI, an emotion engine, voice conversion technology, and a user device.

[0210] Terminal

[0211] First, the user puts on a wearable device. The device is equipped with a heart rate sensor, pedometer, body temperature sensor, sleep tracker, sweat sensor, brainwave sensor, and camera, and collects real-time physical information from the user's daily life. The device is also equipped with an emotion engine that recognizes emotions from the user's facial and voice data. The device has the function of periodically sending the collected data to a server.

[0212] server

[0213] The server receives the user's physical and emotional information sent from the device and stores it in a database. The stored data is then analyzed using an AI model. The AI ​​model uses machine learning and deep learning algorithms to diagnose the user's mental state based on data such as the user's heart rate, number of steps, sleep time, body temperature, sweating, brain waves, facial expressions, and voice tone.

[0214] Once the AI ​​model has completed its mental state diagnosis, the generative AI then generates positive suggestions for the user based on the diagnosis. For example, if the user's heart rate is high and they are judged to be stressed, the generative AI may suggest "take a short break." If the collected emotional data indicates that the user is "sad," the generative AI may suggest "taking your mood into consideration and refreshing yourself."

[0215] The generated suggestions are converted into audio format. The server generates the suggestions as audio files and sends them to the user's device. The audio files are generated using Text-to-Speech (TTS) technology.

[0216] User

[0217] The user's device receives and plays audio files from the server. The user listens to the audio suggestions provided by the device and takes action as necessary. In this way, users can manage their mental state in real time in their daily lives and receive positive advice at the right time.

[0218] Specific examples

[0219] For example, if a user begins to show signs of overwork after a day at work and their facial expression is also confirmed to be tired, the wearable device will detect an increase in heart rate, a decrease in steps, lack of sleep, and a change in sadness in their facial expression. This data is immediately sent to the server, and the AI ​​model analyzes it, determining that the state is "fatigue" and "sad." Based on this diagnosis, the generative AI generates positive suggestions such as "I recommend you take a short break" or "Take a walk to change your mood." These suggestions are sent to the user's device as an audio file, which the device plays to notify the user. The user can then take a break and prevent overwork and depression.

[0220] This system will enable the early detection and prevention of mental illness, helping to maintain the mental health of individuals, and is also expected to improve productivity for companies and society as a whole.

[0221] The processing flow will be explained below.

[0222] Step 1:

[0223] The device collects the user's physical information. The wearable device is equipped with a heart rate sensor, pedometer, body temperature sensor, sleep tracker, sweat sensor, brainwave sensor, camera, etc., and obtains data from these sensors in real time.

[0224] Step 2:

[0225] The device transmits the collected data to a server via the internet or wireless communication, either periodically or in real time as needed.

[0226] Step 3:

[0227] The server receives the data sent from the terminal via the Internet or wireless communication and temporarily stores the received data.

[0228] Step 4:

[0229] The server stores the received data in a database, where data for each user is recorded and managed so that it can be accessed later as needed.

[0230] Step 5:

[0231] The server preprocesses the stored data, scaling and normalizing it to convert it into a format that can be input to an AI model.

[0232] Step 6:

[0233] The server inputs the preprocessed data into an AI model that uses machine learning and deep learning algorithms to analyze the user's heart rate, steps, sleep time, body temperature, sweating, brain waves, facial expressions, and other data to diagnose the user's mental state.

[0234] Step 7:

[0235] The server analyzes the user's facial and voice data using an emotion engine, which identifies emotions from the user's facial expressions and voice tone and uses that information as additional data.

[0236] Step 8:

[0237] The server combines the analysis results of the AI ​​model and the emotion engine to diagnose the user's overall mental state, taking into account not only physical information but also emotional information such as "sadness" or "stress."

[0238] Step 9:

[0239] The server uses generative AI to generate positive suggestions based on the diagnosis results. For example, it makes specific suggestions such as "Take a short break" or "Try some light exercise to change your mood" based on a comprehensive assessment of physical information and emotional state.

[0240] Step 10:

[0241] The server converts the generated suggestions into audio format using Text-to-Speech (TTS) technology, creating a generated audio file.

[0242] Step 11:

[0243] The server sends the audio file to the user's device, which is then transferred to the user's device via the Internet or wireless communication.

[0244] Step 12:

[0245] The device receives the audio file sent from the server and stores it in the device's internal memory.

[0246] Step 13:

[0247] The device will play the audio file to notify the user, and the audio will be played through the device's speaker so the user can hear the suggestion.

[0248] Step 14:

[0249] The user follows the audio suggestions and takes appropriate actions, such as taking a break, taking a walk to refresh their mind, or taking deep breaths, to improve their mental state.

[0250] Example 2

[0251] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0252] The challenge is to accurately grasp the user's mental state in real time and provide positive behavioral suggestions at the appropriate time to realize the early detection and prevention of mental illness. In particular, it is necessary to comprehensively analyze the user's physical and emotional information and provide individually tailored suggestions in voice format to relieve stress and fatigue in daily life and maintain mental health.

[0253] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for collecting the user's physical information, a means for transmitting the collected user's physical information and emotional information to the server, a means for diagnosing the user's mental state using an artificial intelligence model based on the collected user's physical information and emotional information, and a means for converting the generated positive suggestions into audio format. This makes it possible to collect and analyze the user's physical information and emotional information in real time and provide accurate positive suggestions in audio format.

[0254] "User" refers to an individual who uses the system and provides physical and emotional information.

[0255] "Physical information" refers to the user's general physiological data, such as heart rate, number of steps, body temperature, sleep, sweat, and brain waves.

[0256] "Emotional information" refers to data about a user's emotional state as determined by their facial expressions and vocal tone.

[0257] A "wearable device" refers to a device worn by a user that collects physical and emotional information using a heart rate sensor, pedometer, body temperature sensor, sleep tracker, sweat sensor, brain wave sensor, camera, etc.

[0258] "Server" refers to a computer system that receives, stores, analyzes, and manages collected user physical and emotional information.

[0259] An "artificial intelligence model" is a program that uses machine learning and deep learning algorithms to analyze collected data and diagnose the user's mental state.

[0260] A "generative artificial intelligence model" refers to an artificial intelligence program that generates positive suggestions appropriate for the user based on diagnostic results.

[0261] "Positive suggestions" refer to suggestions for specific actions or ways of thinking to improve the user's mental state.

[0262] "Audio format" refers to the state in which the generated positive suggestions are converted into audio data using text-to-speech technology.

[0263] "Terminal" refers to a computer device used by a user that has the function of receiving and playing audio data sent from a server.

[0264] This invention is a system that collects a user's physical and emotional information, analyzes and diagnoses the user's mental state using artificial intelligence (AI), and provides appropriate positive suggestions in the form of voice, as needed. This system is composed of a wearable device, a communication means, a server, an AI model, a generative AI, an emotion engine, voice conversion technology, and a user device.

[0265] Terminal

[0266] The user first puts on a wearable device, which is equipped with a heart rate sensor, pedometer, temperature sensor, sleep tracker, sweat sensor, brainwave sensor, and camera to collect real-time physical information from the user's daily life. For example, the heart rate sensor measures the heart rate, the pedometer counts the number of steps, and the temperature sensor monitors the body temperature.

[0267] The device is equipped with an emotion engine that recognizes emotions from the user's facial expressions and voice data, and the collected data is periodically sent to a server via communication methods such as Wi-Fi, Bluetooth, and LTE.

[0268] server

[0269] The server receives the user's physical and emotional information sent from the device and stores it in a database. The stored data is then analyzed using an artificial intelligence model that uses machine learning and deep learning algorithms to diagnose the user's mental state based on data such as the user's heart rate, number of steps, sleep time, body temperature, sweating, brain waves, facial expressions, and voice tone.

[0270] Once the mental state diagnosis is complete, the generative AI then generates positive suggestions for the user based on the diagnosis. For example, if the user's heart rate is high and they are judged to be stressed, the generative AI may suggest "take a short break." If the collected emotional data indicates that the user is "sad," the AI ​​may make specific suggestions such as "consider your mood and we recommend that you refresh yourself."

[0271] The generated suggestions are converted into audio format. The server generates the suggestions as audio files and sends them to the user's device. The audio files are generated using Text-to-Speech (TTS) technology.

[0272] User

[0273] The user's device receives and plays audio files from the server. The user listens to the audio suggestions provided by the device and takes action as needed. This process allows users to manage their mental state in real time in their daily lives and receive positive advice at the right time.

[0274] Specific examples

[0275] For example, if a user begins to show signs of overwork after a day at work and facial fatigue is detected, the wearable device will detect an increase in heart rate, a decrease in steps, lack of sleep, and a change in facial expression to sadness. This data is immediately sent to a server, where an AI model analyzes it and determines that the state is "fatigue" and "sad." Based on this diagnosis, the generative AI generates positive suggestions such as "I recommend you take a short break" or "Take a walk to change your mood." This suggestion is sent to the user's device as an audio file, which the device plays to notify the user. The user can then take a break and prevent overwork and depression.

[0276] Prompt Sentence Examples

[0277] Below are examples of prompts to the system to analyze and diagnose the user's mental state.

[0278] Please provide data such as the user's heart rate, steps, sleep time, body temperature, sweat, brainwaves, facial expressions, and voice tone. Based on this, please diagnose the current mental state and generate appropriate positive suggestions. Please give us some example suggestions if the user's mental state is judged to be "fatigue" and "sad."

[0279] By inputting this prompt into a generative AI model, specific positive suggestions are generated.

[0280] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0281] Step 1: The user puts on the wearable device

[0282] A user puts on a wearable device equipped with a heart rate sensor, pedometer, body temperature sensor, sleep tracker, sweat sensor, brainwave sensor, camera, etc. The device automatically starts up and becomes ready. The input is the user's wearing action, and the output is the wearable device's state when it is ready to start measuring.

[0283] Step 2: The device collects physical and emotional information

[0284] The device measures data such as heart rate, steps, body temperature, sleep, sweat, and brain waves in real time. It uses an emotion engine to analyze the user's facial expressions and voice tone via a camera and microphone to collect emotional information. The input is the user's physical and emotional information, and the output is the collected digital data.

[0285] Step 3: The device sends the data to the server

[0286] The device sends the collected physical and emotional information to a server at regular intervals (e.g., every hour). Wi-Fi, Bluetooth, and LTE are used as communication methods. The input is the collected digital data, and the output is the data sent to the server.

[0287] Step 4: The server receives the data and stores it in the database

[0288] The server receives the data sent from the terminal. The received data is stored in a database and organized by user. The input is the data sent from the terminal, and the output is the data stored in the database.

[0289] Step 5: The server analyzes the data using the AI ​​model

[0290] The server inputs the data stored in the database into the AI ​​model, which then uses machine learning and deep learning algorithms to analyze the data and diagnose the user's mental state. The input is the data in the database, and the output is the diagnosis result.

[0291] Step 6: The server generates positive suggestions using generative AI

[0292] The server inputs a prompt to the generative AI based on the diagnosis result. The generative AI generates a positive suggestion appropriate for the user based on the diagnosis result. The input is the diagnosis result and the prompt, and the output is a specific positive suggestion.

[0293] Step 7: The server converts the proposal into an audio file and sends it to the device.

[0294] The server converts the suggestions output by the generative AI into audio files using text-to-speech (TTS) technology. The generated audio data is sent to the user's device. The input is the text data of the positive suggestions, and the output is an audio file.

[0295] Step 8: The user receives voice suggestions from the device

[0296] The user's device receives the audio file sent from the server. The device plays the audio file and notifies the user of the positive suggestion. The input is the audio file sent from the server, and the output is the played audio suggestion.

[0297] (Application example 2)

[0298] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0299] Currently, improving work efficiency and managing safety are important issues in factories. In harsh working environments, it is necessary to improve productivity and prevent accidents by monitoring workers' stress levels and mental and physical health in real time and encouraging them to rest and refresh at appropriate times. However, it has been difficult to achieve this appropriately using conventional methods. The present invention aims to solve this problem by providing a system that monitors the mental stress and fatigue of factory workers in real time and provides appropriate positive suggestions via voice.

[0300] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0301] In this invention, the server includes means for collecting user's physical information, means for transmitting the collected user's physical information to the server, and means for grasping the mental stress and fatigue of factory workers in real time and notifying them by voice of positive suggestions at appropriate times. This makes it possible to monitor the health status of factory workers in real time and encourage them to take appropriate rest and refresh themselves as needed.

[0302] "User" refers to a subject whose physical and emotional information is collected, and who receives an analysis of their mental state and positive suggestions.

[0303] "Physical information" refers to data that indicates the user's physiological and health status, such as the user's heart rate, number of steps, sleep time, body temperature, sweating, brain waves, muscle movements, and facial expressions.

[0304] "Server" refers to a computer system that stores and manages a user's physical and emotional information and analyzes this data using an AI model.

[0305] An "artificial intelligence model" refers to a program that uses machine learning and deep learning algorithms to diagnose a user's mental state based on their physical and emotional information.

[0306] "Positive suggestions" refer to advice and behavioral instructions to improve the user's mental and physical health based on the diagnosis of the user's mental state.

[0307] "Audio format" refers to a format in which text data is converted into audio, and refers to a means of providing audible feedback to the user.

[0308] "Factory workers" refers to workers who perform various tasks in a factory, and are the target users of this system.

[0309] "Stress" refers to a state in which the user feels strained both physically and mentally, and is diagnosed based on physical and emotional information.

[0310] "Fatigue" refers to a state in which the user feels tired and in need of rest, and is diagnosed based on physical and emotional information.

[0311] "Audio notification" refers to a means of converting generated positive suggestions into audio format and communicating them to the user in real time.

[0312] MODE FOR CARRYING OUT THE INVENTION

[0313] The present invention is a system that collects physical and emotional information from factory workers, analyzes and diagnoses their mental state using artificial intelligence, and provides positive suggestions in the form of voice as needed. To implement this system, the following configuration and procedures are used.

[0314] Hardware and software used

[0315] Wearable devices: Equipped with heart rate sensors, body temperature sensors, pedometers, cameras, etc., they collect real-time physical information from users' daily lives.

[0316] Server: A computer system for running AI models and generative AI. It also stores and analyzes data.

[0317] User device: A smartphone or a specific work device is used to display collected data and provide voice feedback.

[0318] System Configuration

[0319] 1. Data collection: Factory workers wear wearable devices to collect physical information such as heart rate, body temperature, number of steps taken, and facial expression data. This data is sent to a server in real time.

[0320] 2. Data analysis: The server analyzes the received data using an AI model to diagnose the worker's mental state (relaxation, stress, fatigue, etc.). The main software used includes machine learning libraries such as TensorFlow and Keras.

[0321] 3. Positive suggestion generation: Based on the analysis results, the generative AI generates positive suggestions according to the worker's condition. For example, if it determines that the worker is under high stress, it will generate a suggestion such as "Take a short break and take a deep breath."

[0322] 4. Voice notification: The generated suggestions are converted into voice format using Text-to-Speech (TTS) technology. This voice data is sent to the user's device and notified to the worker in real time. The TTS technology uses the pyttsx3 library, among others.

[0323] Specific examples

[0324] If a factory worker sends data to a wearable device showing a "heart rate over 100," "body temperature 37.5 degrees," "2000 steps taken," and a "sad" facial expression, the server will use an AI model to diagnose "fatigue." Based on this diagnosis, the generating AI will generate a suggestion such as "Get hydrated and refresh yourself a bit," which will be communicated to the worker via voice.

[0325] Prompt Sentence Examples

[0326] "Design an application that uses a wearable device to collect heart rate, body temperature, steps, and facial expression data, analyzes them with an AI model, and generates positive suggestions based on the user's mental state and notifies them via voice."

[0327] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0328] Step 1:

[0329] Data collection

[0330] The user wears a wearable device, which collects real-time physical information such as heart rate, body temperature, number of steps, and facial expression data. Specifically, data is collected using a heart rate sensor, body temperature sensor, pedometer, camera, etc. This data is temporarily stored in the wearable device.

[0331] Input: Heart rate, body temperature, steps, facial expression data

[0332] Output: Collected physical information data

[0333] Step 2:

[0334] Data transmission

[0335] The collected physical information data is transmitted in real time from the wearable device to a server using communication methods such as Bluetooth or Wi-Fi.

[0336] Input: Collected physical information data

[0337] Output: Data sent to the server

[0338] Step 3:

[0339] Data storage

[0340] The server receives the transmitted physical information data and stores it in a database, where records for each user are kept and used for later analysis.

[0341] Input: Data sent to the server

[0342] Output: Saved database records

[0343] Step 4:

[0344] Data analysis

[0345] The server analyzes the stored data using an AI model. Specifically, it uses machine learning libraries such as TensorFlow and Keras to diagnose the user's mental state. At this time, parameters such as heart rate, body temperature, number of steps, and facial expression are extracted from the input data and provided as input to the AI ​​model. The model's output is the user's mental state (relaxed, stressed, fatigue, etc.).

[0346] Input: Saved database records

[0347] Output: Diagnosed mental condition

[0348] Step 5:

[0349] Positive suggestion generation

[0350] The server uses the generative AI model to generate positive suggestions for the user based on the diagnostic results of the AI ​​model. The generative AI generates text-based suggestions based on the prompts, creating specific advice tailored to the user's condition.

[0351] Input: diagnosed mental condition

[0352] Output: Generated positive suggestions

[0353] Step 6:

[0354] Audio conversion

[0355] The generated textual positive suggestions are then converted into audio using Text-to-Speech (TTS) technology, such as the pyttsx3 library, to generate a user-friendly audio file.

[0356] Input: Generated positive suggestions

[0357] Output: Proposal converted to audio format

[0358] Step 7:

[0359] Audio data transmission

[0360] The server converts the suggestions into audio format and sends them to the user's device via the Internet or a local network.

[0361] Input: Suggestions converted to audio format

[0362] Output: Audio data sent to the device

[0363] Step 8:

[0364] Audio notifications

[0365] The user's device plays the received audio data and notifies the user. The user listens to the audio suggestions and takes action as necessary. Specific examples include audio instructions such as "Take a short break" or "Drink some water."

[0366] Input: Audio data sent to the device

[0367] Output: Audio suggestion notification

[0368] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0369] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0370] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0371] [Second embodiment]

[0372] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0373] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0374] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0375] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0376] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0377] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0378] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0379] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0380] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0381] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0382] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0383] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0384] MODE FOR CARRYING OUT THE INVENTION

[0385] This system collects a user's physical information, analyzes and diagnoses the user's mental state using artificial intelligence (AI), and provides appropriate positive suggestions in the form of voice, as needed. This system is composed of a wearable device, a communication means, a server, an AI model, a generative AI, voice conversion technology, and a user device.

[0386] Terminal

[0387] First, the user puts on a wearable device. This device is equipped with a heart rate sensor, pedometer, body temperature sensor, sleep tracker, sweat sensor, brain wave sensor, camera, etc., and collects physical information from the user's daily life in real time. The device has the function of periodically sending the collected data to a server.

[0388] server

[0389] The server receives the user's physical information sent from the device and stores it in a database. The stored data is then analyzed using an AI model. The AI ​​model uses machine learning and deep learning algorithms to diagnose the user's mental state based on data such as the user's heart rate, number of steps, sleep time, body temperature, sweating, brain waves, and facial expressions.

[0390] Once the AI ​​model has completed its mental state diagnosis, the generative AI then generates positive suggestions for the user based on the diagnosis. For example, if the user's heart rate is high and they are under stress, the generative AI will suggest "take a short break."

[0391] The generated suggestions are converted into audio format. The server generates the suggestions as audio files and sends them to the user's device. The audio files are generated using Text-to-Speech (TTS) technology.

[0392] User

[0393] The user's device receives and plays audio files from the server. The user listens to the audio suggestions provided by the device and takes action as necessary. In this way, users can manage their mental state in real time in their daily lives and receive positive advice at the right time.

[0394] Specific examples

[0395] For example, if a user begins to show signs of overwork after a day at work, the wearable device will detect an increase in heart rate, a decrease in steps, and lack of sleep. This data is immediately sent to a server, where an AI model analyzes it and determines that the user is in a state of "fatigue." Based on this diagnosis, the generative AI generates a positive suggestion, such as "We recommend that you take a short break." This suggestion is sent to the user's device as an audio file, which the device plays to notify the user. The user can then take a break and prevent overwork.

[0396] This system will enable the early detection and prevention of mental illness, helping to maintain the mental health of individuals, and is also expected to improve productivity for companies and society as a whole.

[0397] The processing flow will be explained below.

[0398] Step 1:

[0399] The device collects the user's physical information. The wearable device is equipped with a heart rate sensor, pedometer, body temperature sensor, sleep tracker, sweat sensor, brainwave sensor, camera, etc., and obtains data from these sensors in real time.

[0400] Step 2:

[0401] The device transmits the collected data to a server via the internet or wireless communication, either periodically or in real time as needed.

[0402] Step 3:

[0403] The server receives the data sent from the terminal via the Internet or wireless communication and temporarily stores the received data.

[0404] Step 4:

[0405] The server stores the received data in a database, where data for each user is recorded and managed so that it can be accessed later as needed.

[0406] Step 5:

[0407] The server preprocesses the stored data, scaling and normalizing it to convert it into a format that can be input to an AI model.

[0408] Step 6:

[0409] The server inputs the preprocessed data into an AI model that uses machine learning and deep learning algorithms to analyze the user's heart rate, steps, sleep time, body temperature, sweating, brain waves, facial expressions, and other data to diagnose the user's mental state.

[0410] Step 7:

[0411] The server uses generative AI to generate positive suggestions based on the diagnosis results. For example, if fatigue is detected, the server will make specific suggestions such as "Take a short break."

[0412] Step 8:

[0413] The server converts the generated suggestions into audio format using Text-to-Speech (TTS) technology, creating a generated audio file.

[0414] Step 9:

[0415] The server sends the audio file to the user's device, which is then transferred to the user's device via the Internet or wireless communication.

[0416] Step 10:

[0417] The device receives the audio file sent from the server and stores it in the device's internal memory.

[0418] Step 11:

[0419] The device will play the audio file to notify the user, and the audio will be played through the device's speaker so the user can hear the suggestion.

[0420] Step 12:

[0421] The user follows the audio suggestions and takes appropriate actions, such as taking a break or taking a deep breath, to improve their mental state.

[0422] Example 1

[0423] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0424] In modern society, many people are prone to high levels of stress and anxiety, but few systems exist that can adequately monitor their mental state and provide effective countermeasures in real time. Even when such systems exist, they face the challenge of accurately acquiring a user's physical information and providing prompt and appropriate advice based on that information. Furthermore, there are currently insufficient technological means to provide effective mental care while protecting the user's privacy.

[0425] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0426] In this invention, the server includes means for transmitting the user's physical information to a central computer, means for storing and managing the collected user's physical information in the central computer, means for diagnosing the mental state using an artificial intelligence model based on the collected user's physical information, voice synthesis means for converting the generated positive suggestions into voice format, means for transmitting the generated positive suggestions as voice data to the user's terminal, and means for the terminal to play back and present the voice data to the user. This enables real-time mental care based on the user's physical information, and can effectively support stress relief and maintenance of mental health.

[0427] "User's physical information" refers to information about the user's physical condition, including data such as heart rate, number of steps, sleep time, body temperature, sweating, brain waves, and facial expressions.

[0428] A "central computer" is a computer system such as a server or database that stores, manages, and analyzes a user's physical information.

[0429] An "artificial intelligence model" is a software model that uses algorithms such as machine learning and deep learning to analyze a user's physical information and diagnose their mental state.

[0430] A "generative model" is an algorithm or software that generates positive suggestions for users based on the diagnostic results analyzed by artificial intelligence.

[0431] "Speech synthesis means" means means, including text-to-speech (TTS) technology, for converting textual positive suggestions into speech form.

[0432] A "terminal" is an electronic device used by a user, such as a smartphone or computer, that has the function of receiving and playing back audio data sent from a server.

[0433] This invention is a system that collects a user's physical information, analyzes and diagnoses the user's mental state using artificial intelligence (AI), and provides positive suggestions in the form of voice. This system is composed of a wearable device, a communication means, a central computer, an AI model, a generative AI model, a voice synthesis means, and a user device.

[0434] Terminal

[0435] The user wears a wearable device equipped with a heart rate sensor, pedometer, body temperature sensor, sleep tracker, sweat sensor, brain wave sensor, camera, etc., to collect real-time physical information from the user's daily life. The device has the function of periodically transmitting the collected data to a central computer.

[0436] Server (central computer)

[0437] The central computer receives the user's physical information sent from the device and stores it in a database. The stored data is analyzed using an AI model. The AI ​​model uses machine learning and deep learning algorithms to diagnose the user's mental state from data such as the user's heart rate, number of steps, sleep time, body temperature, sweating, brain waves, and facial expressions.

[0438] For example, if a user's heart rate is higher than normal and their step count is decreasing, the AI ​​model will diagnose the user's mental state as "stressed." Based on this diagnosis, the generative AI model will generate positive suggestions such as "Take a short break."

[0439] The generated suggestions are converted into an audio file by a speech synthesizer. The server then sends the audio file to the user's device. Text-to-speech (TTS) technology is used to generate the audio file.

[0440] User

[0441] The user's device has the ability to receive and play audio files from the server. The user listens to the audio suggestions presented by the device and takes action as necessary. For example, if the user receives a suggestion to "take a short break," the user can actually take a break and refresh their mind and body.

[0442] This system allows users to manage their mental state in real time in their daily lives and receive positive advice at the appropriate time. This will enable early detection and prevention of mental illness, and help maintain individual mental health. It is also expected to improve productivity in companies and society as a whole.

[0443] Specific examples

[0444] For example, if a user begins to show signs of fatigue after a day at work, the wearable device will detect an increase in heart rate, a decrease in steps, and lack of sleep. This data is immediately sent to a central computer, where an AI model analyzes it and determines that the user is in a "fatigue" state. Based on this diagnosis, the generative AI model generates a positive suggestion, such as "We recommend that you take a short break." This suggestion is sent to the user's device as an audio file, which the device plays to notify the user. The user can then take a break and prevent overwork.

[0445] Prompt Sentence Examples

[0446] Here are some example prompts to input to a generative AI model:

[0447] You have detected that the user's heart rate is higher than normal and their step count is decreasing. Create an appropriate suggestion for this user, such as "Take a short break."

[0448] Using this prompt, the generative AI model generates positive suggestions that are appropriate for the user's current state.

[0449] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0450] Step 1:

[0451] The device collects the user's physical information in real time.

[0452] How it works: The wearable device is equipped with a heart rate sensor, pedometer, body temperature sensor, sleep tracker, sweat sensor, brainwave sensor, and camera, which collect and store data internally.

[0453] Input: User's physical information

[0454] Output: Internally stored physical information data

[0455] Step 2:

[0456] The terminals transmit the collected data to a central computer.

[0457] Specific operation: The terminal uses Bluetooth or Wi-Fi to send all collected data to a central computer at regular intervals or based on specified conditions (e.g., when a certain amount of data is reached).

[0458] Input: Internally stored physical information data

[0459] Output: Data sent to the central computer

[0460] Step 3:

[0461] The server stores and manages the received data.

[0462] Specific operation: When the central computer receives data sent from the terminal, it organizes each sensor data by category and stores it in a database. The database is a NoSQL type, which enables high-speed reading and writing.

[0463] Input: Data sent from the terminal

[0464] Output: Data stored in the database

[0465] Step 4:

[0466] The server analyzes the stored data using an AI model.

[0467] Specific operation: Various sensor data is acquired from the database and input into the AI ​​model for analysis. The AI ​​model uses machine learning and deep learning algorithms to diagnose the user's mental state. For example, if the heart rate is high and the number of steps is low, it will be diagnosed as "under stress."

[0468] Input: Various sensor data stored in the database

[0469] Output: Diagnosis of the user's mental state

[0470] Step 5:

[0471] A generative AI model generates positive suggestions based on the diagnostic results.

[0472] Specific operation: Using the diagnostic results analyzed by the AI ​​model as input, the generative AI model uses prompt sentences to generate appropriate positive suggestions, such as "Your heart rate is high and you are under stress. Take a short break."

[0473] Input: Diagnosis of the user's mental state

[0474] Output: Generated positive suggestions (in text format)

[0475] Step 6:

[0476] The generated suggestions are converted into audio format.

[0477] How it works: The text-based suggestions output by the generative AI model are converted into audio using text-to-speech (TTS) technology.

[0478] Input: Generated positive suggestions (in text format)

[0479] Output: Audio file

[0480] Step 7:

[0481] The server sends the generated audio file to the user's terminal.

[0482] How it works: Once the audio file is generated, a central computer sends it over the internet to the user's smartphone or tablet.

[0483] Input: Audio file

[0484] Output: Audio file sent to the user's device

[0485] Step 8:

[0486] The terminal plays the audio file and presents it to the user.

[0487] Specific behavior: When the user's device receives the audio file, a notification will be displayed, and when the user opens the app, the audio file will be played. The user can decide what to do based on the audio suggestions.

[0488] Input: Audio file sent to the user's device

[0489] Output: Played audio suggestions

[0490] Step 9:

[0491] The user responds to the voice suggestions from the device and takes action.

[0492] Specific behavior: The user hears the suggestion to "take a short break" and actually takes a break, which is expected to bring the user's heart rate back to a normal range and relieve stress.

[0493] Input: Played speech suggestions

[0494] Output: User action (e.g., taking a break)

[0495] (Application example 1)

[0496] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0497] In modern society, real-time monitoring of a user's mental state and early detection of emergencies are important. However, conventional systems have had difficulty in quickly assessing a user's mental state and proposing appropriate security measures. To solve this problem, a system is needed that can accurately diagnose a user's mental state using their physical information and provide prompt advice on countermeasures in emergencies.

[0498] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0499] In this invention, the server includes means for collecting user physical information, means for transmitting the collected user physical information to the server, means for storing and managing the collected user physical information in the server, means for diagnosing the mental state of the user using an artificial intelligence model based on the collected user physical information, means for generating a warning and security measure proposals based on the diagnosis results, means for converting the generated warning and security measure proposals into audio format, means for transmitting the generated warning and security measure proposals as audio data to the user's terminal, and means for the terminal to play the audio data and present it to the user. This makes it possible to monitor the user's mental state in real time, and when an abnormality is detected, to quickly issue a warning and propose appropriate security measures.

[0500] "User's physical information" refers to biometric data such as an individual's heart rate, number of steps, sleep time, body temperature, sweat, brain waves, muscle movements, and facial expressions.

[0501] "Means for collection" refers to a device or system that collects a user's physical information using sensors in a wearable terminal or smart device.

[0502] "Server" refers to a central processing unit for storing, managing, and analyzing collected user physical information.

[0503] "Means for storing and managing" refers to a system that stores a user's physical information in a database and accesses and updates it as needed.

[0504] An "artificial intelligence model" refers to an algorithm that uses machine learning and deep learning techniques to analyze and diagnose a user's mental state from collected data.

[0505] "Mental state" refers to the user's current psychological and emotional state.

[0506] "Diagnostic means" refers to a system that uses an artificial intelligence model to analyze collected data and determine the user's mental state.

[0507] "Warning and suggested security measures" refers to warnings and recommendations for safety measures issued to users based on the diagnostic results.

[0508] "Generating means" refers to a system or algorithm that generates warnings and security recommendations based on diagnostic results.

[0509] "Means for converting to audio format" refers to technology that converts the generated warnings and security recommendations into audio data (e.g., text-to-speech technology).

[0510] "Audio Data" means digital audio files containing warnings and security suggestions in converted audio format.

[0511] "Terminal" refers to a device held by a user (e.g., a smartphone or smart glasses).

[0512] "Means of presentation" refers to the function of the user's device to play audio data and notify the user.

[0513] 1. Data collection and transmission

[0514] Users wear wearable devices or smart devices equipped with heart rate sensors, pedometers, body temperature sensors, sweat sensors, and brain wave sensors. These sensors collect the user's physical information in real time. The collected data is sent to a server via Bluetooth or Wi-Fi.

[0515] 2. Data storage and management

[0516] The server stores and manages the received user's physical information in a database. The database is built using a database management system such as AWS RDS or MySQL, allowing for efficient storage, search, and analysis of large amounts of data.

[0517] 3. Diagnosis of mental conditions

[0518] The user's physical information stored on the server is analyzed using an artificial intelligence (AI) model, which uses deep learning frameworks such as TensorFlow and PyTorch, to diagnose the user's mental state from data such as the user's heart rate, body temperature, sweating, and brain waves.

[0519] 4. Generate warnings and security action suggestions

[0520] Based on the diagnosis results, a generative AI (such as OpenAI's GPT-3) is used to generate warnings and security recommendations for the user. The recommendations are customized to suit the situation, requiring appropriate attention. The recommendations are then converted into audio data using text-to-speech (TTS) technology. The Google Cloud Text-to-Speech API is used as the TTS technology.

[0521] 5. Sending and playing audio data

[0522] The generated voice data is sent from the server to the user's device (such as a smartphone or smart glasses). The device then plays the received voice data and notifies the user. This allows the user to receive warnings and suggestions for security measures at the appropriate time.

[0523] Specific examples

[0524] If a user begins to show signs of stress, the wearable device will detect an increase in heart rate and changes in body temperature. This data is immediately sent to a server, where an AI model analyzes it and diagnoses the user as being in a "high stress state." Based on this diagnosis, the generative AI generates a suggestion such as "Take a break now and try a relaxation technique." This suggestion is sent to the user's device as an audio file, which plays it and notifies the user.

[0525] Example prompt sentence:

[0526] The user is in a high mental state. What are the next security measures that should be taken?

[0527] Hardware and Software Examples

[0528] Wearable device: Equipped with heart rate sensor, body temperature sensor, sweat sensor, and brain wave sensor

[0529] Communication means: Bluetooth, Wi-Fi

[0530] Server: AWS EC2, Database (AWS RDS, MySQL)

[0531] AI models: TensorFlow, PyTorch

[0532] Generative AI: OpenAI GPT-3

[0533] TTS technology: Google Cloud Text-to-Speech API

[0534] User devices: smartphones, smart glasses

[0535] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0536] Step 1: Data collection and transmission

[0537] The user wears a wearable device. This device collects physical information such as heart rate, body temperature, sweating, and brain waves in real time. The collected data is sent to a device (such as a smartphone) via Bluetooth or Wi-Fi. The input is sensor data of physical information, and the output is data sent to the device. Pre-processing (e.g., data format conversion, compression, etc.) is performed within the device.

[0538] Step 2: Send data to the server

[0539] The physical information collected by the device is packetized and sent to a server via an internet connection. The input is the sensor data sent from the device, and the output is the data stored on the server. When the server receives the data, it checks its integrity and stores it in a database.

[0540] Step 3: Data storage and management

[0541] The server stores the received data in a database (e.g., AWS RDS, MySQL). The input is the sensor data sent to the server, and the output is well-formatted data stored in the database. Data processing here includes data validation and indexing as time-series data.

[0542] Step 4: Diagnose your mental condition

[0543] The stored data is analyzed by an AI model (such as TensorFlow or PyTorch). The input is physical information obtained from a database, and the output is a diagnosis of the user's mental state. The AI ​​model extracts various features (heart rate fluctuations, changes in body temperature, etc.) based on the collected data and predicts the user's mental state (e.g., stress level). Specific operations include inputting data into the model, preprocessing, and outputting the predicted results.

[0544] Step 5: Generate warnings and security recommendations

[0545] Based on the diagnosis results, a generative AI (e.g., OpenAI GPT-3) is used to generate a warning for the user and suggest security measures. The input is the mental state diagnosis result, and the output is a text-based warning and suggested measures. The generative AI generates the suggestions based on the diagnosis results, creating appropriate wording and specific actions in text format.

[0546] Step 6: Convert to audio format

[0547] The generated text suggestions are converted into audio data using TTS (such as Google Cloud Text-to-Speech API) technology. The input is the text suggestions from the generative AI, and the output is a digital audio file. Specifically, this involves sending text to the TTS API and receiving it as an audio file (e.g., in MP3 format).

[0548] Step 7: Sending audio data to the user device

[0549] The server sends the generated voice data to the user's terminal. The input is a voice data file, and the output is the transmission of the voice data to the user's terminal. The server packetizes the voice file and sends it to the user's terminal via the Internet.

[0550] Step 8: Playing back audio data and notifying users

[0551] The user's device plays the received audio data and notifies the user. The input is the transmitted audio data, and the output is an audio notification that reaches the user's ears. This includes the specific actions of the device's media player playing the audio file and informing the user of the suggestion.

[0552] The above steps realize a system that allows users to receive warnings and suggestions for security measures at appropriate times.

[0553] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0554] MODE FOR CARRYING OUT THE INVENTION

[0555] This invention is a system that collects a user's physical and emotional information, analyzes and diagnoses the user's mental state using artificial intelligence (AI), and provides appropriate positive suggestions in the form of voice, as needed. This system is composed of a wearable device, a communication means, a server, an AI model, a generative AI, an emotion engine, voice conversion technology, and a user device.

[0556] Terminal

[0557] First, the user puts on a wearable device. The device is equipped with a heart rate sensor, pedometer, body temperature sensor, sleep tracker, sweat sensor, brainwave sensor, and camera, and collects real-time physical information from the user's daily life. The device is also equipped with an emotion engine that recognizes emotions from the user's facial and voice data. The device has the function of periodically sending the collected data to a server.

[0558] server

[0559] The server receives the user's physical and emotional information sent from the device and stores it in a database. The stored data is then analyzed using an AI model. The AI ​​model uses machine learning and deep learning algorithms to diagnose the user's mental state based on data such as the user's heart rate, number of steps, sleep time, body temperature, sweating, brain waves, facial expressions, and voice tone.

[0560] Once the AI ​​model has completed its mental state diagnosis, the generative AI then generates positive suggestions for the user based on the diagnosis. For example, if the user's heart rate is high and they are judged to be stressed, the generative AI may suggest "take a short break." If the collected emotional data indicates that the user is "sad," the generative AI may suggest "taking your mood into consideration and refreshing yourself."

[0561] The generated suggestions are converted into audio format. The server generates the suggestions as audio files and sends them to the user's device. The audio files are generated using Text-to-Speech (TTS) technology.

[0562] User

[0563] The user's device receives and plays audio files from the server. The user listens to the audio suggestions provided by the device and takes action as necessary. In this way, users can manage their mental state in real time in their daily lives and receive positive advice at the right time.

[0564] Specific examples

[0565] For example, if a user begins to show signs of overwork after a day at work and their facial expression is also confirmed to be tired, the wearable device will detect an increase in heart rate, a decrease in steps, lack of sleep, and a change in sadness in their facial expression. This data is immediately sent to the server, and the AI ​​model analyzes it, determining that the state is "fatigue" and "sad." Based on this diagnosis, the generative AI generates positive suggestions such as "I recommend you take a short break" or "Take a walk to change your mood." These suggestions are sent to the user's device as an audio file, which the device plays to notify the user. The user can then take a break and prevent overwork and depression.

[0566] This system will enable the early detection and prevention of mental illness, helping to maintain the mental health of individuals, and is also expected to improve productivity for companies and society as a whole.

[0567] The processing flow will be explained below.

[0568] Step 1:

[0569] The device collects the user's physical information. The wearable device is equipped with a heart rate sensor, pedometer, body temperature sensor, sleep tracker, sweat sensor, brainwave sensor, camera, etc., and obtains data from these sensors in real time.

[0570] Step 2:

[0571] The device transmits the collected data to a server via the internet or wireless communication, either periodically or in real time as needed.

[0572] Step 3:

[0573] The server receives the data sent from the terminal via the Internet or wireless communication and temporarily stores the received data.

[0574] Step 4:

[0575] The server stores the received data in a database, where data for each user is recorded and managed so that it can be accessed later as needed.

[0576] Step 5:

[0577] The server preprocesses the stored data, scaling and normalizing it to convert it into a format that can be input to an AI model.

[0578] Step 6:

[0579] The server inputs the preprocessed data into an AI model that uses machine learning and deep learning algorithms to analyze the user's heart rate, steps, sleep time, body temperature, sweating, brain waves, facial expressions, and other data to diagnose the user's mental state.

[0580] Step 7:

[0581] The server analyzes the user's facial and voice data using an emotion engine, which identifies emotions from the user's facial expressions and voice tone and uses that information as additional data.

[0582] Step 8:

[0583] The server combines the analysis results of the AI ​​model and the emotion engine to diagnose the user's overall mental state, taking into account not only physical information but also emotional information such as "sadness" or "stress."

[0584] Step 9:

[0585] The server uses generative AI to generate positive suggestions based on the diagnosis results. For example, it makes specific suggestions such as "Take a short break" or "Try some light exercise to change your mood" based on a comprehensive assessment of physical information and emotional state.

[0586] Step 10:

[0587] The server converts the generated suggestions into audio format using Text-to-Speech (TTS) technology, creating a generated audio file.

[0588] Step 11:

[0589] The server sends the audio file to the user's device, which is then transferred to the user's device via the Internet or wireless communication.

[0590] Step 12:

[0591] The device receives the audio file sent from the server and stores it in the device's internal memory.

[0592] Step 13:

[0593] The device will play the audio file to notify the user, and the audio will be played through the device's speaker so the user can hear the suggestion.

[0594] Step 14:

[0595] The user follows the audio suggestions and takes appropriate actions, such as taking a break, taking a walk to refresh their mind, or taking deep breaths, to improve their mental state.

[0596] Example 2

[0597] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0598] The challenge is to accurately grasp the user's mental state in real time and provide positive behavioral suggestions at the appropriate time to realize the early detection and prevention of mental illness. In particular, it is necessary to comprehensively analyze the user's physical and emotional information and provide individually tailored suggestions in voice format to relieve stress and fatigue in daily life and maintain mental health.

[0599] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for collecting the user's physical information, a means for transmitting the collected user's physical information and emotional information to the server, a means for diagnosing the user's mental state using an artificial intelligence model based on the collected user's physical information and emotional information, and a means for converting the generated positive suggestions into audio format. This makes it possible to collect and analyze the user's physical information and emotional information in real time and provide accurate positive suggestions in audio format.

[0600] "User" refers to an individual who uses the system and provides physical and emotional information.

[0601] "Physical information" refers to the user's general physiological data, such as heart rate, number of steps, body temperature, sleep, sweat, and brain waves.

[0602] "Emotional information" refers to data about a user's emotional state as determined by their facial expressions and vocal tone.

[0603] A "wearable device" refers to a device worn by a user that collects physical and emotional information using a heart rate sensor, pedometer, body temperature sensor, sleep tracker, sweat sensor, brain wave sensor, camera, etc.

[0604] "Server" refers to a computer system that receives, stores, analyzes, and manages collected user physical and emotional information.

[0605] An "artificial intelligence model" is a program that uses machine learning and deep learning algorithms to analyze collected data and diagnose the user's mental state.

[0606] A "generative artificial intelligence model" refers to an artificial intelligence program that generates positive suggestions appropriate for the user based on diagnostic results.

[0607] "Positive suggestions" refer to suggestions for specific actions or ways of thinking to improve the user's mental state.

[0608] "Audio format" refers to the state in which the generated positive suggestions are converted into audio data using text-to-speech technology.

[0609] "Terminal" refers to a computer device used by a user that has the function of receiving and playing audio data sent from a server.

[0610] This invention is a system that collects a user's physical and emotional information, analyzes and diagnoses the user's mental state using artificial intelligence (AI), and provides appropriate positive suggestions in the form of voice, as needed. This system is composed of a wearable device, a communication means, a server, an AI model, a generative AI, an emotion engine, voice conversion technology, and a user device.

[0611] Terminal

[0612] The user first puts on a wearable device, which is equipped with a heart rate sensor, pedometer, temperature sensor, sleep tracker, sweat sensor, brainwave sensor, and camera to collect real-time physical information from the user's daily life. For example, the heart rate sensor measures the heart rate, the pedometer counts the number of steps, and the temperature sensor monitors the body temperature.

[0613] The device is equipped with an emotion engine that recognizes emotions from the user's facial expressions and voice data, and the collected data is periodically sent to a server via communication methods such as Wi-Fi, Bluetooth, and LTE.

[0614] server

[0615] The server receives the user's physical and emotional information sent from the device and stores it in a database. The stored data is then analyzed using an artificial intelligence model that uses machine learning and deep learning algorithms to diagnose the user's mental state based on data such as the user's heart rate, number of steps, sleep time, body temperature, sweating, brain waves, facial expressions, and voice tone.

[0616] Once the mental state diagnosis is complete, the generative AI then generates positive suggestions for the user based on the diagnosis. For example, if the user's heart rate is high and they are judged to be stressed, the generative AI may suggest "take a short break." If the collected emotional data indicates that the user is "sad," the AI ​​may make specific suggestions such as "consider your mood and we recommend that you refresh yourself."

[0617] The generated suggestions are converted into audio format. The server generates the suggestions as audio files and sends them to the user's device. The audio files are generated using Text-to-Speech (TTS) technology.

[0618] User

[0619] The user's device receives and plays audio files from the server. The user listens to the audio suggestions provided by the device and takes action as needed. This process allows users to manage their mental state in real time in their daily lives and receive positive advice at the right time.

[0620] Specific examples

[0621] For example, if a user begins to show signs of overwork after a day at work and facial fatigue is detected, the wearable device will detect an increase in heart rate, a decrease in steps, lack of sleep, and a change in facial expression to sadness. This data is immediately sent to a server, where an AI model analyzes it and determines that the state is "fatigue" and "sad." Based on this diagnosis, the generative AI generates positive suggestions such as "I recommend you take a short break" or "Take a walk to change your mood." This suggestion is sent to the user's device as an audio file, which the device plays to notify the user. The user can then take a break and prevent overwork and depression.

[0622] Prompt Sentence Examples

[0623] Below are examples of prompts to the system to analyze and diagnose the user's mental state.

[0624] Please provide data such as the user's heart rate, steps, sleep time, body temperature, sweat, brainwaves, facial expressions, and voice tone. Based on this, please diagnose the current mental state and generate appropriate positive suggestions. Please give us some example suggestions if the user's mental state is judged to be "fatigue" and "sad."

[0625] By inputting this prompt into a generative AI model, specific positive suggestions are generated.

[0626] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0627] Step 1: The user puts on the wearable device

[0628] A user puts on a wearable device equipped with a heart rate sensor, pedometer, body temperature sensor, sleep tracker, sweat sensor, brainwave sensor, camera, etc. The device automatically starts up and becomes ready. The input is the user's wearing action, and the output is the wearable device's state when it is ready to start measuring.

[0629] Step 2: The device collects physical and emotional information

[0630] The device measures data such as heart rate, steps, body temperature, sleep, sweat, and brain waves in real time. It uses an emotion engine to analyze the user's facial expressions and voice tone via a camera and microphone to collect emotional information. The input is the user's physical and emotional information, and the output is the collected digital data.

[0631] Step 3: The device sends the data to the server

[0632] The device sends the collected physical and emotional information to a server at regular intervals (e.g., every hour). Wi-Fi, Bluetooth, and LTE are used as communication methods. The input is the collected digital data, and the output is the data sent to the server.

[0633] Step 4: The server receives the data and stores it in the database

[0634] The server receives the data sent from the terminal. The received data is stored in a database and organized by user. The input is the data sent from the terminal, and the output is the data stored in the database.

[0635] Step 5: The server analyzes the data using the AI ​​model

[0636] The server inputs the data stored in the database into the AI ​​model, which then uses machine learning and deep learning algorithms to analyze the data and diagnose the user's mental state. The input is the data in the database, and the output is the diagnosis result.

[0637] Step 6: The server generates positive suggestions using generative AI

[0638] The server inputs a prompt to the generative AI based on the diagnosis result. The generative AI generates a positive suggestion appropriate for the user based on the diagnosis result. The input is the diagnosis result and the prompt, and the output is a specific positive suggestion.

[0639] Step 7: The server converts the proposal into an audio file and sends it to the device.

[0640] The server converts the suggestions output by the generative AI into audio files using text-to-speech (TTS) technology. The generated audio data is sent to the user's device. The input is the text data of the positive suggestions, and the output is an audio file.

[0641] Step 8: The user receives voice suggestions from the device

[0642] The user's device receives the audio file sent from the server. The device plays the audio file and notifies the user of the positive suggestion. The input is the audio file sent from the server, and the output is the played audio suggestion.

[0643] (Application example 2)

[0644] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0645] Currently, improving work efficiency and managing safety are important issues in factories. In harsh working environments, it is necessary to improve productivity and prevent accidents by monitoring workers' stress levels and mental and physical health in real time and encouraging them to rest and refresh at appropriate times. However, it has been difficult to achieve this appropriately using conventional methods. The present invention aims to solve this problem by providing a system that monitors the mental stress and fatigue of factory workers in real time and provides appropriate positive suggestions via voice.

[0646] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0647] In this invention, the server includes means for collecting user's physical information, means for transmitting the collected user's physical information to the server, and means for grasping the mental stress and fatigue of factory workers in real time and notifying them by voice of positive suggestions at appropriate times. This makes it possible to monitor the health status of factory workers in real time and encourage them to take appropriate rest and refresh themselves as needed.

[0648] "User" refers to a subject whose physical and emotional information is collected, and who receives an analysis of their mental state and positive suggestions.

[0649] "Physical information" refers to data that indicates the user's physiological and health status, such as the user's heart rate, number of steps, sleep time, body temperature, sweating, brain waves, muscle movements, and facial expressions.

[0650] "Server" refers to a computer system that stores and manages a user's physical and emotional information and analyzes this data using an AI model.

[0651] An "artificial intelligence model" refers to a program that uses machine learning and deep learning algorithms to diagnose a user's mental state based on their physical and emotional information.

[0652] "Positive suggestions" refer to advice and behavioral instructions to improve the user's mental and physical health based on the diagnosis of the user's mental state.

[0653] "Audio format" refers to a format in which text data is converted into audio, and refers to a means of providing audible feedback to the user.

[0654] "Factory workers" refers to workers who perform various tasks in a factory, and are the target users of this system.

[0655] "Stress" refers to a state in which the user feels strained both physically and mentally, and is diagnosed based on physical and emotional information.

[0656] "Fatigue" refers to a state in which the user feels tired and in need of rest, and is diagnosed based on physical and emotional information.

[0657] "Audio notification" refers to a means of converting generated positive suggestions into audio format and communicating them to the user in real time.

[0658] MODE FOR CARRYING OUT THE INVENTION

[0659] The present invention is a system that collects physical and emotional information from factory workers, analyzes and diagnoses their mental state using artificial intelligence, and provides positive suggestions in the form of voice as needed. To implement this system, the following configuration and procedures are used.

[0660] Hardware and software used

[0661] Wearable devices: Equipped with heart rate sensors, body temperature sensors, pedometers, cameras, etc., they collect real-time physical information from users' daily lives.

[0662] Server: A computer system for running AI models and generative AI. It also stores and analyzes data.

[0663] User device: A smartphone or a specific work device is used to display collected data and provide voice feedback.

[0664] System Configuration

[0665] 1. Data collection: Factory workers wear wearable devices to collect physical information such as heart rate, body temperature, number of steps taken, and facial expression data. This data is sent to a server in real time.

[0666] 2. Data analysis: The server analyzes the received data using an AI model to diagnose the worker's mental state (relaxation, stress, fatigue, etc.). The main software used includes machine learning libraries such as TensorFlow and Keras.

[0667] 3. Positive suggestion generation: Based on the analysis results, the generative AI generates positive suggestions according to the worker's condition. For example, if it determines that the worker is under high stress, it will generate a suggestion such as "Take a short break and take a deep breath."

[0668] 4. Voice notification: The generated suggestions are converted into voice format using Text-to-Speech (TTS) technology. This voice data is sent to the user's device and notified to the worker in real time. The TTS technology uses the pyttsx3 library, among others.

[0669] Specific examples

[0670] If a factory worker sends data to a wearable device showing a "heart rate over 100," "body temperature 37.5 degrees," "2000 steps taken," and a "sad" facial expression, the server will use an AI model to diagnose "fatigue." Based on this diagnosis, the generating AI will generate a suggestion such as "Get hydrated and refresh yourself a bit," which will be communicated to the worker via voice.

[0671] Prompt Sentence Examples

[0672] "Design an application that uses a wearable device to collect heart rate, body temperature, steps, and facial expression data, analyzes them with an AI model, and generates positive suggestions based on the user's mental state and notifies them via voice."

[0673] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0674] Step 1:

[0675] Data collection

[0676] The user wears a wearable device, which collects real-time physical information such as heart rate, body temperature, number of steps, and facial expression data. Specifically, data is collected using a heart rate sensor, body temperature sensor, pedometer, camera, etc. This data is temporarily stored in the wearable device.

[0677] Input: Heart rate, body temperature, steps, facial expression data

[0678] Output: Collected physical information data

[0679] Step 2:

[0680] Data transmission

[0681] The collected physical information data is transmitted in real time from the wearable device to a server using communication methods such as Bluetooth or Wi-Fi.

[0682] Input: Collected physical information data

[0683] Output: Data sent to the server

[0684] Step 3:

[0685] Data storage

[0686] The server receives the transmitted physical information data and stores it in a database, where records for each user are kept and used for later analysis.

[0687] Input: Data sent to the server

[0688] Output: Saved database records

[0689] Step 4:

[0690] Data analysis

[0691] The server analyzes the stored data using an AI model. Specifically, it uses machine learning libraries such as TensorFlow and Keras to diagnose the user's mental state. At this time, parameters such as heart rate, body temperature, number of steps, and facial expression are extracted from the input data and provided as input to the AI ​​model. The model's output is the user's mental state (relaxed, stressed, fatigue, etc.).

[0692] Input: Saved database records

[0693] Output: Diagnosed mental condition

[0694] Step 5:

[0695] Positive suggestion generation

[0696] The server uses the generative AI model to generate positive suggestions for the user based on the diagnostic results of the AI ​​model. The generative AI generates text-based suggestions based on the prompts, creating specific advice tailored to the user's condition.

[0697] Input: diagnosed mental condition

[0698] Output: Generated positive suggestions

[0699] Step 6:

[0700] Audio conversion

[0701] The generated textual positive suggestions are then converted into audio using Text-to-Speech (TTS) technology, such as the pyttsx3 library, to generate a user-friendly audio file.

[0702] Input: Generated positive suggestions

[0703] Output: Proposal converted to audio format

[0704] Step 7:

[0705] Audio data transmission

[0706] The server converts the suggestions into audio format and sends them to the user's device via the Internet or a local network.

[0707] Input: Suggestions converted to audio format

[0708] Output: Audio data sent to the device

[0709] Step 8:

[0710] Audio notifications

[0711] The user's device plays the received audio data and notifies the user. The user listens to the audio suggestions and takes action as necessary. Specific examples include audio instructions such as "Take a short break" or "Drink some water."

[0712] Input: Audio data sent to the device

[0713] Output: Audio suggestion notification

[0714] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0715] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0716] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0717] [Third embodiment]

[0718] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0719] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0720] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0721] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0722] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0723] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0724] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0725] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0726] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0727] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0728] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0729] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0730] MODE FOR CARRYING OUT THE INVENTION

[0731] This system collects a user's physical information, analyzes and diagnoses the user's mental state using artificial intelligence (AI), and provides appropriate positive suggestions in the form of voice, as needed. This system is composed of a wearable device, a communication means, a server, an AI model, a generative AI, voice conversion technology, and a user device.

[0732] Terminal

[0733] First, the user puts on a wearable device. This device is equipped with a heart rate sensor, pedometer, body temperature sensor, sleep tracker, sweat sensor, brain wave sensor, camera, etc., and collects physical information from the user's daily life in real time. The device has the function of periodically sending the collected data to a server.

[0734] server

[0735] The server receives the user's physical information sent from the device and stores it in a database. The stored data is then analyzed using an AI model. The AI ​​model uses machine learning and deep learning algorithms to diagnose the user's mental state based on data such as the user's heart rate, number of steps, sleep time, body temperature, sweating, brain waves, and facial expressions.

[0736] Once the AI ​​model has completed its mental state diagnosis, the generative AI then generates positive suggestions for the user based on the diagnosis. For example, if the user's heart rate is high and they are under stress, the generative AI will suggest "take a short break."

[0737] The generated suggestions are converted into audio format. The server generates the suggestions as audio files and sends them to the user's device. The audio files are generated using Text-to-Speech (TTS) technology.

[0738] User

[0739] The user's device receives and plays audio files from the server. The user listens to the audio suggestions provided by the device and takes action as necessary. In this way, users can manage their mental state in real time in their daily lives and receive positive advice at the right time.

[0740] Specific examples

[0741] For example, if a user begins to show signs of overwork after a day at work, the wearable device will detect an increase in heart rate, a decrease in steps, and lack of sleep. This data is immediately sent to a server, where an AI model analyzes it and determines that the user is in a state of "fatigue." Based on this diagnosis, the generative AI generates a positive suggestion, such as "We recommend that you take a short break." This suggestion is sent to the user's device as an audio file, which the device plays to notify the user. The user can then take a break and prevent overwork.

[0742] This system will enable the early detection and prevention of mental illness, helping to maintain the mental health of individuals, and is also expected to improve productivity for companies and society as a whole.

[0743] The processing flow will be explained below.

[0744] Step 1:

[0745] The device collects the user's physical information. The wearable device is equipped with a heart rate sensor, pedometer, body temperature sensor, sleep tracker, sweat sensor, brainwave sensor, camera, etc., and obtains data from these sensors in real time.

[0746] Step 2:

[0747] The device transmits the collected data to a server via the internet or wireless communication, either periodically or in real time as needed.

[0748] Step 3:

[0749] The server receives the data sent from the terminal via the Internet or wireless communication and temporarily stores the received data.

[0750] Step 4:

[0751] The server stores the received data in a database, where data for each user is recorded and managed so that it can be accessed later as needed.

[0752] Step 5:

[0753] The server preprocesses the stored data, scaling and normalizing it to convert it into a format that can be input to an AI model.

[0754] Step 6:

[0755] The server inputs the preprocessed data into an AI model that uses machine learning and deep learning algorithms to analyze the user's heart rate, steps, sleep time, body temperature, sweating, brain waves, facial expressions, and other data to diagnose the user's mental state.

[0756] Step 7:

[0757] The server uses generative AI to generate positive suggestions based on the diagnosis results. For example, if fatigue is detected, the server will make specific suggestions such as "Take a short break."

[0758] Step 8:

[0759] The server converts the generated suggestions into audio format using Text-to-Speech (TTS) technology, creating a generated audio file.

[0760] Step 9:

[0761] The server sends the audio file to the user's device, which is then transferred to the user's device via the Internet or wireless communication.

[0762] Step 10:

[0763] The device receives the audio file sent from the server and stores it in the device's internal memory.

[0764] Step 11:

[0765] The device will play the audio file to notify the user, and the audio will be played through the device's speaker so the user can hear the suggestion.

[0766] Step 12:

[0767] The user follows the audio suggestions and takes appropriate actions, such as taking a break or taking a deep breath, to improve their mental state.

[0768] Example 1

[0769] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0770] In modern society, many people are prone to high levels of stress and anxiety, but few systems exist that can adequately monitor their mental state and provide effective countermeasures in real time. Even when such systems exist, they face the challenge of accurately acquiring a user's physical information and providing prompt and appropriate advice based on that information. Furthermore, there are currently insufficient technological means to provide effective mental care while protecting the user's privacy.

[0771] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0772] In this invention, the server includes means for transmitting the user's physical information to a central computer, means for storing and managing the collected user's physical information in the central computer, means for diagnosing the mental state using an artificial intelligence model based on the collected user's physical information, voice synthesis means for converting the generated positive suggestions into voice format, means for transmitting the generated positive suggestions as voice data to the user's terminal, and means for the terminal to play back and present the voice data to the user. This enables real-time mental care based on the user's physical information, and can effectively support stress relief and maintenance of mental health.

[0773] "User's physical information" refers to information about the user's physical condition, including data such as heart rate, number of steps, sleep time, body temperature, sweating, brain waves, and facial expressions.

[0774] A "central computer" is a computer system such as a server or database that stores, manages, and analyzes a user's physical information.

[0775] An "artificial intelligence model" is a software model that uses algorithms such as machine learning and deep learning to analyze a user's physical information and diagnose their mental state.

[0776] A "generative model" is an algorithm or software that generates positive suggestions for users based on the diagnostic results analyzed by artificial intelligence.

[0777] "Speech synthesis means" means means, including text-to-speech (TTS) technology, for converting textual positive suggestions into speech form.

[0778] A "terminal" is an electronic device used by a user, such as a smartphone or computer, that has the function of receiving and playing back audio data sent from a server.

[0779] This invention is a system that collects a user's physical information, analyzes and diagnoses the user's mental state using artificial intelligence (AI), and provides positive suggestions in the form of voice. This system is composed of a wearable device, a communication means, a central computer, an AI model, a generative AI model, a voice synthesis means, and a user device.

[0780] Terminal

[0781] The user wears a wearable device equipped with a heart rate sensor, pedometer, body temperature sensor, sleep tracker, sweat sensor, brain wave sensor, camera, etc., to collect real-time physical information from the user's daily life. The device has the function of periodically transmitting the collected data to a central computer.

[0782] Server (central computer)

[0783] The central computer receives the user's physical information sent from the device and stores it in a database. The stored data is analyzed using an AI model. The AI ​​model uses machine learning and deep learning algorithms to diagnose the user's mental state from data such as the user's heart rate, number of steps, sleep time, body temperature, sweating, brain waves, and facial expressions.

[0784] For example, if a user's heart rate is higher than normal and their step count is decreasing, the AI ​​model will diagnose the user's mental state as "stressed." Based on this diagnosis, the generative AI model will generate positive suggestions such as "Take a short break."

[0785] The generated suggestions are converted into an audio file by a speech synthesizer. The server then sends the audio file to the user's device. Text-to-speech (TTS) technology is used to generate the audio file.

[0786] User

[0787] The user's device has the ability to receive and play audio files from the server. The user listens to the audio suggestions presented by the device and takes action as necessary. For example, if the user receives a suggestion to "take a short break," the user can actually take a break and refresh their mind and body.

[0788] This system allows users to manage their mental state in real time in their daily lives and receive positive advice at the appropriate time. This will enable early detection and prevention of mental illness, and help maintain individual mental health. It is also expected to improve productivity in companies and society as a whole.

[0789] Specific examples

[0790] For example, if a user begins to show signs of fatigue after a day at work, the wearable device will detect an increase in heart rate, a decrease in steps, and lack of sleep. This data is immediately sent to a central computer, where an AI model analyzes it and determines that the user is in a "fatigue" state. Based on this diagnosis, the generative AI model generates a positive suggestion, such as "We recommend that you take a short break." This suggestion is sent to the user's device as an audio file, which the device plays to notify the user. The user can then take a break and prevent overwork.

[0791] Prompt Sentence Examples

[0792] Here are some example prompts to input to a generative AI model:

[0793] You have detected that the user's heart rate is higher than normal and their step count is decreasing. Create an appropriate suggestion for this user, such as "Take a short break."

[0794] Using this prompt, the generative AI model generates positive suggestions that are appropriate for the user's current state.

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

[0796] Step 1:

[0797] The device collects the user's physical information in real time.

[0798] How it works: The wearable device is equipped with a heart rate sensor, pedometer, body temperature sensor, sleep tracker, sweat sensor, brainwave sensor, and camera, which collect and store data internally.

[0799] Input: User's physical information

[0800] Output: Internally stored physical information data

[0801] Step 2:

[0802] The terminals transmit the collected data to a central computer.

[0803] Specific operation: The terminal uses Bluetooth or Wi-Fi to send all collected data to a central computer at regular intervals or based on specified conditions (e.g., when a certain amount of data is reached).

[0804] Input: Internally stored physical information data

[0805] Output: Data sent to the central computer

[0806] Step 3:

[0807] The server stores and manages the received data.

[0808] Specific operation: When the central computer receives data sent from the terminal, it organizes each sensor data by category and stores it in a database. The database is a NoSQL type, which enables high-speed reading and writing.

[0809] Input: Data sent from the terminal

[0810] Output: Data stored in the database

[0811] Step 4:

[0812] The server analyzes the stored data using an AI model.

[0813] Specific operation: Various sensor data is acquired from the database and input into the AI ​​model for analysis. The AI ​​model uses machine learning and deep learning algorithms to diagnose the user's mental state. For example, if the heart rate is high and the number of steps is low, it will be diagnosed as "under stress."

[0814] Input: Various sensor data stored in the database

[0815] Output: Diagnosis of the user's mental state

[0816] Step 5:

[0817] A generative AI model generates positive suggestions based on the diagnostic results.

[0818] Specific operation: Using the diagnostic results analyzed by the AI ​​model as input, the generative AI model uses prompt sentences to generate appropriate positive suggestions, such as "Your heart rate is high and you are under stress. Take a short break."

[0819] Input: Diagnosis of the user's mental state

[0820] Output: Generated positive suggestions (in text format)

[0821] Step 6:

[0822] The generated suggestions are converted into audio format.

[0823] How it works: The text-based suggestions output by the generative AI model are converted into audio using text-to-speech (TTS) technology.

[0824] Input: Generated positive suggestions (in text format)

[0825] Output: Audio file

[0826] Step 7:

[0827] The server sends the generated audio file to the user's terminal.

[0828] How it works: Once the audio file is generated, a central computer sends it over the internet to the user's smartphone or tablet.

[0829] Input: Audio file

[0830] Output: Audio file sent to the user's device

[0831] Step 8:

[0832] The terminal plays the audio file and presents it to the user.

[0833] Specific behavior: When the user's device receives the audio file, a notification will be displayed, and when the user opens the app, the audio file will be played. The user can decide what to do based on the audio suggestions.

[0834] Input: Audio file sent to the user's device

[0835] Output: Played audio suggestions

[0836] Step 9:

[0837] The user responds to the voice suggestions from the device and takes action.

[0838] Specific behavior: The user hears the suggestion to "take a short break" and actually takes a break, which is expected to bring the user's heart rate back to a normal range and relieve stress.

[0839] Input: Played speech suggestions

[0840] Output: User action (e.g., taking a break)

[0841] (Application example 1)

[0842] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0843] In modern society, real-time monitoring of a user's mental state and early detection of emergencies are important. However, conventional systems have had difficulty in quickly assessing a user's mental state and proposing appropriate security measures. To solve this problem, a system is needed that can accurately diagnose a user's mental state using their physical information and provide prompt advice on countermeasures in emergencies.

[0844] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0845] In this invention, the server includes means for collecting user physical information, means for transmitting the collected user physical information to the server, means for storing and managing the collected user physical information in the server, means for diagnosing the mental state of the user using an artificial intelligence model based on the collected user physical information, means for generating a warning and security measure proposals based on the diagnosis results, means for converting the generated warning and security measure proposals into audio format, means for transmitting the generated warning and security measure proposals as audio data to the user's terminal, and means for the terminal to play the audio data and present it to the user. This makes it possible to monitor the user's mental state in real time, and when an abnormality is detected, to quickly issue a warning and propose appropriate security measures.

[0846] "User's physical information" refers to biometric data such as an individual's heart rate, number of steps, sleep time, body temperature, sweat, brain waves, muscle movements, and facial expressions.

[0847] "Means for collection" refers to a device or system that collects a user's physical information using sensors in a wearable terminal or smart device.

[0848] "Server" refers to a central processing unit for storing, managing, and analyzing collected user physical information.

[0849] "Means for storing and managing" refers to a system that stores a user's physical information in a database and accesses and updates it as needed.

[0850] An "artificial intelligence model" refers to an algorithm that uses machine learning and deep learning techniques to analyze and diagnose a user's mental state from collected data.

[0851] "Mental state" refers to the user's current psychological and emotional state.

[0852] "Diagnostic means" refers to a system that uses an artificial intelligence model to analyze collected data and determine the user's mental state.

[0853] "Warning and suggested security measures" refers to warnings and recommendations for safety measures issued to users based on the diagnostic results.

[0854] "Generating means" refers to a system or algorithm that generates warnings and security recommendations based on diagnostic results.

[0855] "Means for converting to audio format" refers to technology that converts the generated warnings and security recommendations into audio data (e.g., text-to-speech technology).

[0856] "Audio Data" means digital audio files containing warnings and security suggestions in converted audio format.

[0857] "Terminal" refers to a device held by a user (e.g., a smartphone or smart glasses).

[0858] "Means of presentation" refers to the function of the user's device to play audio data and notify the user.

[0859] 1. Data collection and transmission

[0860] Users wear wearable devices or smart devices equipped with heart rate sensors, pedometers, body temperature sensors, sweat sensors, and brain wave sensors. These sensors collect the user's physical information in real time. The collected data is sent to a server via Bluetooth or Wi-Fi.

[0861] 2. Data storage and management

[0862] The server stores and manages the received user's physical information in a database. The database is built using a database management system such as AWS RDS or MySQL, allowing for efficient storage, search, and analysis of large amounts of data.

[0863] 3. Diagnosis of mental conditions

[0864] The user's physical information stored on the server is analyzed using an artificial intelligence (AI) model, which uses deep learning frameworks such as TensorFlow and PyTorch, to diagnose the user's mental state from data such as the user's heart rate, body temperature, sweating, and brain waves.

[0865] 4. Generate warnings and security action suggestions

[0866] Based on the diagnosis results, a generative AI (such as OpenAI's GPT-3) is used to generate warnings and security recommendations for the user. The recommendations are customized to suit the situation, requiring appropriate attention. The recommendations are then converted into audio data using text-to-speech (TTS) technology. The Google Cloud Text-to-Speech API is used as the TTS technology.

[0867] 5. Sending and playing audio data

[0868] The generated voice data is sent from the server to the user's device (such as a smartphone or smart glasses). The device then plays the received voice data and notifies the user. This allows the user to receive warnings and suggestions for security measures at the appropriate time.

[0869] Specific examples

[0870] If a user begins to show signs of stress, the wearable device will detect an increase in heart rate and changes in body temperature. This data is immediately sent to a server, where an AI model analyzes it and diagnoses the user as being in a "high stress state." Based on this diagnosis, the generative AI generates a suggestion such as "Take a break now and try a relaxation technique." This suggestion is sent to the user's device as an audio file, which plays it and notifies the user.

[0871] Example prompt sentence:

[0872] The user is in a high mental state. What are the next security measures that should be taken?

[0873] Hardware and Software Examples

[0874] Wearable device: Equipped with heart rate sensor, body temperature sensor, sweat sensor, and brain wave sensor

[0875] Communication means: Bluetooth, Wi-Fi

[0876] Server: AWS EC2, Database (AWS RDS, MySQL)

[0877] AI models: TensorFlow, PyTorch

[0878] Generative AI: OpenAI GPT-3

[0879] TTS technology: Google Cloud Text-to-Speech API

[0880] User devices: smartphones, smart glasses

[0881] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0882] Step 1: Data collection and transmission

[0883] The user wears a wearable device. This device collects physical information such as heart rate, body temperature, sweating, and brain waves in real time. The collected data is sent to a device (such as a smartphone) via Bluetooth or Wi-Fi. The input is sensor data of physical information, and the output is data sent to the device. Pre-processing (e.g., data format conversion, compression, etc.) is performed within the device.

[0884] Step 2: Send data to the server

[0885] The physical information collected by the device is packetized and sent to a server via an internet connection. The input is the sensor data sent from the device, and the output is the data stored on the server. When the server receives the data, it checks its integrity and stores it in a database.

[0886] Step 3: Data storage and management

[0887] The server stores the received data in a database (e.g., AWS RDS, MySQL). The input is the sensor data sent to the server, and the output is well-formatted data stored in the database. Data processing here includes data validation and indexing as time-series data.

[0888] Step 4: Diagnose your mental condition

[0889] The stored data is analyzed by an AI model (such as TensorFlow or PyTorch). The input is physical information obtained from a database, and the output is a diagnosis of the user's mental state. The AI ​​model extracts various features (heart rate fluctuations, changes in body temperature, etc.) based on the collected data and predicts the user's mental state (e.g., stress level). Specific operations include inputting data into the model, preprocessing, and outputting the predicted results.

[0890] Step 5: Generate warnings and security recommendations

[0891] Based on the diagnosis results, a generative AI (e.g., OpenAI GPT-3) is used to generate a warning for the user and suggest security measures. The input is the mental state diagnosis result, and the output is a text-based warning and suggested measures. The generative AI generates the suggestions based on the diagnosis results, creating appropriate wording and specific actions in text format.

[0892] Step 6: Convert to audio format

[0893] The generated text suggestions are converted into audio data using TTS (such as Google Cloud Text-to-Speech API) technology. The input is the text suggestions from the generative AI, and the output is a digital audio file. Specifically, this involves sending text to the TTS API and receiving it as an audio file (e.g., in MP3 format).

[0894] Step 7: Sending audio data to the user device

[0895] The server sends the generated voice data to the user's terminal. The input is a voice data file, and the output is the transmission of the voice data to the user's terminal. The server packetizes the voice file and sends it to the user's terminal via the Internet.

[0896] Step 8: Playing back audio data and notifying users

[0897] The user's device plays the received audio data and notifies the user. The input is the transmitted audio data, and the output is an audio notification that reaches the user's ears. This includes the specific actions of the device's media player playing the audio file and informing the user of the suggestion.

[0898] The above steps realize a system that allows users to receive warnings and suggestions for security measures at appropriate times.

[0899] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0900] MODE FOR CARRYING OUT THE INVENTION

[0901] This invention is a system that collects a user's physical and emotional information, analyzes and diagnoses the user's mental state using artificial intelligence (AI), and provides appropriate positive suggestions in the form of voice, as needed. This system is composed of a wearable device, a communication means, a server, an AI model, a generative AI, an emotion engine, voice conversion technology, and a user device.

[0902] Terminal

[0903] First, the user puts on a wearable device. The device is equipped with a heart rate sensor, pedometer, body temperature sensor, sleep tracker, sweat sensor, brainwave sensor, and camera, and collects real-time physical information from the user's daily life. The device is also equipped with an emotion engine that recognizes emotions from the user's facial and voice data. The device has the function of periodically sending the collected data to a server.

[0904] server

[0905] The server receives the user's physical and emotional information sent from the device and stores it in a database. The stored data is then analyzed using an AI model. The AI ​​model uses machine learning and deep learning algorithms to diagnose the user's mental state based on data such as the user's heart rate, number of steps, sleep time, body temperature, sweating, brain waves, facial expressions, and voice tone.

[0906] Once the AI ​​model has completed its mental state diagnosis, the generative AI then generates positive suggestions for the user based on the diagnosis. For example, if the user's heart rate is high and they are judged to be stressed, the generative AI may suggest "take a short break." If the collected emotional data indicates that the user is "sad," the generative AI may suggest "taking your mood into consideration and refreshing yourself."

[0907] The generated suggestions are converted into audio format. The server generates the suggestions as audio files and sends them to the user's device. The audio files are generated using Text-to-Speech (TTS) technology.

[0908] User

[0909] The user's device receives and plays audio files from the server. The user listens to the audio suggestions provided by the device and takes action as necessary. In this way, users can manage their mental state in real time in their daily lives and receive positive advice at the right time.

[0910] Specific examples

[0911] For example, if a user begins to show signs of overwork after a day at work and their facial expression is also confirmed to be tired, the wearable device will detect an increase in heart rate, a decrease in steps, lack of sleep, and a change in sadness in their facial expression. This data is immediately sent to the server, and the AI ​​model analyzes it, determining that the state is "fatigue" and "sad." Based on this diagnosis, the generative AI generates positive suggestions such as "I recommend you take a short break" or "Take a walk to change your mood." These suggestions are sent to the user's device as an audio file, which the device plays to notify the user. The user can then take a break and prevent overwork and depression.

[0912] This system will enable the early detection and prevention of mental illness, helping to maintain the mental health of individuals, and is also expected to improve productivity for companies and society as a whole.

[0913] The processing flow will be explained below.

[0914] Step 1:

[0915] The device collects the user's physical information. The wearable device is equipped with a heart rate sensor, pedometer, body temperature sensor, sleep tracker, sweat sensor, brainwave sensor, camera, etc., and obtains data from these sensors in real time.

[0916] Step 2:

[0917] The device transmits the collected data to a server via the internet or wireless communication, either periodically or in real time as needed.

[0918] Step 3:

[0919] The server receives the data sent from the terminal via the Internet or wireless communication and temporarily stores the received data.

[0920] Step 4:

[0921] The server stores the received data in a database, where data for each user is recorded and managed so that it can be accessed later as needed.

[0922] Step 5:

[0923] The server preprocesses the stored data, scaling and normalizing it to convert it into a format that can be input to an AI model.

[0924] Step 6:

[0925] The server inputs the preprocessed data into an AI model that uses machine learning and deep learning algorithms to analyze the user's heart rate, steps, sleep time, body temperature, sweating, brain waves, facial expressions, and other data to diagnose the user's mental state.

[0926] Step 7:

[0927] The server analyzes the user's facial and voice data using an emotion engine, which identifies emotions from the user's facial expressions and voice tone and uses that information as additional data.

[0928] Step 8:

[0929] The server combines the analysis results of the AI ​​model and the emotion engine to diagnose the user's overall mental state, taking into account not only physical information but also emotional information such as "sadness" or "stress."

[0930] Step 9:

[0931] The server uses generative AI to generate positive suggestions based on the diagnosis results. For example, it makes specific suggestions such as "Take a short break" or "Try some light exercise to change your mood" based on a comprehensive assessment of physical information and emotional state.

[0932] Step 10:

[0933] The server converts the generated suggestions into audio format using Text-to-Speech (TTS) technology, creating a generated audio file.

[0934] Step 11:

[0935] The server sends the audio file to the user's device, which is then transferred to the user's device via the Internet or wireless communication.

[0936] Step 12:

[0937] The device receives the audio file sent from the server and stores it in the device's internal memory.

[0938] Step 13:

[0939] The device will play the audio file to notify the user, and the audio will be played through the device's speaker so the user can hear the suggestion.

[0940] Step 14:

[0941] The user follows the audio suggestions and takes appropriate actions, such as taking a break, taking a walk to refresh their mind, or taking deep breaths, to improve their mental state.

[0942] Example 2

[0943] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0944] The challenge is to accurately grasp the user's mental state in real time and provide positive behavioral suggestions at the appropriate time to realize the early detection and prevention of mental illness. In particular, it is necessary to comprehensively analyze the user's physical and emotional information and provide individually tailored suggestions in voice format to relieve stress and fatigue in daily life and maintain mental health.

[0945] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for collecting the user's physical information, a means for transmitting the collected user's physical information and emotional information to the server, a means for diagnosing the user's mental state using an artificial intelligence model based on the collected user's physical information and emotional information, and a means for converting the generated positive suggestions into audio format. This makes it possible to collect and analyze the user's physical information and emotional information in real time and provide accurate positive suggestions in audio format.

[0946] "User" refers to an individual who uses the system and provides physical and emotional information.

[0947] "Physical information" refers to the user's general physiological data, such as heart rate, number of steps, body temperature, sleep, sweat, and brain waves.

[0948] "Emotional information" refers to data about a user's emotional state as determined by their facial expressions and vocal tone.

[0949] A "wearable device" refers to a device worn by a user that collects physical and emotional information using a heart rate sensor, pedometer, body temperature sensor, sleep tracker, sweat sensor, brain wave sensor, camera, etc.

[0950] "Server" refers to a computer system that receives, stores, analyzes, and manages collected user physical and emotional information.

[0951] An "artificial intelligence model" is a program that uses machine learning and deep learning algorithms to analyze collected data and diagnose the user's mental state.

[0952] A "generative artificial intelligence model" refers to an artificial intelligence program that generates positive suggestions appropriate for the user based on diagnostic results.

[0953] "Positive suggestions" refer to suggestions for specific actions or ways of thinking to improve the user's mental state.

[0954] "Audio format" refers to the state in which the generated positive suggestions are converted into audio data using text-to-speech technology.

[0955] "Terminal" refers to a computer device used by a user that has the function of receiving and playing audio data sent from a server.

[0956] This invention is a system that collects a user's physical and emotional information, analyzes and diagnoses the user's mental state using artificial intelligence (AI), and provides appropriate positive suggestions in the form of voice, as needed. This system is composed of a wearable device, a communication means, a server, an AI model, a generative AI, an emotion engine, voice conversion technology, and a user device.

[0957] Terminal

[0958] The user first puts on a wearable device, which is equipped with a heart rate sensor, pedometer, temperature sensor, sleep tracker, sweat sensor, brainwave sensor, and camera to collect real-time physical information from the user's daily life. For example, the heart rate sensor measures the heart rate, the pedometer counts the number of steps, and the temperature sensor monitors the body temperature.

[0959] The device is equipped with an emotion engine that recognizes emotions from the user's facial expressions and voice data, and the collected data is periodically sent to a server via communication methods such as Wi-Fi, Bluetooth, and LTE.

[0960] server

[0961] The server receives the user's physical and emotional information sent from the device and stores it in a database. The stored data is then analyzed using an artificial intelligence model that uses machine learning and deep learning algorithms to diagnose the user's mental state based on data such as the user's heart rate, number of steps, sleep time, body temperature, sweating, brain waves, facial expressions, and voice tone.

[0962] Once the mental state diagnosis is complete, the generative AI then generates positive suggestions for the user based on the diagnosis. For example, if the user's heart rate is high and they are judged to be stressed, the generative AI may suggest "take a short break." If the collected emotional data indicates that the user is "sad," the AI ​​may make specific suggestions such as "consider your mood and we recommend that you refresh yourself."

[0963] The generated suggestions are converted into audio format. The server generates the suggestions as audio files and sends them to the user's device. The audio files are generated using Text-to-Speech (TTS) technology.

[0964] User

[0965] The user's device receives and plays audio files from the server. The user listens to the audio suggestions provided by the device and takes action as needed. This process allows users to manage their mental state in real time in their daily lives and receive positive advice at the right time.

[0966] Specific examples

[0967] For example, if a user begins to show signs of overwork after a day at work and facial fatigue is detected, the wearable device will detect an increase in heart rate, a decrease in steps, lack of sleep, and a change in facial expression to sadness. This data is immediately sent to a server, where an AI model analyzes it and determines that the state is "fatigue" and "sad." Based on this diagnosis, the generative AI generates positive suggestions such as "I recommend you take a short break" or "Take a walk to change your mood." This suggestion is sent to the user's device as an audio file, which the device plays to notify the user. The user can then take a break and prevent overwork and depression.

[0968] Prompt Sentence Examples

[0969] Below are examples of prompts to the system to analyze and diagnose the user's mental state.

[0970] Please provide data such as the user's heart rate, steps, sleep time, body temperature, sweat, brainwaves, facial expressions, and voice tone. Based on this, please diagnose the current mental state and generate appropriate positive suggestions. Please give us some example suggestions if the user's mental state is judged to be "fatigue" and "sad."

[0971] By inputting this prompt into a generative AI model, specific positive suggestions are generated.

[0972] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0973] Step 1: The user puts on the wearable device

[0974] A user puts on a wearable device equipped with a heart rate sensor, pedometer, body temperature sensor, sleep tracker, sweat sensor, brainwave sensor, camera, etc. The device automatically starts up and becomes ready. The input is the user's wearing action, and the output is the wearable device's state when it is ready to start measuring.

[0975] Step 2: The device collects physical and emotional information

[0976] The device measures data such as heart rate, steps, body temperature, sleep, sweat, and brain waves in real time. It uses an emotion engine to analyze the user's facial expressions and voice tone via a camera and microphone to collect emotional information. The input is the user's physical and emotional information, and the output is the collected digital data.

[0977] Step 3: The device sends the data to the server

[0978] The device sends the collected physical and emotional information to a server at regular intervals (e.g., every hour). Wi-Fi, Bluetooth, and LTE are used as communication methods. The input is the collected digital data, and the output is the data sent to the server.

[0979] Step 4: The server receives the data and stores it in the database

[0980] The server receives the data sent from the terminal. The received data is stored in a database and organized by user. The input is the data sent from the terminal, and the output is the data stored in the database.

[0981] Step 5: The server analyzes the data using the AI ​​model

[0982] The server inputs the data stored in the database into the AI ​​model, which then uses machine learning and deep learning algorithms to analyze the data and diagnose the user's mental state. The input is the data in the database, and the output is the diagnosis result.

[0983] Step 6: The server generates positive suggestions using generative AI

[0984] The server inputs a prompt to the generative AI based on the diagnosis result. The generative AI generates a positive suggestion appropriate for the user based on the diagnosis result. The input is the diagnosis result and the prompt, and the output is a specific positive suggestion.

[0985] Step 7: The server converts the proposal into an audio file and sends it to the device.

[0986] The server converts the suggestions output by the generative AI into audio files using text-to-speech (TTS) technology. The generated audio data is sent to the user's device. The input is the text data of the positive suggestions, and the output is an audio file.

[0987] Step 8: The user receives voice suggestions from the device

[0988] The user's device receives the audio file sent from the server. The device plays the audio file and notifies the user of the positive suggestion. The input is the audio file sent from the server, and the output is the played audio suggestion.

[0989] (Application example 2)

[0990] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0991] Currently, improving work efficiency and managing safety are important issues in factories. In harsh working environments, it is necessary to improve productivity and prevent accidents by monitoring workers' stress levels and mental and physical health in real time and encouraging them to rest and refresh at appropriate times. However, it has been difficult to achieve this appropriately using conventional methods. The present invention aims to solve this problem by providing a system that monitors the mental stress and fatigue of factory workers in real time and provides appropriate positive suggestions via voice.

[0992] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0993] In this invention, the server includes means for collecting user's physical information, means for transmitting the collected user's physical information to the server, and means for grasping the mental stress and fatigue of factory workers in real time and notifying them by voice of positive suggestions at appropriate times. This makes it possible to monitor the health status of factory workers in real time and encourage them to take appropriate rest and refresh themselves as needed.

[0994] "User" refers to a subject whose physical and emotional information is collected, and who receives an analysis of their mental state and positive suggestions.

[0995] "Physical information" refers to data that indicates the user's physiological and health status, such as the user's heart rate, number of steps, sleep time, body temperature, sweating, brain waves, muscle movements, and facial expressions.

[0996] "Server" refers to a computer system that stores and manages a user's physical and emotional information and analyzes this data using an AI model.

[0997] An "artificial intelligence model" refers to a program that uses machine learning and deep learning algorithms to diagnose a user's mental state based on their physical and emotional information.

[0998] "Positive suggestions" refer to advice and behavioral instructions to improve the user's mental and physical health based on the diagnosis of the user's mental state.

[0999] "Audio format" refers to a format in which text data is converted into audio, and refers to a means of providing audible feedback to the user.

[1000] "Factory workers" refers to workers who perform various tasks in a factory, and are the target users of this system.

[1001] "Stress" refers to a state in which the user feels strained both physically and mentally, and is diagnosed based on physical and emotional information.

[1002] "Fatigue" refers to a state in which the user feels tired and in need of rest, and is diagnosed based on physical and emotional information.

[1003] "Audio notification" refers to a means of converting generated positive suggestions into audio format and communicating them to the user in real time.

[1004] MODE FOR CARRYING OUT THE INVENTION

[1005] The present invention is a system that collects physical and emotional information from factory workers, analyzes and diagnoses their mental state using artificial intelligence, and provides positive suggestions in the form of voice as needed. To implement this system, the following configuration and procedures are used.

[1006] Hardware and software used

[1007] Wearable devices: Equipped with heart rate sensors, body temperature sensors, pedometers, cameras, etc., they collect real-time physical information from users' daily lives.

[1008] Server: A computer system for running AI models and generative AI. It also stores and analyzes data.

[1009] User device: A smartphone or a specific work device is used to display collected data and provide voice feedback.

[1010] System Configuration

[1011] 1. Data collection: Factory workers wear wearable devices to collect physical information such as heart rate, body temperature, number of steps taken, and facial expression data. This data is sent to a server in real time.

[1012] 2. Data analysis: The server analyzes the received data using an AI model to diagnose the worker's mental state (relaxation, stress, fatigue, etc.). The main software used includes machine learning libraries such as TensorFlow and Keras.

[1013] 3. Positive suggestion generation: Based on the analysis results, the generative AI generates positive suggestions according to the worker's condition. For example, if it determines that the worker is under high stress, it will generate a suggestion such as "Take a short break and take a deep breath."

[1014] 4. Voice notification: The generated suggestions are converted into voice format using Text-to-Speech (TTS) technology. This voice data is sent to the user's device and notified to the worker in real time. The TTS technology uses the pyttsx3 library, among others.

[1015] Specific examples

[1016] If a factory worker sends data to a wearable device showing a "heart rate over 100," "body temperature 37.5 degrees," "2000 steps taken," and a "sad" facial expression, the server will use an AI model to diagnose "fatigue." Based on this diagnosis, the generating AI will generate a suggestion such as "Get hydrated and refresh yourself a bit," which will be communicated to the worker via voice.

[1017] Prompt Sentence Examples

[1018] "Design an application that uses a wearable device to collect heart rate, body temperature, steps, and facial expression data, analyzes them with an AI model, and generates positive suggestions based on the user's mental state and notifies them via voice."

[1019] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1020] Step 1:

[1021] Data collection

[1022] The user wears a wearable device, which collects real-time physical information such as heart rate, body temperature, number of steps, and facial expression data. Specifically, data is collected using a heart rate sensor, body temperature sensor, pedometer, camera, etc. This data is temporarily stored in the wearable device.

[1023] Input: Heart rate, body temperature, steps, facial expression data

[1024] Output: Collected physical information data

[1025] Step 2:

[1026] Data transmission

[1027] The collected physical information data is transmitted in real time from the wearable device to a server using communication methods such as Bluetooth or Wi-Fi.

[1028] Input: Collected physical information data

[1029] Output: Data sent to the server

[1030] Step 3:

[1031] Data storage

[1032] The server receives the transmitted physical information data and stores it in a database, where records for each user are kept and used for later analysis.

[1033] Input: Data sent to the server

[1034] Output: Saved database records

[1035] Step 4:

[1036] Data analysis

[1037] The server analyzes the stored data using an AI model. Specifically, it uses machine learning libraries such as TensorFlow and Keras to diagnose the user's mental state. At this time, parameters such as heart rate, body temperature, number of steps, and facial expression are extracted from the input data and provided as input to the AI ​​model. The model's output is the user's mental state (relaxed, stressed, fatigue, etc.).

[1038] Input: Saved database records

[1039] Output: Diagnosed mental condition

[1040] Step 5:

[1041] Positive suggestion generation

[1042] The server uses the generative AI model to generate positive suggestions for the user based on the diagnostic results of the AI ​​model. The generative AI generates text-based suggestions based on the prompts, creating specific advice tailored to the user's condition.

[1043] Input: diagnosed mental condition

[1044] Output: Generated positive suggestions

[1045] Step 6:

[1046] Audio conversion

[1047] The generated textual positive suggestions are then converted into audio using Text-to-Speech (TTS) technology, such as the pyttsx3 library, to generate a user-friendly audio file.

[1048] Input: Generated positive suggestions

[1049] Output: Proposal converted to audio format

[1050] Step 7:

[1051] Audio data transmission

[1052] The server converts the suggestions into audio format and sends them to the user's device via the Internet or a local network.

[1053] Input: Suggestions converted to audio format

[1054] Output: Audio data sent to the device

[1055] Step 8:

[1056] Audio notifications

[1057] The user's device plays the received audio data and notifies the user. The user listens to the audio suggestions and takes action as necessary. Specific examples include audio instructions such as "Take a short break" or "Drink some water."

[1058] Input: Audio data sent to the device

[1059] Output: Audio suggestion notification

[1060] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1061] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1062] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1063] [Fourth embodiment]

[1064] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1065] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1066] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1067] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1068] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1069] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1070] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1071] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1072] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1073] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1074] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1075] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1076] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1077] MODE FOR CARRYING OUT THE INVENTION

[1078] This system collects a user's physical information, analyzes and diagnoses the user's mental state using artificial intelligence (AI), and provides appropriate positive suggestions in the form of voice, as needed. This system is composed of a wearable device, a communication means, a server, an AI model, a generative AI, voice conversion technology, and a user device.

[1079] Terminal

[1080] First, the user puts on a wearable device. This device is equipped with a heart rate sensor, pedometer, body temperature sensor, sleep tracker, sweat sensor, brain wave sensor, camera, etc., and collects physical information from the user's daily life in real time. The device has the function of periodically sending the collected data to a server.

[1081] server

[1082] The server receives the user's physical information sent from the device and stores it in a database. The stored data is then analyzed using an AI model. The AI ​​model uses machine learning and deep learning algorithms to diagnose the user's mental state based on data such as the user's heart rate, number of steps, sleep time, body temperature, sweating, brain waves, and facial expressions.

[1083] Once the AI ​​model has completed its mental state diagnosis, the generative AI then generates positive suggestions for the user based on the diagnosis. For example, if the user's heart rate is high and they are under stress, the generative AI will suggest "take a short break."

[1084] The generated suggestions are converted into audio format. The server generates the suggestions as audio files and sends them to the user's device. The audio files are generated using Text-to-Speech (TTS) technology.

[1085] User

[1086] The user's device receives and plays audio files from the server. The user listens to the audio suggestions provided by the device and takes action as necessary. In this way, users can manage their mental state in real time in their daily lives and receive positive advice at the right time.

[1087] Specific examples

[1088] For example, if a user begins to show signs of overwork after a day at work, the wearable device will detect an increase in heart rate, a decrease in steps, and lack of sleep. This data is immediately sent to a server, where an AI model analyzes it and determines that the user is in a state of "fatigue." Based on this diagnosis, the generative AI generates a positive suggestion, such as "We recommend that you take a short break." This suggestion is sent to the user's device as an audio file, which the device plays to notify the user. The user can then take a break and prevent overwork.

[1089] This system will enable the early detection and prevention of mental illness, helping to maintain the mental health of individuals, and is also expected to improve productivity for companies and society as a whole.

[1090] The processing flow will be explained below.

[1091] Step 1:

[1092] The device collects the user's physical information. The wearable device is equipped with a heart rate sensor, pedometer, body temperature sensor, sleep tracker, sweat sensor, brainwave sensor, camera, etc., and obtains data from these sensors in real time.

[1093] Step 2:

[1094] The device transmits the collected data to a server via the internet or wireless communication, either periodically or in real time as needed.

[1095] Step 3:

[1096] The server receives the data sent from the terminal via the Internet or wireless communication and temporarily stores the received data.

[1097] Step 4:

[1098] The server stores the received data in a database, where data for each user is recorded and managed so that it can be accessed later as needed.

[1099] Step 5:

[1100] The server preprocesses the stored data, scaling and normalizing it to convert it into a format that can be input to an AI model.

[1101] Step 6:

[1102] The server inputs the preprocessed data into an AI model that uses machine learning and deep learning algorithms to analyze the user's heart rate, steps, sleep time, body temperature, sweating, brain waves, facial expressions, and other data to diagnose the user's mental state.

[1103] Step 7:

[1104] The server uses generative AI to generate positive suggestions based on the diagnosis results. For example, if fatigue is detected, the server will make specific suggestions such as "Take a short break."

[1105] Step 8:

[1106] The server converts the generated suggestions into audio format using Text-to-Speech (TTS) technology, creating a generated audio file.

[1107] Step 9:

[1108] The server sends the audio file to the user's device, which is then transferred to the user's device via the Internet or wireless communication.

[1109] Step 10:

[1110] The device receives the audio file sent from the server and stores it in the device's internal memory.

[1111] Step 11:

[1112] The device will play the audio file to notify the user, and the audio will be played through the device's speaker so the user can hear the suggestion.

[1113] Step 12:

[1114] The user follows the audio suggestions and takes appropriate actions, such as taking a break or taking a deep breath, to improve their mental state.

[1115] Example 1

[1116] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1117] In modern society, many people are prone to high levels of stress and anxiety, but few systems exist that can adequately monitor their mental state and provide effective countermeasures in real time. Even when such systems exist, they face the challenge of accurately acquiring a user's physical information and providing prompt and appropriate advice based on that information. Furthermore, there are currently insufficient technological means to provide effective mental care while protecting the user's privacy.

[1118] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1119] In this invention, the server includes means for transmitting the user's physical information to a central computer, means for storing and managing the collected user's physical information in the central computer, means for diagnosing the mental state using an artificial intelligence model based on the collected user's physical information, voice synthesis means for converting the generated positive suggestions into voice format, means for transmitting the generated positive suggestions as voice data to the user's terminal, and means for the terminal to play back and present the voice data to the user. This enables real-time mental care based on the user's physical information, and can effectively support stress relief and maintenance of mental health.

[1120] "User's physical information" refers to information about the user's physical condition, including data such as heart rate, number of steps, sleep time, body temperature, sweating, brain waves, and facial expressions.

[1121] A "central computer" is a computer system such as a server or database that stores, manages, and analyzes a user's physical information.

[1122] An "artificial intelligence model" is a software model that uses algorithms such as machine learning and deep learning to analyze a user's physical information and diagnose their mental state.

[1123] A "generative model" is an algorithm or software that generates positive suggestions for users based on the diagnostic results analyzed by artificial intelligence.

[1124] "Speech synthesis means" means means, including text-to-speech (TTS) technology, for converting textual positive suggestions into speech form.

[1125] A "terminal" is an electronic device used by a user, such as a smartphone or computer, that has the function of receiving and playing back audio data sent from a server.

[1126] This invention is a system that collects a user's physical information, analyzes and diagnoses the user's mental state using artificial intelligence (AI), and provides positive suggestions in the form of voice. This system is composed of a wearable device, a communication means, a central computer, an AI model, a generative AI model, a voice synthesis means, and a user device.

[1127] Terminal

[1128] The user wears a wearable device equipped with a heart rate sensor, pedometer, body temperature sensor, sleep tracker, sweat sensor, brain wave sensor, camera, etc., to collect real-time physical information from the user's daily life. The device has the function of periodically transmitting the collected data to a central computer.

[1129] Server (central computer)

[1130] The central computer receives the user's physical information sent from the device and stores it in a database. The stored data is analyzed using an AI model. The AI ​​model uses machine learning and deep learning algorithms to diagnose the user's mental state from data such as the user's heart rate, number of steps, sleep time, body temperature, sweating, brain waves, and facial expressions.

[1131] For example, if a user's heart rate is higher than normal and their step count is decreasing, the AI ​​model will diagnose the user's mental state as "stressed." Based on this diagnosis, the generative AI model will generate positive suggestions such as "Take a short break."

[1132] The generated suggestions are converted into an audio file by a speech synthesizer. The server then sends the audio file to the user's device. Text-to-speech (TTS) technology is used to generate the audio file.

[1133] User

[1134] The user's device has the ability to receive and play audio files from the server. The user listens to the audio suggestions presented by the device and takes action as necessary. For example, if the user receives a suggestion to "take a short break," the user can actually take a break and refresh their mind and body.

[1135] This system allows users to manage their mental state in real time in their daily lives and receive positive advice at the appropriate time. This will enable early detection and prevention of mental illness, and help maintain individual mental health. It is also expected to improve productivity in companies and society as a whole.

[1136] Specific examples

[1137] For example, if a user begins to show signs of fatigue after a day at work, the wearable device will detect an increase in heart rate, a decrease in steps, and lack of sleep. This data is immediately sent to a central computer, where an AI model analyzes it and determines that the user is in a "fatigue" state. Based on this diagnosis, the generative AI model generates a positive suggestion, such as "We recommend that you take a short break." This suggestion is sent to the user's device as an audio file, which the device plays to notify the user. The user can then take a break and prevent overwork.

[1138] Prompt Sentence Examples

[1139] Here are some example prompts to input to a generative AI model:

[1140] You have detected that the user's heart rate is higher than normal and their step count is decreasing. Create an appropriate suggestion for this user, such as "Take a short break."

[1141] Using this prompt, the generative AI model generates positive suggestions that are appropriate for the user's current state.

[1142] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1143] Step 1:

[1144] The device collects the user's physical information in real time.

[1145] How it works: The wearable device is equipped with a heart rate sensor, pedometer, body temperature sensor, sleep tracker, sweat sensor, brainwave sensor, and camera, which collect and store data internally.

[1146] Input: User's physical information

[1147] Output: Internally stored physical information data

[1148] Step 2:

[1149] The terminals transmit the collected data to a central computer.

[1150] Specific operation: The terminal uses Bluetooth or Wi-Fi to send all collected data to a central computer at regular intervals or based on specified conditions (e.g., when a certain amount of data is reached).

[1151] Input: Internally stored physical information data

[1152] Output: Data sent to the central computer

[1153] Step 3:

[1154] The server stores and manages the received data.

[1155] Specific operation: When the central computer receives data sent from the terminal, it organizes each sensor data by category and stores it in a database. The database is a NoSQL type, which enables high-speed reading and writing.

[1156] Input: Data sent from the terminal

[1157] Output: Data stored in the database

[1158] Step 4:

[1159] The server analyzes the stored data using an AI model.

[1160] Specific operation: Various sensor data is acquired from the database and input into the AI ​​model for analysis. The AI ​​model uses machine learning and deep learning algorithms to diagnose the user's mental state. For example, if the heart rate is high and the number of steps is low, it will be diagnosed as "under stress."

[1161] Input: Various sensor data stored in the database

[1162] Output: Diagnosis of the user's mental state

[1163] Step 5:

[1164] A generative AI model generates positive suggestions based on the diagnostic results.

[1165] Specific operation: Using the diagnostic results analyzed by the AI ​​model as input, the generative AI model uses prompt sentences to generate appropriate positive suggestions, such as "Your heart rate is high and you are under stress. Take a short break."

[1166] Input: Diagnosis of the user's mental state

[1167] Output: Generated positive suggestions (in text format)

[1168] Step 6:

[1169] The generated suggestions are converted into audio format.

[1170] How it works: The text-based suggestions output by the generative AI model are converted into audio using text-to-speech (TTS) technology.

[1171] Input: Generated positive suggestions (in text format)

[1172] Output: Audio file

[1173] Step 7:

[1174] The server sends the generated audio file to the user's terminal.

[1175] How it works: Once the audio file is generated, a central computer sends it over the internet to the user's smartphone or tablet.

[1176] Input: Audio file

[1177] Output: Audio file sent to the user's device

[1178] Step 8:

[1179] The terminal plays the audio file and presents it to the user.

[1180] Specific behavior: When the user's device receives the audio file, a notification will be displayed, and when the user opens the app, the audio file will be played. The user can decide what to do based on the audio suggestions.

[1181] Input: Audio file sent to the user's device

[1182] Output: Played audio suggestions

[1183] Step 9:

[1184] The user responds to the voice suggestions from the device and takes action.

[1185] Specific behavior: The user hears the suggestion to "take a short break" and actually takes a break, which is expected to bring the user's heart rate back to a normal range and relieve stress.

[1186] Input: Played speech suggestions

[1187] Output: User action (e.g., taking a break)

[1188] (Application example 1)

[1189] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1190] In modern society, real-time monitoring of a user's mental state and early detection of emergencies are important. However, conventional systems have had difficulty in quickly assessing a user's mental state and proposing appropriate security measures. To solve this problem, a system is needed that can accurately diagnose a user's mental state using their physical information and provide prompt advice on countermeasures in emergencies.

[1191] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1192] In this invention, the server includes means for collecting user physical information, means for transmitting the collected user physical information to the server, means for storing and managing the collected user physical information in the server, means for diagnosing the mental state of the user using an artificial intelligence model based on the collected user physical information, means for generating a warning and security measure proposals based on the diagnosis results, means for converting the generated warning and security measure proposals into audio format, means for transmitting the generated warning and security measure proposals as audio data to the user's terminal, and means for the terminal to play the audio data and present it to the user. This makes it possible to monitor the user's mental state in real time, and when an abnormality is detected, to quickly issue a warning and propose appropriate security measures.

[1193] "User's physical information" refers to biometric data such as an individual's heart rate, number of steps, sleep time, body temperature, sweat, brain waves, muscle movements, and facial expressions.

[1194] "Means for collection" refers to a device or system that collects a user's physical information using sensors in a wearable terminal or smart device.

[1195] "Server" refers to a central processing unit for storing, managing, and analyzing collected user physical information.

[1196] "Means for storing and managing" refers to a system that stores a user's physical information in a database and accesses and updates it as needed.

[1197] An "artificial intelligence model" refers to an algorithm that uses machine learning and deep learning techniques to analyze and diagnose a user's mental state from collected data.

[1198] "Mental state" refers to the user's current psychological and emotional state.

[1199] "Diagnostic means" refers to a system that uses an artificial intelligence model to analyze collected data and determine the user's mental state.

[1200] "Warning and suggested security measures" refers to warnings and recommendations for safety measures issued to users based on the diagnostic results.

[1201] "Generating means" refers to a system or algorithm that generates warnings and security recommendations based on diagnostic results.

[1202] "Means for converting to audio format" refers to technology that converts the generated warnings and security recommendations into audio data (e.g., text-to-speech technology).

[1203] "Audio Data" means digital audio files containing warnings and security suggestions in converted audio format.

[1204] "Terminal" refers to a device held by a user (e.g., a smartphone or smart glasses).

[1205] "Means of presentation" refers to the function of the user's device to play audio data and notify the user.

[1206] 1. Data collection and transmission

[1207] Users wear wearable devices or smart devices equipped with heart rate sensors, pedometers, body temperature sensors, sweat sensors, and brain wave sensors. These sensors collect the user's physical information in real time. The collected data is sent to a server via Bluetooth or Wi-Fi.

[1208] 2. Data storage and management

[1209] The server stores and manages the received user's physical information in a database. The database is built using a database management system such as AWS RDS or MySQL, allowing for efficient storage, search, and analysis of large amounts of data.

[1210] 3. Diagnosis of mental conditions

[1211] The user's physical information stored on the server is analyzed using an artificial intelligence (AI) model, which uses deep learning frameworks such as TensorFlow and PyTorch, to diagnose the user's mental state from data such as the user's heart rate, body temperature, sweating, and brain waves.

[1212] 4. Generate warnings and security action suggestions

[1213] Based on the diagnosis results, a generative AI (such as OpenAI's GPT-3) is used to generate warnings and security recommendations for the user. The recommendations are customized to suit the situation, requiring appropriate attention. The recommendations are then converted into audio data using text-to-speech (TTS) technology. The Google Cloud Text-to-Speech API is used as the TTS technology.

[1214] 5. Sending and playing audio data

[1215] The generated voice data is sent from the server to the user's device (such as a smartphone or smart glasses). The device then plays the received voice data and notifies the user. This allows the user to receive warnings and suggestions for security measures at the appropriate time.

[1216] Specific examples

[1217] If a user begins to show signs of stress, the wearable device will detect an increase in heart rate and changes in body temperature. This data is immediately sent to a server, where an AI model analyzes it and diagnoses the user as being in a "high stress state." Based on this diagnosis, the generative AI generates a suggestion such as "Take a break now and try a relaxation technique." This suggestion is sent to the user's device as an audio file, which plays it and notifies the user.

[1218] Example prompt sentence:

[1219] The user is in a high mental state. What are the next security measures that should be taken?

[1220] Hardware and Software Examples

[1221] Wearable device: Equipped with heart rate sensor, body temperature sensor, sweat sensor, and brain wave sensor

[1222] Communication means: Bluetooth, Wi-Fi

[1223] Server: AWS EC2, Database (AWS RDS, MySQL)

[1224] AI models: TensorFlow, PyTorch

[1225] Generative AI: OpenAI GPT-3

[1226] TTS technology: Google Cloud Text-to-Speech API

[1227] User devices: smartphones, smart glasses

[1228] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1229] Step 1: Data collection and transmission

[1230] The user wears a wearable device. This device collects physical information such as heart rate, body temperature, sweating, and brain waves in real time. The collected data is sent to a device (such as a smartphone) via Bluetooth or Wi-Fi. The input is sensor data of physical information, and the output is data sent to the device. Pre-processing (e.g., data format conversion, compression, etc.) is performed within the device.

[1231] Step 2: Send data to the server

[1232] The physical information collected by the device is packetized and sent to a server via an internet connection. The input is the sensor data sent from the device, and the output is the data stored on the server. When the server receives the data, it checks its integrity and stores it in a database.

[1233] Step 3: Data storage and management

[1234] The server stores the received data in a database (e.g., AWS RDS, MySQL). The input is the sensor data sent to the server, and the output is well-formatted data stored in the database. Data processing here includes data validation and indexing as time-series data.

[1235] Step 4: Diagnose your mental condition

[1236] The stored data is analyzed by an AI model (such as TensorFlow or PyTorch). The input is physical information obtained from a database, and the output is a diagnosis of the user's mental state. The AI ​​model extracts various features (heart rate fluctuations, changes in body temperature, etc.) based on the collected data and predicts the user's mental state (e.g., stress level). Specific operations include inputting data into the model, preprocessing, and outputting the predicted results.

[1237] Step 5: Generate warnings and security recommendations

[1238] Based on the diagnosis results, a generative AI (e.g., OpenAI GPT-3) is used to generate a warning for the user and suggest security measures. The input is the mental state diagnosis result, and the output is a text-based warning and suggested measures. The generative AI generates the suggestions based on the diagnosis results, creating appropriate wording and specific actions in text format.

[1239] Step 6: Convert to audio format

[1240] The generated text suggestions are converted into audio data using TTS (such as Google Cloud Text-to-Speech API) technology. The input is the text suggestions from the generative AI, and the output is a digital audio file. Specifically, this involves sending text to the TTS API and receiving it as an audio file (e.g., in MP3 format).

[1241] Step 7: Sending audio data to the user device

[1242] The server sends the generated voice data to the user's terminal. The input is a voice data file, and the output is the transmission of the voice data to the user's terminal. The server packetizes the voice file and sends it to the user's terminal via the Internet.

[1243] Step 8: Playing back audio data and notifying users

[1244] The user's device plays the received audio data and notifies the user. The input is the transmitted audio data, and the output is an audio notification that reaches the user's ears. This includes the specific actions of the device's media player playing the audio file and informing the user of the suggestion.

[1245] The above steps realize a system that allows users to receive warnings and suggestions for security measures at appropriate times.

[1246] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1247] MODE FOR CARRYING OUT THE INVENTION

[1248] This invention is a system that collects a user's physical and emotional information, analyzes and diagnoses the user's mental state using artificial intelligence (AI), and provides appropriate positive suggestions in the form of voice, as needed. This system is composed of a wearable device, a communication means, a server, an AI model, a generative AI, an emotion engine, voice conversion technology, and a user device.

[1249] Terminal

[1250] First, the user puts on a wearable device. The device is equipped with a heart rate sensor, pedometer, body temperature sensor, sleep tracker, sweat sensor, brainwave sensor, and camera, and collects real-time physical information from the user's daily life. The device is also equipped with an emotion engine that recognizes emotions from the user's facial and voice data. The device has the function of periodically sending the collected data to a server.

[1251] server

[1252] The server receives the user's physical and emotional information sent from the device and stores it in a database. The stored data is then analyzed using an AI model. The AI ​​model uses machine learning and deep learning algorithms to diagnose the user's mental state based on data such as the user's heart rate, number of steps, sleep time, body temperature, sweating, brain waves, facial expressions, and voice tone.

[1253] Once the AI ​​model has completed its mental state diagnosis, the generative AI then generates positive suggestions for the user based on the diagnosis. For example, if the user's heart rate is high and they are judged to be stressed, the generative AI may suggest "take a short break." If the collected emotional data indicates that the user is "sad," the generative AI may suggest "taking your mood into consideration and refreshing yourself."

[1254] The generated suggestions are converted into audio format. The server generates the suggestions as audio files and sends them to the user's device. The audio files are generated using Text-to-Speech (TTS) technology.

[1255] User

[1256] The user's device receives and plays audio files from the server. The user listens to the audio suggestions provided by the device and takes action as necessary. In this way, users can manage their mental state in real time in their daily lives and receive positive advice at the right time.

[1257] Specific examples

[1258] For example, if a user begins to show signs of overwork after a day at work and their facial expression is also confirmed to be tired, the wearable device will detect an increase in heart rate, a decrease in steps, lack of sleep, and a change in sadness in their facial expression. This data is immediately sent to the server, and the AI ​​model analyzes it, determining that the state is "fatigue" and "sad." Based on this diagnosis, the generative AI generates positive suggestions such as "I recommend you take a short break" or "Take a walk to change your mood." These suggestions are sent to the user's device as an audio file, which the device plays to notify the user. The user can then take a break and prevent overwork and depression.

[1259] This system will enable the early detection and prevention of mental illness, helping to maintain the mental health of individuals, and is also expected to improve productivity for companies and society as a whole.

[1260] The processing flow will be explained below.

[1261] Step 1:

[1262] The device collects the user's physical information. The wearable device is equipped with a heart rate sensor, pedometer, body temperature sensor, sleep tracker, sweat sensor, brainwave sensor, camera, etc., and obtains data from these sensors in real time.

[1263] Step 2:

[1264] The device transmits the collected data to a server via the internet or wireless communication, either periodically or in real time as needed.

[1265] Step 3:

[1266] The server receives the data sent from the terminal via the Internet or wireless communication and temporarily stores the received data.

[1267] Step 4:

[1268] The server stores the received data in a database, where data for each user is recorded and managed so that it can be accessed later as needed.

[1269] Step 5:

[1270] The server preprocesses the stored data, scaling and normalizing it to convert it into a format that can be input to an AI model.

[1271] Step 6:

[1272] The server inputs the preprocessed data into an AI model that uses machine learning and deep learning algorithms to analyze the user's heart rate, steps, sleep time, body temperature, sweating, brain waves, facial expressions, and other data to diagnose the user's mental state.

[1273] Step 7:

[1274] The server analyzes the user's facial and voice data using an emotion engine, which identifies emotions from the user's facial expressions and voice tone and uses that information as additional data.

[1275] Step 8:

[1276] The server combines the analysis results of the AI ​​model and the emotion engine to diagnose the user's overall mental state, taking into account not only physical information but also emotional information such as "sadness" or "stress."

[1277] Step 9:

[1278] The server uses generative AI to generate positive suggestions based on the diagnosis results. For example, it makes specific suggestions such as "Take a short break" or "Try some light exercise to change your mood" based on a comprehensive assessment of physical information and emotional state.

[1279] Step 10:

[1280] The server converts the generated suggestions into audio format using Text-to-Speech (TTS) technology, creating a generated audio file.

[1281] Step 11:

[1282] The server sends the audio file to the user's device, which is then transferred to the user's device via the Internet or wireless communication.

[1283] Step 12:

[1284] The device receives the audio file sent from the server and stores it in the device's internal memory.

[1285] Step 13:

[1286] The device will play the audio file to notify the user, and the audio will be played through the device's speaker so the user can hear the suggestion.

[1287] Step 14:

[1288] The user follows the audio suggestions and takes appropriate actions, such as taking a break, taking a walk to refresh their mind, or taking deep breaths, to improve their mental state.

[1289] Example 2

[1290] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1291] The challenge is to accurately grasp the user's mental state in real time and provide positive behavioral suggestions at the appropriate time to realize the early detection and prevention of mental illness. In particular, it is necessary to comprehensively analyze the user's physical and emotional information and provide individually tailored suggestions in voice format to relieve stress and fatigue in daily life and maintain mental health.

[1292] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for collecting the user's physical information, a means for transmitting the collected user's physical information and emotional information to the server, a means for diagnosing the user's mental state using an artificial intelligence model based on the collected user's physical information and emotional information, and a means for converting the generated positive suggestions into audio format. This makes it possible to collect and analyze the user's physical information and emotional information in real time and provide accurate positive suggestions in audio format.

[1293] "User" refers to an individual who uses the system and provides physical and emotional information.

[1294] "Physical information" refers to the user's general physiological data, such as heart rate, number of steps, body temperature, sleep, sweat, and brain waves.

[1295] "Emotional information" refers to data about a user's emotional state as determined by their facial expressions and vocal tone.

[1296] A "wearable device" refers to a device worn by a user that collects physical and emotional information using a heart rate sensor, pedometer, body temperature sensor, sleep tracker, sweat sensor, brain wave sensor, camera, etc.

[1297] "Server" refers to a computer system that receives, stores, analyzes, and manages collected user physical and emotional information.

[1298] An "artificial intelligence model" is a program that uses machine learning and deep learning algorithms to analyze collected data and diagnose the user's mental state.

[1299] A "generative artificial intelligence model" refers to an artificial intelligence program that generates positive suggestions appropriate for the user based on diagnostic results.

[1300] "Positive suggestions" refer to suggestions for specific actions or ways of thinking to improve the user's mental state.

[1301] "Audio format" refers to the state in which the generated positive suggestions are converted into audio data using text-to-speech technology.

[1302] "Terminal" refers to a computer device used by a user that has the function of receiving and playing audio data sent from a server.

[1303] This invention is a system that collects a user's physical and emotional information, analyzes and diagnoses the user's mental state using artificial intelligence (AI), and provides appropriate positive suggestions in the form of voice, as needed. This system is composed of a wearable device, a communication means, a server, an AI model, a generative AI, an emotion engine, voice conversion technology, and a user device.

[1304] Terminal

[1305] The user first puts on a wearable device, which is equipped with a heart rate sensor, pedometer, temperature sensor, sleep tracker, sweat sensor, brainwave sensor, and camera to collect real-time physical information from the user's daily life. For example, the heart rate sensor measures the heart rate, the pedometer counts the number of steps, and the temperature sensor monitors the body temperature.

[1306] The device is equipped with an emotion engine that recognizes emotions from the user's facial expressions and voice data, and the collected data is periodically sent to a server via communication methods such as Wi-Fi, Bluetooth, and LTE.

[1307] server

[1308] The server receives the user's physical and emotional information sent from the device and stores it in a database. The stored data is then analyzed using an artificial intelligence model that uses machine learning and deep learning algorithms to diagnose the user's mental state based on data such as the user's heart rate, number of steps, sleep time, body temperature, sweating, brain waves, facial expressions, and voice tone.

[1309] Once the mental state diagnosis is complete, the generative AI then generates positive suggestions for the user based on the diagnosis. For example, if the user's heart rate is high and they are judged to be stressed, the generative AI may suggest "take a short break." If the collected emotional data indicates that the user is "sad," the AI ​​may make specific suggestions such as "consider your mood and we recommend that you refresh yourself."

[1310] The generated suggestions are converted into audio format. The server generates the suggestions as audio files and sends them to the user's device. The audio files are generated using Text-to-Speech (TTS) technology.

[1311] User

[1312] The user's device receives and plays audio files from the server. The user listens to the audio suggestions provided by the device and takes action as needed. This process allows users to manage their mental state in real time in their daily lives and receive positive advice at the right time.

[1313] Specific examples

[1314] For example, if a user begins to show signs of overwork after a day at work and facial fatigue is detected, the wearable device will detect an increase in heart rate, a decrease in steps, lack of sleep, and a change in facial expression to sadness. This data is immediately sent to a server, where an AI model analyzes it and determines that the state is "fatigue" and "sad." Based on this diagnosis, the generative AI generates positive suggestions such as "I recommend you take a short break" or "Take a walk to change your mood." This suggestion is sent to the user's device as an audio file, which the device plays to notify the user. The user can then take a break and prevent overwork and depression.

[1315] Prompt Sentence Examples

[1316] Below are examples of prompts to the system to analyze and diagnose the user's mental state.

[1317] Please provide data such as the user's heart rate, steps, sleep time, body temperature, sweat, brainwaves, facial expressions, and voice tone. Based on this, please diagnose the current mental state and generate appropriate positive suggestions. Please give us some example suggestions if the user's mental state is judged to be "fatigue" and "sad."

[1318] By inputting this prompt into a generative AI model, specific positive suggestions are generated.

[1319] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1320] Step 1: The user puts on the wearable device

[1321] A user puts on a wearable device equipped with a heart rate sensor, pedometer, body temperature sensor, sleep tracker, sweat sensor, brainwave sensor, camera, etc. The device automatically starts up and becomes ready. The input is the user's wearing action, and the output is the wearable device's state when it is ready to start measuring.

[1322] Step 2: The device collects physical and emotional information

[1323] The device measures data such as heart rate, steps, body temperature, sleep, sweat, and brain waves in real time. It uses an emotion engine to analyze the user's facial expressions and voice tone via a camera and microphone to collect emotional information. The input is the user's physical and emotional information, and the output is the collected digital data.

[1324] Step 3: The device sends the data to the server

[1325] The device sends the collected physical and emotional information to a server at regular intervals (e.g., every hour). Wi-Fi, Bluetooth, and LTE are used as communication methods. The input is the collected digital data, and the output is the data sent to the server.

[1326] Step 4: The server receives the data and stores it in the database

[1327] The server receives the data sent from the terminal. The received data is stored in a database and organized by user. The input is the data sent from the terminal, and the output is the data stored in the database.

[1328] Step 5: The server analyzes the data using the AI ​​model

[1329] The server inputs the data stored in the database into the AI ​​model, which then uses machine learning and deep learning algorithms to analyze the data and diagnose the user's mental state. The input is the data in the database, and the output is the diagnosis result.

[1330] Step 6: The server generates positive suggestions using generative AI

[1331] The server inputs a prompt to the generative AI based on the diagnosis result. The generative AI generates a positive suggestion appropriate for the user based on the diagnosis result. The input is the diagnosis result and the prompt, and the output is a specific positive suggestion.

[1332] Step 7: The server converts the proposal into an audio file and sends it to the device.

[1333] The server converts the suggestions output by the generative AI into audio files using text-to-speech (TTS) technology. The generated audio data is sent to the user's device. The input is the text data of the positive suggestions, and the output is an audio file.

[1334] Step 8: The user receives voice suggestions from the device

[1335] The user's device receives the audio file sent from the server. The device plays the audio file and notifies the user of the positive suggestion. The input is the audio file sent from the server, and the output is the played audio suggestion.

[1336] (Application example 2)

[1337] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1338] Currently, improving work efficiency and managing safety are important issues in factories. In harsh working environments, it is necessary to improve productivity and prevent accidents by monitoring workers' stress levels and mental and physical health in real time and encouraging them to rest and refresh at appropriate times. However, it has been difficult to achieve this appropriately using conventional methods. The present invention aims to solve this problem by providing a system that monitors the mental stress and fatigue of factory workers in real time and provides appropriate positive suggestions via voice.

[1339] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1340] In this invention, the server includes means for collecting user's physical information, means for transmitting the collected user's physical information to the server, and means for grasping the mental stress and fatigue of factory workers in real time and notifying them by voice of positive suggestions at appropriate times. This makes it possible to monitor the health status of factory workers in real time and encourage them to take appropriate rest and refresh themselves as needed.

[1341] "User" refers to a subject whose physical and emotional information is collected, and who receives an analysis of their mental state and positive suggestions.

[1342] "Physical information" refers to data that indicates the user's physiological and health status, such as the user's heart rate, number of steps, sleep time, body temperature, sweating, brain waves, muscle movements, and facial expressions.

[1343] "Server" refers to a computer system that stores and manages a user's physical and emotional information and analyzes this data using an AI model.

[1344] An "artificial intelligence model" refers to a program that uses machine learning and deep learning algorithms to diagnose a user's mental state based on their physical and emotional information.

[1345] "Positive suggestions" refer to advice and behavioral instructions to improve the user's mental and physical health based on the diagnosis of the user's mental state.

[1346] "Audio format" refers to a format in which text data is converted into audio, and refers to a means of providing audible feedback to the user.

[1347] "Factory workers" refers to workers who perform various tasks in a factory, and are the target users of this system.

[1348] "Stress" refers to a state in which the user feels strained both physically and mentally, and is diagnosed based on physical and emotional information.

[1349] "Fatigue" refers to a state in which the user feels tired and in need of rest, and is diagnosed based on physical and emotional information.

[1350] "Audio notification" refers to a means of converting generated positive suggestions into audio format and communicating them to the user in real time.

[1351] MODE FOR CARRYING OUT THE INVENTION

[1352] The present invention is a system that collects physical and emotional information from factory workers, analyzes and diagnoses their mental state using artificial intelligence, and provides positive suggestions in the form of voice as needed. To implement this system, the following configuration and procedures are used.

[1353] Hardware and software used

[1354] Wearable devices: Equipped with heart rate sensors, body temperature sensors, pedometers, cameras, etc., they collect real-time physical information from users' daily lives.

[1355] Server: A computer system for running AI models and generative AI. It also stores and analyzes data.

[1356] User device: A smartphone or a specific work device is used to display collected data and provide voice feedback.

[1357] System Configuration

[1358] 1. Data collection: Factory workers wear wearable devices to collect physical information such as heart rate, body temperature, number of steps taken, and facial expression data. This data is sent to a server in real time.

[1359] 2. Data analysis: The server analyzes the received data using an AI model to diagnose the worker's mental state (relaxation, stress, fatigue, etc.). The main software used includes machine learning libraries such as TensorFlow and Keras.

[1360] 3. Positive suggestion generation: Based on the analysis results, the generative AI generates positive suggestions according to the worker's condition. For example, if it determines that the worker is under high stress, it will generate a suggestion such as "Take a short break and take a deep breath."

[1361] 4. Voice notification: The generated suggestions are converted into voice format using Text-to-Speech (TTS) technology. This voice data is sent to the user's device and notified to the worker in real time. The TTS technology uses the pyttsx3 library, among others.

[1362] Specific examples

[1363] If a factory worker sends data to a wearable device showing a "heart rate over 100," "body temperature 37.5 degrees," "2000 steps taken," and a "sad" facial expression, the server will use an AI model to diagnose "fatigue." Based on this diagnosis, the generating AI will generate a suggestion such as "Get hydrated and refresh yourself a bit," which will be communicated to the worker via voice.

[1364] Prompt Sentence Examples

[1365] "Design an application that uses a wearable device to collect heart rate, body temperature, steps, and facial expression data, analyzes them with an AI model, and generates positive suggestions based on the user's mental state and notifies them via voice."

[1366] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1367] Step 1:

[1368] Data collection

[1369] The user wears a wearable device, which collects real-time physical information such as heart rate, body temperature, number of steps, and facial expression data. Specifically, data is collected using a heart rate sensor, body temperature sensor, pedometer, camera, etc. This data is temporarily stored in the wearable device.

[1370] Input: Heart rate, body temperature, steps, facial expression data

[1371] Output: Collected physical information data

[1372] Step 2:

[1373] Data transmission

[1374] The collected physical information data is transmitted in real time from the wearable device to a server using communication methods such as Bluetooth or Wi-Fi.

[1375] Input: Collected physical information data

[1376] Output: Data sent to the server

[1377] Step 3:

[1378] Data storage

[1379] The server receives the transmitted physical information data and stores it in a database, where records for each user are kept and used for later analysis.

[1380] Input: Data sent to the server

[1381] Output: Saved database records

[1382] Step 4:

[1383] Data analysis

[1384] The server analyzes the stored data using an AI model. Specifically, it uses machine learning libraries such as TensorFlow and Keras to diagnose the user's mental state. At this time, parameters such as heart rate, body temperature, number of steps, and facial expression are extracted from the input data and provided as input to the AI ​​model. The model's output is the user's mental state (relaxed, stressed, fatigue, etc.).

[1385] Input: Saved database records

[1386] Output: Diagnosed mental condition

[1387] Step 5:

[1388] Positive suggestion generation

[1389] The server uses the generative AI model to generate positive suggestions for the user based on the diagnostic results of the AI ​​model. The generative AI generates text-based suggestions based on the prompts, creating specific advice tailored to the user's condition.

[1390] Input: diagnosed mental condition

[1391] Output: Generated positive suggestions

[1392] Step 6:

[1393] Audio conversion

[1394] The generated textual positive suggestions are then converted into audio using Text-to-Speech (TTS) technology, such as the pyttsx3 library, to generate a user-friendly audio file.

[1395] Input: Generated positive suggestions

[1396] Output: Proposal converted to audio format

[1397] Step 7:

[1398] Audio data transmission

[1399] The server converts the suggestions into audio format and sends them to the user's device via the Internet or a local network.

[1400] Input: Suggestions converted to audio format

[1401] Output: Audio data sent to the device

[1402] Step 8:

[1403] Audio notifications

[1404] The user's device plays the received audio data and notifies the user. The user listens to the audio suggestions and takes action as necessary. Specific examples include audio instructions such as "Take a short break" or "Drink some water."

[1405] Input: Audio data sent to the device

[1406] Output: Audio suggestion notification

[1407] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1408] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1409] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1410] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1411] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1412] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1413] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1414] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1415] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1416] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1417] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1418] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1419] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1420] 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.

[1421] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1422] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1423] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1424] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1425] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1426] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1427] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1428] The following is further disclosed regarding the above embodiment.

[1429] (Claim 1)

[1430] A means for collecting physical information of a user;

[1431] means for transmitting the collected user's physical information to a server;

[1432] A means for storing and managing the collected user's physical information in a server;

[1433] A means for diagnosing a mental state using an artificial intelligence model based on the collected physical information of the user;

[1434] means for generating positive suggestions based on the diagnostic results;

[1435] means for converting the generated positive suggestions into audio format;

[1436] means for transmitting the audio data to a user's device;

[1437] a means for the terminal to play and present the audio data to the user;

[1438] A system including:

[1439] (Claim 2)

[1440] The system according to claim 1, wherein the user's physical information is collected as heart rate, number of steps, sleep time, body temperature, sweating, brain waves, muscle movements, and facial expressions.

[1441] (Claim 3)

[1442] 2. The system of claim 1, wherein the generated positive audio suggestions include specific suggestions such as a recommendation to take a break or instruction on breathing techniques.

[1443] "Example 1"

[1444] (Claim 1)

[1445] A means for collecting physical information of a user;

[1446] means for transmitting the collected user's physical information to a central computer;

[1447] A means for storing and managing the collected user's physical information in a central computer;

[1448] A means for diagnosing a mental state using an artificial intelligence model based on the collected physical information of the user;

[1449] a generative model means for generating positive suggestions based on the diagnosis results;

[1450] a speech synthesis means for converting the generated positive suggestions into speech form;

[1451] means for transmitting the voice data to a user's terminal;

[1452] means for the terminal to play back the audio data and present it to the user;

[1453] A system including:

[1454] (Claim 2)

[1455] 2. The system according to claim 1, wherein the user's physical information includes heart rate, number of steps, sleep time, body temperature, sweating, brain waves, and facial expressions.

[1456] (Claim 3)

[1457] 2. The system of claim 1, wherein the generated positive audio suggestions include specific suggestions such as a recommendation to take a break or instruction on breathing techniques.

[1458] "Application Example 1"

[1459] (Claim 1)

[1460] A means for collecting physical information of a user;

[1461] means for transmitting the collected user's physical information to a server;

[1462] A means for storing and managing the collected user's physical information in a server;

[1463] A means for diagnosing a mental state using an artificial intelligence model based on the collected physical information of the user;

[1464] means for generating warnings and security measure suggestions based on the diagnostic results;

[1465] means for converting the generated warning and security suggestion into audio format;

[1466] means for transmitting the audio data to a user's device;

[1467] a means for the terminal to play and present the audio data to the user;

[1468] A system including:

[1469] (Claim 2)

[1470] The system according to claim 1, wherein the user's physical information is collected as heart rate, number of steps, sleep time, body temperature, sweating, brain waves, muscle movements, and facial expressions.

[1471] (Claim 3)

[1472] 2. The system of claim 1, wherein the generated audio warning and security suggestion includes specific suggestions such as a recommendation to take a break and instructions on how to relax.

[1473] "Example 2: Combining Emotion Engines"

[1474] (Claim 1)

[1475] A means for collecting physical information of a user;

[1476] means for transmitting the collected user's physical information and emotional information to a server;

[1477] A means for storing and managing the collected user's physical information and emotional information in a server;

[1478] A means for diagnosing a mental state using an artificial intelligence model based on the collected physical and emotional information of the user;

[1479] A means for generating positive suggestions using a generative artificial intelligence model based on the diagnosis results;

[1480] means for converting the generated positive suggestions into audio format;

[1481] means for transmitting the audio data to a user's device;

[1482] a means for the terminal to play and present the audio data to the user;

[1483] A system including:

[1484] (Claim 2)

[1485] The system according to claim 1, wherein the user's physical information is collected from heart rate, number of steps, body temperature, sleep, sweat, brain waves, facial expressions, and voice tone.

[1486] (Claim 3)

[1487] 2. The system of claim 1, wherein the generated positive suggestions in audio form include specific suggestions such as a recommendation to take a break or a recommendation to engage in a refreshing activity.

[1488] "Application example 2 when combining emotion engines"

[1489] (Claim 1)

[1490] A means for collecting physical information of a user;

[1491] means for transmitting the collected user's physical information to a server;

[1492] A means for storing and managing the collected user's physical information in a server;

[1493] A means for diagnosing a mental state using an artificial intelligence model based on the collected physical information of the user;

[1494] means for generating positive suggestions based on the diagnostic results;

[1495] means for converting the generated positive suggestions into audio format;

[1496] means for transmitting the audio data to a user's device;

[1497] a means for the terminal to play and present the audio data to the user;

[1498] A means to grasp the mental stress and fatigue of factory workers in real time and provide positive audio suggestions at the appropriate time.

[1499] A system including:

[1500] (Claim 2)

[1501] The system according to claim 1, wherein the user's physical information is collected as heart rate, number of steps, sleep time, body temperature, sweating, brain waves, muscle movements, and facial expressions.

[1502] (Claim 3)

[1503] The system of claim 1, wherein the generated positive suggestions in the form of audio include specific suggestions regarding taking a break, teaching breathing exercises, and safety management in the factory. [Explanation of symbols]

[1504] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for collecting physical information of a user; means for transmitting the collected user's physical information to a server; A means for storing and managing the collected user's physical information in a server; A means for diagnosing a mental state using an artificial intelligence model based on the collected physical information of the user; means for generating positive suggestions based on the diagnostic results; means for converting the generated positive suggestions into audio format; means for transmitting the audio data to a user's device; a means for the terminal to play and present the audio data to the user; A system including:

2. 2. The system according to claim 1, wherein the user's physical information includes heart rate, number of steps, sleep time, body temperature, sweating, brain waves, muscle movements, and facial expressions.

3. The system of claim 1 , wherein the generated positive audio suggestions include specific suggestions such as a recommendation to take a break or instruction on breathing techniques.

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