system

Abstract

Provide a system. 【Solution means】 Means for continuously acquiring biological information using a non-contact sensor, Means for transmitting the biological information acquired via a communication network, Means for analyzing the received biological information and detecting abnormalities, Means for constructing a digital twin using data of similar others, Means for predicting health risks based on the generated digital twin, Means for generating optimal interview questions based on the detected abnormalities, Means for collaborating with medical institutions for information, Means for notifying the user of the analysis results and advice, A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] With the progress of the super-aged society, the needs for the health management of the elderly are increasing. At the same time, due to the concentration of the young population in urban areas, it has become more common for parent households and child households to live far apart. Under this situation, it is necessary to grasp the daily health status of the elderly and reduce the burden on the families living far away regarding caregiving and medical treatment. In addition, in order for the elderly to live independently and with peace of mind, it is required to detect abnormalities quickly and accurately and take appropriate measures. However, the current technology lacks a system that can effectively meet these needs.

Means for Solving the Problems

[0005] This invention provides a system that continuously acquires biometric information using non-contact sensors and securely transmits that information via a communication network. The system analyzes the received information to detect anomalies and constructs a digital twin by referencing similar data from other individuals. Based on the generated digital twin, it predicts health risks and generates an optimal medical questionnaire corresponding to the detected anomalies, notifying the user. Furthermore, by linking information with medical institutions as needed and providing backup for professional diagnoses, it can support the stable lives of the elderly. It also provides health advice tailored to the user's attributes and personality based on the analyzed information, promoting improvement in the user's health. This enables appropriate health management and communication among family members living separately and contributes to the efficient use of medical resources.

[0006] "Biometric information" refers to data about an individual's body that indicates their health status, such as heart rate, respiration, and body temperature.

[0007] A "non-contact sensor" is a sensor technology that acquires biometric information without directly touching the body.

[0008] A "communication network" is the infrastructure between computer systems for transmitting and receiving data.

[0009] A "server" is a centralized computer system used to store and analyze collected data.

[0010] A "digital twin" is a virtual model that mimics the health status of a real-world user, and is generated based on relevant data and algorithms.

[0011] "Health risk" refers to factors or signs that may potentially be harmful to an individual's health.

[0012] "Generative AI" is a system that uses artificial intelligence technology to generate appropriate medical questionnaires and advice based on the user's characteristics.

[0013] A "medical institution" refers to organizations such as hospitals and clinics that provide specialized diagnosis and treatment.

[0014] A "user" is an individual who uses this system to manage their own or others' health status and receive notifications. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the language used in the following description will be explained.

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] In one embodiment of this invention, the user first wears a smartphone or wearable device equipped with a non-contact sensor on a daily basis. This allows the device to continuously acquire biometric information such as heart rate, respiration, and body temperature. This biometric information is transmitted to a server via the internet, protected by security protocols.

[0037] The server stores the received biometric information in a database and analyzes it using an AI model. Through data analysis, it detects signs of anomalies and generates anomaly detection alerts as needed. Furthermore, the server builds a digital twin of the user by referencing similar data from others, enabling highly accurate predictions of health risks.

[0038] Based on the predicted health risks, the server uses AI to automatically generate optimal medical questionnaires and health advice tailored to the user's attributes and personality. This information is sent to the user's device, allowing the user to comprehensively understand their own health status.

[0039] Furthermore, by linking with medical institutions, the server can provide them with information necessary to support specialized diagnoses and treatments, if the user grants permission. This enables doctors to make more accurate diagnoses and provide appropriate medical services.

[0040] As a concrete example, users wear a device before going to sleep, and the device measures their breathing and heart rate during sleep. If the server detects abnormal fluctuations that deviate from the normal sleep pattern, it performs a digital twin analysis and notifies the user with advice generated by AI, such as, "Stress may be involved. Try taking a relaxing bath or meditating." Through this kind of feedback, users can take specific actions to reduce their health risks.

[0041] This invention aims to enable users to manage their own daily health and to monitor the health status of family members living separately in real time, thereby realizing a safer and more comfortable life.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The device continuously collects biometric information using contactless sensors. This information includes heart rate, respiration, and body temperature, and is measured periodically.

[0045] Step 2:

[0046] The device transmits the collected biometric information to the server via the communication network, based on security protocols. Data transfer occurs in real time or at regular intervals.

[0047] Step 3:

[0048] The server stores the received biometric information in a database. This allows for the accumulation and retrieval of data over time.

[0049] Step 4:

[0050] The server uses an AI model to analyze data and detect signs of anomalies. The analysis involves comparing the data with past data and calculating deviations from the normal range.

[0051] Step 5:

[0052] The server references data from other users that is similar to the user's biometric information to construct a digital twin. The digital twin is a model that virtually reproduces the user's health status.

[0053] Step 6:

[0054] The server predicts health risks based on the digital twin. If a risk is identified, it performs a more detailed analysis to determine the appropriate course of action.

[0055] Step 7:

[0056] The server uses AI to generate optimal medical questions and appropriate health advice tailored to the user's attributes and personality. This provides personalized feedback to the user.

[0057] Step 8:

[0058] The server sends generated questions and advice to the terminal. The terminal notifies the user and displays the content in an easy-to-understand format.

[0059] Step 9:

[0060] Users can review the questionnaires and advice received on their devices and take corrective actions as needed. They can also send feedback to the server if they have any inquiries.

[0061] Step 10:

[0062] If the user grants permission, the server will share necessary health information with healthcare institutions to help improve the accuracy of diagnoses. This will allow doctors to have a more accurate understanding of the user's health condition and provide appropriate medical care.

[0063] (Example 1)

[0064] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0065] Traditional health management systems were limited to collecting and analyzing individual biometric data, making it difficult to provide highly accurate health risk predictions or personalized health advice. Furthermore, there was a lack of means to share this information with medical facilities in a timely manner, limiting their effectiveness in situations requiring rapid medical response.

[0066] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0067] In this invention, the server includes means for continuously acquiring biometric data using contactless sensors, means for transferring the acquired biometric data via a communication network, and means for generating analysis results and health advice using a generative AI model. This enables real-time health risk prediction, provision of personalized health advice, and rapid information sharing with appropriate medical facilities.

[0068] "Biometric data" refers to physical information such as an individual's heart rate, respiration, and body temperature.

[0069] A "non-contact sensor" refers to a device that acquires biometric data without physical contact.

[0070] A "communication network" refers to the infrastructure used to send and receive data over long distances, and includes the internet and wireless networks.

[0071] A "generative AI model" refers to an algorithm that analyzes a user's biometric data to generate anomaly detection and health advice.

[0072] A "medical facility" refers to a medical institution or hospital that provides diagnosis and treatment.

[0073] "Digital reproduction" refers to a model of a virtual health state based on the user's biometric data.

[0074] "Health risk" refers to the potential health hazards predicted based on specific biometric data.

[0075] "Notification" refers to the act of presenting important information to a recipient, and is carried out as an email or a device alert.

[0076] In an embodiment of this invention, the user routinely uses an information processing device equipped with a non-contact sensor. This information processing device takes the form of a smartphone or wearable device and has the function of continuously acquiring biometric data such as heart rate, respiration, and body temperature. This data is protected by the information processing device's security protocol and transmitted to a server via a communication network.

[0077] The server receives biometric data via the internet and stores it in a database. It then performs data analysis on the stored data using a generative AI model to detect anomalies. This AI model identifies abnormal patterns in real time by analyzing the data using statistical algorithms and machine learning techniques. Furthermore, the server utilizes advanced data mining techniques to construct digital replicas based on similar data from other individuals, predicting individual health risks.

[0078] Based on predicted health risks, the generating AI model creates health advice tailored to the user's characteristics and personality. For example, if an abnormality is detected in the nighttime sleep pattern, the server generates health advice such as "This may be due to stress. Try deep breathing or meditation," and notifies the user's information processing device. This process can use a prompt message to the user in the format of "Please provide stress-related health advice based on current biometric data."

[0079] Furthermore, with the user's permission, the server collaborates with medical facilities to quickly provide analyzed biometric data. This allows doctors to easily obtain information for accurate diagnoses and provide appropriate medical services. This embodiment of the invention enables users to proactively manage their own health and respond quickly when medical intervention is necessary.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The device uses built-in non-contact sensors to acquire biometric data such as the user's heart rate, respiration, and body temperature in real time. During this process, input from the sensors is continuous, and the acquired data is temporarily stored within the device. Specifically, the application on the device collects data at predetermined time intervals and stores it in a buffer as a new dataset.

[0083] Step 2:

[0084] The terminal transmits temporarily stored biometric data to the server via the communication network, while protecting it with security protocols. This transmission occurs at predetermined time intervals and is performed in an encrypted format to ensure data integrity and confidentiality. The output of this step is a packet of biometric data securely transferred from the terminal to the server.

[0085] Step 3:

[0086] The server receives biometric data transmitted from the terminal and stores it in a database. The data received as input is recorded along with a timestamp. The specific operation aims to accurately manage all data through database writing processes.

[0087] Step 4:

[0088] The server analyzes biometric data stored in the database using a generation AI model. During this analysis, an anomaly detection algorithm is applied based on the input data to detect abnormalities in health status. The output includes notifications indicating anomalies and risk indicators. Specifically, the system includes triggering a notification system when the detected anomaly exceeds a threshold.

[0089] Step 5:

[0090] The server uses anomaly detection results and accumulated data to build a digital reconstruction. The data used includes not only individual user data but also data from other similar users. This process is carried out using machine learning models, and the output generates predictions of health risks. Specifically, it calculates health risk scores and personalized analysis results.

[0091] Step 6:

[0092] The server uses a generative AI model to generate health advice tailored to the user's attributes and personality based on predicted health risks. The input consists of analysis results and the user profile, and the output is personalized advice. Specifically, notifications to the user are constructed based on the generated prompt messages.

[0093] Step 7:

[0094] The server sends generated health advice and anomaly notifications to the device, which then notifies the user. The device's notification system is triggered, and the user receives advice and warnings on the screen. The output is displayed as information to prompt user action.

[0095] Step 8:

[0096] The server shares the analyzed biometric data with the relevant medical facility if the user grants permission. This data sharing takes place via a secure channel and serves as crucial input for diagnostic support. Specifically, the information is provided to the medical facility in real time through a data transmission protocol.

[0097] (Application Example 1)

[0098] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0099] Conventional health monitoring systems are limited to managing health information and lack applications in safety and authentication control. Furthermore, the proposal of appropriate countermeasures linked to anomaly detection is limited, failing to adequately guarantee user safety. Therefore, the challenge is to provide a system that integrates health status and safety authentication control using users' biometric data, enabling rapid and appropriate responses.

[0100] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0101] In this invention, the server includes means for continuously acquiring biometric data using a non-contact detection device, means for transmitting the acquired biometric data via a communication infrastructure, means for analyzing the received biometric data and detecting abnormalities, means for constructing a virtual model using similar information from others, means for predicting health risks based on the generated virtual model, means for generating optimal medical questionnaire items based on the detected abnormalities, means for linking information with medical institutions, means for notifying the user of analysis results and advice, and means for performing authentication control that takes into account health status and safety using biometric data. This enables the integration of user health monitoring and authentication control that takes safety into consideration.

[0102] "Biometric data" refers to physiological information that measures an individual's health status, such as heart rate, respiration, and body temperature.

[0103] A "non-contact detection device" is a sensor device that can acquire information about an object without directly touching it.

[0104] "Communication infrastructure" refers to network infrastructure used to transmit data from one location to another.

[0105] A "virtual model" is a simulation model that reproduces a real-world object using data, and is used for prediction and analysis of that object.

[0106] "Health risk" refers to potential dangers to an individual's health or the likelihood of developing a disease in the future.

[0107] A "medical history questionnaire" is a series of questions presented to assess a person's health status.

[0108] "Medical institutions" refer to providers of medical services such as hospitals, clinics, and medical offices that provide medical examinations and treatments.

[0109] "Authentication control" is the process of verifying identity and managing access so that only authorized users can access the system and information.

[0110] "Analysis results" refer to conclusions and insights obtained by analyzing data.

[0111] "Advice" refers to guidance or recommendations given to an individual based on their circumstances.

[0112] In embodiments of the present invention, it is assumed that the user constantly carries a smartphone or wearable device equipped with a contactless detection device. The device periodically acquires biometric data and transmits it to a server in the cloud using a communication infrastructure. Data protection is provided through security protocols during this process.

[0113] The server uses software such as Python and TENSORFLOW® to analyze received biometric data in real time. If an anomaly is detected through the analysis, it utilizes a generated AI model to select the most appropriate countermeasure for each individual user. This selection process includes predicting health risks by comparing them with existing virtual models.

[0114] Users can receive analysis results and advice through a smartphone application. The application displays notifications based on their health status and provides specific guidance and advice tailored to their individual circumstances. When this operation is performed, the system scrutinizes the access location and health data, and implements authentication controls with security in mind.

[0115] As a concrete example, when a user authenticates via smartphone at the office entrance, the system measures their heart rate and body temperature, and issues a warning if any unusual patterns are detected. This allows for a quick response to abnormal situations in the workplace.

[0116] An example of a prompt message could be, "Provide appropriate health advice to users whose body temperature is higher than normal." Using this prompt message, the AI ​​model generates advice immediately and notifies the user's smartphone. This allows users to proactively manage their own health.

[0117] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0118] Step 1:

[0119] The device periodically acquires biometric data such as the user's heart rate, respiration, and body temperature using built-in non-contact sensors. This acquired data is formatted and prepared for transmission to a server via a communication infrastructure. The input is the user's biological state, and the output is formatted biometric data.

[0120] Step 2:

[0121] The device encrypts the acquired biometric data using a security protocol and securely transmits it to the server. Data encryption is crucial for maintaining data confidentiality. The input is formatted biometric data, and the output is encrypted biometric data.

[0122] Step 3:

[0123] The server receives encrypted biometric data, decrypts it, and then performs data analysis using Python and TensorFlow. This analysis allows for the detection of anomalies in the data. The input is encrypted biometric data, and the output is the analysis results.

[0124] Step 4:

[0125] Based on the analysis results, the server uses a generated AI model to assess health risks and determine the optimal countermeasures for each individual user. The risk assessment involves cross-referencing with information from a virtual model. The input is the analysis results, and the output is the health risk assessment and countermeasures.

[0126] Step 5:

[0127] The server generates a prompt message based on the generated countermeasures and inputs it into the AI ​​model to create personalized advice for each user. For example, the prompt message might be "Provide appropriate health advice to users whose body temperature is higher than normal." The input consists of a health risk assessment and a prompt message, while the output is specific advice for the user.

[0128] Step 6:

[0129] The user receives advice sent from the server through an application on their device. The application displays notifications and detailed advice based on the user's status and prepares to provide information to healthcare providers if necessary. The input is the advice from the server, and the output is the notifications and detailed information received by the user.

[0130] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0131] In an embodiment of this invention, the user uses a smartphone or wearable device equipped with a non-contact sensor. The device continuously collects biometric information such as heart rate, respiration, and body temperature. Furthermore, an emotion recognition engine installed in the device analyzes the user's voice patterns and facial expressions to acquire emotion data. This biometric information and emotion data are transmitted to a server via a securely protected communication network.

[0132] The server stores received biometric and emotional data in a database and analyzes it using AI algorithms. Through data analysis, the server detects signs of abnormalities and emotional fluctuations. Furthermore, it constructs a digital twin by referencing similar data from other users, virtually recreating the user's health state.

[0133] Emotional data acquired by the emotion recognition engine is incorporated into a digital twin. The server utilizes this digital twin to predict health risks and generate health advice tailored to the user's emotional state. This advice is personalized and optimized for the user's psychological state and attributes.

[0134] As a concrete example, based on data collected when a user is experiencing stress, the server generates specific advice such as, "Your stress level is high; we recommend trying deep breathing or simple meditation." Furthermore, if emotional data differs from the norm, the server may identify a higher health risk and send a preventative notification.

[0135] These pieces of advice and notifications are delivered to the user's device, allowing the user to review the feedback and take action as needed. If the user consents, the server also collaborates with healthcare institutions to provide this biometric and emotional data to medical professionals. This enables professionals to gain a more comprehensive understanding of the user's health status and improve diagnostic accuracy.

[0136] This invention aims to help users comprehensively understand their own health status and take appropriate action, taking into account their emotional state. It also enables families living separately to confidently monitor each other's health status and promotes safe and independent living for the elderly.

[0137] The following describes the processing flow.

[0138] Step 1:

[0139] The device uses contactless sensors and an emotion recognition engine to continuously acquire biometric information such as the user's heart rate, respiration, and body temperature, as well as emotional data from voice and facial expressions.

[0140] Step 2:

[0141] The device transmits collected biometric and emotional data to the server in real time using a secure communication protocol.

[0142] Step 3:

[0143] The server stores the transmitted biometric and emotional data in a database and performs preprocessing to ensure data quality.

[0144] Step 4:

[0145] The server uses an AI algorithm to analyze the received data. This analysis detects abnormalities in the user's biometric information and fluctuations in their emotions.

[0146] Step 5:

[0147] Based on the data analysis results, the server constructs a digital twin of the user by referencing similar data from other users. The digital twin is a model that virtually reproduces the user's health and emotional state.

[0148] Step 6:

[0149] The server uses the constructed digital twin to predict health risks and generates risk-based warnings and countermeasures, taking emotional data into consideration.

[0150] Step 7:

[0151] The server uses a generation AI to create customized medical questionnaires and health advice tailored to the user's attributes and emotional state.

[0152] Step 8:

[0153] The server sends the generated questions and advice to the user's terminal. The terminal notifies the user of the received information and displays it.

[0154] Step 9:

[0155] Users check notifications received from their devices and decide on actions based on health advice. They also send feedback back to the server via their devices as needed.

[0156] Step 10:

[0157] With the user's permission, the server shares analyzed biometric and emotional data with healthcare institutions. This allows healthcare institutions to improve the accuracy of diagnoses and treatments based on more comprehensive information.

[0158] (Example 2)

[0159] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0160] In modern society, daily stress and health changes have various impacts on individuals' lives. However, there are limited systems that can monitor individual health conditions in real time and provide appropriate advice by predicting emotional changes and health risks. Conventional health management requires consideration of not only biological signals but also emotional signals, and there is a need for technology to efficiently handle this.

[0161] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0162] In this invention, the server includes means for continuously acquiring biosignals using sensors, means for transmitting the acquired biosignals and emotional signals via a communication line, and means for providing health recommendations tailored to the user's psychological characteristics based on the analyzed biosignals and emotional signals. This makes it possible to provide more personalized health recommendations based on the user's health and emotional state.

[0163] "Biosignals" refer to physical or chemical data obtained from the body of an organism, such as heart rate, respiration, and body temperature.

[0164] A "sensor" refers to a device that detects biological signals or emotional signals and converts them into electrical signals.

[0165] "Communication lines" refer to infrastructure for sending and receiving electronic data, including the internet and dedicated lines.

[0166] "Emotional signals" refer to data that indicates an individual's psychological state, analyzed from factors such as voice patterns and facial expressions.

[0167] An "abnormality" refers to a situation where a state or pattern different from the normal one is detected in biological or emotional signals.

[0168] A "digital model" refers to a model that virtually reproduces an individual's health and emotional state, built based on information from similar individuals.

[0169] A "health crisis" refers to a potential health risk predicted from an individual's biosignals and emotional signals.

[0170] "Health recommendations" refer to advice for improving and maintaining health, provided based on an individual's bio-signals and emotional signals.

[0171] "Specialized institutions" refer to medical institutions and organizations that provide healthcare services.

[0172] "Evaluation accuracy" refers to a measure that indicates the accuracy and reliability of the data analyzed by the server.

[0173] This system collects biometric and emotional signals in real time using smartphones or wearable devices equipped with contactless sensors. Specifically, the device continuously acquires biometric signals such as heart rate, respiration, and body temperature, and analyzes emotional signals from voice patterns and facial expressions. This utilizes software called an emotion recognition engine, which enables the rapid generation of emotional data.

[0174] The device uses the internet as its communication line to securely transmit this data to the server. The communication uses encryption protocols (e.g., HTTPS) to ensure security. The server stores the received biometric and emotional signals in a database and then analyzes the data using AI algorithms. This analysis detects abnormalities in health status and emotional fluctuations.

[0175] The server builds a digital model of the user by referencing similar data from other users. This digital model allows for simulations of the user's health in a virtual environment, enabling the prediction of health crises. A generative AI model is used for this analysis, and an example of a prompt is, "Based on your current emotional data, please suggest the most suitable relaxation method."

[0176] Ultimately, the server generates health recommendations and sends them to the user's device. This allows the user to obtain a concrete action plan to reduce stress and discomfort. With the user's permission, this data can also be shared with professional organizations to contribute to improving the accuracy of diagnosis and treatment. These features aim to enable users to more effectively manage their health and emotional state, thereby increasing their well-being in daily life.

[0177] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0178] Step 1:

[0179] The device continuously acquires biosignals (heart rate, respiration, body temperature, etc.) using contactless sensors. The input to this process is raw data obtained from the user's body, and the output is digital signals acquired by the sensor device. These digital signals are temporarily stored within the device.

[0180] Step 2:

[0181] The emotion recognition engine installed in the device analyzes the user's voice patterns and facial expressions to acquire emotion signals. The input is the user's voice data and image data, and the output is emotion data analyzed based on this data. This emotion data is temporarily stored on the device and prepared to be sent to the server later.

[0182] Step 3:

[0183] The device transmits collected biometric signals and emotional data to a server via a communication line (e.g., the internet). Input is digital data stored on the device, and output is sent to the server as securely encrypted data. HTTPS protocol and similar protocols are used for communication to ensure data security.

[0184] Step 4:

[0185] The server stores the biometric and emotional signals it receives in a database. The input is encrypted data sent from the terminal, and the output is the data stored in the database for analysis. This data is recorded along with a timestamp.

[0186] Step 5:

[0187] The server uses a generated AI model to analyze biometric and emotional signals stored in a database. The input is user data stored in the database, and the output is a report that identifies anomalies and emotional changes detected through the analysis. This report is used to assess the user's health status.

[0188] Step 6:

[0189] The server builds a digital model of the user using similar data from other users. The input is user data and similar data, and the output is a digital model that virtually reproduces the user's health status. This step makes it easier to predict the user's health crises.

[0190] Step 7:

[0191] The server generates user health recommendations based on a digital model and analysis results. Inputs are the digital model and anomaly reports, while outputs are specific action plans and advice tailored to the user's condition. For example, it might generate advice such as, "Based on your current emotional data, we suggest the most suitable relaxation method."

[0192] Step 8:

[0193] The server generates health recommendations and sends them to the user's device, notifying them of their health. The input is the generated advice, and the output is the advice message displayed on the user's device. The user can receive this and use it to improve their own health.

[0194] (Application Example 2)

[0195] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0196] In modern society, it is crucial to continuously monitor individuals' health and take appropriate action as needed. However, conventional technologies have not adequately achieved systems that can detect real-time emotional fluctuations and stress levels and provide immediate, appropriate notifications to individual users. As a result, it has been difficult for users to receive appropriate advice when faced with potential dangers or stressful situations.

[0197] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0198] In this invention, the server includes means for securely generating personalized notifications and sending warnings to the user, means for notifying the user of analysis results and advice, and means for continuously acquiring biometric information using non-contact sensors. This enables the user to respond immediately to changes in their emotional state and stress levels and receive appropriate advice.

[0199] "Biometric information" refers to data that indicates a person's physical state, such as heart rate, respiration, and body temperature.

[0200] A "non-contact sensor" is a sensor device that acquires information about an object without requiring direct contact.

[0201] A "communication network" is infrastructure for transmitting information, and includes the internet and other data communication networks.

[0202] A "digital twin" is a digital representation that mimics real-world objects and processes, allowing for analysis and simulation in a virtual space.

[0203] "Health risk" refers to factors or circumstances that may worsen an individual's health condition.

[0204] A "notification" is a message sent to inform a user of specific information or an alert.

[0205] "Analysis results" refer to the conclusions and insights obtained after analyzing data.

[0206] "Advice" refers to providing users with guidelines or suggestions for action, particularly regarding health and mental health.

[0207] A "medical institution" is a facility or organization that provides medical services, including hospitals and clinics.

[0208] To implement this invention, first, a smartphone or wearable device is used as the terminal. The terminal is equipped with a non-contact sensor to acquire biometric information such as heart rate, respiration, and body temperature in real time. It also has an emotion recognition engine that analyzes voice patterns and facial expressions, thereby acquiring emotion data.

[0209] The device securely transmits acquired biometric and emotional data to a server via a communication network. The server receives this data and performs detailed data analysis using AI algorithms. This analysis detects signs of abnormalities and emotional fluctuations. Furthermore, the server references data from similar individuals to construct a virtual model called a digital twin. Based on this digital twin, it predicts health risks and generates health advice tailored to the user's emotional state.

[0210] Specifically, when the server detects an anomaly, it sends a personalized notification to the user's device. For example, if a user is experiencing high stress levels, it might send advice such as, "Your stress levels are high; we recommend trying deep breathing or simple meditation." This allows the user to know how to cope immediately.

[0211] This system incorporates machine learning models using Python and TensorFlow, supporting efficient and accurate data analysis.

[0212] For example, if a user feels stressed while traveling on public transport, the system can sense this change in emotion and send a notification saying, "To relax, we recommend taking deep breaths and listening to your favorite music." An example of an input prompt to the generating AI model would be, "I've experienced significant emotional fluctuations today; please suggest ways to relax. Current location: office, heart rate: 80, breathing: slightly fast, emotional data: stressed."

[0213] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0214] Step 1:

[0215] The device acquires biometric information such as heart rate, respiration, and body temperature through non-contact sensors. At this stage, the input is the user's physical state, and the output is numerical data (e.g., heart rate: 72, body temperature: 36.5°C). The device records this biometric information in preparation for the next step.

[0216] Step 2:

[0217] The device analyzes voice patterns and facial expressions to acquire emotion data. Here, the input is the user's voice and facial image, and the output is data on emotional state (e.g., tension: high, happiness: medium). The device is equipped with an emotion recognition engine, which quantifies specific emotions by performing voice and image analysis.

[0218] Step 3:

[0219] The device collects biometric and emotional data and transmits it to a server via a secure communication network. In this process, the input is the dataset collected by the device, and the output is the secure arrival of the data at the server. The device uses encryption protocols to prevent data leakage.

[0220] Step 4:

[0221] The server analyzes the received data using an AI algorithm. The input is biometric information and emotional data, and the output is the analysis results (e.g., detection of anomalies in heart rate and stress levels). Based on this data, the server operates a machine learning model using Python and TensorFlow to identify anomalous patterns.

[0222] Step 5:

[0223] The server constructs a digital twin by referencing similar data from other users. The inputs here are user data and other users' datasets, and the output is a virtual health model of the user. This process involves using a database to compare vast amounts of historical data with user data to generate the virtual model.

[0224] Step 6:

[0225] The server predicts health risks based on the analysis results and generates personalized health advice. Inputs are a digital twin and analysis results, and output is an appropriate advice message for the user (e.g., suggestions for relaxation techniques). A generative AI model is used to provide user-specific prompts.

[0226] Step 7:

[0227] The server generates advice and notifies the terminal. Here, the input is the generated advice, and the output is the notification displayed on the user's terminal. The terminal is designed to utilize the notification function to allow the user to receive immediate feedback.

[0228] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0229] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0230] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0231] [Second Embodiment]

[0232] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0233] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0234] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0235] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0236] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0237] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0238] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0239] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0240] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0241] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0242] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0243] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0244] In one embodiment of this invention, the user first wears a smartphone or wearable device equipped with a non-contact sensor on a daily basis. This allows the device to continuously acquire biometric information such as heart rate, respiration, and body temperature. This biometric information is transmitted to a server via the internet, protected by security protocols.

[0245] The server stores the received biometric information in a database and analyzes it using an AI model. Through data analysis, it detects signs of anomalies and generates anomaly detection alerts as needed. Furthermore, the server builds a digital twin of the user by referencing similar data from others, enabling highly accurate predictions of health risks.

[0246] Based on the predicted health risks, the server uses AI to automatically generate optimal medical questionnaires and health advice tailored to the user's attributes and personality. This information is sent to the user's device, allowing the user to comprehensively understand their own health status.

[0247] Furthermore, by linking with medical institutions, the server can provide them with information necessary to support specialized diagnoses and treatments, if the user grants permission. This enables doctors to make more accurate diagnoses and provide appropriate medical services.

[0248] As a concrete example, users wear a device before going to sleep, and the device measures their breathing and heart rate during sleep. If the server detects abnormal fluctuations that deviate from the normal sleep pattern, it performs a digital twin analysis and notifies the user with advice generated by AI, such as, "Stress may be involved. Try taking a relaxing bath or meditating." Through this kind of feedback, users can take specific actions to reduce their health risks.

[0249] This invention aims to enable users to manage their own daily health and to monitor the health status of family members living separately in real time, thereby realizing a safer and more comfortable life.

[0250] The following describes the processing flow.

[0251] Step 1:

[0252] The device continuously collects biometric information using contactless sensors. This information includes heart rate, respiration, and body temperature, and is measured periodically.

[0253] Step 2:

[0254] The device transmits the collected biometric information to the server via the communication network, based on security protocols. Data transfer occurs in real time or at regular intervals.

[0255] Step 3:

[0256] The server stores the received biometric information in a database. This allows for the accumulation and retrieval of data over time.

[0257] Step 4:

[0258] The server uses an AI model to analyze data and detect signs of anomalies. The analysis involves comparing the data with past data and calculating deviations from the normal range.

[0259] Step 5:

[0260] The server references data from other users that is similar to the user's biometric information to construct a digital twin. The digital twin is a model that virtually reproduces the user's health status.

[0261] Step 6:

[0262] The server predicts health risks based on the digital twin. If a risk is identified, it performs a more detailed analysis to determine the appropriate course of action.

[0263] Step 7:

[0264] The server uses AI to generate optimal medical questions and appropriate health advice tailored to the user's attributes and personality. This provides personalized feedback to the user.

[0265] Step 8:

[0266] The server sends generated questions and advice to the terminal. The terminal notifies the user and displays the content in an easy-to-understand format.

[0267] Step 9:

[0268] Users can review the questionnaires and advice received on their devices and take corrective actions as needed. They can also send feedback to the server if they have any inquiries.

[0269] Step 10:

[0270] If the user grants permission, the server will share necessary health information with healthcare institutions to help improve the accuracy of diagnoses. This will allow doctors to have a more accurate understanding of the user's health condition and provide appropriate medical care.

[0271] (Example 1)

[0272] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0273] Traditional health management systems were limited to collecting and analyzing individual biometric data, making it difficult to provide highly accurate health risk predictions or personalized health advice. Furthermore, there was a lack of means to share this information with medical facilities in a timely manner, limiting their effectiveness in situations requiring rapid medical response.

[0274] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0275] In this invention, the server includes means for continuously acquiring biometric data using contactless sensors, means for transferring the acquired biometric data via a communication network, and means for generating analysis results and health advice using a generative AI model. This enables real-time health risk prediction, provision of personalized health advice, and rapid information sharing with appropriate medical facilities.

[0276] "Biometric data" refers to physical information such as an individual's heart rate, respiration, and body temperature.

[0277] A "non-contact sensor" refers to a device that acquires biometric data without physical contact.

[0278] A "communication network" refers to the infrastructure used to send and receive data over long distances, and includes the internet and wireless networks.

[0279] A "generative AI model" refers to an algorithm that analyzes a user's biometric data to generate anomaly detection and health advice.

[0280] A "medical facility" refers to a medical institution or hospital that provides diagnosis and treatment.

[0281] "Digital reproduction" refers to a model of a virtual health state based on the user's biometric data.

[0282] "Health risk" refers to the potential health hazards predicted based on specific biometric data.

[0283] "Notification" refers to the act of presenting important information to a recipient, and is carried out as an email or a device alert.

[0284] As a form for implementing this invention, a user routinely uses an information processing device equipped with a contactless sensor. This information processing device takes the form of a smartphone or a wearable device and has a function of continuously acquiring biological data such as heart rate, respiration, body temperature, etc. These data are protected by the security protocol of the information processing device and are transmitted to a server via a communication network.

[0285] The server receives biological data via the Internet and stores it in a database. For the stored data, data analysis is performed using a generative AI model to detect anomalies. This AI model analyzes using statistical algorithms and machine learning techniques to identify abnormal patterns in real time. Also, the server utilizes advanced data mining techniques to construct a digital reproduction based on the data of other similar individuals and predict individual health risks.

[0286] Based on the predicted health risks, the generative AI model creates health advice according to the user's characteristics and personality. As a specific example, when an abnormality is found in the nighttime sleep pattern, the server generates a health advice such as "The influence of stress is considered. Please try deep breathing and meditation" and notifies the user's information processing device. In this process, a format such as "Please provide health advice related to stress based on the current biological data" can be used as a prompt sentence to the user.

[0287] Furthermore, with the user's permission, the server collaborates with medical facilities and promptly provides the analyzed biological data. By this means, doctors can easily obtain information for making an accurate diagnosis and can provide appropriate medical services. According to the form of this invention, users can actively manage their own health conditions and can promptly respond even in situations where medical intervention is required.

[0288] The flow of the specific process in Example 1 will be described using FIG. 11.

[0289] Step 1:

[0290] The device uses built-in non-contact sensors to acquire biometric data such as the user's heart rate, respiration, and body temperature in real time. During this process, input from the sensors is continuous, and the acquired data is temporarily stored within the device. Specifically, the application on the device collects data at predetermined time intervals and stores it in a buffer as a new dataset.

[0291] Step 2:

[0292] The terminal transmits temporarily stored biometric data to the server via the communication network, while protecting it with security protocols. This transmission occurs at predetermined time intervals and is performed in an encrypted format to ensure data integrity and confidentiality. The output of this step is a packet of biometric data securely transferred from the terminal to the server.

[0293] Step 3:

[0294] The server receives biometric data transmitted from the terminal and stores it in a database. The data received as input is recorded along with a timestamp. The specific operation aims to accurately manage all data through database writing processes.

[0295] Step 4:

[0296] The server analyzes biometric data stored in the database using a generation AI model. During this analysis, an anomaly detection algorithm is applied based on the input data to detect abnormalities in health status. The output includes notifications indicating anomalies and risk indicators. Specifically, the system includes triggering a notification system when the detected anomaly exceeds a threshold.

[0297] Step 5:

[0298] The server uses anomaly detection results and accumulated data to build a digital reconstruction. The data used includes not only individual user data but also data from other similar users. This process is carried out using machine learning models, and the output generates predictions of health risks. Specifically, it calculates health risk scores and personalized analysis results.

[0299] Step 6:

[0300] The server uses a generative AI model to generate health advice tailored to the user's attributes and personality based on predicted health risks. The input consists of analysis results and the user profile, and the output is personalized advice. Specifically, notifications to the user are constructed based on the generated prompt messages.

[0301] Step 7:

[0302] The server sends generated health advice and anomaly notifications to the device, which then notifies the user. The device's notification system is triggered, and the user receives advice and warnings on the screen. The output is displayed as information to prompt user action.

[0303] Step 8:

[0304] The server shares the analyzed biometric data with the relevant medical facility if the user grants permission. This data sharing takes place via a secure channel and serves as crucial input for diagnostic support. Specifically, the information is provided to the medical facility in real time through a data transmission protocol.

[0305] (Application Example 1)

[0306] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0307] Conventional systems for monitoring health status are merely for managing health information and lack applications in safety and authentication control. Also, due to limited proposals for appropriate countermeasures in cooperation with anomaly detection, the safety of users cannot be fully ensured. Therefore, it is an issue to provide a system that integrally implements authentication control in terms of health status and safety using users' biometric data and responds promptly and appropriately.

[0308] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0309] In this invention, the server includes means for continuously acquiring biometric data using a non-contact detection device, means for transmitting the biometric data acquired via a communication infrastructure, means for analyzing the received biometric data to detect anomalies, means for constructing a virtual model using information of similar others, means for predicting health risks based on the generated virtual model, means for generating optimal inquiry items based on the detected anomalies, means for collaborating with medical institutions for information, means for notifying users of analysis results and advice, and means for performing authentication control considering health status and safety using biometric data. Thereby, it becomes possible to integrate health monitoring of users and authentication control considering safety.

[0310] "Biometric data" refers to physiological information such as heart rate, respiration, body temperature, etc., which measures an individual's health status.

[0311] "Non-contact detection device" is a sensor device that can acquire information of an object without direct contact.

[0312] "Communication infrastructure" refers to a network infrastructure for transmitting data from one location to another.

[0313] "Virtual model" is a simulation model that reproduces an actual object on data and is used for prediction and analysis of the object.

[0314] "Health risk" refers to potential dangers to an individual's health or the likelihood of developing a disease in the future.

[0315] A "medical history questionnaire" is a series of questions presented to assess a person's health status.

[0316] "Medical institutions" refer to providers of medical services such as hospitals, clinics, and medical offices that provide medical examinations and treatments.

[0317] "Authentication control" is the process of verifying identity and managing access so that only authorized users can access the system and information.

[0318] "Analysis results" refer to conclusions and insights obtained by analyzing data.

[0319] "Advice" refers to guidance or recommendations given to an individual based on their circumstances.

[0320] In embodiments of the present invention, it is assumed that the user constantly carries a smartphone or wearable device equipped with a contactless detection device. The device periodically acquires biometric data and transmits it to a server in the cloud using a communication infrastructure. Data protection is provided through security protocols during this process.

[0321] The server uses software such as Python and TensorFlow to analyze received biometric data in real time. If an anomaly is detected through the analysis, it utilizes a generative AI model to select the most appropriate countermeasure for each individual user. This selection process includes predicting health risks by comparing them with existing virtual models.

[0322] Users can receive analysis results and advice through a smartphone application. The application displays notifications based on their health status and provides specific guidance and advice tailored to their individual circumstances. When this operation is performed, the system scrutinizes the access location and health data, and implements authentication controls with security in mind.

[0323] As a concrete example, when a user authenticates via smartphone at the office entrance, the system measures their heart rate and body temperature, and issues a warning if any unusual patterns are detected. This allows for a quick response to abnormal situations in the workplace.

[0324] An example of a prompt message could be, "Provide appropriate health advice to users whose body temperature is higher than normal." Using this prompt message, the AI ​​model generates advice immediately and notifies the user's smartphone. This allows users to proactively manage their own health.

[0325] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0326] Step 1:

[0327] The device periodically acquires biometric data such as the user's heart rate, respiration, and body temperature using built-in non-contact sensors. This acquired data is formatted and prepared for transmission to a server via a communication infrastructure. The input is the user's biological state, and the output is formatted biometric data.

[0328] Step 2:

[0329] The device encrypts the acquired biometric data using a security protocol and securely transmits it to the server. Data encryption is crucial for maintaining data confidentiality. The input is formatted biometric data, and the output is encrypted biometric data.

[0330] Step 3:

[0331] The server receives encrypted biometric data, decrypts it, and then performs data analysis using Python and TensorFlow. This analysis allows for the detection of anomalies in the data. The input is encrypted biometric data, and the output is the analysis results.

[0332] Step 4:

[0333] Based on the analysis results, the server uses a generated AI model to assess health risks and determine the optimal countermeasures for each individual user. The risk assessment involves cross-referencing with information from a virtual model. The input is the analysis results, and the output is the health risk assessment and countermeasures.

[0334] Step 5:

[0335] The server generates a prompt message based on the generated countermeasures and inputs it into the AI ​​model to create personalized advice for each user. For example, the prompt message might be "Provide appropriate health advice to users whose body temperature is higher than normal." The input consists of a health risk assessment and a prompt message, while the output is specific advice for the user.

[0336] Step 6:

[0337] The user receives advice sent from the server through an application on their device. The application displays notifications and detailed advice based on the user's status and prepares to provide information to healthcare providers if necessary. The input is the advice from the server, and the output is the notifications and detailed information received by the user.

[0338] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0339] In an embodiment of this invention, the user uses a smartphone or wearable device equipped with a non-contact sensor. The device continuously collects biometric information such as heart rate, respiration, and body temperature. Furthermore, an emotion recognition engine installed in the device analyzes the user's voice patterns and facial expressions to acquire emotion data. This biometric information and emotion data are transmitted to a server via a securely protected communication network.

[0340] The server stores received biometric and emotional data in a database and analyzes it using AI algorithms. Through data analysis, the server detects signs of abnormalities and emotional fluctuations. Furthermore, it constructs a digital twin by referencing similar data from other users, virtually recreating the user's health state.

[0341] Emotional data acquired by the emotion recognition engine is incorporated into a digital twin. The server utilizes this digital twin to predict health risks and generate health advice tailored to the user's emotional state. This advice is personalized and optimized for the user's psychological state and attributes.

[0342] As a concrete example, based on data collected when a user is experiencing stress, the server generates specific advice such as, "Your stress level is high; we recommend trying deep breathing or simple meditation." Furthermore, if emotional data differs from the norm, the server may identify a higher health risk and send a preventative notification.

[0343] These pieces of advice and notifications are delivered to the user's device, allowing the user to review the feedback and take action as needed. If the user consents, the server also collaborates with healthcare institutions to provide this biometric and emotional data to medical professionals. This enables professionals to gain a more comprehensive understanding of the user's health status and improve diagnostic accuracy.

[0344] This invention aims to help users comprehensively understand their own health status and take appropriate action, taking into account their emotional state. It also enables families living separately to confidently monitor each other's health status and promotes safe and independent living for the elderly.

[0345] The following describes the processing flow.

[0346] Step 1:

[0347] The device uses contactless sensors and an emotion recognition engine to continuously acquire biometric information such as the user's heart rate, respiration, and body temperature, as well as emotional data from voice and facial expressions.

[0348] Step 2:

[0349] The device transmits collected biometric and emotional data to the server in real time using a secure communication protocol.

[0350] Step 3:

[0351] The server stores the transmitted biometric and emotional data in a database and performs preprocessing to ensure data quality.

[0352] Step 4:

[0353] The server uses an AI algorithm to analyze the received data. This analysis detects abnormalities in the user's biometric information and fluctuations in their emotions.

[0354] Step 5:

[0355] Based on the data analysis results, the server constructs a digital twin of the user by referencing similar data from other users. The digital twin is a model that virtually reproduces the user's health and emotional state.

[0356] Step 6:

[0357] The server uses the constructed digital twin to predict health risks and generates risk-based warnings and countermeasures, taking emotional data into consideration.

[0358] Step 7:

[0359] The server uses a generation AI to create customized medical questionnaires and health advice tailored to the user's attributes and emotional state.

[0360] Step 8:

[0361] The server sends the generated questions and advice to the user's terminal. The terminal notifies the user of the received information and displays it.

[0362] Step 9:

[0363] Users check notifications received from their devices and decide on actions based on health advice. They also send feedback back to the server via their devices as needed.

[0364] Step 10:

[0365] With the user's permission, the server shares analyzed biometric and emotional data with healthcare institutions. This allows healthcare institutions to improve the accuracy of diagnoses and treatments based on more comprehensive information.

[0366] (Example 2)

[0367] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0368] In modern society, daily stress and health changes have various impacts on individuals' lives. However, there are limited systems that can monitor individual health conditions in real time and provide appropriate advice by predicting emotional changes and health risks. Conventional health management requires consideration of not only biological signals but also emotional signals, and there is a need for technology to efficiently handle this.

[0369] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0370] In this invention, the server includes means for continuously acquiring biosignals using sensors, means for transmitting the acquired biosignals and emotional signals via a communication line, and means for providing health recommendations tailored to the user's psychological characteristics based on the analyzed biosignals and emotional signals. This makes it possible to provide more personalized health recommendations based on the user's health and emotional state.

[0371] "Biosignals" refer to physical or chemical data obtained from the body of an organism, such as heart rate, respiration, and body temperature.

[0372] A "sensor" refers to a device that detects biological signals or emotional signals and converts them into electrical signals.

[0373] "Communication lines" refer to infrastructure for sending and receiving electronic data, including the internet and dedicated lines.

[0374] "Emotional signals" refer to data that indicates an individual's psychological state, analyzed from factors such as voice patterns and facial expressions.

[0375] An "abnormality" refers to a situation where a state or pattern different from the normal one is detected in biological or emotional signals.

[0376] A "digital model" refers to a model that virtually reproduces an individual's health and emotional state, built based on information from similar individuals.

[0377] A "health crisis" refers to a potential health risk predicted from an individual's biosignals and emotional signals.

[0378] "Health recommendations" refer to advice for improving and maintaining health, provided based on an individual's bio-signals and emotional signals.

[0379] "Specialized institutions" refer to medical institutions and organizations that provide healthcare services.

[0380] "Evaluation accuracy" refers to a measure that indicates the accuracy and reliability of the data analyzed by the server.

[0381] This system collects biometric and emotional signals in real time using smartphones or wearable devices equipped with contactless sensors. Specifically, the device continuously acquires biometric signals such as heart rate, respiration, and body temperature, and analyzes emotional signals from voice patterns and facial expressions. This utilizes software called an emotion recognition engine, which enables the rapid generation of emotional data.

[0382] The device uses the internet as its communication line to securely transmit this data to the server. The communication uses encryption protocols (e.g., HTTPS) to ensure security. The server stores the received biometric and emotional signals in a database and then analyzes the data using AI algorithms. This analysis detects abnormalities in health status and emotional fluctuations.

[0383] The server builds a digital model of the user by referencing similar data from other users. This digital model allows for simulations of the user's health in a virtual environment, enabling the prediction of health crises. A generative AI model is used for this analysis, and an example of a prompt is, "Based on your current emotional data, please suggest the most suitable relaxation method."

[0384] Ultimately, the server generates health recommendations and sends them to the user's device. This allows the user to obtain a concrete action plan to reduce stress and discomfort. With the user's permission, this data can also be shared with professional organizations to contribute to improving the accuracy of diagnosis and treatment. These features aim to enable users to more effectively manage their health and emotional state, thereby increasing their well-being in daily life.

[0385] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0386] Step 1:

[0387] The device continuously acquires biosignals (heart rate, respiration, body temperature, etc.) using contactless sensors. The input to this process is raw data obtained from the user's body, and the output is digital signals acquired by the sensor device. These digital signals are temporarily stored within the device.

[0388] Step 2:

[0389] The emotion recognition engine installed in the device analyzes the user's voice patterns and facial expressions to acquire emotion signals. The input is the user's voice data and image data, and the output is emotion data analyzed based on this data. This emotion data is temporarily stored on the device and prepared to be sent to the server later.

[0390] Step 3:

[0391] The device transmits collected biometric signals and emotional data to a server via a communication line (e.g., the internet). Input is digital data stored on the device, and output is sent to the server as securely encrypted data. HTTPS protocol and similar protocols are used for communication to ensure data security.

[0392] Step 4:

[0393] The server stores the biometric and emotional signals it receives in a database. The input is encrypted data sent from the terminal, and the output is the data stored in the database for analysis. This data is recorded along with a timestamp.

[0394] Step 5:

[0395] The server uses a generated AI model to analyze biometric and emotional signals stored in a database. The input is user data stored in the database, and the output is a report that identifies anomalies and emotional changes detected through the analysis. This report is used to assess the user's health status.

[0396] Step 6:

[0397] The server builds a digital model of the user using similar data from other users. The input is user data and similar data, and the output is a digital model that virtually reproduces the user's health status. This step makes it easier to predict the user's health crises.

[0398] Step 7:

[0399] The server generates user health recommendations based on a digital model and analysis results. Inputs are the digital model and anomaly reports, while outputs are specific action plans and advice tailored to the user's condition. For example, it might generate advice such as, "Based on your current emotional data, we suggest the most suitable relaxation method."

[0400] Step 8:

[0401] The server generates health recommendations and sends them to the user's device, notifying them of their health. The input is the generated advice, and the output is the advice message displayed on the user's device. The user can receive this and use it to improve their own health.

[0402] (Application Example 2)

[0403] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0404] In modern society, it is crucial to continuously monitor individuals' health and take appropriate action as needed. However, conventional technologies have not adequately achieved systems that can detect real-time emotional fluctuations and stress levels and provide immediate, appropriate notifications to individual users. As a result, it has been difficult for users to receive appropriate advice when faced with potential dangers or stressful situations.

[0405] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0406] In this invention, the server includes means for securely generating personalized notifications and sending warnings to the user, means for notifying the user of analysis results and advice, and means for continuously acquiring biometric information using non-contact sensors. This enables the user to respond immediately to changes in their emotional state and stress levels and receive appropriate advice.

[0407] "Biometric information" refers to data that indicates a person's physical state, such as heart rate, respiration, and body temperature.

[0408] A "non-contact sensor" is a sensor device that acquires information about an object without requiring direct contact.

[0409] A "communication network" is infrastructure for transmitting information, and includes the internet and other data communication networks.

[0410] A "digital twin" is a digital representation that mimics real-world objects and processes, allowing for analysis and simulation in a virtual space.

[0411] "Health risk" refers to factors or circumstances that may worsen an individual's health condition.

[0412] A "notification" is a message sent to inform a user of specific information or an alert.

[0413] "Analysis results" refer to the conclusions and insights obtained after analyzing data.

[0414] "Advice" refers to providing users with guidelines or suggestions for action, particularly regarding health and mental health.

[0415] A "medical institution" is a facility or organization that provides medical services, including hospitals and clinics.

[0416] To implement this invention, first, a smartphone or wearable device is used as the terminal. The terminal is equipped with a non-contact sensor to acquire biometric information such as heart rate, respiration, and body temperature in real time. It also has an emotion recognition engine that analyzes voice patterns and facial expressions, thereby acquiring emotion data.

[0417] The device securely transmits acquired biometric and emotional data to a server via a communication network. The server receives this data and performs detailed data analysis using AI algorithms. This analysis detects signs of abnormalities and emotional fluctuations. Furthermore, the server references data from similar individuals to construct a virtual model called a digital twin. Based on this digital twin, it predicts health risks and generates health advice tailored to the user's emotional state.

[0418] Specifically, when the server detects an anomaly, it sends a personalized notification to the user's device. For example, if a user is experiencing high stress levels, it might send advice such as, "Your stress levels are high; we recommend trying deep breathing or simple meditation." This allows the user to know how to cope immediately.

[0419] This system incorporates machine learning models using Python and TensorFlow, supporting efficient and accurate data analysis.

[0420] For example, if a user feels stressed while traveling on public transport, the system can sense this change in emotion and send a notification saying, "To relax, we recommend taking deep breaths and listening to your favorite music." An example of an input prompt to the generating AI model would be, "I've experienced significant emotional fluctuations today; please suggest ways to relax. Current location: office, heart rate: 80, breathing: slightly fast, emotional data: stressed."

[0421] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0422] Step 1:

[0423] The device acquires biometric information such as heart rate, respiration, and body temperature through non-contact sensors. At this stage, the input is the user's physical state, and the output is numerical data (e.g., heart rate: 72, body temperature: 36.5°C). The device records this biometric information in preparation for the next step.

[0424] Step 2:

[0425] The device analyzes voice patterns and facial expressions to acquire emotion data. Here, the input is the user's voice and facial image, and the output is data on emotional state (e.g., tension: high, happiness: medium). The device is equipped with an emotion recognition engine, which quantifies specific emotions by performing voice and image analysis.

[0426] Step 3:

[0427] The device collects biometric and emotional data and transmits it to a server via a secure communication network. In this process, the input is the dataset collected by the device, and the output is the secure arrival of the data at the server. The device uses encryption protocols to prevent data leakage.

[0428] Step 4:

[0429] The server analyzes the received data using an AI algorithm. The input is biometric information and emotional data, and the output is the analysis results (e.g., detection of anomalies in heart rate and stress levels). Based on this data, the server operates a machine learning model using Python and TensorFlow to identify anomalous patterns.

[0430] Step 5:

[0431] The server constructs a digital twin by referencing similar data from other users. The inputs here are user data and other users' datasets, and the output is a virtual health model of the user. This process involves using a database to compare vast amounts of historical data with user data to generate the virtual model.

[0432] Step 6:

[0433] The server predicts health risks based on the analysis results and generates personalized health advice. Inputs are a digital twin and analysis results, and output is an appropriate advice message for the user (e.g., suggestions for relaxation techniques). A generative AI model is used to provide user-specific prompts.

[0434] Step 7:

[0435] The server generates advice and notifies the terminal. Here, the input is the generated advice, and the output is the notification displayed on the user's terminal. The terminal is designed to utilize the notification function to allow the user to receive immediate feedback.

[0436] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0437] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0438] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0439] [Third Embodiment]

[0440] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0441] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0442] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0443] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0444] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0445] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0446] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0447] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0448] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0449] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0450] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0451] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0452] In one embodiment of this invention, the user first wears a smartphone or wearable device equipped with a non-contact sensor on a daily basis. This allows the device to continuously acquire biometric information such as heart rate, respiration, and body temperature. This biometric information is transmitted to a server via the internet, protected by security protocols.

[0453] The server stores the received biometric information in a database and analyzes it using an AI model. Through data analysis, it detects signs of anomalies and generates anomaly detection alerts as needed. Furthermore, the server builds a digital twin of the user by referencing similar data from others, enabling highly accurate predictions of health risks.

[0454] Based on the predicted health risks, the server uses AI to automatically generate optimal medical questionnaires and health advice tailored to the user's attributes and personality. This information is sent to the user's device, allowing the user to comprehensively understand their own health status.

[0455] Furthermore, by linking with medical institutions, the server can provide them with information necessary to support specialized diagnoses and treatments, if the user grants permission. This enables doctors to make more accurate diagnoses and provide appropriate medical services.

[0456] As a concrete example, users wear a device before going to sleep, and the device measures their breathing and heart rate during sleep. If the server detects abnormal fluctuations that deviate from the normal sleep pattern, it performs a digital twin analysis and notifies the user with advice generated by AI, such as, "Stress may be involved. Try taking a relaxing bath or meditating." Through this kind of feedback, users can take specific actions to reduce their health risks.

[0457] This invention aims to enable users to manage their own daily health and to monitor the health status of family members living separately in real time, thereby realizing a safer and more comfortable life.

[0458] The following describes the processing flow.

[0459] Step 1:

[0460] The device continuously collects biometric information using contactless sensors. This information includes heart rate, respiration, and body temperature, and is measured periodically.

[0461] Step 2:

[0462] The device transmits the collected biometric information to the server via the communication network, based on security protocols. Data transfer occurs in real time or at regular intervals.

[0463] Step 3:

[0464] The server stores the received biometric information in a database. This allows for the accumulation and retrieval of data over time.

[0465] Step 4:

[0466] The server uses an AI model to analyze data and detect signs of anomalies. The analysis involves comparing the data with past data and calculating deviations from the normal range.

[0467] Step 5:

[0468] The server references data from other users that is similar to the user's biometric information to construct a digital twin. The digital twin is a model that virtually reproduces the user's health status.

[0469] Step 6:

[0470] The server predicts health risks based on the digital twin. If a risk is identified, it performs a more detailed analysis to determine the appropriate course of action.

[0471] Step 7:

[0472] The server uses AI to generate optimal medical questions and appropriate health advice tailored to the user's attributes and personality. This provides personalized feedback to the user.

[0473] Step 8:

[0474] The server sends generated questions and advice to the terminal. The terminal notifies the user and displays the content in an easy-to-understand format.

[0475] Step 9:

[0476] Users can review the questionnaires and advice received on their devices and take corrective actions as needed. They can also send feedback to the server if they have any inquiries.

[0477] Step 10:

[0478] If the user grants permission, the server will share necessary health information with healthcare institutions to help improve the accuracy of diagnoses. This will allow doctors to have a more accurate understanding of the user's health condition and provide appropriate medical care.

[0479] (Example 1)

[0480] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0481] Traditional health management systems were limited to collecting and analyzing individual biometric data, making it difficult to provide highly accurate health risk predictions or personalized health advice. Furthermore, there was a lack of means to share this information with medical facilities in a timely manner, limiting their effectiveness in situations requiring rapid medical response.

[0482] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0483] In this invention, the server includes means for continuously acquiring biometric data using contactless sensors, means for transferring the acquired biometric data via a communication network, and means for generating analysis results and health advice using a generative AI model. This enables real-time health risk prediction, provision of personalized health advice, and rapid information sharing with appropriate medical facilities.

[0484] "Biometric data" refers to physical information such as an individual's heart rate, respiration, and body temperature.

[0485] A "non-contact sensor" refers to a device that acquires biometric data without physical contact.

[0486] A "communication network" refers to the infrastructure used to send and receive data over long distances, and includes the internet and wireless networks.

[0487] A "generative AI model" refers to an algorithm that analyzes a user's biometric data to generate anomaly detection and health advice.

[0488] A "medical facility" refers to a medical institution or hospital that provides diagnosis and treatment.

[0489] "Digital reproduction" refers to a model of a virtual health state based on the user's biometric data.

[0490] "Health risk" refers to the potential health hazards predicted based on specific biometric data.

[0491] "Notification" refers to the act of presenting important information to a recipient, and is carried out as an email or a device alert.

[0492] In an embodiment of this invention, the user routinely uses an information processing device equipped with a non-contact sensor. This information processing device takes the form of a smartphone or wearable device and has the function of continuously acquiring biometric data such as heart rate, respiration, and body temperature. This data is protected by the information processing device's security protocol and transmitted to a server via a communication network.

[0493] The server receives biometric data via the internet and stores it in a database. It then performs data analysis on the stored data using a generative AI model to detect anomalies. This AI model identifies abnormal patterns in real time by analyzing the data using statistical algorithms and machine learning techniques. Furthermore, the server utilizes advanced data mining techniques to construct digital replicas based on similar data from other individuals, predicting individual health risks.

[0494] Based on predicted health risks, the generating AI model creates health advice tailored to the user's characteristics and personality. For example, if an abnormality is detected in the nighttime sleep pattern, the server generates health advice such as "This may be due to stress. Try deep breathing or meditation," and notifies the user's information processing device. This process can use a prompt message to the user in the format of "Please provide stress-related health advice based on current biometric data."

[0495] Furthermore, with the user's permission, the server collaborates with medical facilities to quickly provide analyzed biometric data. This allows doctors to easily obtain information for accurate diagnoses and provide appropriate medical services. This embodiment of the invention enables users to proactively manage their own health and respond quickly when medical intervention is necessary.

[0496] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0497] Step 1:

[0498] The device uses built-in non-contact sensors to acquire biometric data such as the user's heart rate, respiration, and body temperature in real time. During this process, input from the sensors is continuous, and the acquired data is temporarily stored within the device. Specifically, the application on the device collects data at predetermined time intervals and stores it in a buffer as a new dataset.

[0499] Step 2:

[0500] The terminal transmits temporarily stored biometric data to the server via the communication network, while protecting it with security protocols. This transmission occurs at predetermined time intervals and is performed in an encrypted format to ensure data integrity and confidentiality. The output of this step is a packet of biometric data securely transferred from the terminal to the server.

[0501] Step 3:

[0502] The server receives biometric data transmitted from the terminal and stores it in a database. The data received as input is recorded along with a timestamp. The specific operation aims to accurately manage all data through database writing processes.

[0503] Step 4:

[0504] The server analyzes biometric data stored in the database using a generation AI model. During this analysis, an anomaly detection algorithm is applied based on the input data to detect abnormalities in health status. The output includes notifications indicating anomalies and risk indicators. Specifically, the system includes triggering a notification system when the detected anomaly exceeds a threshold.

[0505] Step 5:

[0506] The server uses anomaly detection results and accumulated data to build a digital reconstruction. The data used includes not only individual user data but also data from other similar users. This process is carried out using machine learning models, and the output generates predictions of health risks. Specifically, it calculates health risk scores and personalized analysis results.

[0507] Step 6:

[0508] The server uses a generative AI model to generate health advice tailored to the user's attributes and personality based on predicted health risks. The input consists of analysis results and the user profile, and the output is personalized advice. Specifically, notifications to the user are constructed based on the generated prompt messages.

[0509] Step 7:

[0510] The server sends generated health advice and anomaly notifications to the device, which then notifies the user. The device's notification system is triggered, and the user receives advice and warnings on the screen. The output is displayed as information to prompt user action.

[0511] Step 8:

[0512] The server shares the analyzed biometric data with the relevant medical facility if the user grants permission. This data sharing takes place via a secure channel and serves as crucial input for diagnostic support. Specifically, the information is provided to the medical facility in real time through a data transmission protocol.

[0513] (Application Example 1)

[0514] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0515] Conventional health monitoring systems are limited to managing health information and lack applications in safety and authentication control. Furthermore, the proposal of appropriate countermeasures linked to anomaly detection is limited, failing to adequately guarantee user safety. Therefore, the challenge is to provide a system that integrates health status and safety authentication control using users' biometric data, enabling rapid and appropriate responses.

[0516] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0517] In this invention, the server includes means for continuously acquiring biometric data using a non-contact detection device, means for transmitting the acquired biometric data via a communication infrastructure, means for analyzing the received biometric data and detecting abnormalities, means for constructing a virtual model using similar information from others, means for predicting health risks based on the generated virtual model, means for generating optimal medical questionnaire items based on the detected abnormalities, means for linking information with medical institutions, means for notifying the user of analysis results and advice, and means for performing authentication control that takes into account health status and safety using biometric data. This enables the integration of user health monitoring and authentication control that takes safety into consideration.

[0518] "Biometric data" refers to physiological information that measures an individual's health status, such as heart rate, respiration, and body temperature.

[0519] A "non-contact detection device" is a sensor device that can acquire information about an object without directly touching it.

[0520] "Communication infrastructure" refers to network infrastructure used to transmit data from one location to another.

[0521] A "virtual model" is a simulation model that reproduces a real-world object using data, and is used for prediction and analysis of that object.

[0522] "Health risk" refers to potential dangers to an individual's health or the likelihood of developing a disease in the future.

[0523] A "medical history questionnaire" is a series of questions presented to assess a person's health status.

[0524] "Medical institutions" refer to providers of medical services such as hospitals, clinics, and medical offices that provide medical examinations and treatments.

[0525] "Authentication control" is the process of verifying identity and managing access so that only authorized users can access the system and information.

[0526] "Analysis results" refer to conclusions and insights obtained by analyzing data.

[0527] "Advice" refers to guidance or recommendations given to an individual based on their circumstances.

[0528] In embodiments of the present invention, it is assumed that the user constantly carries a smartphone or wearable device equipped with a contactless detection device. The device periodically acquires biometric data and transmits it to a server in the cloud using a communication infrastructure. Data protection is provided through security protocols during this process.

[0529] The server uses software such as Python and TensorFlow to analyze received biometric data in real time. If an anomaly is detected through the analysis, it utilizes a generative AI model to select the most appropriate countermeasure for each individual user. This selection process includes predicting health risks by comparing them with existing virtual models.

[0530] Users can receive analysis results and advice through a smartphone application. The application displays notifications based on their health status and provides specific guidance and advice tailored to their individual circumstances. When this operation is performed, the system scrutinizes the access location and health data, and implements authentication controls with security in mind.

[0531] As a concrete example, when a user authenticates via smartphone at the office entrance, the system measures their heart rate and body temperature, and issues a warning if any unusual patterns are detected. This allows for a quick response to abnormal situations in the workplace.

[0532] An example of a prompt message could be, "Provide appropriate health advice to users whose body temperature is higher than normal." Using this prompt message, the AI ​​model generates advice immediately and notifies the user's smartphone. This allows users to proactively manage their own health.

[0533] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0534] Step 1:

[0535] The device periodically acquires biometric data such as the user's heart rate, respiration, and body temperature using built-in non-contact sensors. This acquired data is formatted and prepared for transmission to a server via a communication infrastructure. The input is the user's biological state, and the output is formatted biometric data.

[0536] Step 2:

[0537] The device encrypts the acquired biometric data using a security protocol and securely transmits it to the server. Data encryption is crucial for maintaining data confidentiality. The input is formatted biometric data, and the output is encrypted biometric data.

[0538] Step 3:

[0539] The server receives encrypted biometric data, decrypts it, and then performs data analysis using Python and TensorFlow. This analysis allows for the detection of anomalies in the data. The input is encrypted biometric data, and the output is the analysis results.

[0540] Step 4:

[0541] Based on the analysis results, the server uses a generated AI model to assess health risks and determine the optimal countermeasures for each individual user. The risk assessment involves cross-referencing with information from a virtual model. The input is the analysis results, and the output is the health risk assessment and countermeasures.

[0542] Step 5:

[0543] The server generates a prompt message based on the generated countermeasures and inputs it into the AI ​​model to create personalized advice for each user. For example, the prompt message might be "Provide appropriate health advice to users whose body temperature is higher than normal." The input consists of a health risk assessment and a prompt message, while the output is specific advice for the user.

[0544] Step 6:

[0545] The user receives advice sent from the server through an application on their device. The application displays notifications and detailed advice based on the user's status and prepares to provide information to healthcare providers if necessary. The input is the advice from the server, and the output is the notifications and detailed information received by the user.

[0546] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0547] In an embodiment of this invention, the user uses a smartphone or wearable device equipped with a non-contact sensor. The device continuously collects biometric information such as heart rate, respiration, and body temperature. Furthermore, an emotion recognition engine installed in the device analyzes the user's voice patterns and facial expressions to acquire emotion data. This biometric information and emotion data are transmitted to a server via a securely protected communication network.

[0548] The server stores received biometric and emotional data in a database and analyzes it using AI algorithms. Through data analysis, the server detects signs of abnormalities and emotional fluctuations. Furthermore, it constructs a digital twin by referencing similar data from other users, virtually recreating the user's health state.

[0549] Emotional data acquired by the emotion recognition engine is incorporated into a digital twin. The server utilizes this digital twin to predict health risks and generate health advice tailored to the user's emotional state. This advice is personalized and optimized for the user's psychological state and attributes.

[0550] As a concrete example, based on data collected when a user is experiencing stress, the server generates specific advice such as, "Your stress level is high; we recommend trying deep breathing or simple meditation." Furthermore, if emotional data differs from the norm, the server may identify a higher health risk and send a preventative notification.

[0551] These pieces of advice and notifications are delivered to the user's device, allowing the user to review the feedback and take action as needed. If the user consents, the server also collaborates with healthcare institutions to provide this biometric and emotional data to medical professionals. This enables professionals to gain a more comprehensive understanding of the user's health status and improve diagnostic accuracy.

[0552] This invention aims to help users comprehensively understand their own health status and take appropriate action, taking into account their emotional state. It also enables families living separately to confidently monitor each other's health status and promotes safe and independent living for the elderly.

[0553] The following describes the processing flow.

[0554] Step 1:

[0555] The device uses contactless sensors and an emotion recognition engine to continuously acquire biometric information such as the user's heart rate, respiration, and body temperature, as well as emotional data from voice and facial expressions.

[0556] Step 2:

[0557] The device transmits collected biometric and emotional data to the server in real time using a secure communication protocol.

[0558] Step 3:

[0559] The server stores the transmitted biometric and emotional data in a database and performs preprocessing to ensure data quality.

[0560] Step 4:

[0561] The server uses an AI algorithm to analyze the received data. This analysis detects abnormalities in the user's biometric information and fluctuations in their emotions.

[0562] Step 5:

[0563] Based on the data analysis results, the server constructs a digital twin of the user by referencing similar data from other users. The digital twin is a model that virtually reproduces the user's health and emotional state.

[0564] Step 6:

[0565] The server uses the constructed digital twin to predict health risks and generates risk-based warnings and countermeasures, taking emotional data into consideration.

[0566] Step 7:

[0567] The server uses a generation AI to create customized medical questionnaires and health advice tailored to the user's attributes and emotional state.

[0568] Step 8:

[0569] The server sends the generated questions and advice to the user's terminal. The terminal notifies the user of the received information and displays it.

[0570] Step 9:

[0571] Users check notifications received from their devices and decide on actions based on health advice. They also send feedback back to the server via their devices as needed.

[0572] Step 10:

[0573] With the user's permission, the server shares analyzed biometric and emotional data with healthcare institutions. This allows healthcare institutions to improve the accuracy of diagnoses and treatments based on more comprehensive information.

[0574] (Example 2)

[0575] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0576] In modern society, daily stress and health changes have various impacts on individuals' lives. However, there are limited systems that can monitor individual health conditions in real time and provide appropriate advice by predicting emotional changes and health risks. Conventional health management requires consideration of not only biological signals but also emotional signals, and there is a need for technology to efficiently handle this.

[0577] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0578] In this invention, the server includes means for continuously acquiring biosignals using sensors, means for transmitting the acquired biosignals and emotional signals via a communication line, and means for providing health recommendations tailored to the user's psychological characteristics based on the analyzed biosignals and emotional signals. This makes it possible to provide more personalized health recommendations based on the user's health and emotional state.

[0579] "Biosignals" refer to physical or chemical data obtained from the body of an organism, such as heart rate, respiration, and body temperature.

[0580] A "sensor" refers to a device that detects biological signals or emotional signals and converts them into electrical signals.

[0581] "Communication lines" refer to infrastructure for sending and receiving electronic data, including the internet and dedicated lines.

[0582] "Emotional signals" refer to data that indicates an individual's psychological state, analyzed from factors such as voice patterns and facial expressions.

[0583] An "abnormality" refers to a situation where a state or pattern different from the normal one is detected in biological or emotional signals.

[0584] A "digital model" refers to a model that virtually reproduces an individual's health and emotional state, built based on information from similar individuals.

[0585] A "health crisis" refers to a potential health risk predicted from an individual's biosignals and emotional signals.

[0586] "Health recommendations" refer to advice for improving and maintaining health, provided based on an individual's bio-signals and emotional signals.

[0587] "Specialized institutions" refer to medical institutions and organizations that provide healthcare services.

[0588] "Evaluation accuracy" refers to a measure that indicates the accuracy and reliability of the data analyzed by the server.

[0589] This system collects biometric and emotional signals in real time using smartphones or wearable devices equipped with contactless sensors. Specifically, the device continuously acquires biometric signals such as heart rate, respiration, and body temperature, and analyzes emotional signals from voice patterns and facial expressions. This utilizes software called an emotion recognition engine, which enables the rapid generation of emotional data.

[0590] The device uses the internet as its communication line to securely transmit this data to the server. The communication uses encryption protocols (e.g., HTTPS) to ensure security. The server stores the received biometric and emotional signals in a database and then analyzes the data using AI algorithms. This analysis detects abnormalities in health status and emotional fluctuations.

[0591] The server builds a digital model of the user by referencing similar data from other users. This digital model allows for simulations of the user's health in a virtual environment, enabling the prediction of health crises. A generative AI model is used for this analysis, and an example of a prompt is, "Based on your current emotional data, please suggest the most suitable relaxation method."

[0592] Ultimately, the server generates health recommendations and sends them to the user's device. This allows the user to obtain a concrete action plan to reduce stress and discomfort. With the user's permission, this data can also be shared with professional organizations to contribute to improving the accuracy of diagnosis and treatment. These features aim to enable users to more effectively manage their health and emotional state, thereby increasing their well-being in daily life.

[0593] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0594] Step 1:

[0595] The device continuously acquires biosignals (heart rate, respiration, body temperature, etc.) using contactless sensors. The input to this process is raw data obtained from the user's body, and the output is digital signals acquired by the sensor device. These digital signals are temporarily stored within the device.

[0596] Step 2:

[0597] The emotion recognition engine installed in the device analyzes the user's voice patterns and facial expressions to acquire emotion signals. The input is the user's voice data and image data, and the output is emotion data analyzed based on this data. This emotion data is temporarily stored on the device and prepared to be sent to the server later.

[0598] Step 3:

[0599] The device transmits collected biometric signals and emotional data to a server via a communication line (e.g., the internet). Input is digital data stored on the device, and output is sent to the server as securely encrypted data. HTTPS protocol and similar protocols are used for communication to ensure data security.

[0600] Step 4:

[0601] The server stores the biometric and emotional signals it receives in a database. The input is encrypted data sent from the terminal, and the output is the data stored in the database for analysis. This data is recorded along with a timestamp.

[0602] Step 5:

[0603] The server uses a generated AI model to analyze biometric and emotional signals stored in a database. The input is user data stored in the database, and the output is a report that identifies anomalies and emotional changes detected through the analysis. This report is used to assess the user's health status.

[0604] Step 6:

[0605] The server builds a digital model of the user using similar data from other users. The input is user data and similar data, and the output is a digital model that virtually reproduces the user's health status. This step makes it easier to predict the user's health crises.

[0606] Step 7:

[0607] The server generates user health recommendations based on a digital model and analysis results. Inputs are the digital model and anomaly reports, while outputs are specific action plans and advice tailored to the user's condition. For example, it might generate advice such as, "Based on your current emotional data, we suggest the most suitable relaxation method."

[0608] Step 8:

[0609] The server generates health recommendations and sends them to the user's device, notifying them of their health. The input is the generated advice, and the output is the advice message displayed on the user's device. The user can receive this and use it to improve their own health.

[0610] (Application Example 2)

[0611] Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0612] In modern society, it is crucial to continuously monitor individuals' health and take appropriate action as needed. However, conventional technologies have not adequately achieved systems that can detect real-time emotional fluctuations and stress levels and provide immediate, appropriate notifications to individual users. As a result, it has been difficult for users to receive appropriate advice when faced with potential dangers or stressful situations.

[0613] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0614] In this invention, the server includes means for securely generating personalized notifications and sending warnings to the user, means for notifying the user of analysis results and advice, and means for continuously acquiring biometric information using non-contact sensors. This enables the user to respond immediately to changes in their emotional state and stress levels and receive appropriate advice.

[0615] "Biometric information" refers to data that indicates a person's physical state, such as heart rate, respiration, and body temperature.

[0616] A "non-contact sensor" is a sensor device that acquires information about an object without requiring direct contact.

[0617] A "communication network" is infrastructure for transmitting information, and includes the internet and other data communication networks.

[0618] A "digital twin" is a digital representation that mimics real-world objects and processes, allowing for analysis and simulation in a virtual space.

[0619] "Health risk" refers to factors or circumstances that may worsen an individual's health condition.

[0620] A "notification" is a message sent to inform a user of specific information or an alert.

[0621] "Analysis results" refer to the conclusions and insights obtained after analyzing data.

[0622] "Advice" refers to providing users with guidelines or suggestions for action, particularly regarding health and mental health.

[0623] A "medical institution" is a facility or organization that provides medical services, including hospitals and clinics.

[0624] To implement this invention, first, a smartphone or wearable device is used as the terminal. The terminal is equipped with a non-contact sensor to acquire biometric information such as heart rate, respiration, and body temperature in real time. It also has an emotion recognition engine that analyzes voice patterns and facial expressions, thereby acquiring emotion data.

[0625] The device securely transmits acquired biometric and emotional data to a server via a communication network. The server receives this data and performs detailed data analysis using AI algorithms. This analysis detects signs of abnormalities and emotional fluctuations. Furthermore, the server references data from similar individuals to construct a virtual model called a digital twin. Based on this digital twin, it predicts health risks and generates health advice tailored to the user's emotional state.

[0626] Specifically, when the server detects an anomaly, it sends a personalized notification to the user's device. For example, if a user is experiencing high stress levels, it might send advice such as, "Your stress levels are high; we recommend trying deep breathing or simple meditation." This allows the user to know how to cope immediately.

[0627] This system incorporates machine learning models using Python and TensorFlow, supporting efficient and accurate data analysis.

[0628] For example, if a user feels stressed while traveling on public transport, the system can sense this change in emotion and send a notification saying, "To relax, we recommend taking deep breaths and listening to your favorite music." An example of an input prompt to the generating AI model would be, "I've experienced significant emotional fluctuations today; please suggest ways to relax. Current location: office, heart rate: 80, breathing: slightly fast, emotional data: stressed."

[0629] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0630] Step 1:

[0631] The device acquires biometric information such as heart rate, respiration, and body temperature through non-contact sensors. At this stage, the input is the user's physical state, and the output is numerical data (e.g., heart rate: 72, body temperature: 36.5°C). The device records this biometric information in preparation for the next step.

[0632] Step 2:

[0633] The device analyzes voice patterns and facial expressions to acquire emotion data. Here, the input is the user's voice and facial image, and the output is data on emotional state (e.g., tension: high, happiness: medium). The device is equipped with an emotion recognition engine, which quantifies specific emotions by performing voice and image analysis.

[0634] Step 3:

[0635] The device collects biometric and emotional data and transmits it to a server via a secure communication network. In this process, the input is the dataset collected by the device, and the output is the secure arrival of the data at the server. The device uses encryption protocols to prevent data leakage.

[0636] Step 4:

[0637] The server analyzes the received data using an AI algorithm. The input is biometric information and emotional data, and the output is the analysis results (e.g., detection of anomalies in heart rate and stress levels). Based on this data, the server operates a machine learning model using Python and TensorFlow to identify anomalous patterns.

[0638] Step 5:

[0639] The server constructs a digital twin by referencing similar data from other users. The inputs here are user data and other users' datasets, and the output is a virtual health model of the user. This process involves using a database to compare vast amounts of historical data with user data to generate the virtual model.

[0640] Step 6:

[0641] The server predicts health risks based on the analysis results and generates personalized health advice. Inputs are a digital twin and analysis results, and output is an appropriate advice message for the user (e.g., suggestions for relaxation techniques). A generative AI model is used to provide user-specific prompts.

[0642] Step 7:

[0643] The server generates advice and notifies the terminal. Here, the input is the generated advice, and the output is the notification displayed on the user's terminal. The terminal is designed to utilize the notification function to allow the user to receive immediate feedback.

[0644] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0645] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0646] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0647] [Fourth Embodiment]

[0648] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0649] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0650] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0651] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0652] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0653] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0654] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0655] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0656] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0657] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0658] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0659] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0660] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0661] In one embodiment of this invention, the user first wears a smartphone or wearable device equipped with a non-contact sensor on a daily basis. This allows the device to continuously acquire biometric information such as heart rate, respiration, and body temperature. This biometric information is transmitted to a server via the internet, protected by security protocols.

[0662] The server stores the received biometric information in a database and analyzes it using an AI model. Through data analysis, it detects signs of anomalies and generates anomaly detection alerts as needed. Furthermore, the server builds a digital twin of the user by referencing similar data from others, enabling highly accurate predictions of health risks.

[0663] Based on the predicted health risks, the server uses AI to automatically generate optimal medical questionnaires and health advice tailored to the user's attributes and personality. This information is sent to the user's device, allowing the user to comprehensively understand their own health status.

[0664] Furthermore, by linking with medical institutions, the server can provide them with information necessary to support specialized diagnoses and treatments, if the user grants permission. This enables doctors to make more accurate diagnoses and provide appropriate medical services.

[0665] As a concrete example, users wear a device before going to sleep, and the device measures their breathing and heart rate during sleep. If the server detects abnormal fluctuations that deviate from the normal sleep pattern, it performs a digital twin analysis and notifies the user with advice generated by AI, such as, "Stress may be involved. Try taking a relaxing bath or meditating." Through this kind of feedback, users can take specific actions to reduce their health risks.

[0666] This invention aims to enable users to manage their own daily health and to monitor the health status of family members living separately in real time, thereby realizing a safer and more comfortable life.

[0667] The following describes the processing flow.

[0668] Step 1:

[0669] The device continuously collects biometric information using contactless sensors. This information includes heart rate, respiration, and body temperature, and is measured periodically.

[0670] Step 2:

[0671] The device transmits the collected biometric information to the server via the communication network, based on security protocols. Data transfer occurs in real time or at regular intervals.

[0672] Step 3:

[0673] The server stores the received biometric information in a database. This allows for the accumulation and retrieval of data over time.

[0674] Step 4:

[0675] The server uses an AI model to analyze data and detect signs of anomalies. The analysis involves comparing the data with past data and calculating deviations from the normal range.

[0676] Step 5:

[0677] The server references data from other users that is similar to the user's biometric information to construct a digital twin. The digital twin is a model that virtually reproduces the user's health status.

[0678] Step 6:

[0679] The server predicts health risks based on the digital twin. If a risk is identified, it performs a more detailed analysis to determine the appropriate course of action.

[0680] Step 7:

[0681] The server uses AI to generate optimal medical questions and appropriate health advice tailored to the user's attributes and personality. This provides personalized feedback to the user.

[0682] Step 8:

[0683] The server sends generated questions and advice to the terminal. The terminal notifies the user and displays the content in an easy-to-understand format.

[0684] Step 9:

[0685] Users can review the questionnaires and advice received on their devices and take corrective actions as needed. They can also send feedback to the server if they have any inquiries.

[0686] Step 10:

[0687] If the user grants permission, the server will share necessary health information with healthcare institutions to help improve the accuracy of diagnoses. This will allow doctors to have a more accurate understanding of the user's health condition and provide appropriate medical care.

[0688] (Example 1)

[0689] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0690] Traditional health management systems were limited to collecting and analyzing individual biometric data, making it difficult to provide highly accurate health risk predictions or personalized health advice. Furthermore, there was a lack of means to share this information with medical facilities in a timely manner, limiting their effectiveness in situations requiring rapid medical response.

[0691] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0692] In this invention, the server includes means for continuously acquiring biometric data using contactless sensors, means for transferring the acquired biometric data via a communication network, and means for generating analysis results and health advice using a generative AI model. This enables real-time health risk prediction, provision of personalized health advice, and rapid information sharing with appropriate medical facilities.

[0693] "Biometric data" refers to physical information such as an individual's heart rate, respiration, and body temperature.

[0694] A "non-contact sensor" refers to a device that acquires biometric data without physical contact.

[0695] A "communication network" refers to the infrastructure used to send and receive data over long distances, and includes the internet and wireless networks.

[0696] A "generative AI model" refers to an algorithm that analyzes a user's biometric data to generate anomaly detection and health advice.

[0697] A "medical facility" refers to a medical institution or hospital that provides diagnosis and treatment.

[0698] "Digital reproduction" refers to a model of a virtual health state based on the user's biometric data.

[0699] "Health risk" refers to the potential health hazards predicted based on specific biometric data.

[0700] "Notification" refers to the act of presenting important information to a recipient, and is carried out as an email or a device alert.

[0701] In an embodiment of this invention, the user routinely uses an information processing device equipped with a non-contact sensor. This information processing device takes the form of a smartphone or wearable device and has the function of continuously acquiring biometric data such as heart rate, respiration, and body temperature. This data is protected by the information processing device's security protocol and transmitted to a server via a communication network.

[0702] The server receives biometric data via the internet and stores it in a database. It then performs data analysis on the stored data using a generative AI model to detect anomalies. This AI model identifies abnormal patterns in real time by analyzing the data using statistical algorithms and machine learning techniques. Furthermore, the server utilizes advanced data mining techniques to construct digital replicas based on similar data from other individuals, predicting individual health risks.

[0703] Based on predicted health risks, the generating AI model creates health advice tailored to the user's characteristics and personality. For example, if an abnormality is detected in the nighttime sleep pattern, the server generates health advice such as "This may be due to stress. Try deep breathing or meditation," and notifies the user's information processing device. This process can use a prompt message to the user in the format of "Please provide stress-related health advice based on current biometric data."

[0704] Furthermore, with the user's permission, the server collaborates with medical facilities to quickly provide analyzed biometric data. This allows doctors to easily obtain information for accurate diagnoses and provide appropriate medical services. This embodiment of the invention enables users to proactively manage their own health and respond quickly when medical intervention is necessary.

[0705] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0706] Step 1:

[0707] The device uses built-in non-contact sensors to acquire biometric data such as the user's heart rate, respiration, and body temperature in real time. During this process, input from the sensors is continuous, and the acquired data is temporarily stored within the device. Specifically, the application on the device collects data at predetermined time intervals and stores it in a buffer as a new dataset.

[0708] Step 2:

[0709] The terminal transmits temporarily stored biometric data to the server via the communication network, while protecting it with security protocols. This transmission occurs at predetermined time intervals and is performed in an encrypted format to ensure data integrity and confidentiality. The output of this step is a packet of biometric data securely transferred from the terminal to the server.

[0710] Step 3:

[0711] The server receives biometric data transmitted from the terminal and stores it in a database. The data received as input is recorded along with a timestamp. The specific operation aims to accurately manage all data through database writing processes.

[0712] Step 4:

[0713] The server analyzes biometric data stored in the database using a generation AI model. During this analysis, an anomaly detection algorithm is applied based on the input data to detect abnormalities in health status. The output includes notifications indicating anomalies and risk indicators. Specifically, the system includes triggering a notification system when the detected anomaly exceeds a threshold.

[0714] Step 5:

[0715] The server uses anomaly detection results and accumulated data to build a digital reconstruction. The data used includes not only individual user data but also data from other similar users. This process is carried out using machine learning models, and the output generates predictions of health risks. Specifically, it calculates health risk scores and personalized analysis results.

[0716] Step 6:

[0717] The server uses a generative AI model to generate health advice tailored to the user's attributes and personality based on predicted health risks. The input consists of analysis results and the user profile, and the output is personalized advice. Specifically, notifications to the user are constructed based on the generated prompt messages.

[0718] Step 7:

[0719] The server sends generated health advice and anomaly notifications to the device, which then notifies the user. The device's notification system is triggered, and the user receives advice and warnings on the screen. The output is displayed as information to prompt user action.

[0720] Step 8:

[0721] The server shares the analyzed biometric data with the relevant medical facility if the user grants permission. This data sharing takes place via a secure channel and serves as crucial input for diagnostic support. Specifically, the information is provided to the medical facility in real time through a data transmission protocol.

[0722] (Application Example 1)

[0723] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0724] Conventional health monitoring systems are limited to managing health information and lack applications in safety and authentication control. Furthermore, the proposal of appropriate countermeasures linked to anomaly detection is limited, failing to adequately guarantee user safety. Therefore, the challenge is to provide a system that integrates health status and safety authentication control using users' biometric data, enabling rapid and appropriate responses.

[0725] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0726] In this invention, the server includes means for continuously acquiring biometric data using a non-contact detection device, means for transmitting the acquired biometric data via a communication infrastructure, means for analyzing the received biometric data and detecting abnormalities, means for constructing a virtual model using similar information from others, means for predicting health risks based on the generated virtual model, means for generating optimal medical questionnaire items based on the detected abnormalities, means for linking information with medical institutions, means for notifying the user of analysis results and advice, and means for performing authentication control that takes into account health status and safety using biometric data. This enables the integration of user health monitoring and authentication control that takes safety into consideration.

[0727] "Biometric data" refers to physiological information that measures an individual's health status, such as heart rate, respiration, and body temperature.

[0728] A "non-contact detection device" is a sensor device that can acquire information about an object without directly touching it.

[0729] "Communication infrastructure" refers to network infrastructure used to transmit data from one location to another.

[0730] A "virtual model" is a simulation model that reproduces a real-world object using data, and is used for prediction and analysis of that object.

[0731] "Health risk" refers to potential dangers to an individual's health or the likelihood of developing a disease in the future.

[0732] A "medical history questionnaire" is a series of questions presented to assess a person's health status.

[0733] "Medical institutions" refer to providers of medical services such as hospitals, clinics, and medical offices that provide medical examinations and treatments.

[0734] "Authentication control" is the process of verifying identity and managing access so that only authorized users can access the system and information.

[0735] "Analysis results" refer to conclusions and insights obtained by analyzing data.

[0736] "Advice" refers to guidance or recommendations given to an individual based on their circumstances.

[0737] In embodiments of the present invention, it is assumed that the user constantly carries a smartphone or wearable device equipped with a contactless detection device. The device periodically acquires biometric data and transmits it to a server in the cloud using a communication infrastructure. Data protection is provided through security protocols during this process.

[0738] The server uses software such as Python and TensorFlow to analyze received biometric data in real time. If an anomaly is detected through the analysis, it utilizes a generative AI model to select the most appropriate countermeasure for each individual user. This selection process includes predicting health risks by comparing them with existing virtual models.

[0739] Users can receive analysis results and advice through a smartphone application. The application displays notifications based on their health status and provides specific guidance and advice tailored to their individual circumstances. When this operation is performed, the system scrutinizes the access location and health data, and implements authentication controls with security in mind.

[0740] As a concrete example, when a user authenticates via smartphone at the office entrance, the system measures their heart rate and body temperature, and issues a warning if any unusual patterns are detected. This allows for a quick response to abnormal situations in the workplace.

[0741] An example of a prompt message could be, "Provide appropriate health advice to users whose body temperature is higher than normal." Using this prompt message, the AI ​​model generates advice immediately and notifies the user's smartphone. This allows users to proactively manage their own health.

[0742] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0743] Step 1:

[0744] The device periodically acquires biometric data such as the user's heart rate, respiration, and body temperature using built-in non-contact sensors. This acquired data is formatted and prepared for transmission to a server via a communication infrastructure. The input is the user's biological state, and the output is formatted biometric data.

[0745] Step 2:

[0746] The device encrypts the acquired biometric data using a security protocol and securely transmits it to the server. Data encryption is crucial for maintaining data confidentiality. The input is formatted biometric data, and the output is encrypted biometric data.

[0747] Step 3:

[0748] The server receives encrypted biometric data, decrypts it, and then performs data analysis using Python and TensorFlow. This analysis allows for the detection of anomalies in the data. The input is encrypted biometric data, and the output is the analysis results.

[0749] Step 4:

[0750] Based on the analysis results, the server uses a generated AI model to assess health risks and determine the optimal countermeasures for each individual user. The risk assessment involves cross-referencing with information from a virtual model. The input is the analysis results, and the output is the health risk assessment and countermeasures.

[0751] Step 5:

[0752] The server generates a prompt message based on the generated countermeasures and inputs it into the AI ​​model to create personalized advice for each user. For example, the prompt message might be "Provide appropriate health advice to users whose body temperature is higher than normal." The input consists of a health risk assessment and a prompt message, while the output is specific advice for the user.

[0753] Step 6:

[0754] The user receives advice sent from the server through an application on their device. The application displays notifications and detailed advice based on the user's status and prepares to provide information to healthcare providers if necessary. The input is the advice from the server, and the output is the notifications and detailed information received by the user.

[0755] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0756] In an embodiment of this invention, the user uses a smartphone or wearable device equipped with a non-contact sensor. The device continuously collects biometric information such as heart rate, respiration, and body temperature. Furthermore, an emotion recognition engine installed in the device analyzes the user's voice patterns and facial expressions to acquire emotion data. This biometric information and emotion data are transmitted to a server via a securely protected communication network.

[0757] The server stores received biometric and emotional data in a database and analyzes it using AI algorithms. Through data analysis, the server detects signs of abnormalities and emotional fluctuations. Furthermore, it constructs a digital twin by referencing similar data from other users, virtually recreating the user's health state.

[0758] Emotional data acquired by the emotion recognition engine is incorporated into a digital twin. The server utilizes this digital twin to predict health risks and generate health advice tailored to the user's emotional state. This advice is personalized and optimized for the user's psychological state and attributes.

[0759] As a concrete example, based on data collected when a user is experiencing stress, the server generates specific advice such as, "Your stress level is high; we recommend trying deep breathing or simple meditation." Furthermore, if emotional data differs from the norm, the server may identify a higher health risk and send a preventative notification.

[0760] These pieces of advice and notifications are delivered to the user's device, allowing the user to review the feedback and take action as needed. If the user consents, the server also collaborates with healthcare institutions to provide this biometric and emotional data to medical professionals. This enables professionals to gain a more comprehensive understanding of the user's health status and improve diagnostic accuracy.

[0761] This invention aims to help users comprehensively understand their own health status and take appropriate action, taking into account their emotional state. It also enables families living separately to confidently monitor each other's health status and promotes safe and independent living for the elderly.

[0762] The following describes the processing flow.

[0763] Step 1:

[0764] The device uses contactless sensors and an emotion recognition engine to continuously acquire biometric information such as the user's heart rate, respiration, and body temperature, as well as emotional data from voice and facial expressions.

[0765] Step 2:

[0766] The device transmits collected biometric and emotional data to the server in real time using a secure communication protocol.

[0767] Step 3:

[0768] The server stores the transmitted biometric and emotional data in a database and performs preprocessing to ensure data quality.

[0769] Step 4:

[0770] The server uses an AI algorithm to analyze the received data. This analysis detects abnormalities in the user's biometric information and fluctuations in their emotions.

[0771] Step 5:

[0772] Based on the data analysis results, the server constructs a digital twin of the user by referencing similar data from other users. The digital twin is a model that virtually reproduces the user's health and emotional state.

[0773] Step 6:

[0774] The server uses the constructed digital twin to predict health risks and generates risk-based warnings and countermeasures, taking emotional data into consideration.

[0775] Step 7:

[0776] The server uses a generation AI to create customized medical questionnaires and health advice tailored to the user's attributes and emotional state.

[0777] Step 8:

[0778] The server sends the generated questions and advice to the user's terminal. The terminal notifies the user of the received information and displays it.

[0779] Step 9:

[0780] Users check notifications received from their devices and decide on actions based on health advice. They also send feedback back to the server via their devices as needed.

[0781] Step 10:

[0782] With the user's permission, the server shares analyzed biometric and emotional data with healthcare institutions. This allows healthcare institutions to improve the accuracy of diagnoses and treatments based on more comprehensive information.

[0783] (Example 2)

[0784] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0785] In modern society, daily stress and health changes have various impacts on individuals' lives. However, there are limited systems that can monitor individual health conditions in real time and provide appropriate advice by predicting emotional changes and health risks. Conventional health management requires consideration of not only biological signals but also emotional signals, and there is a need for technology to efficiently handle this.

[0786] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0787] In this invention, the server includes means for continuously acquiring biosignals using sensors, means for transmitting the acquired biosignals and emotional signals via a communication line, and means for providing health recommendations tailored to the user's psychological characteristics based on the analyzed biosignals and emotional signals. This makes it possible to provide more personalized health recommendations based on the user's health and emotional state.

[0788] "Biosignals" refer to physical or chemical data obtained from the body of an organism, such as heart rate, respiration, and body temperature.

[0789] A "sensor" refers to a device that detects biological signals or emotional signals and converts them into electrical signals.

[0790] "Communication lines" refer to infrastructure for sending and receiving electronic data, including the internet and dedicated lines.

[0791] "Emotional signals" refer to data that indicates an individual's psychological state, analyzed from factors such as voice patterns and facial expressions.

[0792] An "abnormality" refers to a situation where a state or pattern different from the normal one is detected in biological or emotional signals.

[0793] A "digital model" refers to a model that virtually reproduces an individual's health and emotional state, built based on information from similar individuals.

[0794] A "health crisis" refers to a potential health risk predicted from an individual's biosignals and emotional signals.

[0795] "Health recommendations" refer to advice for improving and maintaining health, provided based on an individual's bio-signals and emotional signals.

[0796] "Specialized institutions" refer to medical institutions and organizations that provide healthcare services.

[0797] "Evaluation accuracy" refers to a measure that indicates the accuracy and reliability of the data analyzed by the server.

[0798] This system collects biometric and emotional signals in real time using smartphones or wearable devices equipped with contactless sensors. Specifically, the device continuously acquires biometric signals such as heart rate, respiration, and body temperature, and analyzes emotional signals from voice patterns and facial expressions. This utilizes software called an emotion recognition engine, which enables the rapid generation of emotional data.

[0799] The device uses the internet as its communication line to securely transmit this data to the server. The communication uses encryption protocols (e.g., HTTPS) to ensure security. The server stores the received biometric and emotional signals in a database and then analyzes the data using AI algorithms. This analysis detects abnormalities in health status and emotional fluctuations.

[0800] The server builds a digital model of the user by referencing similar data from other users. This digital model allows for simulations of the user's health in a virtual environment, enabling the prediction of health crises. A generative AI model is used for this analysis, and an example of a prompt is, "Based on your current emotional data, please suggest the most suitable relaxation method."

[0801] Ultimately, the server generates health recommendations and sends them to the user's device. This allows the user to obtain a concrete action plan to reduce stress and discomfort. With the user's permission, this data can also be shared with professional organizations to contribute to improving the accuracy of diagnosis and treatment. These features aim to enable users to more effectively manage their health and emotional state, thereby increasing their well-being in daily life.

[0802] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0803] Step 1:

[0804] The device continuously acquires biosignals (heart rate, respiration, body temperature, etc.) using contactless sensors. The input to this process is raw data obtained from the user's body, and the output is digital signals acquired by the sensor device. These digital signals are temporarily stored within the device.

[0805] Step 2:

[0806] The emotion recognition engine installed in the device analyzes the user's voice patterns and facial expressions to acquire emotion signals. The input is the user's voice data and image data, and the output is emotion data analyzed based on this data. This emotion data is temporarily stored on the device and prepared to be sent to the server later.

[0807] Step 3:

[0808] The device transmits collected biometric signals and emotional data to a server via a communication line (e.g., the internet). Input is digital data stored on the device, and output is sent to the server as securely encrypted data. HTTPS protocol and similar protocols are used for communication to ensure data security.

[0809] Step 4:

[0810] The server stores the biometric and emotional signals it receives in a database. The input is encrypted data sent from the terminal, and the output is the data stored in the database for analysis. This data is recorded along with a timestamp.

[0811] Step 5:

[0812] The server uses a generated AI model to analyze biometric and emotional signals stored in a database. The input is user data stored in the database, and the output is a report that identifies anomalies and emotional changes detected through the analysis. This report is used to assess the user's health status.

[0813] Step 6:

[0814] The server builds a digital model of the user using similar data from other users. The input is user data and similar data, and the output is a digital model that virtually reproduces the user's health status. This step makes it easier to predict the user's health crises.

[0815] Step 7:

[0816] The server generates user health recommendations based on a digital model and analysis results. Inputs are the digital model and anomaly reports, while outputs are specific action plans and advice tailored to the user's condition. For example, it might generate advice such as, "Based on your current emotional data, we suggest the most suitable relaxation method."

[0817] Step 8:

[0818] The server generates health recommendations and sends them to the user's device, notifying them of their health. The input is the generated advice, and the output is the advice message displayed on the user's device. The user can receive this and use it to improve their own health.

[0819] (Application Example 2)

[0820] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0821] In modern society, it is crucial to continuously monitor individuals' health and take appropriate action as needed. However, conventional technologies have not adequately achieved systems that can detect real-time emotional fluctuations and stress levels and provide immediate, appropriate notifications to individual users. As a result, it has been difficult for users to receive appropriate advice when faced with potential dangers or stressful situations.

[0822] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0823] In this invention, the server includes means for securely generating personalized notifications and sending warnings to the user, means for notifying the user of analysis results and advice, and means for continuously acquiring biometric information using non-contact sensors. This enables the user to respond immediately to changes in their emotional state and stress levels and receive appropriate advice.

[0824] "Biometric information" refers to data that indicates a person's physical state, such as heart rate, respiration, and body temperature.

[0825] A "non-contact sensor" is a sensor device that acquires information about an object without requiring direct contact.

[0826] A "communication network" is infrastructure for transmitting information, and includes the internet and other data communication networks.

[0827] A "digital twin" is a digital representation that mimics real-world objects and processes, allowing for analysis and simulation in a virtual space.

[0828] "Health risk" refers to factors or circumstances that may worsen an individual's health condition.

[0829] A "notification" is a message sent to inform a user of specific information or an alert.

[0830] "Analysis results" refer to the conclusions and insights obtained after analyzing data.

[0831] "Advice" refers to providing users with guidelines or suggestions for action, particularly regarding health and mental health.

[0832] A "medical institution" is a facility or organization that provides medical services, including hospitals and clinics.

[0833] To implement this invention, first, a smartphone or wearable device is used as the terminal. The terminal is equipped with a non-contact sensor to acquire biometric information such as heart rate, respiration, and body temperature in real time. It also has an emotion recognition engine that analyzes voice patterns and facial expressions, thereby acquiring emotion data.

[0834] The device securely transmits acquired biometric and emotional data to a server via a communication network. The server receives this data and performs detailed data analysis using AI algorithms. This analysis detects signs of abnormalities and emotional fluctuations. Furthermore, the server references data from similar individuals to construct a virtual model called a digital twin. Based on this digital twin, it predicts health risks and generates health advice tailored to the user's emotional state.

[0835] Specifically, when the server detects an anomaly, it sends a personalized notification to the user's device. For example, if a user is experiencing high stress levels, it might send advice such as, "Your stress levels are high; we recommend trying deep breathing or simple meditation." This allows the user to know how to cope immediately.

[0836] This system incorporates machine learning models using Python and TensorFlow, supporting efficient and accurate data analysis.

[0837] For example, if a user feels stressed while traveling on public transport, the system can sense this change in emotion and send a notification saying, "To relax, we recommend taking deep breaths and listening to your favorite music." An example of an input prompt to the generating AI model would be, "I've experienced significant emotional fluctuations today; please suggest ways to relax. Current location: office, heart rate: 80, breathing: slightly fast, emotional data: stressed."

[0838] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0839] Step 1:

[0840] The device acquires biometric information such as heart rate, respiration, and body temperature through non-contact sensors. At this stage, the input is the user's physical state, and the output is numerical data (e.g., heart rate: 72, body temperature: 36.5°C). The device records this biometric information in preparation for the next step.

[0841] Step 2:

[0842] The device analyzes voice patterns and facial expressions to acquire emotion data. Here, the input is the user's voice and facial image, and the output is data on emotional state (e.g., tension: high, happiness: medium). The device is equipped with an emotion recognition engine, which quantifies specific emotions by performing voice and image analysis.

[0843] Step 3:

[0844] The device collects biometric and emotional data and transmits it to a server via a secure communication network. In this process, the input is the dataset collected by the device, and the output is the secure arrival of the data at the server. The device uses encryption protocols to prevent data leakage.

[0845] Step 4:

[0846] The server analyzes the received data using an AI algorithm. The input is biometric information and emotional data, and the output is the analysis results (e.g., detection of anomalies in heart rate and stress levels). Based on this data, the server operates a machine learning model using Python and TensorFlow to identify anomalous patterns.

[0847] Step 5:

[0848] The server constructs a digital twin by referencing similar data from other users. The inputs here are user data and other users' datasets, and the output is a virtual health model of the user. This process involves using a database to compare vast amounts of historical data with user data to generate the virtual model.

[0849] Step 6:

[0850] The server predicts health risks based on the analysis results and generates personalized health advice. Inputs are a digital twin and analysis results, and output is an appropriate advice message for the user (e.g., suggestions for relaxation techniques). A generative AI model is used to provide user-specific prompts.

[0851] Step 7:

[0852] The server generates advice and notifies the terminal. Here, the input is the generated advice, and the output is the notification displayed on the user's terminal. The terminal is designed to utilize the notification function to allow the user to receive immediate feedback.

[0853] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0854] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0855] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0856] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0857] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0858] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0859] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0860] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0861] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0862] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0863] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0864] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0865] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0866] 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.

[0867] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0868] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0869] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0870] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0871] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0872] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0873] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0874] The following is further disclosed regarding the embodiments described above.

[0875] (Claim 1)

[0876] A means for continuously acquiring biometric information using non-contact sensors,

[0877] A means for transmitting biometric information acquired via a communication network,

[0878] A means for analyzing received biometric information and detecting abnormalities,

[0879] A means of constructing a digital twin using similar data from other parties,

[0880] A method for predicting health risks based on the generated digital twin,

[0881] A means for generating optimal medical questionnaire questions based on detected anomalies,

[0882] Means of sharing information with medical institutions,

[0883] A means of notifying the user of analysis results and advice,

[0884] A system that includes this.

[0885] (Claim 2)

[0886] The system according to claim 1, further comprising means for providing health advice tailored to the user's attributes and personality based on analyzed biometric information.

[0887] (Claim 3)

[0888] The system according to claim 1, further comprising means for providing biometric information to a medical institution with the user's permission to improve diagnostic accuracy.

[0889] "Example 1"

[0890] (Claim 1)

[0891] A means of continuously acquiring biometric data using non-contact sensors,

[0892] A means for transferring biometric data acquired via a communication network,

[0893] A means of analyzing received biometric data and detecting abnormalities,

[0894] A means of constructing a digital reproduction using similar data from others,

[0895] A means of predicting health risks based on the generated digital reproduction,

[0896] A means of using a generative AI model to generate optimal medical questionnaires and health advice based on detected anomalies,

[0897] Means for sharing information with medical facilities,

[0898] Means of notifying users of analysis results and health advice,

[0899] A system that includes this.

[0900] (Claim 2)

[0901] The system according to claim 1, further comprising means for providing health advice tailored to the user's characteristics and personality based on analyzed biometric data.

[0902] (Claim 3)

[0903] The system according to claim 1, further comprising means for providing biometric data to a medical facility with the user's permission to improve diagnostic accuracy.

[0904] "Application Example 1"

[0905] (Claim 1)

[0906] A means for continuously acquiring biometric data using a non-contact detection device,

[0907] A means for transmitting biometric data acquired via a communication infrastructure,

[0908] A means of analyzing received biometric data and detecting abnormalities,

[0909] A means of constructing a virtual model using similar information from others,

[0910] A means of predicting health risks based on the generated virtual model,

[0911] A means for generating optimal questionnaire items based on detected abnormalities,

[0912] Means of sharing information with medical institutions,

[0913] A means of notifying users of analysis results and advice,

[0914] A means of performing authentication control that takes into account health status and safety using biometric data,

[0915] A system that includes this.

[0916] (Claim 2)

[0917] The system according to claim 1, further comprising means for providing health advice tailored to the user's attributes and personality based on analyzed biometric data.

[0918] (Claim 3)

[0919] The system according to claim 1, further comprising means for providing biometric information to a medical institution with the user's permission, improving diagnostic accuracy, and implementing security-based access authentication.

[0920] "Example 2 of combining an emotion engine"

[0921] (Claim 1)

[0922] A means for continuously acquiring biological signals using sensors,

[0923] A means for transmitting biometric signals and emotional signals acquired via a communication line,

[0924] A means for analyzing received biosignals and emotional signals to detect abnormalities and emotional changes,

[0925] A means of constructing a digital model using similar information from others,

[0926] A means of predicting health crises based on the generated digital models,

[0927] A means of generating health advice based on analyzed emotional data,

[0928] Means of coordinating information with specialized organizations,

[0929] A means of notifying users of analysis results and advice,

[0930] A system that includes this.

[0931] (Claim 2)

[0932] The system according to claim 1, further comprising means for providing health recommendations tailored to the user's psychological characteristics based on analyzed biosignals and emotional signals.

[0933] (Claim 3)

[0934] The system according to claim 1, further comprising means for providing biosignals and emotional signals to a specialized institution with the user's permission to improve evaluation accuracy.

[0935] "Application example 2 when combining with an emotional engine"

[0936] (Claim 1)

[0937] A means for continuously acquiring biometric information using non-contact sensors,

[0938] A means for transmitting biometric information acquired via a communication network,

[0939] A means for analyzing received biometric information and detecting abnormalities,

[0940] A means of constructing a digital twin using similar data from other parties,

[0941] A method for predicting health risks based on the generated digital twin,

[0942] A means for generating optimal medical questionnaire questions based on detected anomalies,

[0943] A means of securely generating personalized notifications and sending alerts to users,

[0944] Means of sharing information with medical institutions,

[0945] A means of notifying the user of analysis results and advice,

[0946] A system that includes this.

[0947] (Claim 2)

[0948] The system according to claim 1, further comprising means for providing health advice tailored to the user's attributes and personality based on analyzed biometric information.

[0949] (Claim 3)

[0950] The system according to claim 1, further comprising means for providing biometric information to a medical institution with the user's permission to improve diagnostic accuracy. [Explanation of symbols]

[0951] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for continuously acquiring biometric information using non-contact sensors, A means for transmitting biometric information acquired via a communication network, A means for analyzing received biometric information and detecting abnormalities, A means of constructing a digital twin using similar data from other parties, A method for predicting health risks based on the generated digital twin, A means for generating optimal medical questionnaire questions based on detected anomalies, Means of sharing information with medical institutions, A means of notifying the user of analysis results and advice, A system that includes this.

2. The system according to claim 1, further comprising means for providing health advice tailored to the user's attributes and personality based on analyzed biometric information.

3. The system according to claim 1, further comprising means for providing biometric information to a medical institution with the user's permission to improve diagnostic accuracy.

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