System

Abstract

A system is provided.SOLUTION: A system, comprising: means for obtaining physiological data of a user; means for obtaining location data; means for obtaining environmental data; means for analyzing the obtained physiological data, location data, and environmental data to suggest a training menu in real time; means for monitoring the physiological data and the environmental data in real time during a training performed by the user and dynamically changing the training menu based on changed data; and means for analyzing the data and providing feedback after the training.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] With the recent spread of smart devices, it has become possible to acquire and check users' physiological and environmental data, such as heart rate and location information, in real time. However, current running support applications and devices are unable to effectively utilize this data to provide users with training menus optimized for their individual needs. As a result, users are often unable to train in a way that is appropriate for their physical condition and external environment, resulting in problems such as injury and reduced efficiency. The present invention aims to solve these problems and enable users to safely perform optimal training. [Means for solving the problem]

[0005] The present invention is a system that provides a means for acquiring a user's physiological data, a means for acquiring location data, a means for acquiring environmental data, a means for analyzing the acquired physiological data, location data, and environmental data to propose a training menu in real time, a means for monitoring the physiological data and environmental data in real time while the user is training and dynamically changing the training menu based on the changed data, and a means for analyzing data and providing feedback after training. By acquiring heart rate as physiological data and GPS data as location data, the system provides an optimal training menu in real time based on the user's condition and external environment, supporting safe and effective training.

[0006] "Physiological data" is data that indicates the user's physical condition, and includes information about the internal state of the body, such as heart rate, blood pressure, and oxygen saturation.

[0007] "Location Data" means data indicating a user's current location, including the user's latitude and longitude information obtained using a GPS or other geographic location information system.

[0008] "Environmental data" refers to data that indicates the environmental conditions around the user, and includes information about the external environment such as temperature, humidity, weather conditions, and air quality.

[0009] A "training menu" refers to an exercise plan or schedule that a user should follow, and includes details such as specific exercise content, intensity, time, and distance.

[0010] "Monitoring" is the act of observing the user's status in real time, acquiring and analyzing that data, and is used to detect abnormalities and fluctuations and take appropriate action.

[0011] "Suggestion" refers to the act of providing information to encourage optimal behavior or choices to the user, and includes giving specific instructions or advice based on the user's condition or external environment.

[0012] "Feedback" refers to the act of providing a user with evaluations and advice regarding the exercise or behavior they have performed, and includes information such as post-training analysis results and areas for improvement.

[0013] "Analysis" is the act of investigating and examining acquired data in detail, understanding and interpreting its meaning and patterns, and includes the process of deriving useful information and conclusions from it. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] The present invention is a system that collects and analyzes a user's physiological data, location data, and environmental data in real time, and proposes an optimal training menu for the user. The specific implementation of this system is shown below.

[0036] System Overview

[0037] The system works by collecting physiological and location data from the user's smartwatch or smartphone and sending it to a server. The server then uses the collected data to generate and suggest optimal training menus based on the user's physical condition and the weather conditions of the day. Furthermore, the system monitors and analyzes data in real time while running and provides dynamic advice.

[0038] Program Overview

[0039] The server receives the data from the user, analyzes it, and generates an optimal training menu. Specifically, the process is carried out in the following steps:

[0040] Data collection and transmission

[0041] The user wears a smartwatch or smartphone and starts running. The smartwatch measures physiological data such as heart rate in real time, and the smartphone collects location data using GPS. This data is then sent to a server at regular intervals.

[0042] Data analysis on the server

[0043] When the server receives data sent from the user, it first analyzes the physiological data (e.g., heart rate) and location data (e.g., GPS data). In addition, the server uses an external API to obtain current environmental data (e.g., weather information).

[0044] Training menu generation

[0045] The server uses AI algorithms to generate optimal training plans based on physiological, location, and environmental data. For example, if the current temperature is high, it may suggest training on an indoor treadmill, while if the weather is fine, it may suggest running outdoors.

[0046] Real-time monitoring and advice

[0047] While the user is training, the smartwatch and smartphone continue to send physiological and location data to the server, which monitors and analyzes this data in real time and provides appropriate advice based on the situation. For example, if the user's heart rate spikes, the server can advise them to slow down.

[0048] Post-training feedback

[0049] Once the workout is complete, the server analyzes all the data and provides feedback to the user, including injury risk assessment, advice for the next workout, and recommended diet and sleep.

[0050] Specific examples

[0051] 1. Examples of user behavior

[0052] The user launches the app using their smartwatch and smartphone and enters their user data. When they start running, the smartwatch begins measuring their heart rate and the smartphone collects GPS data.

[0053] 2. Examples of Device Actions

[0054] The device sends the collected heart rate data and GPS data to the server. For example, the device sends a heart rate of 75 bpm and location data (35.658034, 139.701636) to the server.

[0055] 3. Examples of Server Actions

[0056] The server uses the received data to determine the user's current heart rate and location, and then uses an external API to obtain current weather information, such as a temperature of 25 degrees, humidity of 60%, and clear skies.

[0057] Based on this data, the server uses an AI algorithm to suggest the optimal training menu, such as "running 5 kilometers outdoors."

[0058] While running, the server monitors data in real time and provides advice to the user based on the situation, for example, telling them to "slow down" if their heart rate gets too high.

[0059] After the training, the server analyzes all the data and provides feedback to the user, such as "Great performance. Make sure you get plenty of rest and drink plenty of water before your next training session."

[0060] The system allows users to train safely and effectively while receiving real-time, personalized training advice.

[0061] The processing flow will be explained below.

[0062] Step 1:

[0063] The user launches the app using a smartwatch or smartphone and enters their own user data (level, weight, goals, etc.). This data is used by the system to suggest training menus that are optimal for the user's condition and goals.

[0064] Step 2:

[0065] The device sends user data to the server to initiate a session along with login information, which the server stores for later analysis.

[0066] Step 3:

[0067] The device will begin to measure physiological data (e.g., heart rate) in real time using sensors in the smartwatch or smartphone, and location data (e.g., GPS data) will also be collected.

[0068] Step 4:

[0069] The device periodically sends the collected physiological data and location data to the server, for example, sending a set of heart rate data and GPS data to the server every minute.

[0070] Step 5:

[0071] The server analyzes the received data and uses an external API to obtain current environmental data (e.g., temperature, humidity, weather) based on the acquired physiological data (heart rate) and location data (GPS data).

[0072] Step 6:

[0073] The server uses AI algorithms to generate optimal training plans based on the user's physiological, location, and environmental data, suggesting, for example, indoor treadmill training on hot days and outdoor running on sunny days.

[0074] Step 7:

[0075] The server sends the generated training menu to the terminal, which displays it to the user, who then starts running according to the training menu.

[0076] Step 8:

[0077] While the user is training, the device continues to collect physiological data (heart rate) and location data (GPS data) and transmits them to the server.

[0078] Step 9:

[0079] The server monitors the data it receives in real time and immediately generates advice if there are any fluctuations. For example, if your heart rate spikes, the server generates an alert saying "Slow down" and sends it to the device. The device then displays the alert to the user.

[0080] Step 10:

[0081] When the user finishes the training, the terminal sends a training completion notification to the server.

[0082] Step 11:

[0083] The server analyzes the data from the entire training session and generates feedback, such as an injury risk assessment, advice for the next training session, and recommendations for food and rest.

[0084] Step 12:

[0085] The server generates feedback and sends it to the device, which displays it to the user, who can use it to plan their next workout.

[0086] These detailed steps allow the system to understand the user's condition in real time and support safe and effective training.

[0087] Example 1

[0088] 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."

[0089] Conventional training support systems have had difficulty integrating and analyzing a user's physiological data, location data, and environmental data to provide an optimal training menu. Furthermore, they lacked the functionality to monitor the user's training status in real time and dynamically change the menu. This prevented users from optimally training for their own condition and environment, leading to safety and effectiveness issues.

[0090] 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.

[0091] In this invention, the server includes means for acquiring physiological data of a user, means for acquiring position data, means for acquiring environmental data, means for analyzing the acquired physiological data, position data, and environmental data and proposing a generated training menu, means for monitoring the physiological data and position data in real time during training performed by the user and dynamically changing the training menu according to the situation, and means for analyzing data after training and providing feedback. This allows the user to receive an individually optimized training menu in real time, enabling them to train safely and effectively.

[0092] "Physiological data" refers to data that indicates the user's physical condition, and specifically includes heart rate, blood pressure, body temperature, etc.

[0093] "Location data" refers to data indicating the user's current location, and specifically includes latitude and longitude information obtained from the Global Positioning System (GPS).

[0094] "Environmental data" refers to data that indicates the external environmental conditions, and specifically includes weather, temperature, humidity, wind speed, and the like.

[0095] "Analyzing" refers to the act of analyzing acquired data through computer processing to find specific patterns or trends.

[0096] A "training menu" indicates a plan or guidelines for the exercise or training that a user should do, and specifically includes running distance, time, intensity, and the like.

[0097] "Real-time" refers to processing ongoing processes and events instantly and providing results immediately.

[0098] "Monitoring" refers to the act of continuously observing the user's condition and environment, and responding immediately if there are any abnormalities or changes.

[0099] "Dynamic change" means to flexibly change fixed settings or plans according to the situation at hand, and to respond immediately.

[0100] "Providing feedback" refers to the act of providing improvements and guidelines for next actions based on the results and evaluation of the training the user has completed.

[0101] This invention is a system that collects and analyzes a user's physiological data, location data, and environmental data in real time, and proposes an optimal training menu for the user. The specific implementation of this system is shown below.

[0102] Hardware and software used

[0103] This system mainly uses the following hardware and software:

[0104] Smartwatch: A device that measures a user's physiological data, such as heart rate.

[0105] Smartphone: A device that uses GPS to collect location data and transmits it along with physiological data to a server.

[0106] Server: A device that analyzes data, generates and proposes training menus, and provides feedback to users.

[0107] External API: An interface for obtaining environmental data such as weather information.

[0108] System Operation

[0109] Data collection and transmission

[0110] The user puts on the smartwatch and launches the smartphone application, where the user enters basic data such as age, weight, and gender, and the application is then ready to reference the user's individual data.

[0111] The smartwatch measures the user's physiological data, such as heart rate, in real time, while the smartphone collects the user's location data using GPS, which is then sent to a server at regular intervals.

[0112] Data analysis on the server

[0113] The server receives the heart rate and location data sent by the user in real time and begins analysis. The server uses an external API to obtain current environmental data, such as weather information (temperature, humidity, weather).

[0114] Training menu generation

[0115] The server uses AI algorithms to generate optimal training plans based on physiological, location, and environmental data. For example, it suggests indoor treadmill training when the temperature is high, and a 5-kilometer run outdoors when the weather is good.

[0116] Real-time monitoring and advice

[0117] While the user is training, the smartwatch and smartphone continue to send physiological and location data to the server, which monitors this data in real time and provides appropriate advice based on the situation. For example, if the user's heart rate spikes, the server will advise them to "slow down."

[0118] Post-training feedback

[0119] Once the training is over, the server analyzes all the data and provides feedback to the user. This feedback includes injury risk assessment, advice for the next training session, and recommended diet and sleep. For example, a message might be given saying, "Great performance! Make sure you get plenty of rest and stay properly hydrated before your next training session."

[0120] Specific examples

[0121] 1. Examples of user behavior

[0122] The user launches the app using their smartwatch and smartphone and enters their user data. When they start running, the smartwatch begins measuring their heart rate and the smartphone collects GPS data.

[0123] 2. Examples of Device Actions

[0124] The device sends the collected heart rate data and GPS data to the server, for example, a heart rate of 75 bpm and location data (35.658034, 139.701636).

[0125] 3. Examples of Server Actions

[0126] The server determines the user's current heart rate and location based on the received data, and also uses an external API to obtain current weather information, such as a temperature of 25 degrees, humidity of 60%, and clear skies.

[0127] Based on this data, the server uses an AI algorithm to suggest the optimal training menu, for example, "running 5 kilometers outdoors."

[0128] While running, the server monitors data in real time and provides advice to the user, such as "slow down your pace," depending on the situation.

[0129] After the training, the server analyzes all the data and provides the user with feedback such as, "Great performance. Make sure you get plenty of rest and stay properly hydrated before your next training session."

[0130] Prompt Sentence Examples

[0131] "Describe a system where a user can use a smartphone and a smartwatch to start a run and collect and transmit data in real time."

[0132] This system allows users to receive personalized training advice in real time, enabling them to train more safely and effectively.

[0133] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0134] Step 1:

[0135] The user puts on the smartwatch and launches the smartphone application, where the user enters basic data such as age, weight, and gender. This is the initial input, and the application is ready to reference this information.

[0136] Input: Basic data such as age, weight, and gender

[0137] Output: User profile initial setup complete

[0138] Specific behavior:

[0139] The user launches the application and enters personal data such as "30 years old, 70 kg, male."

[0140] Step 2:

[0141] The smartwatch, which is the device, measures the user's physiological data such as heart rate in real time. At the same time, the smartphone collects location data using GPS. This data is continuously collected and sent to a server at regular intervals.

[0142] Input: Real-time heart rate data, GPS location data

[0143] Output: Acquired physiological and location data

[0144] Specific behavior:

[0145] The smartwatch measures your heart rate in real time and records, for example, "Heart rate: 75 bpm."

[0146] The smartphone collects GPS location information and records, for example, "Location data: (35.658034, 139.701636)".

[0147] Step 3:

[0148] The device sends the collected heart rate data and GPS data to a server, which updates the data in real time by setting the data to be sent to the server at regular intervals.

[0149] Input: Collected heart rate data, GPS location data

[0150] Output: Data sent to the server

[0151] Specific behavior:

[0152] For example, every 10 seconds, updated heart rate data and GPS location data are sent to a server.

[0153] Step 4:

[0154] The server receives the heart rate and location data sent by the user in real time and begins analysis, which involves processing the data to understand the user's physiological condition and movement status.

[0155] Input: Transmitted physiological and location data

[0156] Output: Parsed user state data

[0157] Specific behavior:

[0158] The server receives "Heart rate: 75 bpm, and location data: (35.658034, 139.701636)" and analyzes it.

[0159] Step 5:

[0160] The server uses an external API to retrieve current environmental data, including weather, temperature, humidity, etc. This environmental data is also subject to analysis.

[0161] Input: Environmental data request obtained from external API

[0162] Output: Current environmental data (e.g. temperature, humidity, weather)

[0163] Specific behavior:

[0164] The server sends a request to an external API and retrieves the data "Temperature: 25 degrees, Humidity: 60%, Weather: Sunny".

[0165] Step 6:

[0166] The server uses AI algorithms to generate optimal training menus based on physiological, location, and environmental data, recommending indoor training when temperatures are high and outdoor training when the weather is good.

[0167] Input: Analyzed physiological data, location data, and environmental data

[0168] Output: Generated training menu

[0169] Specific behavior:

[0170] The server uses an AI algorithm to generate an optimal training menu, such as "running 5 kilometers outdoors."

[0171] Step 7:

[0172] While the user is performing the workout, the smartwatch and smartphone continue to transmit physiological and location data to the server.

[0173] Input: Real-time training data

[0174] Output: Data that continues to be sent to the server

[0175] Specific behavior:

[0176] For example, the user's heart rate rises to "80 bpm" and the data is sent to the server.

[0177] Step 8:

[0178] The server monitors this data in real time and provides appropriate advice depending on the situation: if your heart rate spikes, the server will instruct you to "slow down."

[0179] Input: Physiological and location data transmitted in real time

[0180] Output: Real-time advice

[0181] Specific behavior:

[0182] The server sends a notification to the user's smartphone with advice such as "Your heart rate has risen sharply, so slow down your pace."

[0183] Step 9:

[0184] Once the workout is complete, the server analyzes all the data and provides feedback to the user, including advice for the next workout and diet and sleep recommendations.

[0185] Input: All data from completed training

[0186] Output: Parsed feedback

[0187] Specific behavior:

[0188] For example, they might provide feedback like, "Great performance! Make sure you get plenty of rest and stay properly hydrated before your next training session."

[0189] (Application example 1)

[0190] 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."

[0191] When a user rides in an autonomous vehicle, safety and comfort are important, but conventional systems have had difficulty making real-time adjustments based on the user's physiological state and environment.In addition, by adjusting the vehicle's operation based on physiological data such as heart rate and body temperature, it is necessary to reduce user stress and respond quickly if an abnormality is detected.

[0192] 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.

[0193] In this invention, the server includes means for acquiring user physiological data, means for acquiring location data, means for acquiring environmental data, means for analyzing the acquired physiological data, location data, and environmental data and proposing a training menu in real time, means for monitoring the physiological data and environmental data in real time during training by the user and dynamically changing the training menu based on the changed data, means for analyzing the data and providing feedback after training, means for adjusting and proposing vehicle operation in real time according to the user's condition, means for transmitting data to a vehicle control system and determining vehicle operation based on the data, and means for adjusting the vehicle speed and generating an alert if the user's physiological data indicates an abnormality. This makes it possible to optimize the operation of an autonomous vehicle in real time according to the user's condition, thereby improving safety and comfort.

[0194] "User physiological data" refers to data that indicates the user's physical condition in real time, such as heart rate, body temperature, and blood pressure.

[0195] "Location data" is data that indicates the current location of a user or vehicle, and is usually obtained as GPS data.

[0196] "Environmental data" refers to data relating to the user's surrounding environment, including temperature, humidity, and temperature inside the vehicle.

[0197] "Means for proposing training menus in real time" refers to systems or algorithms that instantly analyze acquired data and present the optimal training menu to the user.

[0198] "Means for monitoring physiological and environmental data" refers to sensors and software that constantly monitor the user's physical state and environmental conditions during training.

[0199] "Means for dynamically changing training menus" refers to systems or algorithms that instantly change training menus based on data acquired in real time.

[0200] "Means for providing feedback" refers to a function that provides guidance and advice to the user based on data analyzed after training.

[0201] "Means for adjusting and suggesting vehicle behavior in real time" refers to systems or functions that instantly correct or suggest the speed or route of an autonomous vehicle based on user status data.

[0202] "Means for transmitting data to the vehicle's control system" refers to the communication functions and protocols for transmitting the acquired physiological data and environmental data to the control device in the vehicle.

[0203] "Means for determining vehicle behavior" refers to the systems or algorithms that control the vehicle's movement based on the transmitted data.

[0204] The "means for generating an alert" refers to a function that issues a warning when an abnormality is detected in the user's physiological data.

[0205] This invention is a system that collects and analyzes a user's physiological data, location data, and environmental data in real time, and provides the user with optimal driving advice. How this system is implemented will be described below.

[0206] System Overview

[0207] This system works by collecting physiological and location data from the user's smartwatch or smartphone and transmitting it to the autonomous vehicle's control system. The control system uses the collected data to generate and provide optimal driving advice based on the user's physiological state and the surrounding environment of the day. Furthermore, while driving, the system monitors and analyzes data in real time and provides dynamic driving advice.

[0208] Program Overview

[0209] The server receives the data from the user, analyzes it, and generates optimal driving advice. Specifically, the process involves the following steps:

[0210] 1. Data collection and transmission

[0211] Users ride in autonomous vehicles wearing a smartwatch or smartphone. The smartwatch measures physiological data such as heart rate and body temperature in real time, while the smartphone collects location data using GPS. This data is sent to the vehicle's control system at regular intervals.

[0212] 2. Data analysis in the control system

[0213] When the control system receives data sent from the user, it first analyzes the physiological data (e.g., heart rate) and location data (e.g., GPS data). In addition, the control system uses external data acquisition means to acquire current environmental data (e.g., temperature and humidity inside the vehicle).

[0214] 3. Driving advice generation

[0215] Based on the acquired physiological, location, and environmental data, the control system uses AI algorithms to generate optimal driving advice, such as suggesting slowing the vehicle and heading to the nearest rest stop if the user's heart rate is high.

[0216] 4. Real-time monitoring and advice

[0217] While the user is riding, the smartwatch and smartphone continue to transmit physiological and location data to the control system, which monitors and analyzes this data in real time and provides appropriate driving advice based on the situation. For example, if the user's heart rate spikes, the system can issue an alert to "take a deep breath."

[0218] 5. Post-driving feedback

[0219] Once the trip is over, the control system analyzes all the data and provides feedback to the user, including a risk assessment of any anomalies and recommendations for the next trip.

[0220] Hardware and Software

[0221] Hardware: Smartwatches (e.g., smartwatches), smartphones (e.g., smartphones), autonomous vehicle control systems (e.g., in-vehicle control systems)

[0222] Software: AI algorithms (e.g., TensorFlow, PyTorch), environmental data acquisition APIs (e.g., Environmental Data APIs)

[0223] The server uses the above hardware and software to acquire and analyze data in real time and provide appropriate driving advice to users.

[0224] Specific examples

[0225] User: A user wearing a smartwatch and carrying a smartphone with GPS functionality gets into an autonomous vehicle.

[0226] Terminal: The smartwatch and smartphone each transmit physiological data and location data to the control system within the terminal.

[0227] Server: Based on the data received by the in-vehicle control system, the server uses AI algorithms to generate and provide optimal driving advice in real time.

[0228] Prompt Sentence Examples

[0229] The smartwatch collects the user's heart rate, body temperature, and physiological data in real time and sends it to the vehicle's control system. The control system then uses AI algorithms to analyze the data and determine the vehicle's behavior based on the user's condition. Specifically, if the user's heart rate is high, the system will slow down the vehicle or change the route. After the drive, the system will provide detailed feedback.

[0230] This system allows users to enjoy safe and comfortable autonomous vehicle driving while receiving personalized driving advice in real time.

[0231] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0232] Step 1:

[0233] A user wears a smartwatch and a smartphone and gets into an autonomous vehicle. The smartwatch measures physiological data such as heart rate and body temperature in real time, while the smartphone collects location data using GPS. The input data includes heart rate (e.g., 75 bpm), body temperature (e.g., 36.6°C), and location data (e.g., GPS coordinates). This data is sent to the vehicle's control system via the smartphone.

[0234] Step 2:

[0235] The server receives physiological and location data sent from the user's smartwatch and smartphone. It also acquires environmental data such as interior temperature and humidity from environmental data acquisition sensors inside the vehicle. The input data includes heart rate, body temperature, GPS data, interior temperature (e.g., 24°C), and humidity (e.g., 50%). The server aggregates this data and creates a dataset for analysis.

[0236] Step 3:

[0237] The server analyzes the acquired physiological data, location data, and environmental data. AI algorithms (e.g., TensorFlow, PyTorch) are used for the analysis. The AI ​​model evaluates the user's state under certain conditions and generates driving advice based on that evaluation. For example, if the heart rate is high, the AI ​​model will decide to slow down the vehicle. The driving advice generated as a result of the analysis is output.

[0238] Step 4:

[0239] The server sends the generated driving advice to the vehicle's control system, which then adjusts the vehicle's behavior in real time by adjusting the vehicle's speed and, if necessary, changing its route. The input data is the driving advice, and the output data is the controlled vehicle behavior.

[0240] Step 5:

[0241] While the user is driving, the smartwatch and smartphone continue to transmit physiological and location data to the server. The server continues to monitor and analyze this data in real time, dynamically generating driving advice as needed and sending it to the control system. For example, if the user's heart rate spikes, it will issue an alert saying, "Take a deep breath."

[0242] Step 6:

[0243] Once the drive is over, the server analyzes all the data and provides feedback to the user, including an abnormality risk assessment and advice for the next drive. All collected data (heart rate data, body temperature data, GPS data, interior temperature, humidity, and driving behavior data) are used as input data, and a feedback report is generated as output data.

[0244] Through these steps, users can receive real-time personalized driving advice and enjoy a safe and comfortable autonomous vehicle experience.

[0245] 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.

[0246] The present invention is a system that combines a user's physiological data, location data, and environmental data with an emotion engine that recognizes the user's emotions to propose optimal training menus to the user in real time. The specific implementation of this system is shown below.

[0247] System Overview

[0248] The system collects various data using a smartwatch, smartphone, and emotion engine, and analyzes it on a server to propose an optimized training menu for the user. The system also provides feedback during and after training.

[0249] Program Overview

[0250] The server receives the data from the user, analyzes it, and generates an optimal training menu. Specifically, the process is carried out in the following steps:

[0251] Data collection and transmission

[0252] The user puts on a smartwatch or smartphone, launches an application equipped with the emotion engine, and starts running. The smartwatch measures physiological data such as heart rate in real time, while the smartphone collects location data using GPS. The emotion engine also analyzes the user's voice data and facial expression data to generate emotion data. This data is sent to a server at regular intervals.

[0253] Data analysis on the server

[0254] The server receives and analyzes all data sent by the user, including physiological data (e.g., heart rate) and location data (e.g., GPS data), as well as current environmental data (e.g., temperature, humidity, weather) using external APIs, and emotion data generated by the emotion engine.

[0255] Training menu generation

[0256] The server uses AI algorithms to generate optimal training menus based on physiological, location, environmental, and emotional data. For example, if the emotion engine recognizes that the user is feeling stressed, it can suggest light exercise that will have a relaxing effect. On the other hand, if the user is feeling refreshed, it can suggest more strenuous exercise.

[0257] Real-time monitoring and advice

[0258] While the user is training, the smartwatch and smartphone continue to send physiological, location, and emotional data to the server. The server monitors and analyzes this data in real time, dynamically adjusting the training menu based on the situation and providing appropriate advice. For example, if the user feels fatigued or anxious during training, the server will advise them to slow down.

[0259] Post-training feedback

[0260] Once the training is complete, the server analyzes all the acquired data and provides feedback to the user. The feedback includes not only advice based on physical data, but also suggestions for mental care based on emotional data. For example, feedback such as "Excellent performance. Please try to relax before your next training session" may be provided.

[0261] Specific examples

[0262] 1. Examples of user behavior

[0263] The user launches the app using their smartwatch and smartphone and enters their user data. The emotion engine also starts up and begins analyzing voice and facial expression data. When the user starts running, the smartwatch begins measuring their heart rate and the smartphone collects GPS data.

[0264] 2. Examples of Device Actions

[0265] The device sends the collected heart rate data, GPS data, and emotion data to the server. For example, the heart rate is 75 bpm, the location data is (35.658034, 139.701636), and the emotion data is "stress."

[0266] 3. Examples of Server Actions

[0267] The server starts analysis based on this data. It also obtains current weather information using an external API. For example, it obtains weather data such as temperature 25 degrees, humidity 60%, and clear skies.

[0268] The server uses an AI algorithm to suggest optimal training menus based on physiological, location, environmental, and emotional data. For example, if the emotional data indicates "stress," it will suggest "relaxing exercises indoors."

[0269] While running, the server monitors the data in real time and provides advice to the user depending on the situation. For example, if the heart rate gets too high, it will instruct the user to "slow down your pace." If the emotional data indicates "anxiety," it will advise the user to "try to relax and take deep breaths."

[0270] After the training, the server analyzes all the data and provides feedback to the user, such as "Great performance. Please try to relax until the next training session."

[0271] This system allows users to perform optimal training that takes into account not only their physical condition but also their emotional state, supporting safe and effective training.

[0272] The processing flow will be explained below.

[0273] Step 1:

[0274] The user launches the app using a smartwatch or smartphone and enters their own user data (level, weight, goals, etc.). This data is used by the system to suggest training menus that are optimal for the user's condition and goals.

[0275] Step 2:

[0276] The device sends user data to the server to initiate a session along with login information, which the server stores for later analysis.

[0277] Step 3:

[0278] The device will begin to measure physiological data (e.g., heart rate) in real time using sensors in the smartwatch or smartphone, and location data (e.g., GPS data) will also be collected.

[0279] Step 4:

[0280] The device uses an emotion engine to analyze the user's voice and facial expression data and generate emotion data in real time, which indicates emotional states such as "stress," "refreshment," and "anxiety."

[0281] Step 5:

[0282] The device periodically collects physiological data, location data, and emotion data and sends them to the server. For example, the device sends heart rate 75 bpm, location data (35.658034, 139.701636), and emotion data "refresh" to the server.

[0283] Step 6:

[0284] The server analyzes the received data and uses an external API to obtain current environmental data (e.g., temperature, humidity, weather) based on the acquired physiological data (heart rate), location data (GPS data), and emotion data.

[0285] Step 7:

[0286] The server uses an AI algorithm to generate an optimal training menu based on the user's physiological, location, environmental, and emotional data. For example, if the user is in a "refreshed" emotional state, it will suggest a hard training menu, and if the user is feeling "stressed," it will suggest relaxing exercises.

[0287] Step 8:

[0288] The server sends the generated training menu to the terminal, which displays it to the user, who then starts running according to the training menu.

[0289] Step 9:

[0290] While the user is training, the device continues to collect physiological data, location data, and emotional data and transmits them to the server. For example, if the user's heart rate rises to 85 bpm during training and the emotional data changes to "anxiety," the device will transmit the data to the server.

[0291] Step 10:

[0292] The server monitors the data it receives in real time and immediately generates advice if there are any fluctuations. For example, if the heart rate suddenly increases, it generates an alert saying "Slow down your pace." If the emotion data indicates "anxiety," it sends the advice "Try to relax and take deep breaths" to the device. The device then displays the alert to the user.

[0293] Step 11:

[0294] When the user finishes the training, the terminal sends a training completion notification to the server.

[0295] Step 12:

[0296] The server analyzes the data from the entire training session and generates feedback. For example, it assesses the risk of injury, provides advice for the next training session, and provides recommended diet and rest. Feedback also includes suggestions for mental care, such as "That was a great performance. Please try to relax before your next training session."

[0297] Step 13:

[0298] The server generates feedback and sends it to the device, which displays it to the user, who can use it to plan their next workout.

[0299] These detailed steps enable the system to understand the user's condition in real time and support safe and effective training. The addition of an emotion engine makes it possible to provide training advice that takes the user's mental state into account.

[0300] Example 2

[0301] 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."

[0302] Conventional training systems could propose training menus based on a user's physiological data, location data, and environmental data, but they were unable to take the user's emotional state into account. This made it difficult to provide an optimal training menu that matched the user's mental state, preventing the training from being fully effective. Real-time feedback and dynamic changes to the training menu were also insufficient. The present invention aims to solve these problems and provide an optimal training menu that takes into account both the user's physiological and emotional states.

[0303] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring physiological data of the user, means for acquiring location data, means for acquiring environmental data, means for analyzing and acquiring emotional data of the user, means for analyzing the acquired physiological data, location data, environmental data, and emotional data and proposing a training menu in real time, means for monitoring the physiological data, environmental data, and emotional data in real time during training performed by the user and dynamically changing the training menu based on the changed data, and means for analyzing data after training and providing feedback. This makes it possible to propose an optimal training menu in real time that takes into account both the physiological state and emotional state of the user, thereby maximizing the effectiveness of the training.

[0304] "User's physiological data" refers to data that indicates the user's physical condition, such as heart rate, blood pressure, and body temperature.

[0305] "Location data" refers to data including the latitude and longitude of the user's current location, obtained using a GPS module or the like.

[0306] "Environmental data" refers to data that indicates the conditions of the environment in which the user is located, such as temperature, humidity, and weather.

[0307] "Emotion data" refers to data that indicates the mental state of the user, analyzed from their voice and facial expressions.

[0308] "Means for suggesting training menus in real time" refers to systems or algorithms that instantly provide users with appropriate training menus based on collected and analyzed data.

[0309] "Monitoring physiological and environmental data in real time during a workout" refers to the process of continuously collecting and monitoring physiological and environmental data while a user is working out.

[0310] "Means for dynamically changing training menus" refers to systems or algorithms that update and change training menus in real time based on acquired and analyzed data.

[0311] "Means for analyzing data and providing feedback after training" refers to a system or algorithm that analyzes all data collected after training is completed and provides advice or evaluation to the user based on the results.

[0312] This invention is a system that combines a user's physiological data, location data, and environmental data with an emotion engine that recognizes the user's emotions to propose optimal training menus to the user in real time. This system is realized by collecting and analyzing various data using a smartwatch, smartphone, server, and emotion engine.

[0313] System Overview

[0314] The system works as follows: Users collect data using a smartwatch or smartphone, and the emotion engine analyzes the user's emotional data. The data is then sent to a server, which analyzes the data and generates an optimal training menu. Additionally, feedback is provided during and after training.

[0315] Program processing

[0316] Data collection and transmission

[0317] 1. The user wears the smartwatch on their wrist and launches a dedicated app on their smartphone, which collects physiological data such as heart rate, blood pressure, and body temperature.

[0318] 2. The device (smartwatch) collects these physiological data in real time and stores them in its internal memory.

[0319] 3. The device (smartphone) collects location data using the GPS function. For example, the GPS module obtains the latitude and longitude of the current location every minute.

[0320] 4. The device (emotion engine) generates emotion data from the user's voice and facial expressions. It uses the smartphone's camera and microphone to recognize facial expressions and analyze voice, determining emotions such as "stress" or "relaxation."

[0321] 5. The device sends the collected heart rate data, location data, and emotion data to the server at regular intervals, for example, every 5 minutes, batch-processing the data and sending it to the server using HTTPS.

[0322] Data analysis on the server

[0323] 1. The server receives the transmitted physiological data, location data, and emotion data and stores them in a database.

[0324] 2. The server uses an external API to obtain current environmental data (temperature, humidity, weather), sends a request to the weather API, analyzes the response data, and saves it.

[0325] 3. The server performs analysis based on physiological, location, environmental, and emotional data, and processes the data using AI algorithms to evaluate the correlation between each data point.

[0326] Training menu generation

[0327] The server generates an optimal training menu based on the analysis results. For example, if the emotion engine detects "stress," it will suggest light exercise with a relaxing effect. If the user indicates "relaxation," it will suggest jogging or yoga.

[0328] Real-time monitoring and advice

[0329] 1. During training, the device continuously transmits physiological data, location data, and emotional data to the server. The data is updated every minute and transmitted to the server in real time.

[0330] 2. The server analyzes the received data in real time and provides advice to the user based on the situation. For example, if the user's heart rate exceeds 160 bpm and the user feels fatigued, the server will advise the user to "slow down."

[0331] Post-training feedback

[0332] 1. The server re-analyzes all data after training is complete. At the end of training, it performs a batch analysis and generates comprehensive feedback.

[0333] 2. The server provides final feedback to the user via an in-app notification or email with a message such as "Great performance! Please try to relax until your next training session."

[0334] Examples of prompt statements

[0335] User: Launches the app using a smartwatch and smartphone, enters data, and starts running.

[0336] Device: The smartwatch measures the heart rate, the smartphone collects GPS data, and the emotion engine generates emotion data.

[0337] Server: Analyzes the collected data, obtains weather information via an external API, and suggests optimal training menus.

[0338] As described above, this system can propose training menus in real time that take into account both the user's physiological and emotional states, providing the optimal training environment for the user.

[0339] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0340] Step 1:

[0341] The user wears the smartwatch on their wrist and launches the dedicated app on their smartphone. The input is basic data such as the user's heart rate, blood pressure, and body temperature. The output is the start of collection of this data. Specifically, the user presses the "Start" button on the app's startup screen.

[0342] Step 2:

[0343] The device (smartwatch) collects physiological data in real time. The input is the user's physiological data, such as heart rate, blood pressure, and body temperature, which are measured every second. The output is the storage of the measured data in internal memory. Specifically, the heart rate sensor collects data and stores it in internal memory.

[0344] Step 3:

[0345] The device (smartphone) uses GPS to collect location data. The input is location information from the smartphone's GPS module, which acquires latitude and longitude every minute. The output is the storage of the latitude and longitude of the current location. Specifically, the GPS module acquires information about the current location and stores it in the smartphone.

[0346] Step 4:

[0347] The device (emotion engine) generates the user's emotional data from voice and facial expressions. The input is voice and facial expression data captured using the smartphone's camera and microphone. The output is emotional data (such as "stress" or "relaxed") as the result of analysis. Specifically, it analyzes camera images and voice to determine the emotion.

[0348] Step 5:

[0349] The device sends the collected physiological data, location data, and emotional data to the server at regular intervals. The input is the collected data (heart rate, location data, emotional data), which is batch-processed every 5 minutes. The output is transmission to the server. Specifically, the data is sent to the server using the HTTPS protocol.

[0350] Step 6:

[0351] The server receives the transmitted physiological data, location data, and emotion data. The input is the data transmitted from the device, and the output is to store it in a database. Specifically, a dedicated API endpoint receives the data and stores it in the database.

[0352] Step 7:

[0353] The server uses an external API to obtain environmental data (temperature, humidity, weather). The input is a request to the weather API, and the output is the obtained environmental data. Specifically, it periodically sends requests to the weather API, analyzes the response data, and saves it.

[0354] Step 8:

[0355] The server performs analysis based on physiological, location, environmental, and emotional data. The input is all stored data, and the output is the analysis results (optimal training menu). Specifically, it applies AI algorithms to analyze the data and evaluates the correlation between each data point.

[0356] Step 9:

[0357] The server generates an optimal training menu and proposes it to the user. The input is the analysis results, and the output is a training menu proposal. Specifically, if the emotional data indicates "stress," the server proposes light exercise that has a relaxing effect.

[0358] Step 10:

[0359] The device continues to send physiological, location, and emotional data to the server during training. The input is the data collected in real time, and the output is sending it to the server. Specifically, the data is updated every minute and sent to the server in real time.

[0360] Step 11:

[0361] The server analyzes the data received in real time and provides advice to the user as needed. The input is real-time data, and the output is dynamic training menu changes and advice. Specifically, if the heart rate exceeds 160 bpm and the user shows signs of fatigue, the server instructs the user to "slow down."

[0362] Step 12:

[0363] After the training is complete, the server reanalyzes all data and provides feedback to the user. The input is all training data, and the output is a feedback message. Specifically, the server performs a batch analysis at the end of the training and sends an in-app notification or email with a message saying, "Excellent performance. Please try to relax until the next training session."

[0364] (Application example 2)

[0365] 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."

[0366] Modern factories require effective management systems that balance employee health with efficient work. However, there are currently no systems that can analyze employees' physiological data and emotional state in real time and make appropriate suggestions based on that state. This can lead to the accumulation of employee fatigue and stress, which can lead to a decline in productivity. It can also lead to a deterioration in employee health. A new system is needed to solve these issues.

[0367] 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.

[0368] In this invention, the server includes means for acquiring physiological data of a user, means for acquiring location data, means for acquiring environmental data, means for acquiring emotional data, means for analyzing the acquired physiological data, location data, environmental data, and emotional data to propose a training menu in real time, means for monitoring the physiological data and environmental data in real time during training performed by the user and dynamically changing the training menu based on the changed data, means for analyzing data after training and providing feedback, and means for evaluating the fatigue level of employees and proposing appropriate break timing and workload adjustments in real time, thereby enabling health management of factory employees and improving work efficiency.

[0369] "User" refers to a person who uses the system, and specifically includes factory workers.

[0370] "Physiological data" refers to physiological information obtained from the human body, such as heart rate.

[0371] "Location data" refers to geographical location information obtained by GPS or other means.

[0372] "Environmental data" refers to information about the external environment, such as temperature, humidity, and weather.

[0373] "Emotional data" refers to information about the user's emotional state obtained by analyzing voice and facial expressions.

[0374] "Analysis" refers to information processing to make appropriate decisions and suggestions based on acquired data.

[0375] "Real-time" refers to data collection and processing occurring immediately, without delay.

[0376] A "training menu" refers to a series of exercises or tasks that a user performs.

[0377] "Dynamic change" refers to instantly changing training menus and suggestions in response to data that changes in real time.

[0378] "Feedback" refers to providing advice on improvements and next steps based on the user's behavior and status.

[0379] "Fatigue level" refers to an index that quantitatively evaluates the degree of fatigue of the user.

[0380] "Break timing" refers to the appropriate moment to temporarily stop work and take a rest.

[0381] "Workload" refers to the difficulty or intensity of the work performed by the user.

[0382] This paper outlines a system that applies this invention to factory robots. This system uses physiological data, location data, environmental data, and emotional data of factory workers to monitor their health status in real time and propose appropriate break times and workload adjustments.

[0383] System configuration

[0384] Data collection

[0385] The user wears a smartwatch and a smartphone and launches an application equipped with an emotion engine. The smartwatch measures physiological data such as heart rate in real time, while the smartphone collects location data using GPS. The smartphone's emotion engine then analyzes voice and facial expressions to generate emotion data.

[0386] Sending data

[0387] This data is sent to a server via the Internet at regular intervals, where it is analyzed and appropriate suggestions are made to the user.

[0388] Data analysis on the server

[0389] The server integrates the collected physiological, location, environmental, and emotional data and analyzes it using an AI algorithm. Based on the analysis results, it evaluates the employee's fatigue level and suggests appropriate break times and workload adjustments.

[0390] Specifically, if the user's heart rate is high or the emotion engine indicates "stress," the system will notify them, for example, to "take a break." On the other hand, if the user's condition is good, the system will provide instructions such as "keep working at this pace."

[0391] Real-time monitoring and feedback

[0392] While the user is working, the smartwatch and smartphone continue to collect and transmit data to the server. The server monitors and analyzes this data in real time and dynamically adjusts the suggestions. For example, if the user's heart rate rises too much, the server can provide advice such as "Take a deep breath and relax."

[0393] After the task is completed, the server analyzes all the acquired data and provides the user with comprehensive feedback, such as "Today's task performance was excellent. Let's try harder next time."

[0394] Specific examples

[0395] An example of a prompt for a generative AI model might look like this:

[0396] User: I seem to be stressed. My heart rate is over 90 and the emotion engine is showing "stress." What advice would you give me?

[0397] AI: I would recommend you take a break and relax for a bit. Take a deep breath and take a five-minute break.

[0398] Such a system is expected to effectively manage the health of factory employees, improving productivity and work efficiency, and also contributing to maintaining the long-term health of employees and improving the efficiency of the entire company.

[0399] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0400] Step 1:

[0401] The user puts on the smartwatch and smartphone and launches the application. At this time, the emotion engine is also launched. At this stage, the smartwatch begins measuring the user's heart rate, and the smartphone acquires location information using GPS. The emotion engine analyzes the user's voice and facial expressions to generate emotion data. This data is sent to the smartphone in real time.

[0402] Input: User's heart rate, location information, voice and facial expression data

[0403] Output: physiological data, location data, emotional data

[0404] Step 2:

[0405] The device (smartphone) transmits the collected physiological data, location data, and emotional data to a server at regular intervals. The transmitted data is accumulated in the server in real time and used for subsequent analysis.

[0406] Input: physiological data, location data, emotional data

[0407] Output: Send data to the server

[0408] Step 3:

[0409] The server analyzes the received data using an AI algorithm to assess the user's fatigue level and emotional state. For example, if the heart rate is high and the emotional data indicates "stress," the server determines that the user is highly fatigued.

[0410] Input: physiological data, location data, emotional data

[0411] Output: Analysis results (fatigue level evaluation, emotion evaluation)

[0412] Step 4:

[0413] Based on the analysis results, the server will suggest an appropriate training menu to the user in real time. For example, if the heart rate is high and the emotional data indicates "stress," the server will suggest "take a break." On the other hand, if the user's condition is good, the server will suggest "keep working at this pace."

[0414] Input: Analysis results

[0415] Output: Training menu suggestions

[0416] Step 5:

[0417] While the user is working, the smartwatch and smartphone continue to collect data and send it to the server. The server monitors this data in real time and dynamically changes the training menu and suggestions depending on the situation. For example, if the user's heart rate spikes while working, the server will provide advice such as "Take a deep breath and relax."

[0418] Input: Real-time data (physiological data, location data, emotional data)

[0419] Output: Dynamic suggestions and training menu changes

[0420] Step 6:

[0421] Once the task is completed, the server compiles and analyzes all the acquired data and provides feedback to the user. The feedback includes the results of the training and advice for the next time. For example, the server may provide feedback such as, "Today's task performance was excellent. Let's try harder next time."

[0422] Input: All collected data

[0423] Output: Comprehensive feedback

[0424] These steps allow users to monitor their health status in real time and receive appropriate breaks and workload adjustments, which is expected to improve work efficiency and reduce health risks.

[0425] 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.

[0426] 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.

[0427] 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.

[0428] [Second embodiment]

[0429] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0430] 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.

[0431] 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).

[0432] 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.

[0433] 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.

[0434] 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).

[0435] 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. 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.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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."

[0441] The present invention is a system that collects and analyzes a user's physiological data, location data, and environmental data in real time, and proposes an optimal training menu for the user. The specific implementation of this system is shown below.

[0442] System Overview

[0443] The system works by collecting physiological and location data from the user's smartwatch or smartphone and sending it to a server. The server then uses the collected data to generate and suggest optimal training menus based on the user's physical condition and the weather conditions of the day. Furthermore, the system monitors and analyzes data in real time while running and provides dynamic advice.

[0444] Program Overview

[0445] The server receives the data from the user, analyzes it, and generates an optimal training menu. Specifically, the process is carried out in the following steps:

[0446] Data collection and transmission

[0447] The user wears a smartwatch or smartphone and starts running. The smartwatch measures physiological data such as heart rate in real time, and the smartphone collects location data using GPS. This data is then sent to a server at regular intervals.

[0448] Data analysis on the server

[0449] When the server receives data sent from the user, it first analyzes the physiological data (e.g., heart rate) and location data (e.g., GPS data). In addition, the server uses an external API to obtain current environmental data (e.g., weather information).

[0450] Training menu generation

[0451] The server uses AI algorithms to generate optimal training plans based on physiological, location, and environmental data. For example, if the current temperature is high, it may suggest training on an indoor treadmill, while if the weather is fine, it may suggest running outdoors.

[0452] Real-time monitoring and advice

[0453] While the user is training, the smartwatch and smartphone continue to send physiological and location data to the server, which monitors and analyzes this data in real time and provides appropriate advice based on the situation. For example, if the user's heart rate spikes, the server can advise them to slow down.

[0454] Post-training feedback

[0455] Once the workout is complete, the server analyzes all the data and provides feedback to the user, including injury risk assessment, advice for the next workout, and recommended diet and sleep.

[0456] Specific examples

[0457] 1. Examples of user behavior

[0458] The user launches the app using their smartwatch and smartphone and enters their user data. When they start running, the smartwatch begins measuring their heart rate and the smartphone collects GPS data.

[0459] 2. Examples of Device Actions

[0460] The device sends the collected heart rate data and GPS data to the server. For example, the device sends a heart rate of 75 bpm and location data (35.658034, 139.701636) to the server.

[0461] 3. Examples of Server Actions

[0462] The server uses the received data to determine the user's current heart rate and location, and then uses an external API to obtain current weather information, such as a temperature of 25 degrees, humidity of 60%, and clear skies.

[0463] Based on this data, the server uses an AI algorithm to suggest the optimal training menu, such as "running 5 kilometers outdoors."

[0464] While running, the server monitors data in real time and provides advice to the user based on the situation, for example, telling them to "slow down" if their heart rate gets too high.

[0465] After the training, the server analyzes all the data and provides feedback to the user, such as "Great performance. Make sure you get plenty of rest and drink plenty of water before your next training session."

[0466] The system allows users to train safely and effectively while receiving real-time, personalized training advice.

[0467] The processing flow will be explained below.

[0468] Step 1:

[0469] The user launches the app using a smartwatch or smartphone and enters their own user data (level, weight, goals, etc.). This data is used by the system to suggest training menus that are optimal for the user's condition and goals.

[0470] Step 2:

[0471] The device sends user data to the server to initiate a session along with login information, which the server stores for later analysis.

[0472] Step 3:

[0473] The device will begin to measure physiological data (e.g., heart rate) in real time using sensors in the smartwatch or smartphone, and location data (e.g., GPS data) will also be collected.

[0474] Step 4:

[0475] The device periodically sends the collected physiological data and location data to the server, for example, sending a set of heart rate data and GPS data to the server every minute.

[0476] Step 5:

[0477] The server analyzes the received data and uses an external API to obtain current environmental data (e.g., temperature, humidity, weather) based on the acquired physiological data (heart rate) and location data (GPS data).

[0478] Step 6:

[0479] The server uses AI algorithms to generate optimal training plans based on the user's physiological, location, and environmental data, suggesting, for example, indoor treadmill training on hot days and outdoor running on sunny days.

[0480] Step 7:

[0481] The server sends the generated training menu to the terminal, which displays it to the user, who then starts running according to the training menu.

[0482] Step 8:

[0483] While the user is training, the device continues to collect physiological data (heart rate) and location data (GPS data) and transmits them to the server.

[0484] Step 9:

[0485] The server monitors the data it receives in real time and immediately generates advice if there are any fluctuations. For example, if your heart rate spikes, the server generates an alert saying "Slow down" and sends it to the device. The device then displays the alert to the user.

[0486] Step 10:

[0487] When the user finishes the training, the terminal sends a training completion notification to the server.

[0488] Step 11:

[0489] The server analyzes the data from the entire training session and generates feedback, such as an injury risk assessment, advice for the next training session, and recommendations for food and rest.

[0490] Step 12:

[0491] The server generates feedback and sends it to the device, which displays it to the user, who can use it to plan their next workout.

[0492] These detailed steps allow the system to understand the user's condition in real time and support safe and effective training.

[0493] Example 1

[0494] 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."

[0495] Conventional training support systems have had difficulty integrating and analyzing a user's physiological data, location data, and environmental data to provide an optimal training menu. Furthermore, they lacked the functionality to monitor the user's training status in real time and dynamically change the menu. This prevented users from optimally training for their own condition and environment, leading to safety and effectiveness issues.

[0496] 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.

[0497] In this invention, the server includes means for acquiring physiological data of a user, means for acquiring position data, means for acquiring environmental data, means for analyzing the acquired physiological data, position data, and environmental data and proposing a generated training menu, means for monitoring the physiological data and position data in real time during training performed by the user and dynamically changing the training menu according to the situation, and means for analyzing data after training and providing feedback. This allows the user to receive an individually optimized training menu in real time, enabling them to train safely and effectively.

[0498] "Physiological data" refers to data that indicates the user's physical condition, and specifically includes heart rate, blood pressure, body temperature, etc.

[0499] "Location data" refers to data indicating the user's current location, and specifically includes latitude and longitude information obtained from the Global Positioning System (GPS).

[0500] "Environmental data" refers to data that indicates the external environmental conditions, and specifically includes weather, temperature, humidity, wind speed, and the like.

[0501] "Analyzing" refers to the act of analyzing acquired data through computer processing to find specific patterns or trends.

[0502] A "training menu" indicates a plan or guidelines for the exercise or training that a user should do, and specifically includes running distance, time, intensity, and the like.

[0503] "Real-time" refers to processing ongoing processes and events instantly and providing results immediately.

[0504] "Monitoring" refers to the act of continuously observing the user's condition and environment, and responding immediately if there are any abnormalities or changes.

[0505] "Dynamic change" means to flexibly change fixed settings or plans according to the situation at hand, and to respond immediately.

[0506] "Providing feedback" refers to the act of providing improvements and guidelines for next actions based on the results and evaluation of the training the user has completed.

[0507] This invention is a system that collects and analyzes a user's physiological data, location data, and environmental data in real time, and proposes an optimal training menu for the user. The specific implementation of this system is shown below.

[0508] Hardware and software used

[0509] This system mainly uses the following hardware and software:

[0510] Smartwatch: A device that measures a user's physiological data, such as heart rate.

[0511] Smartphone: A device that uses GPS to collect location data and transmits it along with physiological data to a server.

[0512] Server: A device that analyzes data, generates and proposes training menus, and provides feedback to users.

[0513] External API: An interface for obtaining environmental data such as weather information.

[0514] System Operation

[0515] Data collection and transmission

[0516] The user puts on the smartwatch and launches the smartphone application, where the user enters basic data such as age, weight, and gender, and the application is then ready to reference the user's individual data.

[0517] The smartwatch measures the user's physiological data, such as heart rate, in real time, while the smartphone collects the user's location data using GPS, which is then sent to a server at regular intervals.

[0518] Data analysis on the server

[0519] The server receives the heart rate and location data sent by the user in real time and begins analysis. The server uses an external API to obtain current environmental data, such as weather information (temperature, humidity, weather).

[0520] Training menu generation

[0521] The server uses AI algorithms to generate optimal training plans based on physiological, location, and environmental data. For example, it suggests indoor treadmill training when the temperature is high, and a 5-kilometer run outdoors when the weather is good.

[0522] Real-time monitoring and advice

[0523] While the user is training, the smartwatch and smartphone continue to send physiological and location data to the server, which monitors this data in real time and provides appropriate advice based on the situation. For example, if the user's heart rate spikes, the server will advise them to "slow down."

[0524] Post-training feedback

[0525] Once the training is over, the server analyzes all the data and provides feedback to the user. This feedback includes injury risk assessment, advice for the next training session, and recommended diet and sleep. For example, a message might be given saying, "Great performance! Make sure you get plenty of rest and stay properly hydrated before your next training session."

[0526] Specific examples

[0527] 1. Examples of user behavior

[0528] The user launches the app using their smartwatch and smartphone and enters their user data. When they start running, the smartwatch begins measuring their heart rate and the smartphone collects GPS data.

[0529] 2. Examples of Device Actions

[0530] The device sends the collected heart rate data and GPS data to the server, for example, a heart rate of 75 bpm and location data (35.658034, 139.701636).

[0531] 3. Examples of Server Actions

[0532] The server determines the user's current heart rate and location based on the received data, and also uses an external API to obtain current weather information, such as a temperature of 25 degrees, humidity of 60%, and clear skies.

[0533] Based on this data, the server uses an AI algorithm to suggest the optimal training menu, for example, "running 5 kilometers outdoors."

[0534] While running, the server monitors data in real time and provides advice to the user, such as "slow down your pace," depending on the situation.

[0535] After the training, the server analyzes all the data and provides the user with feedback such as, "Great performance. Make sure you get plenty of rest and stay properly hydrated before your next training session."

[0536] Prompt Sentence Examples

[0537] "Describe a system where a user can use a smartphone and a smartwatch to start a run and collect and transmit data in real time."

[0538] This system allows users to receive personalized training advice in real time, enabling them to train more safely and effectively.

[0539] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0540] Step 1:

[0541] The user puts on the smartwatch and launches the smartphone application, where the user enters basic data such as age, weight, and gender. This is the initial input, and the application is ready to reference this information.

[0542] Input: Basic data such as age, weight, and gender

[0543] Output: User profile initial setup complete

[0544] Specific behavior:

[0545] The user launches the application and enters personal data such as "30 years old, 70 kg, male."

[0546] Step 2:

[0547] The smartwatch, which is the device, measures the user's physiological data such as heart rate in real time. At the same time, the smartphone collects location data using GPS. This data is continuously collected and sent to a server at regular intervals.

[0548] Input: Real-time heart rate data, GPS location data

[0549] Output: Acquired physiological and location data

[0550] Specific behavior:

[0551] The smartwatch measures your heart rate in real time and records, for example, "Heart rate: 75 bpm."

[0552] The smartphone collects GPS location information and records, for example, "Location data: (35.658034, 139.701636)".

[0553] Step 3:

[0554] The device sends the collected heart rate data and GPS data to a server, which updates the data in real time by setting the data to be sent to the server at regular intervals.

[0555] Input: Collected heart rate data, GPS location data

[0556] Output: Data sent to the server

[0557] Specific behavior:

[0558] For example, every 10 seconds, updated heart rate data and GPS location data are sent to a server.

[0559] Step 4:

[0560] The server receives the heart rate and location data sent by the user in real time and begins analysis, which involves processing the data to understand the user's physiological condition and movement status.

[0561] Input: Transmitted physiological and location data

[0562] Output: Parsed user state data

[0563] Specific behavior:

[0564] The server receives "Heart rate: 75 bpm, and location data: (35.658034, 139.701636)" and analyzes it.

[0565] Step 5:

[0566] The server uses an external API to retrieve current environmental data, including weather, temperature, humidity, etc. This environmental data is also subject to analysis.

[0567] Input: Environmental data request obtained from external API

[0568] Output: Current environmental data (e.g. temperature, humidity, weather)

[0569] Specific behavior:

[0570] The server sends a request to an external API and retrieves the data "Temperature: 25 degrees, Humidity: 60%, Weather: Sunny".

[0571] Step 6:

[0572] The server uses AI algorithms to generate optimal training menus based on physiological, location, and environmental data, recommending indoor training when temperatures are high and outdoor training when the weather is good.

[0573] Input: Analyzed physiological data, location data, and environmental data

[0574] Output: Generated training menu

[0575] Specific behavior:

[0576] The server uses an AI algorithm to generate an optimal training menu, such as "running 5 kilometers outdoors."

[0577] Step 7:

[0578] While the user is performing the workout, the smartwatch and smartphone continue to transmit physiological and location data to the server.

[0579] Input: Real-time training data

[0580] Output: Data that continues to be sent to the server

[0581] Specific behavior:

[0582] For example, the user's heart rate rises to "80 bpm" and the data is sent to the server.

[0583] Step 8:

[0584] The server monitors this data in real time and provides appropriate advice depending on the situation: if your heart rate spikes, the server will instruct you to "slow down."

[0585] Input: Physiological and location data transmitted in real time

[0586] Output: Real-time advice

[0587] Specific behavior:

[0588] The server sends a notification to the user's smartphone with advice such as "Your heart rate has risen sharply, so slow down your pace."

[0589] Step 9:

[0590] Once the workout is complete, the server analyzes all the data and provides feedback to the user, including advice for the next workout and diet and sleep recommendations.

[0591] Input: All data from completed training

[0592] Output: Parsed feedback

[0593] Specific behavior:

[0594] For example, they might provide feedback like, "Great performance! Make sure you get plenty of rest and stay properly hydrated before your next training session."

[0595] (Application example 1)

[0596] 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."

[0597] When a user rides in an autonomous vehicle, safety and comfort are important, but conventional systems have had difficulty making real-time adjustments based on the user's physiological state and environment.In addition, by adjusting the vehicle's operation based on physiological data such as heart rate and body temperature, it is necessary to reduce user stress and respond quickly if an abnormality is detected.

[0598] 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.

[0599] In this invention, the server includes means for acquiring user physiological data, means for acquiring location data, means for acquiring environmental data, means for analyzing the acquired physiological data, location data, and environmental data and proposing a training menu in real time, means for monitoring the physiological data and environmental data in real time during training by the user and dynamically changing the training menu based on the changed data, means for analyzing the data and providing feedback after training, means for adjusting and proposing vehicle operation in real time according to the user's condition, means for transmitting data to a vehicle control system and determining vehicle operation based on the data, and means for adjusting the vehicle speed and generating an alert if the user's physiological data indicates an abnormality. This makes it possible to optimize the operation of an autonomous vehicle in real time according to the user's condition, thereby improving safety and comfort.

[0600] "User physiological data" refers to data that indicates the user's physical condition in real time, such as heart rate, body temperature, and blood pressure.

[0601] "Location data" is data that indicates the current location of a user or vehicle, and is usually obtained as GPS data.

[0602] "Environmental data" refers to data relating to the user's surrounding environment, including temperature, humidity, and temperature inside the vehicle.

[0603] "Means for proposing training menus in real time" refers to systems or algorithms that instantly analyze acquired data and present the optimal training menu to the user.

[0604] "Means for monitoring physiological and environmental data" refers to sensors and software that constantly monitor the user's physical state and environmental conditions during training.

[0605] "Means for dynamically changing training menus" refers to systems or algorithms that instantly change training menus based on data acquired in real time.

[0606] "Means for providing feedback" refers to a function that provides guidance and advice to the user based on data analyzed after training.

[0607] "Means for adjusting and suggesting vehicle behavior in real time" refers to systems or functions that instantly correct or suggest the speed or route of an autonomous vehicle based on user status data.

[0608] "Means for transmitting data to the vehicle's control system" refers to the communication functions and protocols for transmitting the acquired physiological data and environmental data to the control device in the vehicle.

[0609] "Means for determining vehicle behavior" refers to the systems or algorithms that control the vehicle's movement based on the transmitted data.

[0610] The "means for generating an alert" refers to a function that issues a warning when an abnormality is detected in the user's physiological data.

[0611] This invention is a system that collects and analyzes a user's physiological data, location data, and environmental data in real time, and provides the user with optimal driving advice. How this system is implemented will be described below.

[0612] System Overview

[0613] This system works by collecting physiological and location data from the user's smartwatch or smartphone and transmitting it to the autonomous vehicle's control system. The control system uses the collected data to generate and provide optimal driving advice based on the user's physiological state and the surrounding environment of the day. Furthermore, while driving, the system monitors and analyzes data in real time and provides dynamic driving advice.

[0614] Program Overview

[0615] The server receives the data from the user, analyzes it, and generates optimal driving advice. Specifically, the process involves the following steps:

[0616] 1. Data collection and transmission

[0617] Users ride in autonomous vehicles wearing a smartwatch or smartphone. The smartwatch measures physiological data such as heart rate and body temperature in real time, while the smartphone collects location data using GPS. This data is sent to the vehicle's control system at regular intervals.

[0618] 2. Data analysis in the control system

[0619] When the control system receives data sent from the user, it first analyzes the physiological data (e.g., heart rate) and location data (e.g., GPS data). In addition, the control system uses external data acquisition means to acquire current environmental data (e.g., temperature and humidity inside the vehicle).

[0620] 3. Driving advice generation

[0621] Based on the acquired physiological, location, and environmental data, the control system uses AI algorithms to generate optimal driving advice, such as suggesting slowing the vehicle and heading to the nearest rest stop if the user's heart rate is high.

[0622] 4. Real-time monitoring and advice

[0623] While the user is riding, the smartwatch and smartphone continue to transmit physiological and location data to the control system, which monitors and analyzes this data in real time and provides appropriate driving advice based on the situation. For example, if the user's heart rate spikes, the system can issue an alert to "take a deep breath."

[0624] 5. Post-driving feedback

[0625] Once the trip is over, the control system analyzes all the data and provides feedback to the user, including a risk assessment of any anomalies and recommendations for the next trip.

[0626] Hardware and Software

[0627] Hardware: Smartwatches (e.g., smartwatches), smartphones (e.g., smartphones), autonomous vehicle control systems (e.g., in-vehicle control systems)

[0628] Software: AI algorithms (e.g., TensorFlow, PyTorch), environmental data acquisition APIs (e.g., Environmental Data APIs)

[0629] The server uses the above hardware and software to acquire and analyze data in real time and provide appropriate driving advice to users.

[0630] Specific examples

[0631] User: A user wearing a smartwatch and carrying a smartphone with GPS functionality gets into an autonomous vehicle.

[0632] Terminal: The smartwatch and smartphone each transmit physiological data and location data to the control system within the terminal.

[0633] Server: Based on the data received by the in-vehicle control system, the server uses AI algorithms to generate and provide optimal driving advice in real time.

[0634] Prompt Sentence Examples

[0635] The smartwatch collects the user's heart rate, body temperature, and physiological data in real time and sends it to the vehicle's control system. The control system then uses AI algorithms to analyze the data and determine the vehicle's behavior based on the user's condition. Specifically, if the user's heart rate is high, the system will slow down the vehicle or change the route. After the drive, the system will provide detailed feedback.

[0636] This system allows users to enjoy safe and comfortable autonomous vehicle driving while receiving personalized driving advice in real time.

[0637] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0638] Step 1:

[0639] A user wears a smartwatch and a smartphone and gets into an autonomous vehicle. The smartwatch measures physiological data such as heart rate and body temperature in real time, while the smartphone collects location data using GPS. The input data includes heart rate (e.g., 75 bpm), body temperature (e.g., 36.6°C), and location data (e.g., GPS coordinates). This data is sent to the vehicle's control system via the smartphone.

[0640] Step 2:

[0641] The server receives physiological and location data sent from the user's smartwatch and smartphone. It also acquires environmental data such as interior temperature and humidity from environmental data acquisition sensors inside the vehicle. The input data includes heart rate, body temperature, GPS data, interior temperature (e.g., 24°C), and humidity (e.g., 50%). The server aggregates this data and creates a dataset for analysis.

[0642] Step 3:

[0643] The server analyzes the acquired physiological data, location data, and environmental data. AI algorithms (e.g., TensorFlow, PyTorch) are used for the analysis. The AI ​​model evaluates the user's state under certain conditions and generates driving advice based on that evaluation. For example, if the heart rate is high, the AI ​​model will decide to slow down the vehicle. The driving advice generated as a result of the analysis is output.

[0644] Step 4:

[0645] The server sends the generated driving advice to the vehicle's control system, which then adjusts the vehicle's behavior in real time by adjusting the vehicle's speed and, if necessary, changing its route. The input data is the driving advice, and the output data is the controlled vehicle behavior.

[0646] Step 5:

[0647] While the user is driving, the smartwatch and smartphone continue to transmit physiological and location data to the server. The server continues to monitor and analyze this data in real time, dynamically generating driving advice as needed and sending it to the control system. For example, if the user's heart rate spikes, it will issue an alert saying, "Take a deep breath."

[0648] Step 6:

[0649] Once the drive is over, the server analyzes all the data and provides feedback to the user, including an abnormality risk assessment and advice for the next drive. All collected data (heart rate data, body temperature data, GPS data, interior temperature, humidity, and driving behavior data) are used as input data, and a feedback report is generated as output data.

[0650] Through these steps, users can receive real-time personalized driving advice and enjoy a safe and comfortable autonomous vehicle experience.

[0651] 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.

[0652] The present invention is a system that combines a user's physiological data, location data, and environmental data with an emotion engine that recognizes the user's emotions to propose optimal training menus to the user in real time. The specific implementation of this system is shown below.

[0653] System Overview

[0654] The system collects various data using a smartwatch, smartphone, and emotion engine, and analyzes it on a server to propose an optimized training menu for the user. The system also provides feedback during and after training.

[0655] Program Overview

[0656] The server receives the data from the user, analyzes it, and generates an optimal training menu. Specifically, the process is carried out in the following steps:

[0657] Data collection and transmission

[0658] The user puts on a smartwatch or smartphone, launches an application equipped with the emotion engine, and starts running. The smartwatch measures physiological data such as heart rate in real time, while the smartphone collects location data using GPS. The emotion engine also analyzes the user's voice data and facial expression data to generate emotion data. This data is sent to a server at regular intervals.

[0659] Data analysis on the server

[0660] The server receives and analyzes all data sent by the user, including physiological data (e.g., heart rate) and location data (e.g., GPS data), as well as current environmental data (e.g., temperature, humidity, weather) using external APIs, and emotion data generated by the emotion engine.

[0661] Training menu generation

[0662] The server uses AI algorithms to generate optimal training menus based on physiological, location, environmental, and emotional data. For example, if the emotion engine recognizes that the user is feeling stressed, it can suggest light exercise that will have a relaxing effect. On the other hand, if the user is feeling refreshed, it can suggest more strenuous exercise.

[0663] Real-time monitoring and advice

[0664] While the user is training, the smartwatch and smartphone continue to send physiological, location, and emotional data to the server. The server monitors and analyzes this data in real time, dynamically adjusting the training menu based on the situation and providing appropriate advice. For example, if the user feels fatigued or anxious during training, the server will advise them to slow down.

[0665] Post-training feedback

[0666] Once the training is complete, the server analyzes all the acquired data and provides feedback to the user. The feedback includes not only advice based on physical data, but also suggestions for mental care based on emotional data. For example, feedback such as "Excellent performance. Please try to relax before your next training session" may be provided.

[0667] Specific examples

[0668] 1. Examples of user behavior

[0669] The user launches the app using their smartwatch and smartphone and enters their user data. The emotion engine also starts up and begins analyzing voice and facial expression data. When the user starts running, the smartwatch begins measuring their heart rate and the smartphone collects GPS data.

[0670] 2. Examples of Device Actions

[0671] The device sends the collected heart rate data, GPS data, and emotion data to the server. For example, the heart rate is 75 bpm, the location data is (35.658034, 139.701636), and the emotion data is "stress."

[0672] 3. Examples of Server Actions

[0673] The server starts analysis based on this data. It also obtains current weather information using an external API. For example, it obtains weather data such as temperature 25 degrees, humidity 60%, and clear skies.

[0674] The server uses an AI algorithm to suggest optimal training menus based on physiological, location, environmental, and emotional data. For example, if the emotional data indicates "stress," it will suggest "relaxing exercises indoors."

[0675] While running, the server monitors the data in real time and provides advice to the user depending on the situation. For example, if the heart rate gets too high, it will instruct the user to "slow down your pace." If the emotional data indicates "anxiety," it will advise the user to "try to relax and take deep breaths."

[0676] After the training, the server analyzes all the data and provides feedback to the user, such as "Great performance. Please try to relax until the next training session."

[0677] This system allows users to perform optimal training that takes into account not only their physical condition but also their emotional state, supporting safe and effective training.

[0678] The processing flow will be explained below.

[0679] Step 1:

[0680] The user launches the app using a smartwatch or smartphone and enters their own user data (level, weight, goals, etc.). This data is used by the system to suggest training menus that are optimal for the user's condition and goals.

[0681] Step 2:

[0682] The device sends user data to the server to initiate a session along with login information, which the server stores for later analysis.

[0683] Step 3:

[0684] The device will begin to measure physiological data (e.g., heart rate) in real time using sensors in the smartwatch or smartphone, and location data (e.g., GPS data) will also be collected.

[0685] Step 4:

[0686] The device uses an emotion engine to analyze the user's voice and facial expression data and generate emotion data in real time, which indicates emotional states such as "stress," "refreshment," and "anxiety."

[0687] Step 5:

[0688] The device periodically collects physiological data, location data, and emotion data and sends them to the server. For example, the device sends heart rate 75 bpm, location data (35.658034, 139.701636), and emotion data "refresh" to the server.

[0689] Step 6:

[0690] The server analyzes the received data and uses an external API to obtain current environmental data (e.g., temperature, humidity, weather) based on the acquired physiological data (heart rate), location data (GPS data), and emotion data.

[0691] Step 7:

[0692] The server uses an AI algorithm to generate an optimal training menu based on the user's physiological, location, environmental, and emotional data. For example, if the user is in a "refreshed" emotional state, it will suggest a hard training menu, and if the user is feeling "stressed," it will suggest relaxing exercises.

[0693] Step 8:

[0694] The server sends the generated training menu to the terminal, which displays it to the user, who then starts running according to the training menu.

[0695] Step 9:

[0696] While the user is training, the device continues to collect physiological data, location data, and emotional data and transmits them to the server. For example, if the user's heart rate rises to 85 bpm during training and the emotional data changes to "anxiety," the device will transmit the data to the server.

[0697] Step 10:

[0698] The server monitors the data it receives in real time and immediately generates advice if there are any fluctuations. For example, if the heart rate suddenly increases, it generates an alert saying "Slow down your pace." If the emotion data indicates "anxiety," it sends the advice "Try to relax and take deep breaths" to the device. The device then displays the alert to the user.

[0699] Step 11:

[0700] When the user finishes the training, the terminal sends a training completion notification to the server.

[0701] Step 12:

[0702] The server analyzes the data from the entire training session and generates feedback. For example, it assesses the risk of injury, provides advice for the next training session, and provides recommended diet and rest. Feedback also includes suggestions for mental care, such as "That was a great performance. Please try to relax before your next training session."

[0703] Step 13:

[0704] The server generates feedback and sends it to the device, which displays it to the user, who can use it to plan their next workout.

[0705] These detailed steps enable the system to understand the user's condition in real time and support safe and effective training. The addition of an emotion engine makes it possible to provide training advice that takes the user's mental state into account.

[0706] Example 2

[0707] 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."

[0708] Conventional training systems could propose training menus based on a user's physiological data, location data, and environmental data, but they were unable to take the user's emotional state into account. This made it difficult to provide an optimal training menu that matched the user's mental state, preventing the training from being fully effective. Real-time feedback and dynamic changes to the training menu were also insufficient. The present invention aims to solve these problems and provide an optimal training menu that takes into account both the user's physiological and emotional states.

[0709] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring physiological data of the user, means for acquiring location data, means for acquiring environmental data, means for analyzing and acquiring emotional data of the user, means for analyzing the acquired physiological data, location data, environmental data, and emotional data and proposing a training menu in real time, means for monitoring the physiological data, environmental data, and emotional data in real time during training performed by the user and dynamically changing the training menu based on the changed data, and means for analyzing data after training and providing feedback. This makes it possible to propose an optimal training menu in real time that takes into account both the physiological state and emotional state of the user, thereby maximizing the effectiveness of the training.

[0710] "User's physiological data" refers to data that indicates the user's physical condition, such as heart rate, blood pressure, and body temperature.

[0711] "Location data" refers to data including the latitude and longitude of the user's current location, obtained using a GPS module or the like.

[0712] "Environmental data" refers to data that indicates the conditions of the environment in which the user is located, such as temperature, humidity, and weather.

[0713] "Emotion data" refers to data that indicates the mental state of the user, analyzed from their voice and facial expressions.

[0714] "Means for suggesting training menus in real time" refers to systems or algorithms that instantly provide users with appropriate training menus based on collected and analyzed data.

[0715] "Monitoring physiological and environmental data in real time during a workout" refers to the process of continuously collecting and monitoring physiological and environmental data while a user is working out.

[0716] "Means for dynamically changing training menus" refers to systems or algorithms that update and change training menus in real time based on acquired and analyzed data.

[0717] "Means for analyzing data and providing feedback after training" refers to a system or algorithm that analyzes all data collected after training is completed and provides advice or evaluation to the user based on the results.

[0718] This invention is a system that combines a user's physiological data, location data, and environmental data with an emotion engine that recognizes the user's emotions to propose optimal training menus to the user in real time. This system is realized by collecting and analyzing various data using a smartwatch, smartphone, server, and emotion engine.

[0719] System Overview

[0720] The system works as follows: Users collect data using a smartwatch or smartphone, and the emotion engine analyzes the user's emotional data. The data is then sent to a server, which analyzes the data and generates an optimal training menu. Additionally, feedback is provided during and after training.

[0721] Program processing

[0722] Data collection and transmission

[0723] 1. The user wears the smartwatch on their wrist and launches a dedicated app on their smartphone, which collects physiological data such as heart rate, blood pressure, and body temperature.

[0724] 2. The device (smartwatch) collects these physiological data in real time and stores them in its internal memory.

[0725] 3. The device (smartphone) collects location data using the GPS function. For example, the GPS module obtains the latitude and longitude of the current location every minute.

[0726] 4. The device (emotion engine) generates emotion data from the user's voice and facial expressions. It uses the smartphone's camera and microphone to recognize facial expressions and analyze voice, determining emotions such as "stress" or "relaxation."

[0727] 5. The device sends the collected heart rate data, location data, and emotion data to the server at regular intervals, for example, every 5 minutes, batch-processing the data and sending it to the server using HTTPS.

[0728] Data analysis on the server

[0729] 1. The server receives the transmitted physiological data, location data, and emotion data and stores them in a database.

[0730] 2. The server uses an external API to obtain current environmental data (temperature, humidity, weather), sends a request to the weather API, analyzes the response data, and saves it.

[0731] 3. The server performs analysis based on physiological, location, environmental, and emotional data, and processes the data using AI algorithms to evaluate the correlation between each data point.

[0732] Training menu generation

[0733] The server generates an optimal training menu based on the analysis results. For example, if the emotion engine detects "stress," it will suggest light exercise with a relaxing effect. If the user indicates "relaxation," it will suggest jogging or yoga.

[0734] Real-time monitoring and advice

[0735] 1. During training, the device continuously transmits physiological data, location data, and emotional data to the server. The data is updated every minute and transmitted to the server in real time.

[0736] 2. The server analyzes the received data in real time and provides advice to the user based on the situation. For example, if the user's heart rate exceeds 160 bpm and the user feels fatigued, the server will advise the user to "slow down."

[0737] Post-training feedback

[0738] 1. The server re-analyzes all data after training is complete. At the end of training, it performs a batch analysis and generates comprehensive feedback.

[0739] 2. The server provides final feedback to the user via an in-app notification or email with a message such as "Great performance! Please try to relax until your next training session."

[0740] Examples of prompt statements

[0741] User: Launches the app using a smartwatch and smartphone, enters data, and starts running.

[0742] Device: The smartwatch measures the heart rate, the smartphone collects GPS data, and the emotion engine generates emotion data.

[0743] Server: Analyzes the collected data, obtains weather information via an external API, and suggests optimal training menus.

[0744] As described above, this system can propose training menus in real time that take into account both the user's physiological and emotional states, providing the optimal training environment for the user.

[0745] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0746] Step 1:

[0747] The user wears the smartwatch on their wrist and launches the dedicated app on their smartphone. The input is basic data such as the user's heart rate, blood pressure, and body temperature. The output is the start of collection of this data. Specifically, the user presses the "Start" button on the app's startup screen.

[0748] Step 2:

[0749] The device (smartwatch) collects physiological data in real time. The input is the user's physiological data, such as heart rate, blood pressure, and body temperature, which are measured every second. The output is the storage of the measured data in internal memory. Specifically, the heart rate sensor collects data and stores it in internal memory.

[0750] Step 3:

[0751] The device (smartphone) uses GPS to collect location data. The input is location information from the smartphone's GPS module, which acquires latitude and longitude every minute. The output is the storage of the latitude and longitude of the current location. Specifically, the GPS module acquires information about the current location and stores it in the smartphone.

[0752] Step 4:

[0753] The device (emotion engine) generates the user's emotional data from voice and facial expressions. The input is voice and facial expression data captured using the smartphone's camera and microphone. The output is emotional data (such as "stress" or "relaxed") as the result of analysis. Specifically, it analyzes camera images and voice to determine the emotion.

[0754] Step 5:

[0755] The device sends the collected physiological data, location data, and emotional data to the server at regular intervals. The input is the collected data (heart rate, location data, emotional data), which is batch-processed every 5 minutes. The output is transmission to the server. Specifically, the data is sent to the server using the HTTPS protocol.

[0756] Step 6:

[0757] The server receives the transmitted physiological data, location data, and emotion data. The input is the data transmitted from the device, and the output is to store it in a database. Specifically, a dedicated API endpoint receives the data and stores it in the database.

[0758] Step 7:

[0759] The server uses an external API to obtain environmental data (temperature, humidity, weather). The input is a request to the weather API, and the output is the obtained environmental data. Specifically, it periodically sends requests to the weather API, analyzes the response data, and saves it.

[0760] Step 8:

[0761] The server performs analysis based on physiological, location, environmental, and emotional data. The input is all stored data, and the output is the analysis results (optimal training menu). Specifically, it applies AI algorithms to analyze the data and evaluates the correlation between each data point.

[0762] Step 9:

[0763] The server generates an optimal training menu and proposes it to the user. The input is the analysis results, and the output is a training menu proposal. Specifically, if the emotional data indicates "stress," the server proposes light exercise that has a relaxing effect.

[0764] Step 10:

[0765] The device continues to send physiological, location, and emotional data to the server during training. The input is the data collected in real time, and the output is sending it to the server. Specifically, the data is updated every minute and sent to the server in real time.

[0766] Step 11:

[0767] The server analyzes the data received in real time and provides advice to the user as needed. The input is real-time data, and the output is dynamic training menu changes and advice. Specifically, if the heart rate exceeds 160 bpm and the user shows signs of fatigue, the server instructs the user to "slow down."

[0768] Step 12:

[0769] After the training is complete, the server reanalyzes all data and provides feedback to the user. The input is all training data, and the output is a feedback message. Specifically, the server performs a batch analysis at the end of the training and sends an in-app notification or email with a message saying, "Excellent performance. Please try to relax until the next training session."

[0770] (Application example 2)

[0771] 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."

[0772] Modern factories require effective management systems that balance employee health with efficient work. However, there are currently no systems that can analyze employees' physiological data and emotional state in real time and make appropriate suggestions based on that state. This can lead to the accumulation of employee fatigue and stress, which can lead to a decline in productivity. It can also lead to a deterioration in employee health. A new system is needed to solve these issues.

[0773] 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.

[0774] In this invention, the server includes means for acquiring physiological data of a user, means for acquiring location data, means for acquiring environmental data, means for acquiring emotional data, means for analyzing the acquired physiological data, location data, environmental data, and emotional data to propose a training menu in real time, means for monitoring the physiological data and environmental data in real time during training performed by the user and dynamically changing the training menu based on the changed data, means for analyzing data after training and providing feedback, and means for evaluating the fatigue level of employees and proposing appropriate break timing and workload adjustments in real time, thereby enabling health management of factory employees and improving work efficiency.

[0775] "User" refers to a person who uses the system, and specifically includes factory workers.

[0776] "Physiological data" refers to physiological information obtained from the human body, such as heart rate.

[0777] "Location data" refers to geographical location information obtained by GPS or other means.

[0778] "Environmental data" refers to information about the external environment, such as temperature, humidity, and weather.

[0779] "Emotional data" refers to information about the user's emotional state obtained by analyzing voice and facial expressions.

[0780] "Analysis" refers to information processing to make appropriate decisions and suggestions based on acquired data.

[0781] "Real-time" refers to data collection and processing occurring immediately, without delay.

[0782] A "training menu" refers to a series of exercises or tasks that a user performs.

[0783] "Dynamic change" refers to instantly changing training menus and suggestions in response to data that changes in real time.

[0784] "Feedback" refers to providing advice on improvements and next steps based on the user's behavior and status.

[0785] "Fatigue level" refers to an index that quantitatively evaluates the degree of fatigue of the user.

[0786] "Break timing" refers to the appropriate moment to temporarily stop work and take a rest.

[0787] "Workload" refers to the difficulty or intensity of the work performed by the user.

[0788] This paper outlines a system that applies this invention to factory robots. This system uses physiological data, location data, environmental data, and emotional data of factory workers to monitor their health status in real time and propose appropriate break times and workload adjustments.

[0789] System configuration

[0790] Data collection

[0791] The user wears a smartwatch and a smartphone and launches an application equipped with an emotion engine. The smartwatch measures physiological data such as heart rate in real time, while the smartphone collects location data using GPS. The smartphone's emotion engine then analyzes voice and facial expressions to generate emotion data.

[0792] Sending data

[0793] This data is sent to a server via the Internet at regular intervals, where it is analyzed and appropriate suggestions are made to the user.

[0794] Data analysis on the server

[0795] The server integrates the collected physiological, location, environmental, and emotional data and analyzes it using an AI algorithm. Based on the analysis results, it evaluates the employee's fatigue level and suggests appropriate break times and workload adjustments.

[0796] Specifically, if the user's heart rate is high or the emotion engine indicates "stress," the system will notify them, for example, to "take a break." On the other hand, if the user's condition is good, the system will provide instructions such as "keep working at this pace."

[0797] Real-time monitoring and feedback

[0798] While the user is working, the smartwatch and smartphone continue to collect and transmit data to the server. The server monitors and analyzes this data in real time and dynamically adjusts the suggestions. For example, if the user's heart rate rises too much, the server can provide advice such as "Take a deep breath and relax."

[0799] After the task is completed, the server analyzes all the acquired data and provides the user with comprehensive feedback, such as "Today's task performance was excellent. Let's try harder next time."

[0800] Specific examples

[0801] An example of a prompt for a generative AI model might look like this:

[0802] User: I seem to be stressed. My heart rate is over 90 and the emotion engine is showing "stress." What advice would you give me?

[0803] AI: I would recommend you take a break and relax for a bit. Take a deep breath and take a five-minute break.

[0804] Such a system is expected to effectively manage the health of factory employees, improving productivity and work efficiency, and also contributing to maintaining the long-term health of employees and improving the efficiency of the entire company.

[0805] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0806] Step 1:

[0807] The user puts on the smartwatch and smartphone and launches the application. At this time, the emotion engine is also launched. At this stage, the smartwatch begins measuring the user's heart rate, and the smartphone acquires location information using GPS. The emotion engine analyzes the user's voice and facial expressions to generate emotion data. This data is sent to the smartphone in real time.

[0808] Input: User's heart rate, location information, voice and facial expression data

[0809] Output: physiological data, location data, emotional data

[0810] Step 2:

[0811] The device (smartphone) transmits the collected physiological data, location data, and emotional data to a server at regular intervals. The transmitted data is accumulated in the server in real time and used for subsequent analysis.

[0812] Input: physiological data, location data, emotional data

[0813] Output: Send data to the server

[0814] Step 3:

[0815] The server analyzes the received data using an AI algorithm to assess the user's fatigue level and emotional state. For example, if the heart rate is high and the emotional data indicates "stress," the server determines that the user is highly fatigued.

[0816] Input: physiological data, location data, emotional data

[0817] Output: Analysis results (fatigue level evaluation, emotion evaluation)

[0818] Step 4:

[0819] Based on the analysis results, the server will suggest an appropriate training menu to the user in real time. For example, if the heart rate is high and the emotional data indicates "stress," the server will suggest "take a break." On the other hand, if the user's condition is good, the server will suggest "keep working at this pace."

[0820] Input: Analysis results

[0821] Output: Training menu suggestions

[0822] Step 5:

[0823] While the user is working, the smartwatch and smartphone continue to collect data and send it to the server. The server monitors this data in real time and dynamically changes the training menu and suggestions depending on the situation. For example, if the user's heart rate spikes while working, the server will provide advice such as "Take a deep breath and relax."

[0824] Input: Real-time data (physiological data, location data, emotional data)

[0825] Output: Dynamic suggestions and training menu changes

[0826] Step 6:

[0827] Once the task is completed, the server compiles and analyzes all the acquired data and provides feedback to the user. The feedback includes the results of the training and advice for the next time. For example, the server may provide feedback such as, "Today's task performance was excellent. Let's try harder next time."

[0828] Input: All collected data

[0829] Output: Comprehensive feedback

[0830] These steps allow users to monitor their health status in real time and receive appropriate breaks and workload adjustments, which is expected to improve work efficiency and reduce health risks.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] [Third embodiment]

[0835] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0836] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0837] 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).

[0838] 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.

[0839] 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.

[0840] 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).

[0841] 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. 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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."

[0847] The present invention is a system that collects and analyzes a user's physiological data, location data, and environmental data in real time, and proposes an optimal training menu for the user. The specific implementation of this system is shown below.

[0848] System Overview

[0849] The system works by collecting physiological and location data from the user's smartwatch or smartphone and sending it to a server. The server then uses the collected data to generate and suggest optimal training menus based on the user's physical condition and the weather conditions of the day. Furthermore, the system monitors and analyzes data in real time while running and provides dynamic advice.

[0850] Program Overview

[0851] The server receives the data from the user, analyzes it, and generates an optimal training menu. Specifically, the process is carried out in the following steps:

[0852] Data collection and transmission

[0853] The user wears a smartwatch or smartphone and starts running. The smartwatch measures physiological data such as heart rate in real time, and the smartphone collects location data using GPS. This data is then sent to a server at regular intervals.

[0854] Data analysis on the server

[0855] When the server receives data sent from the user, it first analyzes the physiological data (e.g., heart rate) and location data (e.g., GPS data). In addition, the server uses an external API to obtain current environmental data (e.g., weather information).

[0856] Training menu generation

[0857] The server uses AI algorithms to generate optimal training plans based on physiological, location, and environmental data. For example, if the current temperature is high, it may suggest training on an indoor treadmill, while if the weather is fine, it may suggest running outdoors.

[0858] Real-time monitoring and advice

[0859] While the user is training, the smartwatch and smartphone continue to send physiological and location data to the server, which monitors and analyzes this data in real time and provides appropriate advice based on the situation. For example, if the user's heart rate spikes, the server can advise them to slow down.

[0860] Post-training feedback

[0861] Once the workout is complete, the server analyzes all the data and provides feedback to the user, including injury risk assessment, advice for the next workout, and recommended diet and sleep.

[0862] Specific examples

[0863] 1. Examples of user behavior

[0864] The user launches the app using their smartwatch and smartphone and enters their user data. When they start running, the smartwatch begins measuring their heart rate and the smartphone collects GPS data.

[0865] 2. Examples of Device Actions

[0866] The device sends the collected heart rate data and GPS data to the server. For example, the device sends a heart rate of 75 bpm and location data (35.658034, 139.701636) to the server.

[0867] 3. Examples of Server Actions

[0868] The server uses the received data to determine the user's current heart rate and location, and then uses an external API to obtain current weather information, such as a temperature of 25 degrees, humidity of 60%, and clear skies.

[0869] Based on this data, the server uses an AI algorithm to suggest the optimal training menu, such as "running 5 kilometers outdoors."

[0870] While running, the server monitors data in real time and provides advice to the user based on the situation, for example, telling them to "slow down" if their heart rate gets too high.

[0871] After the training, the server analyzes all the data and provides feedback to the user, such as "Great performance. Make sure you get plenty of rest and drink plenty of water before your next training session."

[0872] The system allows users to train safely and effectively while receiving real-time, personalized training advice.

[0873] The processing flow will be explained below.

[0874] Step 1:

[0875] The user launches the app using a smartwatch or smartphone and enters their own user data (level, weight, goals, etc.). This data is used by the system to suggest training menus that are optimal for the user's condition and goals.

[0876] Step 2:

[0877] The device sends user data to the server to initiate a session along with login information, which the server stores for later analysis.

[0878] Step 3:

[0879] The device will begin to measure physiological data (e.g., heart rate) in real time using sensors in the smartwatch or smartphone, and location data (e.g., GPS data) will also be collected.

[0880] Step 4:

[0881] The device periodically sends the collected physiological data and location data to the server, for example, sending a set of heart rate data and GPS data to the server every minute.

[0882] Step 5:

[0883] The server analyzes the received data and uses an external API to obtain current environmental data (e.g., temperature, humidity, weather) based on the acquired physiological data (heart rate) and location data (GPS data).

[0884] Step 6:

[0885] The server uses AI algorithms to generate optimal training plans based on the user's physiological, location, and environmental data, suggesting, for example, indoor treadmill training on hot days and outdoor running on sunny days.

[0886] Step 7:

[0887] The server sends the generated training menu to the terminal, which displays it to the user, who then starts running according to the training menu.

[0888] Step 8:

[0889] While the user is training, the device continues to collect physiological data (heart rate) and location data (GPS data) and transmits them to the server.

[0890] Step 9:

[0891] The server monitors the data it receives in real time and immediately generates advice if there are any fluctuations. For example, if your heart rate spikes, the server generates an alert saying "Slow down" and sends it to the device. The device then displays the alert to the user.

[0892] Step 10:

[0893] When the user finishes the training, the terminal sends a training completion notification to the server.

[0894] Step 11:

[0895] The server analyzes the data from the entire training session and generates feedback, such as an injury risk assessment, advice for the next training session, and recommendations for food and rest.

[0896] Step 12:

[0897] The server generates feedback and sends it to the device, which displays it to the user, who can use it to plan their next workout.

[0898] These detailed steps allow the system to understand the user's condition in real time and support safe and effective training.

[0899] Example 1

[0900] 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."

[0901] Conventional training support systems have had difficulty integrating and analyzing a user's physiological data, location data, and environmental data to provide an optimal training menu. Furthermore, they lacked the functionality to monitor the user's training status in real time and dynamically change the menu. This prevented users from optimally training for their own condition and environment, leading to safety and effectiveness issues.

[0902] 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.

[0903] In this invention, the server includes means for acquiring physiological data of a user, means for acquiring position data, means for acquiring environmental data, means for analyzing the acquired physiological data, position data, and environmental data and proposing a generated training menu, means for monitoring the physiological data and position data in real time during training performed by the user and dynamically changing the training menu according to the situation, and means for analyzing data after training and providing feedback. This allows the user to receive an individually optimized training menu in real time, enabling them to train safely and effectively.

[0904] "Physiological data" refers to data that indicates the user's physical condition, and specifically includes heart rate, blood pressure, body temperature, etc.

[0905] "Location data" refers to data indicating the user's current location, and specifically includes latitude and longitude information obtained from the Global Positioning System (GPS).

[0906] "Environmental data" refers to data that indicates the external environmental conditions, and specifically includes weather, temperature, humidity, wind speed, and the like.

[0907] "Analyzing" refers to the act of analyzing acquired data through computer processing to find specific patterns or trends.

[0908] A "training menu" indicates a plan or guidelines for the exercise or training that a user should do, and specifically includes running distance, time, intensity, and the like.

[0909] "Real-time" refers to processing ongoing processes and events instantly and providing results immediately.

[0910] "Monitoring" refers to the act of continuously observing the user's condition and environment, and responding immediately if there are any abnormalities or changes.

[0911] "Dynamic change" means to flexibly change fixed settings or plans according to the situation at hand, and to respond immediately.

[0912] "Providing feedback" refers to the act of providing improvements and guidelines for next actions based on the results and evaluation of the training the user has completed.

[0913] This invention is a system that collects and analyzes a user's physiological data, location data, and environmental data in real time, and proposes an optimal training menu for the user. The specific implementation of this system is shown below.

[0914] Hardware and software used

[0915] This system mainly uses the following hardware and software:

[0916] Smartwatch: A device that measures a user's physiological data, such as heart rate.

[0917] Smartphone: A device that uses GPS to collect location data and transmits it along with physiological data to a server.

[0918] Server: A device that analyzes data, generates and proposes training menus, and provides feedback to users.

[0919] External API: An interface for obtaining environmental data such as weather information.

[0920] System Operation

[0921] Data collection and transmission

[0922] The user puts on the smartwatch and launches the smartphone application, where the user enters basic data such as age, weight, and gender, and the application is then ready to reference the user's individual data.

[0923] The smartwatch measures the user's physiological data, such as heart rate, in real time, while the smartphone collects the user's location data using GPS, which is then sent to a server at regular intervals.

[0924] Data analysis on the server

[0925] The server receives the heart rate and location data sent by the user in real time and begins analysis. The server uses an external API to obtain current environmental data, such as weather information (temperature, humidity, weather).

[0926] Training menu generation

[0927] The server uses AI algorithms to generate optimal training plans based on physiological, location, and environmental data. For example, it suggests indoor treadmill training when the temperature is high, and a 5-kilometer run outdoors when the weather is good.

[0928] Real-time monitoring and advice

[0929] While the user is training, the smartwatch and smartphone continue to send physiological and location data to the server, which monitors this data in real time and provides appropriate advice based on the situation. For example, if the user's heart rate spikes, the server will advise them to "slow down."

[0930] Post-training feedback

[0931] Once the training is over, the server analyzes all the data and provides feedback to the user. This feedback includes injury risk assessment, advice for the next training session, and recommended diet and sleep. For example, a message might be given saying, "Great performance! Make sure you get plenty of rest and stay properly hydrated before your next training session."

[0932] Specific examples

[0933] 1. Examples of user behavior

[0934] The user launches the app using their smartwatch and smartphone and enters their user data. When they start running, the smartwatch begins measuring their heart rate and the smartphone collects GPS data.

[0935] 2. Examples of Device Actions

[0936] The device sends the collected heart rate data and GPS data to the server, for example, a heart rate of 75 bpm and location data (35.658034, 139.701636).

[0937] 3. Examples of Server Actions

[0938] The server determines the user's current heart rate and location based on the received data, and also uses an external API to obtain current weather information, such as a temperature of 25 degrees, humidity of 60%, and clear skies.

[0939] Based on this data, the server uses an AI algorithm to suggest the optimal training menu, for example, "running 5 kilometers outdoors."

[0940] While running, the server monitors data in real time and provides advice to the user, such as "slow down your pace," depending on the situation.

[0941] After the training, the server analyzes all the data and provides the user with feedback such as, "Great performance. Make sure you get plenty of rest and stay properly hydrated before your next training session."

[0942] Prompt Sentence Examples

[0943] "Describe a system where a user can use a smartphone and a smartwatch to start a run and collect and transmit data in real time."

[0944] This system allows users to receive personalized training advice in real time, enabling them to train more safely and effectively.

[0945] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0946] Step 1:

[0947] The user puts on the smartwatch and launches the smartphone application, where the user enters basic data such as age, weight, and gender. This is the initial input, and the application is ready to reference this information.

[0948] Input: Basic data such as age, weight, and gender

[0949] Output: User profile initial setup complete

[0950] Specific behavior:

[0951] The user launches the application and enters personal data such as "30 years old, 70 kg, male."

[0952] Step 2:

[0953] The smartwatch, which is the device, measures the user's physiological data such as heart rate in real time. At the same time, the smartphone collects location data using GPS. This data is continuously collected and sent to a server at regular intervals.

[0954] Input: Real-time heart rate data, GPS location data

[0955] Output: Acquired physiological and location data

[0956] Specific behavior:

[0957] The smartwatch measures your heart rate in real time and records, for example, "Heart rate: 75 bpm."

[0958] The smartphone collects GPS location information and records, for example, "Location data: (35.658034, 139.701636)".

[0959] Step 3:

[0960] The device sends the collected heart rate data and GPS data to a server, which updates the data in real time by setting the data to be sent to the server at regular intervals.

[0961] Input: Collected heart rate data, GPS location data

[0962] Output: Data sent to the server

[0963] Specific behavior:

[0964] For example, every 10 seconds, updated heart rate data and GPS location data are sent to a server.

[0965] Step 4:

[0966] The server receives the heart rate and location data sent by the user in real time and begins analysis, which involves processing the data to understand the user's physiological condition and movement status.

[0967] Input: Transmitted physiological and location data

[0968] Output: Parsed user state data

[0969] Specific behavior:

[0970] The server receives "Heart rate: 75 bpm, and location data: (35.658034, 139.701636)" and analyzes it.

[0971] Step 5:

[0972] The server uses an external API to retrieve current environmental data, including weather, temperature, humidity, etc. This environmental data is also subject to analysis.

[0973] Input: Environmental data request obtained from external API

[0974] Output: Current environmental data (e.g. temperature, humidity, weather)

[0975] Specific behavior:

[0976] The server sends a request to an external API and retrieves the data "Temperature: 25 degrees, Humidity: 60%, Weather: Sunny".

[0977] Step 6:

[0978] The server uses AI algorithms to generate optimal training menus based on physiological, location, and environmental data, recommending indoor training when temperatures are high and outdoor training when the weather is good.

[0979] Input: Analyzed physiological data, location data, and environmental data

[0980] Output: Generated training menu

[0981] Specific behavior:

[0982] The server uses an AI algorithm to generate an optimal training menu, such as "running 5 kilometers outdoors."

[0983] Step 7:

[0984] While the user is performing the workout, the smartwatch and smartphone continue to transmit physiological and location data to the server.

[0985] Input: Real-time training data

[0986] Output: Data that continues to be sent to the server

[0987] Specific behavior:

[0988] For example, the user's heart rate rises to "80 bpm" and the data is sent to the server.

[0989] Step 8:

[0990] The server monitors this data in real time and provides appropriate advice depending on the situation: if your heart rate spikes, the server will instruct you to "slow down."

[0991] Input: Physiological and location data transmitted in real time

[0992] Output: Real-time advice

[0993] Specific behavior:

[0994] The server sends a notification to the user's smartphone with advice such as "Your heart rate has risen sharply, so slow down your pace."

[0995] Step 9:

[0996] Once the workout is complete, the server analyzes all the data and provides feedback to the user, including advice for the next workout and diet and sleep recommendations.

[0997] Input: All data from completed training

[0998] Output: Parsed feedback

[0999] Specific behavior:

[1000] For example, they might provide feedback like, "Great performance! Make sure you get plenty of rest and stay properly hydrated before your next training session."

[1001] (Application example 1)

[1002] 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."

[1003] When a user rides in an autonomous vehicle, safety and comfort are important, but conventional systems have had difficulty making real-time adjustments based on the user's physiological state and environment.In addition, by adjusting the vehicle's operation based on physiological data such as heart rate and body temperature, it is necessary to reduce user stress and respond quickly if an abnormality is detected.

[1004] 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.

[1005] In this invention, the server includes means for acquiring user physiological data, means for acquiring location data, means for acquiring environmental data, means for analyzing the acquired physiological data, location data, and environmental data and proposing a training menu in real time, means for monitoring the physiological data and environmental data in real time during training by the user and dynamically changing the training menu based on the changed data, means for analyzing the data and providing feedback after training, means for adjusting and proposing vehicle operation in real time according to the user's condition, means for transmitting data to a vehicle control system and determining vehicle operation based on the data, and means for adjusting the vehicle speed and generating an alert if the user's physiological data indicates an abnormality. This makes it possible to optimize the operation of an autonomous vehicle in real time according to the user's condition, thereby improving safety and comfort.

[1006] "User physiological data" refers to data that indicates the user's physical condition in real time, such as heart rate, body temperature, and blood pressure.

[1007] "Location data" is data that indicates the current location of a user or vehicle, and is usually obtained as GPS data.

[1008] "Environmental data" refers to data relating to the user's surrounding environment, including temperature, humidity, and temperature inside the vehicle.

[1009] "Means for proposing training menus in real time" refers to systems or algorithms that instantly analyze acquired data and present the optimal training menu to the user.

[1010] "Means for monitoring physiological and environmental data" refers to sensors and software that constantly monitor the user's physical state and environmental conditions during training.

[1011] "Means for dynamically changing training menus" refers to systems or algorithms that instantly change training menus based on data acquired in real time.

[1012] "Means for providing feedback" refers to a function that provides guidance and advice to the user based on data analyzed after training.

[1013] "Means for adjusting and suggesting vehicle behavior in real time" refers to systems or functions that instantly correct or suggest the speed or route of an autonomous vehicle based on user status data.

[1014] "Means for transmitting data to the vehicle's control system" refers to the communication functions and protocols for transmitting the acquired physiological data and environmental data to the control device in the vehicle.

[1015] "Means for determining vehicle behavior" refers to the systems or algorithms that control the vehicle's movement based on the transmitted data.

[1016] The "means for generating an alert" refers to a function that issues a warning when an abnormality is detected in the user's physiological data.

[1017] This invention is a system that collects and analyzes a user's physiological data, location data, and environmental data in real time, and provides the user with optimal driving advice. How this system is implemented will be described below.

[1018] System Overview

[1019] This system works by collecting physiological and location data from the user's smartwatch or smartphone and transmitting it to the autonomous vehicle's control system. The control system uses the collected data to generate and provide optimal driving advice based on the user's physiological state and the surrounding environment of the day. Furthermore, while driving, the system monitors and analyzes data in real time and provides dynamic driving advice.

[1020] Program Overview

[1021] The server receives the data from the user, analyzes it, and generates optimal driving advice. Specifically, the process involves the following steps:

[1022] 1. Data collection and transmission

[1023] Users ride in autonomous vehicles wearing a smartwatch or smartphone. The smartwatch measures physiological data such as heart rate and body temperature in real time, while the smartphone collects location data using GPS. This data is sent to the vehicle's control system at regular intervals.

[1024] 2. Data analysis in the control system

[1025] When the control system receives data sent from the user, it first analyzes the physiological data (e.g., heart rate) and location data (e.g., GPS data). In addition, the control system uses external data acquisition means to acquire current environmental data (e.g., temperature and humidity inside the vehicle).

[1026] 3. Driving advice generation

[1027] Based on the acquired physiological, location, and environmental data, the control system uses AI algorithms to generate optimal driving advice, such as suggesting slowing the vehicle and heading to the nearest rest stop if the user's heart rate is high.

[1028] 4. Real-time monitoring and advice

[1029] While the user is riding, the smartwatch and smartphone continue to transmit physiological and location data to the control system, which monitors and analyzes this data in real time and provides appropriate driving advice based on the situation. For example, if the user's heart rate spikes, the system can issue an alert to "take a deep breath."

[1030] 5. Post-driving feedback

[1031] Once the trip is over, the control system analyzes all the data and provides feedback to the user, including a risk assessment of any anomalies and recommendations for the next trip.

[1032] Hardware and Software

[1033] Hardware: Smartwatches (e.g., smartwatches), smartphones (e.g., smartphones), autonomous vehicle control systems (e.g., in-vehicle control systems)

[1034] Software: AI algorithms (e.g., TensorFlow, PyTorch), environmental data acquisition APIs (e.g., Environmental Data APIs)

[1035] The server uses the above hardware and software to acquire and analyze data in real time and provide appropriate driving advice to users.

[1036] Specific examples

[1037] User: A user wearing a smartwatch and carrying a smartphone with GPS functionality gets into an autonomous vehicle.

[1038] Terminal: The smartwatch and smartphone each transmit physiological data and location data to the control system within the terminal.

[1039] Server: Based on the data received by the in-vehicle control system, the server uses AI algorithms to generate and provide optimal driving advice in real time.

[1040] Prompt Sentence Examples

[1041] The smartwatch collects the user's heart rate, body temperature, and physiological data in real time and sends it to the vehicle's control system. The control system then uses AI algorithms to analyze the data and determine the vehicle's behavior based on the user's condition. Specifically, if the user's heart rate is high, the system will slow down the vehicle or change the route. After the drive, the system will provide detailed feedback.

[1042] This system allows users to enjoy safe and comfortable autonomous vehicle driving while receiving personalized driving advice in real time.

[1043] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1044] Step 1:

[1045] A user wears a smartwatch and a smartphone and gets into an autonomous vehicle. The smartwatch measures physiological data such as heart rate and body temperature in real time, while the smartphone collects location data using GPS. The input data includes heart rate (e.g., 75 bpm), body temperature (e.g., 36.6°C), and location data (e.g., GPS coordinates). This data is sent to the vehicle's control system via the smartphone.

[1046] Step 2:

[1047] The server receives physiological and location data sent from the user's smartwatch and smartphone. It also acquires environmental data such as interior temperature and humidity from environmental data acquisition sensors inside the vehicle. The input data includes heart rate, body temperature, GPS data, interior temperature (e.g., 24°C), and humidity (e.g., 50%). The server aggregates this data and creates a dataset for analysis.

[1048] Step 3:

[1049] The server analyzes the acquired physiological data, location data, and environmental data. AI algorithms (e.g., TensorFlow, PyTorch) are used for the analysis. The AI ​​model evaluates the user's state under certain conditions and generates driving advice based on that evaluation. For example, if the heart rate is high, the AI ​​model will decide to slow down the vehicle. The driving advice generated as a result of the analysis is output.

[1050] Step 4:

[1051] The server sends the generated driving advice to the vehicle's control system, which then adjusts the vehicle's behavior in real time by adjusting the vehicle's speed and, if necessary, changing its route. The input data is the driving advice, and the output data is the controlled vehicle behavior.

[1052] Step 5:

[1053] While the user is driving, the smartwatch and smartphone continue to transmit physiological and location data to the server. The server continues to monitor and analyze this data in real time, dynamically generating driving advice as needed and sending it to the control system. For example, if the user's heart rate spikes, it will issue an alert saying, "Take a deep breath."

[1054] Step 6:

[1055] Once the drive is over, the server analyzes all the data and provides feedback to the user, including an abnormality risk assessment and advice for the next drive. All collected data (heart rate data, body temperature data, GPS data, interior temperature, humidity, and driving behavior data) are used as input data, and a feedback report is generated as output data.

[1056] Through these steps, users can receive real-time personalized driving advice and enjoy a safe and comfortable autonomous vehicle experience.

[1057] 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.

[1058] The present invention is a system that combines a user's physiological data, location data, and environmental data with an emotion engine that recognizes the user's emotions to propose optimal training menus to the user in real time. The specific implementation of this system is shown below.

[1059] System Overview

[1060] The system collects various data using a smartwatch, smartphone, and emotion engine, and analyzes it on a server to propose an optimized training menu for the user. The system also provides feedback during and after training.

[1061] Program Overview

[1062] The server receives the data from the user, analyzes it, and generates an optimal training menu. Specifically, the process is carried out in the following steps:

[1063] Data collection and transmission

[1064] The user puts on a smartwatch or smartphone, launches an application equipped with the emotion engine, and starts running. The smartwatch measures physiological data such as heart rate in real time, while the smartphone collects location data using GPS. The emotion engine also analyzes the user's voice data and facial expression data to generate emotion data. This data is sent to a server at regular intervals.

[1065] Data analysis on the server

[1066] The server receives and analyzes all data sent by the user, including physiological data (e.g., heart rate) and location data (e.g., GPS data), as well as current environmental data (e.g., temperature, humidity, weather) using external APIs, and emotion data generated by the emotion engine.

[1067] Training menu generation

[1068] The server uses AI algorithms to generate optimal training menus based on physiological, location, environmental, and emotional data. For example, if the emotion engine recognizes that the user is feeling stressed, it can suggest light exercise that will have a relaxing effect. On the other hand, if the user is feeling refreshed, it can suggest more strenuous exercise.

[1069] Real-time monitoring and advice

[1070] While the user is training, the smartwatch and smartphone continue to send physiological, location, and emotional data to the server. The server monitors and analyzes this data in real time, dynamically adjusting the training menu based on the situation and providing appropriate advice. For example, if the user feels fatigued or anxious during training, the server will advise them to slow down.

[1071] Post-training feedback

[1072] Once the training is complete, the server analyzes all the acquired data and provides feedback to the user. The feedback includes not only advice based on physical data, but also suggestions for mental care based on emotional data. For example, feedback such as "Excellent performance. Please try to relax before your next training session" may be provided.

[1073] Specific examples

[1074] 1. Examples of user behavior

[1075] The user launches the app using their smartwatch and smartphone and enters their user data. The emotion engine also starts up and begins analyzing voice and facial expression data. When the user starts running, the smartwatch begins measuring their heart rate and the smartphone collects GPS data.

[1076] 2. Examples of Device Actions

[1077] The device sends the collected heart rate data, GPS data, and emotion data to the server. For example, the heart rate is 75 bpm, the location data is (35.658034, 139.701636), and the emotion data is "stress."

[1078] 3. Examples of Server Actions

[1079] The server starts analysis based on this data. It also obtains current weather information using an external API. For example, it obtains weather data such as temperature 25 degrees, humidity 60%, and clear skies.

[1080] The server uses an AI algorithm to suggest optimal training menus based on physiological, location, environmental, and emotional data. For example, if the emotional data indicates "stress," it will suggest "relaxing exercises indoors."

[1081] While running, the server monitors the data in real time and provides advice to the user depending on the situation. For example, if the heart rate gets too high, it will instruct the user to "slow down your pace." If the emotional data indicates "anxiety," it will advise the user to "try to relax and take deep breaths."

[1082] After the training, the server analyzes all the data and provides feedback to the user, such as "Great performance. Please try to relax until the next training session."

[1083] This system allows users to perform optimal training that takes into account not only their physical condition but also their emotional state, supporting safe and effective training.

[1084] The processing flow will be explained below.

[1085] Step 1:

[1086] The user launches the app using a smartwatch or smartphone and enters their own user data (level, weight, goals, etc.). This data is used by the system to suggest training menus that are optimal for the user's condition and goals.

[1087] Step 2:

[1088] The device sends user data to the server to initiate a session along with login information, which the server stores for later analysis.

[1089] Step 3:

[1090] The device will begin to measure physiological data (e.g., heart rate) in real time using sensors in the smartwatch or smartphone, and location data (e.g., GPS data) will also be collected.

[1091] Step 4:

[1092] The device uses an emotion engine to analyze the user's voice and facial expression data and generate emotion data in real time, which indicates emotional states such as "stress," "refreshment," and "anxiety."

[1093] Step 5:

[1094] The device periodically collects physiological data, location data, and emotion data and sends them to the server. For example, the device sends heart rate 75 bpm, location data (35.658034, 139.701636), and emotion data "refresh" to the server.

[1095] Step 6:

[1096] The server analyzes the received data and uses an external API to obtain current environmental data (e.g., temperature, humidity, weather) based on the acquired physiological data (heart rate), location data (GPS data), and emotion data.

[1097] Step 7:

[1098] The server uses an AI algorithm to generate an optimal training menu based on the user's physiological, location, environmental, and emotional data. For example, if the user is in a "refreshed" emotional state, it will suggest a hard training menu, and if the user is feeling "stressed," it will suggest relaxing exercises.

[1099] Step 8:

[1100] The server sends the generated training menu to the terminal, which displays it to the user, who then starts running according to the training menu.

[1101] Step 9:

[1102] While the user is training, the device continues to collect physiological data, location data, and emotional data and transmits them to the server. For example, if the user's heart rate rises to 85 bpm during training and the emotional data changes to "anxiety," the device will transmit the data to the server.

[1103] Step 10:

[1104] The server monitors the data it receives in real time and immediately generates advice if there are any fluctuations. For example, if the heart rate suddenly increases, it generates an alert saying "Slow down your pace." If the emotion data indicates "anxiety," it sends the advice "Try to relax and take deep breaths" to the device. The device then displays the alert to the user.

[1105] Step 11:

[1106] When the user finishes the training, the terminal sends a training completion notification to the server.

[1107] Step 12:

[1108] The server analyzes the data from the entire training session and generates feedback. For example, it assesses the risk of injury, provides advice for the next training session, and provides recommended diet and rest. Feedback also includes suggestions for mental care, such as "That was a great performance. Please try to relax before your next training session."

[1109] Step 13:

[1110] The server generates feedback and sends it to the device, which displays it to the user, who can use it to plan their next workout.

[1111] These detailed steps enable the system to understand the user's condition in real time and support safe and effective training. The addition of an emotion engine makes it possible to provide training advice that takes the user's mental state into account.

[1112] Example 2

[1113] 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."

[1114] Conventional training systems could propose training menus based on a user's physiological data, location data, and environmental data, but they were unable to take the user's emotional state into account. This made it difficult to provide an optimal training menu that matched the user's mental state, preventing the training from being fully effective. Real-time feedback and dynamic changes to the training menu were also insufficient. The present invention aims to solve these problems and provide an optimal training menu that takes into account both the user's physiological and emotional states.

[1115] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring physiological data of the user, means for acquiring location data, means for acquiring environmental data, means for analyzing and acquiring emotional data of the user, means for analyzing the acquired physiological data, location data, environmental data, and emotional data and proposing a training menu in real time, means for monitoring the physiological data, environmental data, and emotional data in real time during training performed by the user and dynamically changing the training menu based on the changed data, and means for analyzing data after training and providing feedback. This makes it possible to propose an optimal training menu in real time that takes into account both the physiological state and emotional state of the user, thereby maximizing the effectiveness of the training.

[1116] "User's physiological data" refers to data that indicates the user's physical condition, such as heart rate, blood pressure, and body temperature.

[1117] "Location data" refers to data including the latitude and longitude of the user's current location, obtained using a GPS module or the like.

[1118] "Environmental data" refers to data that indicates the conditions of the environment in which the user is located, such as temperature, humidity, and weather.

[1119] "Emotion data" refers to data that indicates the mental state of the user, analyzed from their voice and facial expressions.

[1120] "Means for suggesting training menus in real time" refers to systems or algorithms that instantly provide users with appropriate training menus based on collected and analyzed data.

[1121] "Monitoring physiological and environmental data in real time during a workout" refers to the process of continuously collecting and monitoring physiological and environmental data while a user is working out.

[1122] "Means for dynamically changing training menus" refers to systems or algorithms that update and change training menus in real time based on acquired and analyzed data.

[1123] "Means for analyzing data and providing feedback after training" refers to a system or algorithm that analyzes all data collected after training is completed and provides advice or evaluation to the user based on the results.

[1124] This invention is a system that combines a user's physiological data, location data, and environmental data with an emotion engine that recognizes the user's emotions to propose optimal training menus to the user in real time. This system is realized by collecting and analyzing various data using a smartwatch, smartphone, server, and emotion engine.

[1125] System Overview

[1126] The system works as follows: Users collect data using a smartwatch or smartphone, and the emotion engine analyzes the user's emotional data. The data is then sent to a server, which analyzes the data and generates an optimal training menu. Additionally, feedback is provided during and after training.

[1127] Program processing

[1128] Data collection and transmission

[1129] 1. The user wears the smartwatch on their wrist and launches a dedicated app on their smartphone, which collects physiological data such as heart rate, blood pressure, and body temperature.

[1130] 2. The device (smartwatch) collects these physiological data in real time and stores them in its internal memory.

[1131] 3. The device (smartphone) collects location data using the GPS function. For example, the GPS module obtains the latitude and longitude of the current location every minute.

[1132] 4. The device (emotion engine) generates emotion data from the user's voice and facial expressions. It uses the smartphone's camera and microphone to recognize facial expressions and analyze voice, determining emotions such as "stress" or "relaxation."

[1133] 5. The device sends the collected heart rate data, location data, and emotion data to the server at regular intervals, for example, every 5 minutes, batch-processing the data and sending it to the server using HTTPS.

[1134] Data analysis on the server

[1135] 1. The server receives the transmitted physiological data, location data, and emotion data and stores them in a database.

[1136] 2. The server uses an external API to obtain current environmental data (temperature, humidity, weather), sends a request to the weather API, analyzes the response data, and saves it.

[1137] 3. The server performs analysis based on physiological, location, environmental, and emotional data, and processes the data using AI algorithms to evaluate the correlation between each data point.

[1138] Training menu generation

[1139] The server generates an optimal training menu based on the analysis results. For example, if the emotion engine detects "stress," it will suggest light exercise with a relaxing effect. If the user indicates "relaxation," it will suggest jogging or yoga.

[1140] Real-time monitoring and advice

[1141] 1. During training, the device continuously transmits physiological data, location data, and emotional data to the server. The data is updated every minute and transmitted to the server in real time.

[1142] 2. The server analyzes the received data in real time and provides advice to the user based on the situation. For example, if the user's heart rate exceeds 160 bpm and the user feels fatigued, the server will advise the user to "slow down."

[1143] Post-training feedback

[1144] 1. The server re-analyzes all data after training is complete. At the end of training, it performs a batch analysis and generates comprehensive feedback.

[1145] 2. The server provides final feedback to the user via an in-app notification or email with a message such as "Great performance! Please try to relax until your next training session."

[1146] Examples of prompt statements

[1147] User: Launches the app using a smartwatch and smartphone, enters data, and starts running.

[1148] Device: The smartwatch measures the heart rate, the smartphone collects GPS data, and the emotion engine generates emotion data.

[1149] Server: Analyzes the collected data, obtains weather information via an external API, and suggests optimal training menus.

[1150] As described above, this system can propose training menus in real time that take into account both the user's physiological and emotional states, providing the optimal training environment for the user.

[1151] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1152] Step 1:

[1153] The user wears the smartwatch on their wrist and launches the dedicated app on their smartphone. The input is basic data such as the user's heart rate, blood pressure, and body temperature. The output is the start of collection of this data. Specifically, the user presses the "Start" button on the app's startup screen.

[1154] Step 2:

[1155] The device (smartwatch) collects physiological data in real time. The input is the user's physiological data, such as heart rate, blood pressure, and body temperature, which are measured every second. The output is the storage of the measured data in internal memory. Specifically, the heart rate sensor collects data and stores it in internal memory.

[1156] Step 3:

[1157] The device (smartphone) uses GPS to collect location data. The input is location information from the smartphone's GPS module, which acquires latitude and longitude every minute. The output is the storage of the latitude and longitude of the current location. Specifically, the GPS module acquires information about the current location and stores it in the smartphone.

[1158] Step 4:

[1159] The device (emotion engine) generates the user's emotional data from voice and facial expressions. The input is voice and facial expression data captured using the smartphone's camera and microphone. The output is emotional data (such as "stress" or "relaxed") as the result of analysis. Specifically, it analyzes camera images and voice to determine the emotion.

[1160] Step 5:

[1161] The device sends the collected physiological data, location data, and emotional data to the server at regular intervals. The input is the collected data (heart rate, location data, emotional data), which is batch-processed every 5 minutes. The output is transmission to the server. Specifically, the data is sent to the server using the HTTPS protocol.

[1162] Step 6:

[1163] The server receives the transmitted physiological data, location data, and emotion data. The input is the data transmitted from the device, and the output is to store it in a database. Specifically, a dedicated API endpoint receives the data and stores it in the database.

[1164] Step 7:

[1165] The server uses an external API to obtain environmental data (temperature, humidity, weather). The input is a request to the weather API, and the output is the obtained environmental data. Specifically, it periodically sends requests to the weather API, analyzes the response data, and saves it.

[1166] Step 8:

[1167] The server performs analysis based on physiological, location, environmental, and emotional data. The input is all stored data, and the output is the analysis results (optimal training menu). Specifically, it applies AI algorithms to analyze the data and evaluates the correlation between each data point.

[1168] Step 9:

[1169] The server generates an optimal training menu and proposes it to the user. The input is the analysis results, and the output is a training menu proposal. Specifically, if the emotional data indicates "stress," the server proposes light exercise that has a relaxing effect.

[1170] Step 10:

[1171] The device continues to send physiological, location, and emotional data to the server during training. The input is the data collected in real time, and the output is sending it to the server. Specifically, the data is updated every minute and sent to the server in real time.

[1172] Step 11:

[1173] The server analyzes the data received in real time and provides advice to the user as needed. The input is real-time data, and the output is dynamic training menu changes and advice. Specifically, if the heart rate exceeds 160 bpm and the user shows signs of fatigue, the server instructs the user to "slow down."

[1174] Step 12:

[1175] After the training is complete, the server reanalyzes all data and provides feedback to the user. The input is all training data, and the output is a feedback message. Specifically, the server performs a batch analysis at the end of the training and sends an in-app notification or email with a message saying, "Excellent performance. Please try to relax until the next training session."

[1176] (Application example 2)

[1177] 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."

[1178] Modern factories require effective management systems that balance employee health with efficient work. However, there are currently no systems that can analyze employees' physiological data and emotional state in real time and make appropriate suggestions based on that state. This can lead to the accumulation of employee fatigue and stress, which can lead to a decline in productivity. It can also lead to a deterioration in employee health. A new system is needed to solve these issues.

[1179] 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.

[1180] In this invention, the server includes means for acquiring physiological data of a user, means for acquiring location data, means for acquiring environmental data, means for acquiring emotional data, means for analyzing the acquired physiological data, location data, environmental data, and emotional data to propose a training menu in real time, means for monitoring the physiological data and environmental data in real time during training performed by the user and dynamically changing the training menu based on the changed data, means for analyzing data after training and providing feedback, and means for evaluating the fatigue level of employees and proposing appropriate break timing and workload adjustments in real time, thereby enabling health management of factory employees and improving work efficiency.

[1181] "User" refers to a person who uses the system, and specifically includes factory workers.

[1182] "Physiological data" refers to physiological information obtained from the human body, such as heart rate.

[1183] "Location data" refers to geographical location information obtained by GPS or other means.

[1184] "Environmental data" refers to information about the external environment, such as temperature, humidity, and weather.

[1185] "Emotional data" refers to information about the user's emotional state obtained by analyzing voice and facial expressions.

[1186] "Analysis" refers to information processing to make appropriate decisions and suggestions based on acquired data.

[1187] "Real-time" refers to data collection and processing occurring immediately, without delay.

[1188] A "training menu" refers to a series of exercises or tasks that a user performs.

[1189] "Dynamic change" refers to instantly changing training menus and suggestions in response to data that changes in real time.

[1190] "Feedback" refers to providing advice on improvements and next steps based on the user's behavior and status.

[1191] "Fatigue level" refers to an index that quantitatively evaluates the degree of fatigue of the user.

[1192] "Break timing" refers to the appropriate moment to temporarily stop work and take a rest.

[1193] "Workload" refers to the difficulty or intensity of the work performed by the user.

[1194] This paper outlines a system that applies this invention to factory robots. This system uses physiological data, location data, environmental data, and emotional data of factory workers to monitor their health status in real time and propose appropriate break times and workload adjustments.

[1195] System configuration

[1196] Data collection

[1197] The user wears a smartwatch and a smartphone and launches an application equipped with an emotion engine. The smartwatch measures physiological data such as heart rate in real time, while the smartphone collects location data using GPS. The smartphone's emotion engine then analyzes voice and facial expressions to generate emotion data.

[1198] Sending data

[1199] This data is sent to a server via the Internet at regular intervals, where it is analyzed and appropriate suggestions are made to the user.

[1200] Data analysis on the server

[1201] The server integrates the collected physiological, location, environmental, and emotional data and analyzes it using an AI algorithm. Based on the analysis results, it evaluates the employee's fatigue level and suggests appropriate break times and workload adjustments.

[1202] Specifically, if the user's heart rate is high or the emotion engine indicates "stress," the system will notify them, for example, to "take a break." On the other hand, if the user's condition is good, the system will provide instructions such as "keep working at this pace."

[1203] Real-time monitoring and feedback

[1204] While the user is working, the smartwatch and smartphone continue to collect and transmit data to the server. The server monitors and analyzes this data in real time and dynamically adjusts the suggestions. For example, if the user's heart rate rises too much, the server can provide advice such as "Take a deep breath and relax."

[1205] After the task is completed, the server analyzes all the acquired data and provides the user with comprehensive feedback, such as "Today's task performance was excellent. Let's try harder next time."

[1206] Specific examples

[1207] An example of a prompt for a generative AI model might look like this:

[1208] User: I seem to be stressed. My heart rate is over 90 and the emotion engine is showing "stress." What advice would you give me?

[1209] AI: I would recommend you take a break and relax for a bit. Take a deep breath and take a five-minute break.

[1210] Such a system is expected to effectively manage the health of factory employees, improving productivity and work efficiency, and also contributing to maintaining the long-term health of employees and improving the efficiency of the entire company.

[1211] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1212] Step 1:

[1213] The user puts on the smartwatch and smartphone and launches the application. At this time, the emotion engine is also launched. At this stage, the smartwatch begins measuring the user's heart rate, and the smartphone acquires location information using GPS. The emotion engine analyzes the user's voice and facial expressions to generate emotion data. This data is sent to the smartphone in real time.

[1214] Input: User's heart rate, location information, voice and facial expression data

[1215] Output: physiological data, location data, emotional data

[1216] Step 2:

[1217] The device (smartphone) transmits the collected physiological data, location data, and emotional data to a server at regular intervals. The transmitted data is accumulated in the server in real time and used for subsequent analysis.

[1218] Input: physiological data, location data, emotional data

[1219] Output: Send data to the server

[1220] Step 3:

[1221] The server analyzes the received data using an AI algorithm to assess the user's fatigue level and emotional state. For example, if the heart rate is high and the emotional data indicates "stress," the server determines that the user is highly fatigued.

[1222] Input: physiological data, location data, emotional data

[1223] Output: Analysis results (fatigue level evaluation, emotion evaluation)

[1224] Step 4:

[1225] Based on the analysis results, the server will suggest an appropriate training menu to the user in real time. For example, if the heart rate is high and the emotional data indicates "stress," the server will suggest "take a break." On the other hand, if the user's condition is good, the server will suggest "keep working at this pace."

[1226] Input: Analysis results

[1227] Output: Training menu suggestions

[1228] Step 5:

[1229] While the user is working, the smartwatch and smartphone continue to collect data and send it to the server. The server monitors this data in real time and dynamically changes the training menu and suggestions depending on the situation. For example, if the user's heart rate spikes while working, the server will provide advice such as "Take a deep breath and relax."

[1230] Input: Real-time data (physiological data, location data, emotional data)

[1231] Output: Dynamic suggestions and training menu changes

[1232] Step 6:

[1233] Once the task is completed, the server compiles and analyzes all the acquired data and provides feedback to the user. The feedback includes the results of the training and advice for the next time. For example, the server may provide feedback such as, "Today's task performance was excellent. Let's try harder next time."

[1234] Input: All collected data

[1235] Output: Comprehensive feedback

[1236] These steps allow users to monitor their health status in real time and receive appropriate breaks and workload adjustments, which is expected to improve work efficiency and reduce health risks.

[1237] 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.

[1238] 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.

[1239] 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.

[1240] [Fourth embodiment]

[1241] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1242] 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.

[1243] 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).

[1244] 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.

[1245] 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.

[1246] 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).

[1247] 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. 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.

[1248] 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.

[1249] 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.

[1250] 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.

[1251] 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.

[1252] 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.

[1253] 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."

[1254] The present invention is a system that collects and analyzes a user's physiological data, location data, and environmental data in real time, and proposes an optimal training menu for the user. The specific implementation of this system is shown below.

[1255] System Overview

[1256] The system works by collecting physiological and location data from the user's smartwatch or smartphone and sending it to a server. The server then uses the collected data to generate and suggest optimal training menus based on the user's physical condition and the weather conditions of the day. Furthermore, the system monitors and analyzes data in real time while running and provides dynamic advice.

[1257] Program Overview

[1258] The server receives the data from the user, analyzes it, and generates an optimal training menu. Specifically, the process is carried out in the following steps:

[1259] Data collection and transmission

[1260] The user wears a smartwatch or smartphone and starts running. The smartwatch measures physiological data such as heart rate in real time, and the smartphone collects location data using GPS. This data is then sent to a server at regular intervals.

[1261] Data analysis on the server

[1262] When the server receives data sent from the user, it first analyzes the physiological data (e.g., heart rate) and location data (e.g., GPS data). In addition, the server uses an external API to obtain current environmental data (e.g., weather information).

[1263] Training menu generation

[1264] The server uses AI algorithms to generate optimal training plans based on physiological, location, and environmental data. For example, if the current temperature is high, it may suggest training on an indoor treadmill, while if the weather is fine, it may suggest running outdoors.

[1265] Real-time monitoring and advice

[1266] While the user is training, the smartwatch and smartphone continue to send physiological and location data to the server, which monitors and analyzes this data in real time and provides appropriate advice based on the situation. For example, if the user's heart rate spikes, the server can advise them to slow down.

[1267] Post-training feedback

[1268] Once the workout is complete, the server analyzes all the data and provides feedback to the user, including injury risk assessment, advice for the next workout, and recommended diet and sleep.

[1269] Specific examples

[1270] 1. Examples of user behavior

[1271] The user launches the app using their smartwatch and smartphone and enters their user data. When they start running, the smartwatch begins measuring their heart rate and the smartphone collects GPS data.

[1272] 2. Examples of Device Actions

[1273] The device sends the collected heart rate data and GPS data to the server. For example, the device sends a heart rate of 75 bpm and location data (35.658034, 139.701636) to the server.

[1274] 3. Examples of Server Actions

[1275] The server uses the received data to determine the user's current heart rate and location, and then uses an external API to obtain current weather information, such as a temperature of 25 degrees, humidity of 60%, and clear skies.

[1276] Based on this data, the server uses an AI algorithm to suggest the optimal training menu, such as "running 5 kilometers outdoors."

[1277] While running, the server monitors data in real time and provides advice to the user based on the situation, for example, telling them to "slow down" if their heart rate gets too high.

[1278] After the training, the server analyzes all the data and provides feedback to the user, such as "Great performance. Make sure you get plenty of rest and drink plenty of water before your next training session."

[1279] The system allows users to train safely and effectively while receiving real-time, personalized training advice.

[1280] The processing flow will be explained below.

[1281] Step 1:

[1282] The user launches the app using a smartwatch or smartphone and enters their own user data (level, weight, goals, etc.). This data is used by the system to suggest training menus that are optimal for the user's condition and goals.

[1283] Step 2:

[1284] The device sends user data to the server to initiate a session along with login information, which the server stores for later analysis.

[1285] Step 3:

[1286] The device will begin to measure physiological data (e.g., heart rate) in real time using sensors in the smartwatch or smartphone, and location data (e.g., GPS data) will also be collected.

[1287] Step 4:

[1288] The device periodically sends the collected physiological data and location data to the server, for example, sending a set of heart rate data and GPS data to the server every minute.

[1289] Step 5:

[1290] The server analyzes the received data and uses an external API to obtain current environmental data (e.g., temperature, humidity, weather) based on the acquired physiological data (heart rate) and location data (GPS data).

[1291] Step 6:

[1292] The server uses AI algorithms to generate optimal training plans based on the user's physiological, location, and environmental data, suggesting, for example, indoor treadmill training on hot days and outdoor running on sunny days.

[1293] Step 7:

[1294] The server sends the generated training menu to the terminal, which displays it to the user, who then starts running according to the training menu.

[1295] Step 8:

[1296] While the user is training, the device continues to collect physiological data (heart rate) and location data (GPS data) and transmits them to the server.

[1297] Step 9:

[1298] The server monitors the data it receives in real time and immediately generates advice if there are any fluctuations. For example, if your heart rate spikes, the server generates an alert saying "Slow down" and sends it to the device. The device then displays the alert to the user.

[1299] Step 10:

[1300] When the user finishes the training, the terminal sends a training completion notification to the server.

[1301] Step 11:

[1302] The server analyzes the data from the entire training session and generates feedback, such as an injury risk assessment, advice for the next training session, and recommendations for food and rest.

[1303] Step 12:

[1304] The server generates feedback and sends it to the device, which displays it to the user, who can use it to plan their next workout.

[1305] These detailed steps allow the system to understand the user's condition in real time and support safe and effective training.

[1306] Example 1

[1307] 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."

[1308] Conventional training support systems have had difficulty integrating and analyzing a user's physiological data, location data, and environmental data to provide an optimal training menu. Furthermore, they lacked the functionality to monitor the user's training status in real time and dynamically change the menu. This prevented users from optimally training for their own condition and environment, leading to safety and effectiveness issues.

[1309] 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.

[1310] In this invention, the server includes means for acquiring physiological data of a user, means for acquiring position data, means for acquiring environmental data, means for analyzing the acquired physiological data, position data, and environmental data and proposing a generated training menu, means for monitoring the physiological data and position data in real time during training performed by the user and dynamically changing the training menu according to the situation, and means for analyzing data after training and providing feedback. This allows the user to receive an individually optimized training menu in real time, enabling them to train safely and effectively.

[1311] "Physiological data" refers to data that indicates the user's physical condition, and specifically includes heart rate, blood pressure, body temperature, etc.

[1312] "Location data" refers to data indicating the user's current location, and specifically includes latitude and longitude information obtained from the Global Positioning System (GPS).

[1313] "Environmental data" refers to data that indicates the external environmental conditions, and specifically includes weather, temperature, humidity, wind speed, and the like.

[1314] "Analyzing" refers to the act of analyzing acquired data through computer processing to find specific patterns or trends.

[1315] A "training menu" indicates a plan or guidelines for the exercise or training that a user should do, and specifically includes running distance, time, intensity, and the like.

[1316] "Real-time" refers to processing ongoing processes and events instantly and providing results immediately.

[1317] "Monitoring" refers to the act of continuously observing the user's condition and environment, and responding immediately if there are any abnormalities or changes.

[1318] "Dynamic change" means to flexibly change fixed settings or plans according to the situation at hand, and to respond immediately.

[1319] "Providing feedback" refers to the act of providing improvements and guidelines for next actions based on the results and evaluation of the training the user has completed.

[1320] This invention is a system that collects and analyzes a user's physiological data, location data, and environmental data in real time, and proposes an optimal training menu for the user. The specific implementation of this system is shown below.

[1321] Hardware and software used

[1322] This system mainly uses the following hardware and software:

[1323] Smartwatch: A device that measures a user's physiological data, such as heart rate.

[1324] Smartphone: A device that uses GPS to collect location data and transmits it along with physiological data to a server.

[1325] Server: A device that analyzes data, generates and proposes training menus, and provides feedback to users.

[1326] External API: An interface for obtaining environmental data such as weather information.

[1327] System Operation

[1328] Data collection and transmission

[1329] The user puts on the smartwatch and launches the smartphone application, where the user enters basic data such as age, weight, and gender, and the application is then ready to reference the user's individual data.

[1330] The smartwatch measures the user's physiological data, such as heart rate, in real time, while the smartphone collects the user's location data using GPS, which is then sent to a server at regular intervals.

[1331] Data analysis on the server

[1332] The server receives the heart rate and location data sent by the user in real time and begins analysis. The server uses an external API to obtain current environmental data, such as weather information (temperature, humidity, weather).

[1333] Training menu generation

[1334] The server uses AI algorithms to generate optimal training plans based on physiological, location, and environmental data. For example, it suggests indoor treadmill training when the temperature is high, and a 5-kilometer run outdoors when the weather is good.

[1335] Real-time monitoring and advice

[1336] While the user is training, the smartwatch and smartphone continue to send physiological and location data to the server, which monitors this data in real time and provides appropriate advice based on the situation. For example, if the user's heart rate spikes, the server will advise them to "slow down."

[1337] Post-training feedback

[1338] Once the training is over, the server analyzes all the data and provides feedback to the user. This feedback includes injury risk assessment, advice for the next training session, and recommended diet and sleep. For example, a message might be given saying, "Great performance! Make sure you get plenty of rest and stay properly hydrated before your next training session."

[1339] Specific examples

[1340] 1. Examples of user behavior

[1341] The user launches the app using their smartwatch and smartphone and enters their user data. When they start running, the smartwatch begins measuring their heart rate and the smartphone collects GPS data.

[1342] 2. Examples of Device Actions

[1343] The device sends the collected heart rate data and GPS data to the server, for example, a heart rate of 75 bpm and location data (35.658034, 139.701636).

[1344] 3. Examples of Server Actions

[1345] The server determines the user's current heart rate and location based on the received data, and also uses an external API to obtain current weather information, such as a temperature of 25 degrees, humidity of 60%, and clear skies.

[1346] Based on this data, the server uses an AI algorithm to suggest the optimal training menu, for example, "running 5 kilometers outdoors."

[1347] While running, the server monitors data in real time and provides advice to the user, such as "slow down your pace," depending on the situation.

[1348] After the training, the server analyzes all the data and provides the user with feedback such as, "Great performance. Make sure you get plenty of rest and stay properly hydrated before your next training session."

[1349] Prompt Sentence Examples

[1350] "Describe a system where a user can use a smartphone and a smartwatch to start a run and collect and transmit data in real time."

[1351] This system allows users to receive personalized training advice in real time, enabling them to train more safely and effectively.

[1352] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1353] Step 1:

[1354] The user puts on the smartwatch and launches the smartphone application, where the user enters basic data such as age, weight, and gender. This is the initial input, and the application is ready to reference this information.

[1355] Input: Basic data such as age, weight, and gender

[1356] Output: User profile initial setup complete

[1357] Specific behavior:

[1358] The user launches the application and enters personal data such as "30 years old, 70 kg, male."

[1359] Step 2:

[1360] The smartwatch, which is the device, measures the user's physiological data such as heart rate in real time. At the same time, the smartphone collects location data using GPS. This data is continuously collected and sent to a server at regular intervals.

[1361] Input: Real-time heart rate data, GPS location data

[1362] Output: Acquired physiological and location data

[1363] Specific behavior:

[1364] The smartwatch measures your heart rate in real time and records, for example, "Heart rate: 75 bpm."

[1365] The smartphone collects GPS location information and records, for example, "Location data: (35.658034, 139.701636)".

[1366] Step 3:

[1367] The device sends the collected heart rate data and GPS data to a server, which updates the data in real time by setting the data to be sent to the server at regular intervals.

[1368] Input: Collected heart rate data, GPS location data

[1369] Output: Data sent to the server

[1370] Specific behavior:

[1371] For example, every 10 seconds, updated heart rate data and GPS location data are sent to a server.

[1372] Step 4:

[1373] The server receives the heart rate and location data sent by the user in real time and begins analysis, which involves processing the data to understand the user's physiological condition and movement status.

[1374] Input: Transmitted physiological and location data

[1375] Output: Parsed user state data

[1376] Specific behavior:

[1377] The server receives "Heart rate: 75 bpm, and location data: (35.658034, 139.701636)" and analyzes it.

[1378] Step 5:

[1379] The server uses an external API to retrieve current environmental data, including weather, temperature, humidity, etc. This environmental data is also subject to analysis.

[1380] Input: Environmental data request obtained from external API

[1381] Output: Current environmental data (e.g. temperature, humidity, weather)

[1382] Specific behavior:

[1383] The server sends a request to an external API and retrieves the data "Temperature: 25 degrees, Humidity: 60%, Weather: Sunny".

[1384] Step 6:

[1385] The server uses AI algorithms to generate optimal training menus based on physiological, location, and environmental data, recommending indoor training when temperatures are high and outdoor training when the weather is good.

[1386] Input: Analyzed physiological data, location data, and environmental data

[1387] Output: Generated training menu

[1388] Specific behavior:

[1389] The server uses an AI algorithm to generate an optimal training menu, such as "running 5 kilometers outdoors."

[1390] Step 7:

[1391] While the user is performing the workout, the smartwatch and smartphone continue to transmit physiological and location data to the server.

[1392] Input: Real-time training data

[1393] Output: Data that continues to be sent to the server

[1394] Specific behavior:

[1395] For example, the user's heart rate rises to "80 bpm" and the data is sent to the server.

[1396] Step 8:

[1397] The server monitors this data in real time and provides appropriate advice depending on the situation: if your heart rate spikes, the server will instruct you to "slow down."

[1398] Input: Physiological and location data transmitted in real time

[1399] Output: Real-time advice

[1400] Specific behavior:

[1401] The server sends a notification to the user's smartphone with advice such as "Your heart rate has risen sharply, so slow down your pace."

[1402] Step 9:

[1403] Once the workout is complete, the server analyzes all the data and provides feedback to the user, including advice for the next workout and diet and sleep recommendations.

[1404] Input: All data from completed training

[1405] Output: Parsed feedback

[1406] Specific behavior:

[1407] For example, they might provide feedback like, "Great performance! Make sure you get plenty of rest and stay properly hydrated before your next training session."

[1408] (Application example 1)

[1409] 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."

[1410] When a user rides in an autonomous vehicle, safety and comfort are important, but conventional systems have had difficulty making real-time adjustments based on the user's physiological state and environment.In addition, by adjusting the vehicle's operation based on physiological data such as heart rate and body temperature, it is necessary to reduce user stress and respond quickly if an abnormality is detected.

[1411] 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.

[1412] In this invention, the server includes means for acquiring user physiological data, means for acquiring location data, means for acquiring environmental data, means for analyzing the acquired physiological data, location data, and environmental data and proposing a training menu in real time, means for monitoring the physiological data and environmental data in real time during training by the user and dynamically changing the training menu based on the changed data, means for analyzing the data and providing feedback after training, means for adjusting and proposing vehicle operation in real time according to the user's condition, means for transmitting data to a vehicle control system and determining vehicle operation based on the data, and means for adjusting the vehicle speed and generating an alert if the user's physiological data indicates an abnormality. This makes it possible to optimize the operation of an autonomous vehicle in real time according to the user's condition, thereby improving safety and comfort.

[1413] "User physiological data" refers to data that indicates the user's physical condition in real time, such as heart rate, body temperature, and blood pressure.

[1414] "Location data" is data that indicates the current location of a user or vehicle, and is usually obtained as GPS data.

[1415] "Environmental data" refers to data relating to the user's surrounding environment, including temperature, humidity, and temperature inside the vehicle.

[1416] "Means for proposing training menus in real time" refers to systems or algorithms that instantly analyze acquired data and present the optimal training menu to the user.

[1417] "Means for monitoring physiological and environmental data" refers to sensors and software that constantly monitor the user's physical state and environmental conditions during training.

[1418] "Means for dynamically changing training menus" refers to systems or algorithms that instantly change training menus based on data acquired in real time.

[1419] "Means for providing feedback" refers to a function that provides guidance and advice to the user based on data analyzed after training.

[1420] "Means for adjusting and suggesting vehicle behavior in real time" refers to systems or functions that instantly correct or suggest the speed or route of an autonomous vehicle based on user status data.

[1421] "Means for transmitting data to the vehicle's control system" refers to the communication functions and protocols for transmitting the acquired physiological data and environmental data to the control device in the vehicle.

[1422] "Means for determining vehicle behavior" refers to the systems or algorithms that control the vehicle's movement based on the transmitted data.

[1423] The "means for generating an alert" refers to a function that issues a warning when an abnormality is detected in the user's physiological data.

[1424] This invention is a system that collects and analyzes a user's physiological data, location data, and environmental data in real time, and provides the user with optimal driving advice. How this system is implemented will be described below.

[1425] System Overview

[1426] This system works by collecting physiological and location data from the user's smartwatch or smartphone and transmitting it to the autonomous vehicle's control system. The control system uses the collected data to generate and provide optimal driving advice based on the user's physiological state and the surrounding environment of the day. Furthermore, while driving, the system monitors and analyzes data in real time and provides dynamic driving advice.

[1427] Program Overview

[1428] The server receives the data from the user, analyzes it, and generates optimal driving advice. Specifically, the process involves the following steps:

[1429] 1. Data collection and transmission

[1430] Users ride in autonomous vehicles wearing a smartwatch or smartphone. The smartwatch measures physiological data such as heart rate and body temperature in real time, while the smartphone collects location data using GPS. This data is sent to the vehicle's control system at regular intervals.

[1431] 2. Data analysis in the control system

[1432] When the control system receives data sent from the user, it first analyzes the physiological data (e.g., heart rate) and location data (e.g., GPS data). In addition, the control system uses external data acquisition means to acquire current environmental data (e.g., temperature and humidity inside the vehicle).

[1433] 3. Driving advice generation

[1434] Based on the acquired physiological, location, and environmental data, the control system uses AI algorithms to generate optimal driving advice, such as suggesting slowing the vehicle and heading to the nearest rest stop if the user's heart rate is high.

[1435] 4. Real-time monitoring and advice

[1436] While the user is riding, the smartwatch and smartphone continue to transmit physiological and location data to the control system, which monitors and analyzes this data in real time and provides appropriate driving advice based on the situation. For example, if the user's heart rate spikes, the system can issue an alert to "take a deep breath."

[1437] 5. Post-driving feedback

[1438] Once the trip is over, the control system analyzes all the data and provides feedback to the user, including a risk assessment of any anomalies and recommendations for the next trip.

[1439] Hardware and Software

[1440] Hardware: Smartwatches (e.g., smartwatches), smartphones (e.g., smartphones), autonomous vehicle control systems (e.g., in-vehicle control systems)

[1441] Software: AI algorithms (e.g., TensorFlow, PyTorch), environmental data acquisition APIs (e.g., Environmental Data APIs)

[1442] The server uses the above hardware and software to acquire and analyze data in real time and provide appropriate driving advice to users.

[1443] Specific examples

[1444] User: A user wearing a smartwatch and carrying a smartphone with GPS functionality gets into an autonomous vehicle.

[1445] Terminal: The smartwatch and smartphone each transmit physiological data and location data to the control system within the terminal.

[1446] Server: Based on the data received by the in-vehicle control system, the server uses AI algorithms to generate and provide optimal driving advice in real time.

[1447] Prompt Sentence Examples

[1448] The smartwatch collects the user's heart rate, body temperature, and physiological data in real time and sends it to the vehicle's control system. The control system then uses AI algorithms to analyze the data and determine the vehicle's behavior based on the user's condition. Specifically, if the user's heart rate is high, the system will slow down the vehicle or change the route. After the drive, the system will provide detailed feedback.

[1449] This system allows users to enjoy safe and comfortable autonomous vehicle driving while receiving personalized driving advice in real time.

[1450] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1451] Step 1:

[1452] A user wears a smartwatch and a smartphone and gets into an autonomous vehicle. The smartwatch measures physiological data such as heart rate and body temperature in real time, while the smartphone collects location data using GPS. The input data includes heart rate (e.g., 75 bpm), body temperature (e.g., 36.6°C), and location data (e.g., GPS coordinates). This data is sent to the vehicle's control system via the smartphone.

[1453] Step 2:

[1454] The server receives physiological and location data sent from the user's smartwatch and smartphone. It also acquires environmental data such as interior temperature and humidity from environmental data acquisition sensors inside the vehicle. The input data includes heart rate, body temperature, GPS data, interior temperature (e.g., 24°C), and humidity (e.g., 50%). The server aggregates this data and creates a dataset for analysis.

[1455] Step 3:

[1456] The server analyzes the acquired physiological data, location data, and environmental data. AI algorithms (e.g., TensorFlow, PyTorch) are used for the analysis. The AI ​​model evaluates the user's state under certain conditions and generates driving advice based on that evaluation. For example, if the heart rate is high, the AI ​​model will decide to slow down the vehicle. The driving advice generated as a result of the analysis is output.

[1457] Step 4:

[1458] The server sends the generated driving advice to the vehicle's control system, which then adjusts the vehicle's behavior in real time by adjusting the vehicle's speed and, if necessary, changing its route. The input data is the driving advice, and the output data is the controlled vehicle behavior.

[1459] Step 5:

[1460] While the user is driving, the smartwatch and smartphone continue to transmit physiological and location data to the server. The server continues to monitor and analyze this data in real time, dynamically generating driving advice as needed and sending it to the control system. For example, if the user's heart rate spikes, it will issue an alert saying, "Take a deep breath."

[1461] Step 6:

[1462] Once the drive is over, the server analyzes all the data and provides feedback to the user, including an abnormality risk assessment and advice for the next drive. All collected data (heart rate data, body temperature data, GPS data, interior temperature, humidity, and driving behavior data) are used as input data, and a feedback report is generated as output data.

[1463] Through these steps, users can receive real-time personalized driving advice and enjoy a safe and comfortable autonomous vehicle experience.

[1464] 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.

[1465] The present invention is a system that combines a user's physiological data, location data, and environmental data with an emotion engine that recognizes the user's emotions to propose optimal training menus to the user in real time. The specific implementation of this system is shown below.

[1466] System Overview

[1467] The system collects various data using a smartwatch, smartphone, and emotion engine, and analyzes it on a server to propose an optimized training menu for the user. The system also provides feedback during and after training.

[1468] Program Overview

[1469] The server receives the data from the user, analyzes it, and generates an optimal training menu. Specifically, the process is carried out in the following steps:

[1470] Data collection and transmission

[1471] The user puts on a smartwatch or smartphone, launches an application equipped with the emotion engine, and starts running. The smartwatch measures physiological data such as heart rate in real time, while the smartphone collects location data using GPS. The emotion engine also analyzes the user's voice data and facial expression data to generate emotion data. This data is sent to a server at regular intervals.

[1472] Data analysis on the server

[1473] The server receives and analyzes all data sent by the user, including physiological data (e.g., heart rate) and location data (e.g., GPS data), as well as current environmental data (e.g., temperature, humidity, weather) using external APIs, and emotion data generated by the emotion engine.

[1474] Training menu generation

[1475] The server uses AI algorithms to generate optimal training menus based on physiological, location, environmental, and emotional data. For example, if the emotion engine recognizes that the user is feeling stressed, it can suggest light exercise that will have a relaxing effect. On the other hand, if the user is feeling refreshed, it can suggest more strenuous exercise.

[1476] Real-time monitoring and advice

[1477] While the user is training, the smartwatch and smartphone continue to send physiological, location, and emotional data to the server. The server monitors and analyzes this data in real time, dynamically adjusting the training menu based on the situation and providing appropriate advice. For example, if the user feels fatigued or anxious during training, the server will advise them to slow down.

[1478] Post-training feedback

[1479] Once the training is complete, the server analyzes all the acquired data and provides feedback to the user. The feedback includes not only advice based on physical data, but also suggestions for mental care based on emotional data. For example, feedback such as "Excellent performance. Please try to relax before your next training session" may be provided.

[1480] Specific examples

[1481] 1. Examples of user behavior

[1482] The user launches the app using their smartwatch and smartphone and enters their user data. The emotion engine also starts up and begins analyzing voice and facial expression data. When the user starts running, the smartwatch begins measuring their heart rate and the smartphone collects GPS data.

[1483] 2. Examples of Device Actions

[1484] The device sends the collected heart rate data, GPS data, and emotion data to the server. For example, the heart rate is 75 bpm, the location data is (35.658034, 139.701636), and the emotion data is "stress."

[1485] 3. Examples of Server Actions

[1486] The server starts analysis based on this data. It also obtains current weather information using an external API. For example, it obtains weather data such as temperature 25 degrees, humidity 60%, and clear skies.

[1487] The server uses an AI algorithm to suggest optimal training menus based on physiological, location, environmental, and emotional data. For example, if the emotional data indicates "stress," it will suggest "relaxing exercises indoors."

[1488] While running, the server monitors the data in real time and provides advice to the user depending on the situation. For example, if the heart rate gets too high, it will instruct the user to "slow down your pace." If the emotional data indicates "anxiety," it will advise the user to "try to relax and take deep breaths."

[1489] After the training, the server analyzes all the data and provides feedback to the user, such as "Great performance. Please try to relax until the next training session."

[1490] This system allows users to perform optimal training that takes into account not only their physical condition but also their emotional state, supporting safe and effective training.

[1491] The processing flow will be explained below.

[1492] Step 1:

[1493] The user launches the app using a smartwatch or smartphone and enters their own user data (level, weight, goals, etc.). This data is used by the system to suggest training menus that are optimal for the user's condition and goals.

[1494] Step 2:

[1495] The device sends user data to the server to initiate a session along with login information, which the server stores for later analysis.

[1496] Step 3:

[1497] The device will begin to measure physiological data (e.g., heart rate) in real time using sensors in the smartwatch or smartphone, and location data (e.g., GPS data) will also be collected.

[1498] Step 4:

[1499] The device uses an emotion engine to analyze the user's voice and facial expression data and generate emotion data in real time, which indicates emotional states such as "stress," "refreshment," and "anxiety."

[1500] Step 5:

[1501] The device periodically collects physiological data, location data, and emotion data and sends them to the server. For example, the device sends heart rate 75 bpm, location data (35.658034, 139.701636), and emotion data "refresh" to the server.

[1502] Step 6:

[1503] The server analyzes the received data and uses an external API to obtain current environmental data (e.g., temperature, humidity, weather) based on the acquired physiological data (heart rate), location data (GPS data), and emotion data.

[1504] Step 7:

[1505] The server uses an AI algorithm to generate an optimal training menu based on the user's physiological, location, environmental, and emotional data. For example, if the user is in a "refreshed" emotional state, it will suggest a hard training menu, and if the user is feeling "stressed," it will suggest relaxing exercises.

[1506] Step 8:

[1507] The server sends the generated training menu to the terminal, which displays it to the user, who then starts running according to the training menu.

[1508] Step 9:

[1509] While the user is training, the device continues to collect physiological data, location data, and emotional data and transmits them to the server. For example, if the user's heart rate rises to 85 bpm during training and the emotional data changes to "anxiety," the device will transmit the data to the server.

[1510] Step 10:

[1511] The server monitors the data it receives in real time and immediately generates advice if there are any fluctuations. For example, if the heart rate suddenly increases, it generates an alert saying "Slow down your pace." If the emotion data indicates "anxiety," it sends the advice "Try to relax and take deep breaths" to the device. The device then displays the alert to the user.

[1512] Step 11:

[1513] When the user finishes the training, the terminal sends a training completion notification to the server.

[1514] Step 12:

[1515] The server analyzes the data from the entire training session and generates feedback. For example, it assesses the risk of injury, provides advice for the next training session, and provides recommended diet and rest. Feedback also includes suggestions for mental care, such as "That was a great performance. Please try to relax before your next training session."

[1516] Step 13:

[1517] The server generates feedback and sends it to the device, which displays it to the user, who can use it to plan their next workout.

[1518] These detailed steps enable the system to understand the user's condition in real time and support safe and effective training. The addition of an emotion engine makes it possible to provide training advice that takes the user's mental state into account.

[1519] Example 2

[1520] 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."

[1521] Conventional training systems could propose training menus based on a user's physiological data, location data, and environmental data, but they were unable to take the user's emotional state into account. This made it difficult to provide an optimal training menu that matched the user's mental state, preventing the training from being fully effective. Real-time feedback and dynamic changes to the training menu were also insufficient. The present invention aims to solve these problems and provide an optimal training menu that takes into account both the user's physiological and emotional states.

[1522] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for acquiring physiological data of the user, means for acquiring location data, means for acquiring environmental data, means for analyzing and acquiring emotional data of the user, means for analyzing the acquired physiological data, location data, environmental data, and emotional data and proposing a training menu in real time, means for monitoring the physiological data, environmental data, and emotional data in real time during training performed by the user and dynamically changing the training menu based on the changed data, and means for analyzing data after training and providing feedback. This makes it possible to propose an optimal training menu in real time that takes into account both the physiological state and emotional state of the user, thereby maximizing the effectiveness of the training.

[1523] "User's physiological data" refers to data that indicates the user's physical condition, such as heart rate, blood pressure, and body temperature.

[1524] "Location data" refers to data including the latitude and longitude of the user's current location, obtained using a GPS module or the like.

[1525] "Environmental data" refers to data that indicates the conditions of the environment in which the user is located, such as temperature, humidity, and weather.

[1526] "Emotion data" refers to data that indicates the mental state of the user, analyzed from their voice and facial expressions.

[1527] "Means for suggesting training menus in real time" refers to systems or algorithms that instantly provide users with appropriate training menus based on collected and analyzed data.

[1528] "Monitoring physiological and environmental data in real time during a workout" refers to the process of continuously collecting and monitoring physiological and environmental data while a user is working out.

[1529] "Means for dynamically changing training menus" refers to systems or algorithms that update and change training menus in real time based on acquired and analyzed data.

[1530] "Means for analyzing data and providing feedback after training" refers to a system or algorithm that analyzes all data collected after training is completed and provides advice or evaluation to the user based on the results.

[1531] This invention is a system that combines a user's physiological data, location data, and environmental data with an emotion engine that recognizes the user's emotions to propose optimal training menus to the user in real time. This system is realized by collecting and analyzing various data using a smartwatch, smartphone, server, and emotion engine.

[1532] System Overview

[1533] The system works as follows: Users collect data using a smartwatch or smartphone, and the emotion engine analyzes the user's emotional data. The data is then sent to a server, which analyzes the data and generates an optimal training menu. Additionally, feedback is provided during and after training.

[1534] Program processing

[1535] Data collection and transmission

[1536] 1. The user wears the smartwatch on their wrist and launches a dedicated app on their smartphone, which collects physiological data such as heart rate, blood pressure, and body temperature.

[1537] 2. The device (smartwatch) collects these physiological data in real time and stores them in its internal memory.

[1538] 3. The device (smartphone) collects location data using the GPS function. For example, the GPS module obtains the latitude and longitude of the current location every minute.

[1539] 4. The device (emotion engine) generates emotion data from the user's voice and facial expressions. It uses the smartphone's camera and microphone to recognize facial expressions and analyze voice, determining emotions such as "stress" or "relaxation."

[1540] 5. The device sends the collected heart rate data, location data, and emotion data to the server at regular intervals, for example, every 5 minutes, batch-processing the data and sending it to the server using HTTPS.

[1541] Data analysis on the server

[1542] 1. The server receives the transmitted physiological data, location data, and emotion data and stores them in a database.

[1543] 2. The server uses an external API to obtain current environmental data (temperature, humidity, weather), sends a request to the weather API, analyzes the response data, and saves it.

[1544] 3. The server performs analysis based on physiological, location, environmental, and emotional data, and processes the data using AI algorithms to evaluate the correlation between each data point.

[1545] Training menu generation

[1546] The server generates an optimal training menu based on the analysis results. For example, if the emotion engine detects "stress," it will suggest light exercise with a relaxing effect. If the user indicates "relaxation," it will suggest jogging or yoga.

[1547] Real-time monitoring and advice

[1548] 1. During training, the device continuously transmits physiological data, location data, and emotional data to the server. The data is updated every minute and transmitted to the server in real time.

[1549] 2. The server analyzes the received data in real time and provides advice to the user based on the situation. For example, if the user's heart rate exceeds 160 bpm and the user feels fatigued, the server will advise the user to "slow down."

[1550] Post-training feedback

[1551] 1. The server re-analyzes all data after training is complete. At the end of training, it performs a batch analysis and generates comprehensive feedback.

[1552] 2. The server provides final feedback to the user via an in-app notification or email with a message such as "Great performance! Please try to relax until your next training session."

[1553] Examples of prompt statements

[1554] User: Launches the app using a smartwatch and smartphone, enters data, and starts running.

[1555] Device: The smartwatch measures the heart rate, the smartphone collects GPS data, and the emotion engine generates emotion data.

[1556] Server: Analyzes the collected data, obtains weather information via an external API, and suggests optimal training menus.

[1557] As described above, this system can propose training menus in real time that take into account both the user's physiological and emotional states, providing the optimal training environment for the user.

[1558] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1559] Step 1:

[1560] The user wears the smartwatch on their wrist and launches the dedicated app on their smartphone. The input is basic data such as the user's heart rate, blood pressure, and body temperature. The output is the start of collection of this data. Specifically, the user presses the "Start" button on the app's startup screen.

[1561] Step 2:

[1562] The device (smartwatch) collects physiological data in real time. The input is the user's physiological data, such as heart rate, blood pressure, and body temperature, which are measured every second. The output is the storage of the measured data in internal memory. Specifically, the heart rate sensor collects data and stores it in internal memory.

[1563] Step 3:

[1564] The device (smartphone) uses GPS to collect location data. The input is location information from the smartphone's GPS module, which acquires latitude and longitude every minute. The output is the storage of the latitude and longitude of the current location. Specifically, the GPS module acquires information about the current location and stores it in the smartphone.

[1565] Step 4:

[1566] The device (emotion engine) generates the user's emotional data from voice and facial expressions. The input is voice and facial expression data captured using the smartphone's camera and microphone. The output is emotional data (such as "stress" or "relaxed") as the result of analysis. Specifically, it analyzes camera images and voice to determine the emotion.

[1567] Step 5:

[1568] The device sends the collected physiological data, location data, and emotional data to the server at regular intervals. The input is the collected data (heart rate, location data, emotional data), which is batch-processed every 5 minutes. The output is transmission to the server. Specifically, the data is sent to the server using the HTTPS protocol.

[1569] Step 6:

[1570] The server receives the transmitted physiological data, location data, and emotion data. The input is the data transmitted from the device, and the output is to store it in a database. Specifically, a dedicated API endpoint receives the data and stores it in the database.

[1571] Step 7:

[1572] The server uses an external API to obtain environmental data (temperature, humidity, weather). The input is a request to the weather API, and the output is the obtained environmental data. Specifically, it periodically sends requests to the weather API, analyzes the response data, and saves it.

[1573] Step 8:

[1574] The server performs analysis based on physiological, location, environmental, and emotional data. The input is all stored data, and the output is the analysis results (optimal training menu). Specifically, it applies AI algorithms to analyze the data and evaluates the correlation between each data point.

[1575] Step 9:

[1576] The server generates an optimal training menu and proposes it to the user. The input is the analysis results, and the output is a training menu proposal. Specifically, if the emotional data indicates "stress," the server proposes light exercise that has a relaxing effect.

[1577] Step 10:

[1578] The device continues to send physiological, location, and emotional data to the server during training. The input is the data collected in real time, and the output is sending it to the server. Specifically, the data is updated every minute and sent to the server in real time.

[1579] Step 11:

[1580] The server analyzes the data received in real time and provides advice to the user as needed. The input is real-time data, and the output is dynamic training menu changes and advice. Specifically, if the heart rate exceeds 160 bpm and the user shows signs of fatigue, the server instructs the user to "slow down."

[1581] Step 12:

[1582] After the training is complete, the server reanalyzes all data and provides feedback to the user. The input is all training data, and the output is a feedback message. Specifically, the server performs a batch analysis at the end of the training and sends an in-app notification or email with a message saying, "Excellent performance. Please try to relax until the next training session."

[1583] (Application example 2)

[1584] 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."

[1585] Modern factories require effective management systems that balance employee health with efficient work. However, there are currently no systems that can analyze employees' physiological data and emotional state in real time and make appropriate suggestions based on that state. This can lead to the accumulation of employee fatigue and stress, which can lead to a decline in productivity. It can also lead to a deterioration in employee health. A new system is needed to solve these issues.

[1586] 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.

[1587] In this invention, the server includes means for acquiring physiological data of a user, means for acquiring location data, means for acquiring environmental data, means for acquiring emotional data, means for analyzing the acquired physiological data, location data, environmental data, and emotional data to propose a training menu in real time, means for monitoring the physiological data and environmental data in real time during training performed by the user and dynamically changing the training menu based on the changed data, means for analyzing data after training and providing feedback, and means for evaluating the fatigue level of employees and proposing appropriate break timing and workload adjustments in real time, thereby enabling health management of factory employees and improving work efficiency.

[1588] "User" refers to a person who uses the system, and specifically includes factory workers.

[1589] "Physiological data" refers to physiological information obtained from the human body, such as heart rate.

[1590] "Location data" refers to geographical location information obtained by GPS or other means.

[1591] "Environmental data" refers to information about the external environment, such as temperature, humidity, and weather.

[1592] "Emotional data" refers to information about the user's emotional state obtained by analyzing voice and facial expressions.

[1593] "Analysis" refers to information processing to make appropriate decisions and suggestions based on acquired data.

[1594] "Real-time" refers to data collection and processing occurring immediately, without delay.

[1595] A "training menu" refers to a series of exercises or tasks that a user performs.

[1596] "Dynamic change" refers to instantly changing training menus and suggestions in response to data that changes in real time.

[1597] "Feedback" refers to providing advice on improvements and next steps based on the user's behavior and status.

[1598] "Fatigue level" refers to an index that quantitatively evaluates the degree of fatigue of the user.

[1599] "Break timing" refers to the appropriate moment to temporarily stop work and take a rest.

[1600] "Workload" refers to the difficulty or intensity of the work performed by the user.

[1601] This paper outlines a system that applies this invention to factory robots. This system uses physiological data, location data, environmental data, and emotional data of factory workers to monitor their health status in real time and propose appropriate break times and workload adjustments.

[1602] System configuration

[1603] Data collection

[1604] The user wears a smartwatch and a smartphone and launches an application equipped with an emotion engine. The smartwatch measures physiological data such as heart rate in real time, while the smartphone collects location data using GPS. The smartphone's emotion engine then analyzes voice and facial expressions to generate emotion data.

[1605] Sending data

[1606] This data is sent to a server via the Internet at regular intervals, where it is analyzed and appropriate suggestions are made to the user.

[1607] Data analysis on the server

[1608] The server integrates the collected physiological, location, environmental, and emotional data and analyzes it using an AI algorithm. Based on the analysis results, it evaluates the employee's fatigue level and suggests appropriate break times and workload adjustments.

[1609] Specifically, if the user's heart rate is high or the emotion engine indicates "stress," the system will notify them, for example, to "take a break." On the other hand, if the user's condition is good, the system will provide instructions such as "keep working at this pace."

[1610] Real-time monitoring and feedback

[1611] While the user is working, the smartwatch and smartphone continue to collect and transmit data to the server. The server monitors and analyzes this data in real time and dynamically adjusts the suggestions. For example, if the user's heart rate rises too much, the server can provide advice such as "Take a deep breath and relax."

[1612] After the task is completed, the server analyzes all the acquired data and provides the user with comprehensive feedback, such as "Today's task performance was excellent. Let's try harder next time."

[1613] Specific examples

[1614] An example of a prompt for a generative AI model might look like this:

[1615] User: I seem to be stressed. My heart rate is over 90 and the emotion engine is showing "stress." What advice would you give me?

[1616] AI: I would recommend you take a break and relax for a bit. Take a deep breath and take a five-minute break.

[1617] Such a system is expected to effectively manage the health of factory employees, improving productivity and work efficiency, and also contributing to maintaining the long-term health of employees and improving the efficiency of the entire company.

[1618] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1619] Step 1:

[1620] The user puts on the smartwatch and smartphone and launches the application. At this time, the emotion engine is also launched. At this stage, the smartwatch begins measuring the user's heart rate, and the smartphone acquires location information using GPS. The emotion engine analyzes the user's voice and facial expressions to generate emotion data. This data is sent to the smartphone in real time.

[1621] Input: User's heart rate, location information, voice and facial expression data

[1622] Output: physiological data, location data, emotional data

[1623] Step 2:

[1624] The device (smartphone) transmits the collected physiological data, location data, and emotional data to a server at regular intervals. The transmitted data is accumulated in the server in real time and used for subsequent analysis.

[1625] Input: physiological data, location data, emotional data

[1626] Output: Send data to the server

[1627] Step 3:

[1628] The server analyzes the received data using an AI algorithm to assess the user's fatigue level and emotional state. For example, if the heart rate is high and the emotional data indicates "stress," the server determines that the user is highly fatigued.

[1629] Input: physiological data, location data, emotional data

[1630] Output: Analysis results (fatigue level evaluation, emotion evaluation)

[1631] Step 4:

[1632] Based on the analysis results, the server will suggest an appropriate training menu to the user in real time. For example, if the heart rate is high and the emotional data indicates "stress," the server will suggest "take a break." On the other hand, if the user's condition is good, the server will suggest "keep working at this pace."

[1633] Input: Analysis results

[1634] Output: Training menu suggestions

[1635] Step 5:

[1636] While the user is working, the smartwatch and smartphone continue to collect data and send it to the server. The server monitors this data in real time and dynamically changes the training menu and suggestions depending on the situation. For example, if the user's heart rate spikes while working, the server will provide advice such as "Take a deep breath and relax."

[1637] Input: Real-time data (physiological data, location data, emotional data)

[1638] Output: Dynamic suggestions and training menu changes

[1639] Step 6:

[1640] Once the task is completed, the server compiles and analyzes all the acquired data and provides feedback to the user. The feedback includes the results of the training and advice for the next time. For example, the server may provide feedback such as, "Today's task performance was excellent. Let's try harder next time."

[1641] Input: All collected data

[1642] Output: Comprehensive feedback

[1643] These steps allow users to monitor their health status in real time and receive appropriate breaks and workload adjustments, which is expected to improve work efficiency and reduce health risks.

[1644] 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.

[1645] 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.

[1646] 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 robot 414.

[1647] 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.

[1648] 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.

[1649] 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.

[1650] 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).

[1651] 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, motorcycles, and other devices, 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.

[1652] 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."

[1653] 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.

[1654] 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).

[1655] 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.

[1656] 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.

[1657] 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.

[1658] 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.

[1659] 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.

[1660] 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.

[1661] 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.

[1662] 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.

[1663] 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.

[1664] 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.

[1665] The following is further disclosed regarding the above embodiment.

[1666] (Claim 1)

[1667] means for acquiring physiological data of a user;

[1668] means for obtaining location data;

[1669] a means for acquiring environmental data;

[1670] A means for analyzing the acquired physiological data, location data, and environmental data to suggest training menus in real time;

[1671] a means for monitoring physiological data and environmental data in real time during a training session performed by a user, and dynamically changing a training menu based on the changed data;

[1672] a means of analyzing the data and providing feedback after training;

[1673] A system including:

[1674] (Claim 2)

[1675] 10. The system of claim 1, wherein the physiological data is acquired as heart rate.

[1676] (Claim 3)

[1677] 10. The system of claim 1, wherein the location data is obtained from GPS data.

[1678] "Example 1"

[1679] (Claim 1)

[1680] means for acquiring physiological data of a user;

[1681] means for obtaining location data;

[1682] a means for acquiring environmental data;

[1683] A means for analyzing the acquired physiological data, position data, and environmental data and generating and proposing a training menu;

[1684] A means for monitoring physiological data and position data in real time during training performed by a user and dynamically changing a training menu according to the situation;

[1685] a means of analyzing the data and providing feedback after training;

[1686] A system including:

[1687] (Claim 2)

[1688] 10. The system of claim 1, wherein the physiological data is acquired as heart rate.

[1689] (Claim 3)

[1690] 10. The system of claim 1, wherein the location data is obtained from global positioning system data.

[1691] "Application Example 1"

[1692] (Claim 1)

[1693] means for acquiring physiological data of a user;

[1694] means for obtaining location data;

[1695] a means for acquiring environmental data;

[1696] A means for analyzing the acquired physiological data, location data, and environmental data to suggest training menus in real time;

[1697] a means for monitoring physiological data and environmental data in real time during a training session performed by a user, and dynamically changing a training menu based on the changed data;

[1698] a means of analyzing the data and providing feedback after training;

[1699] A means to adjust and propose vehicle operation in real time according to the user's condition;

[1700] means for transmitting data to a vehicle control system and determining vehicle operation based thereon;

[1701] means for adjusting the speed of the vehicle and generating an alert when the physiological data of the user indicates an abnormality;

[1702] A system including:

[1703] (Claim 2)

[1704] 10. The system of claim 1, wherein the physiological data is acquired as heart rate.

[1705] (Claim 3)

[1706] 10. The system of claim 1, wherein the location data is obtained from GPS data.

[1707] "Example 2: Combining Emotion Engines"

[1708] (Claim 1)

[1709] means for acquiring physiological data of a user;

[1710] means for obtaining location data;

[1711] a means for acquiring environmental data;

[1712] A means for analyzing and acquiring user emotion data;

[1713] A means for analyzing the acquired physiological data, location data, environmental data, and emotional data to suggest training menus in real time;

[1714] a means for monitoring physiological data, environmental data, and emotional data in real time during a training session performed by a user, and dynamically changing a training menu based on the changed data;

[1715] a means of analyzing the data and providing feedback after training;

[1716] A system including:

[1717] (Claim 2)

[1718] 10. The system of claim 1, wherein the physiological data is acquired as heart rate.

[1719] (Claim 3)

[1720] 10. The system of claim 1, wherein the location data is obtained from GPS data.

[1721] "Application example 2 when combining emotion engines"

[1722] (Claim 1)

[1723] means for acquiring physiological data of a user;

[1724] means for obtaining location data;

[1725] a means for acquiring environmental data;

[1726] A means for acquiring emotion data;

[1727] A means for analyzing the acquired physiological data, location data, environmental data, and emotional data to suggest training menus in real time;

[1728] a means for monitoring physiological data and environmental data in real time during a training session performed by a user, and dynamically changing a training menu based on the changed data;

[1729] a means of analyzing the data and providing feedback after training;

[1730] A means to evaluate employee fatigue levels and suggest appropriate break timing and workload adjustments in real time;

[1731] A system including:

[1732] (Claim 2)

[1733] 10. The system of claim 1, wherein the physiological data is acquired as heart rate.

[1734] (Claim 3)

[1735] 10. The system of claim 1, wherein the location data is obtained from GPS data. [Explanation of symbols]

[1736] 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. means for acquiring physiological data of a user; means for obtaining location data; a means for acquiring environmental data; A means for analyzing the acquired physiological data, location data, and environmental data to suggest training menus in real time; means for monitoring physiological data and environmental data in real time during training performed by a user and dynamically changing a training menu based on the changed data; a means of analyzing the data and providing feedback after training; A system including:

2. The system of claim 1 , wherein the physiological data is acquired as heart rate.

3. 2. The system of claim 1, wherein the location data is GPS data.

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