system

Abstract

We provide the system. [Solution] A means having a measuring device for collecting user health data, An analytical means for analyzing the aforementioned health data and estimating stress levels and psychological state, A generation means for generating information to provide appropriate recommendations or promotions to users based on the analysis results, A transmission means for notifying the user's terminal device of the generated information, A report generation means for reporting the analysis results of the user to the administrator of the organization, A system that includes this.

Description

Technical Field

[0005]

[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 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 as a 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 spread of the remote work environment, there is a problem that the sense of loneliness and stress experienced by employees are increasing, and mental health problems are becoming more serious. This problem often appears as a lack of communication, the breakdown of work-life balance due to the ambiguity of the boundary between work and private life, and a decline in productivity. Therefore, there is an increasing need for means to timely detect such mental health problems and automatically provide appropriate improvement measures.

Means for Solving the Problems

[0005] This invention solves the above-mentioned problems by providing a system that collects users' health data through a measuring device and analyzes that data on the cloud. Based on the stress levels and psychological state obtained from the analysis, the system generates individually tailored advice, recommendations, and nudges for the user and provides means to notify the user's terminal. Furthermore, by reporting these analysis results to the organization's administrator, it provides support for managing overall mental health. This makes it possible to detect individual problems early and support the creation of a healthy workplace environment for the entire organization.

[0006] A "measuring device" is a device used to collect users' health data, and specifically refers to smartwatches, fitness trackers, and similar devices.

[0007] "Health data" refers to a collection of numerical data or information that indicates a user's physical and psychological state, such as heart rate, exercise level, and voice tone.

[0008] "Analysis means" refers to software or algorithms used to analyze collected health data and estimate the user's stress level and psychological state.

[0009] "Generation means" refers to the process or apparatus for creating information to provide appropriate recommendations and guidance to users based on analysis results.

[0010] "Transmission means" refers to communication technology or equipment used to notify the user's terminal device of the generated information.

[0011] "Report generation means" refers to a device or process that generates documents or electronic data for reporting the analysis results obtained by the analysis means to the organization's administrators.

[0012] A "terminal device" is a device used by a user to receive and verify generated information, and specifically refers to smartphones and personal computers.

[0013] "Analysis results" refer to information regarding users' stress levels and psychological states obtained through the analysis of health data. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]A sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), etc.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a 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, etc.

[0020] 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).

[0021] 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."

[0022] [First Embodiment]

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

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0029] 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.

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

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention is implemented as a system to support the mental health of employees in a remote work environment. This system consists of multiple components that work together to analyze the user's mental health status and provide appropriate feedback based on the results.

[0036] First, the smartwatch worn by the user collects health data such as heart rate, activity level, and voice tone in real time. This data is sent to the user's smartphone, where it is temporarily stored. The smartphone then periodically sends the collected data to a server in the cloud.

[0037] The server stores the received data in a cloud database and inputs it into an AI analysis model. This AI analysis model estimates the user's stress level and psychological state and generates specific stress indicators. Based on the results, it automatically generates advice and nudges, i.e., suggestions to encourage action, tailored to each user.

[0038] For example, if a user experiences a consistently high heart rate for several days, the server may interpret this as a sign of stress and send a notification to their smartphone suggesting actions such as "Try taking a deep breath" or "Take a 5-minute break." The user can then receive this notification and take the suggested action.

[0039] Furthermore, the server aggregates the analysis results and creates a report that visualizes the overall mental health status of the organization. This report is provided to administrators and the human resources department and used to build individual support systems and consider improvement measures for the entire organization.

[0040] In this way, this system aims to improve communication in remote environments, maintain productivity, and provide stress-free mental health care.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The device measures health data such as heart rate, activity level, and voice tone through the user's smartwatch and transfers this data to the smartphone.

[0044] Step 2:

[0045] The device is configured to periodically upload health data stored on the smartphone to a cloud server. Data transmission occurs automatically at regular intervals.

[0046] Step 3:

[0047] The server records the received health data in a database and manages it as stored data. This data is then prepared for analysis by AI analysis models.

[0048] Step 4:

[0049] The server inputs health data from the database into an AI analysis model, which then analyzes the user's stress level and psychological state. The AI ​​analysis model uses past patterns and learning algorithms to determine the current state.

[0050] Step 5:

[0051] The server generates personalized advice and nudges for the user based on the analysis results. Examples of generated content include "reminders to rest" and "suggestions for simple stretches."

[0052] Step 6:

[0053] The server issues instructions to send the generated advice and nudges to the user's smartphone. This information is delivered as a notification so that the user can take immediate action.

[0054] Step 7:

[0055] The device receives notifications sent to the user's smartphone and displays an alert to the user. This allows the user to take the recommended action immediately.

[0056] Step 8:

[0057] The server analyzes the organization's mental health trends and generates a detailed report for managers or the human resources department. This report can be used to plan the organization's health initiatives.

[0058] (Example 1)

[0059] 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."

[0060] In recent years, as remote work has become commonplace, individual psychological stress and mental health are impacting the overall performance of organizations. However, effectively monitoring employees' mental health and taking timely and appropriate action is difficult. Therefore, there is a growing need for methods to accurately assess the mental health of individual employees and provide personalized support.

[0061] 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.

[0062] In this invention, the server includes means having an electronic device for acquiring biometric information, computation means for aggregating biometric information and evaluating psychological state, and generation means for creating specific advice based on the evaluation results. This makes it possible to monitor an individual's mental health in real time and provide necessary support quickly and appropriately.

[0063] "Biometric information" refers to information that indicates the user's physical condition, and includes data such as heart rate, activity level, and voice tone.

[0064] "Electronic device" refers to a hardware device for acquiring biological information, specifically a measuring instrument that can be worn on the user's body.

[0065] "Computational means" refers to processes and algorithms for analyzing aggregated biometric information and evaluating the user's psychological state.

[0066] "Generation means" refers to a function that creates specific advice and action-promoting messages for users based on analyzed data.

[0067] "Communication means" refers to methods or technologies for transmitting generated information to a user's mobile device, such as wireless communication or the use of Internet protocols.

[0068] "Reporting methods" refer to the processes and tools used to aggregate and visualize analytical results for presentation to decision-makers within an organization.

[0069] This invention is a system for supporting the mental health of users in a remote environment, with each component working in coordination. First, the user wears a smartwatch, an electronic device, which acquires biometric information such as heart rate, activity level, and voice tone in real time. This smartwatch uses sensor technology to collect this data. The collected data is transmitted to the user's smartphone via Bluetooth.

[0070] Smartphones play a role in temporarily storing received biometric information and sending it to a cloud server. HTTPS, a secure communication protocol, is used for data transmission. The cloud server stores the transmitted data in a database and has the computational means to analyze it. Specifically, it uses an AI analysis model to evaluate the user's psychological state and stress level, and calculates stress indicators through a generative AI model.

[0071] Based on the analysis results, the server generates information aimed at providing useful advice and encouraging actions to the user. The generated information is written in natural language and sent as a push notification to the smartphone. The user can check the notification on their smartphone and take action on the suggested methods (e.g., "Try taking a deep breath," "Take a 5-minute break," etc.).

[0072] For example, if a user's heart rate remains higher than normal, the server will consider this a sign of stress and notify them of suggestions such as deep breathing. Furthermore, the server will compile the analysis results and provide a visualized report to the organization's administrators. This report will serve as important guidance for improving the overall mental health situation of the organization.

[0073] Examples of prompts include, "If the user's heart rate has been elevated for a week, suggest specific advice for stress reduction," and "Based on the collected health data, list suggestions for actions the user should take."

[0074] In this way, the present invention provides mental health support tailored to the individual circumstances of users and promotes healthy working styles in remote work.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The user wears a smartwatch, which acquires biometric information such as heart rate, activity level, and voice tone in real time. Specifically, sensors within the smartwatch collect this data. The inputs are the user's heart rate, voice, and movement, and the output is a dataset of these data.

[0078] Step 2:

[0079] The user's smartwatch transmits collected biometric information to their smartphone via Bluetooth. The smartphone receives the data and temporarily stores it in its storage. In this process, the input is data from the smartwatch, and the output is data stored in the smartphone.

[0080] Step 3:

[0081] The smartphone, acting as the terminal, periodically sends collected data to a cloud server. The secure HTTPS protocol is used for data transmission. The input is biometric data stored on the smartphone, and the output is data transmission to the cloud server.

[0082] Step 4:

[0083] The server receives biometric data on the cloud and stores it in a database. Since the stored data is analyzed by an AI analysis model, the server preprocesses the data for the AI ​​model and performs data cleansing. The input is data sent from a smartphone, and the output is an analyzable dataset.

[0084] Step 5:

[0085] The server uses an AI analysis model to assess the user's stress level and psychological state. By analyzing the data, it generates stress indicators and psychological state evaluation points. The input is pre-processed data, and the output is the analysis result.

[0086] Step 6:

[0087] Based on the analysis results, the server generates specific advice for the user using an AI model. This generated information is then converted into an easily understandable form using natural language. The input is the analysis results, and the output is the generated advice message.

[0088] Step 7:

[0089] The server pushes the generated advice to the user's smartphone. The user receives the notification on their smartphone and can then take the suggested action. The input is the generated message, and the output is the notification to the smartphone.

[0090] (Application Example 1)

[0091] 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."

[0092] The aim is to improve the work environment in a remote setting by utilizing employees' biometric information to appropriately assess individual mental stress and psychological states, and by promptly providing actionable suggestions based on the results. Furthermore, it provides managers with a foundation for more effective organizational management by visualizing the overall mental health status of employees.

[0093] 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.

[0094] In this invention, the server includes means having a measuring device for collecting the user's biometric information, analysis means for analyzing the biometric information and estimating mental stress and psychological state, and generation means for generating information to provide appropriate action suggestions or encouragement to the user based on the analysis results. This makes it possible to grasp the stress and psychological state of individual employees in real time and provide appropriate support.

[0095] "Biometric information" refers to data related to the user's physical condition and function, such as heart rate, exercise level, and voice tone.

[0096] A "measuring device" is a device or group of devices used to collect biological information, and includes devices such as smartwatches.

[0097] "Analysis means" refers to the process or function of analyzing collected biometric information to estimate the user's mental burden and psychological state.

[0098] "Generative means" refers to the procedures and processes for generating information that provides action suggestions or promotions based on the analysis results.

[0099] "Transmission means" refers to the communication path and protocol used to notify the user's information terminal of the generated information.

[0100] "Output generation means" refers to methods and functions for creating materials and reports to inform the person in charge of the organization of the user's analysis results.

[0101] "Response mechanisms" refer to interfaces or tools designed to encourage users to take the suggested actions.

[0102] This system is designed to support the mental health of employees in remote work environments. Its main components include a measurement device worn by the user, a server that processes and analyzes the information, and a terminal device that provides feedback to the user.

[0103] As a measuring device, a smartwatch collects biometric information such as heart rate, activity level, and voice tone in real time. This data is transmitted to the user's smartphone and uploaded to a cloud server via the internet.

[0104] The server uses software such as Python and TENSORFLOW® to estimate mental stress and psychological state using an AI analysis model based on collected data. Based on the analysis results, personalized action suggestions are generated for the user. The suggestions are notified to the user through an application installed on their smartphone. This may include suggestions such as "Take a 5-minute break to relax."

[0105] Furthermore, the server aggregates the data for organizational leaders and creates a report outlining the overall mental health status. This report is used as a reference for organizational improvement measures.

[0106] For example, if the AI ​​model determines that the user's stress level is very high in the morning, the server will generate a nudge suggesting, "Let's practice deep breathing." The user receives this notification via their smartphone and can take stress-reducing action by following the instructions.

[0107] Examples of prompts for generative AI models:

[0108] "Analyze the mental stress associated with changes in heart rate, and if the stress level exceeds a certain level, propose specific mitigation measures."

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The device acquires biometric information such as heart rate, activity level, and voice tone from the smartwatch, which acts as a measuring device. This data is temporarily stored on the device. The input data is biometric information, and the output is temporarily stored data on the device.

[0112] Step 2:

[0113] The device transmits temporarily stored biometric information to a cloud server via the internet. Specifically, the device verifies the network connection and uploads the data to the cloud using a secure communication protocol. The input is the biometric data stored on the device, and the output is the data stored on the cloud server.

[0114] Step 3:

[0115] The server inputs biometric information received on the cloud into an AI analysis model to estimate mental stress and psychological state. Specifically, it uses Python and TensorFlow to extract data features and calculate stress levels. The input is data stored on the cloud server, and the output is the estimated result of the psychological state.

[0116] Step 4:

[0117] The server automatically generates appropriate action suggestions for the user based on the analysis results. Specifically, it uses an AI model to generate suggestions such as "promote relaxation" based on the evaluation of the analysis results. The input is the estimated result of the user's psychological state, and the output is the text of the action suggestion.

[0118] Step 5:

[0119] The generated action suggestions are notified to the device. The device presents the suggestions to the user through on-screen pop-up notifications, audio notifications, etc. Specific actions such as vibration or alarm sounds may be used to attract the user's attention. The input is the text of the action suggestion, and the output is the notification to the user.

[0120] Step 6:

[0121] The user chooses whether to perform the action suggested through their device. Based on this choice, the response is recorded and may be sent to the server as feedback. Specifically, the record is updated according to the user's actions. The input is the user's response, and the output is feedback data.

[0122] Step 7:

[0123] The server continuously improves the AI ​​analysis model by incorporating user feedback and additional biometric information. Specifically, it accumulates feedback data and retrains the model to improve its performance. The input is the feedback data, and the output is the improved analysis model.

[0124] 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.

[0125] This invention is implemented as a system for monitoring the mental health of users in a remote work environment and prompting appropriate responses. This system has multiple main components that work together to analyze the user's psychological and emotional state.

[0126] The smartwatch worn by the user collects basic health data such as heart rate, activity level, and voice tone. This data is temporarily stored on the user's smartphone. The smartphone then periodically sends the collected data to a server in the cloud.

[0127] The server organizes the data received on the cloud and performs analysis using an emotion engine. This emotion engine recognizes emotions from the user's voice and facial expression data and has the technology to estimate stress levels and psychological states with greater precision. The analyzed data is evaluated while taking into account the user's individual history data, making it possible to gain insights into the user's current emotional state.

[0128] Based on the analysis results, the server automatically generates personalized advice and nudges for the user. These nudges may include mental support information tailored to the user's emotional state. For example, if the server determines that the user is experiencing excessive stress, it will create a notification suggesting relaxation methods or recommending a short break.

[0129] Next, the server sends the generated notification to the smartphone, delivering it to the user as an alert via the device. Upon receiving this alert, the user can then take specific action.

[0130] Furthermore, the server manages the mental health of the entire organization by generating reports for administrators based on aggregated data. These reports can be used to develop individual care plans for employees and to plan organizational healthcare initiatives.

[0131] This invention aims to efficiently understand the emotional state of users working remotely by combining emotion recognition technology, thereby facilitating timely countermeasures.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The device uses the user's smartwatch to measure health data such as heart rate, activity level, and voice tone, and transfers this data to the smartphone.

[0135] Step 2:

[0136] The device uploads health data stored on the smartphone to a cloud server at regular intervals, making it easier to collect data in real time.

[0137] Step 3:

[0138] The server retrieves health data received from the cloud and records it in a database. This recorded data is then prepared for further detailed analysis.

[0139] Step 4:

[0140] The server uses an emotion engine to analyze health data and recognize the user's emotional state from their voice and facial expression data. This allows for highly accurate estimation of stress levels and psychological state.

[0141] Step 5:

[0142] The server generates personalized advice and nudges for the user based on their perceived emotional state. This may include suggestions for relaxation techniques or ways to improve work-life balance.

[0143] Step 6:

[0144] The server issues instructions to send the generated advice and nudges to the user's smartphone. The device then delivers this to the user as a notification.

[0145] Step 7:

[0146] By receiving notifications on their smartphones and taking specific actions based on their content, users can reduce stress and improve their mental health.

[0147] Step 8:

[0148] The server aggregates mental health data across the entire organization and generates detailed reports for administrators and the human resources department. These reports are used to develop the organization's health initiatives.

[0149] (Example 2)

[0150] 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".

[0151] The aim is to improve the current situation where there is insufficient infrastructure to efficiently monitor the mental health of employees working remotely and to automatically prompt appropriate responses. In particular, there is a need for a method that analyzes employees' emotional states and stress levels in real time and provides individually customized advice, thereby contributing to the maintenance of individual health while improving the mental health of the entire organization.

[0152] 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.

[0153] In this invention, the server includes information collection means for collecting the user's biometric information, analysis means for analyzing the biometric information and estimating the emotional state and mental health, and generation means for generating information that provides individually tailored recommendations to the user based on the analysis results. This enables accurate understanding of the user's psychological state and appropriate responses in real time.

[0154] "User" refers to an individual who provides their biometric information using the system.

[0155] "Biometric information" refers to all data that indicates a user's health status, such as heart rate, activity level, and voice tone.

[0156] "Information gathering means" refers to devices and methods for acquiring biometric information from users.

[0157] "Analysis methods" refer to methods and processes for evaluating a user's emotional state and mental health based on collected biometric information.

[0158] "Generation method" refers to the process of creating tailored advice and recommendations for users based on the analysis results.

[0159] "Means of communication" refers to methods for notifying users of generated advice and information on their devices.

[0160] "Report creation method" refers to a method for providing users with their analysis results in an easily understandable format for the organization's responsible person.

[0161] This invention is a system that collects biometric information from users working in a remote environment and provides personalized health advice based on that information. This system has the following specific components, which work together to analyze the user's emotional state and stress level.

[0162] Body devices such as smartwatches worn by users acquire biometric information such as heart rate, activity level, and voice tone. This information is temporarily stored on the user's smartphone. This smartphone receives biometric information via Bluetooth and periodically transmits it to a server in the cloud. Standard Wi-Fi or mobile data communication is used for this purpose.

[0163] The server receives data in the cloud and performs data organization and analysis. The analysis utilizes emotion recognition technologies, including voice data analysis and facial expression estimation. This allows for an assessment of the user's emotional state and stress level. General cloud computing services are used for the analysis.

[0164] Furthermore, the server uses a generative AI model to create prompt messages based on the analysis results. This generation method takes individual historical data into consideration to provide personalized advice. For example, a prompt such as, "Based on the observed data, your stress level appears to be high. We recommend incorporating 10 minutes of meditation," might be generated.

[0165] The server sends the generated prompt to the smartphone, displaying an alert to the user via the device. Upon receiving this alert, the user can take the suggested action, which is expected to improve their health.

[0166] This system also provides organizational administrators with reports that assess the overall mental health status of employees based on aggregated data. These reports can be used to plan for maintaining employee well-being.

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] The user wears a smartwatch, which acquires biometric information (heart rate, activity level, voice tone) in real time. Specifically, the smartwatch uses sensors to continuously measure this data and transmits the acquired data to the user's smartphone via Bluetooth. The input here is the user's biological behavior, and the output is digitized biometric data.

[0170] Step 2:

[0171] The device, i.e., the user's smartphone, temporarily stores the received biometric data in its storage. Furthermore, it periodically sends this data to a server in the cloud. Specifically, it sends the data at regular intervals using Wi-Fi or a mobile data network. The input is biometric data from the smartwatch, and the output is the data sent to the server.

[0172] Step 3:

[0173] The server receives data in the cloud and performs organization and preprocessing. Specifically, it classifies the received data by user, completes missing data, and corrects outliers. The input is biometric data sent from smartphones, and the output is data prepared for analysis.

[0174] Step 4:

[0175] The server uses emotion recognition technology to evaluate emotional states and stress levels based on organized data. Specifically, it employs methods such as analyzing voice data and comparing it with historical data. The input is pre-processed biometric data, and the output is evaluation data regarding emotional states and stress levels.

[0176] Step 5:

[0177] The server utilizes a generative AI model to generate prompt messages based on the analysis results. These prompts include advice tailored to the user's psychological state. Specifically, prompts such as, "Based on the observed data, your stress level appears to be high. We recommend incorporating 10 minutes of meditation," are generated. The input is emotion evaluation data, and the output is a prompt message containing specific advice for the user.

[0178] Step 6:

[0179] The server sends the generated prompt to the terminal, and the terminal displays the received information as a notification to the user. Specifically, the prompt is shown to the user using the smartphone's notification function. The input is the generated prompt, and the output is a notification that the user can visually confirm.

[0180] Step 7:

[0181] The server evaluates the overall mental health status of the organization based on aggregated user data and generates a report. Specifically, it generates a report in a format viewable by administrators, which includes trends in the organization's stress levels and suggested countermeasures. The input is the analysis results of the users, and the output is a report for administrators.

[0182] (Application Example 2)

[0183] 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".

[0184] In occupations involving long working hours, such as security guards, it is not easy to quickly identify employee stress and mental burden and take appropriate measures. In particular, the inability to offer specific rest and relaxation methods tailored to individual needs can lead to a deterioration of employee mental health and a decrease in work efficiency. Therefore, there is a need for a system that can effectively monitor the mental health of security guards and provide individually tailored mental support.

[0185] 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.

[0186] In this invention, the server includes means having a measuring device for collecting user health data, analysis means for analyzing the health data and estimating stress levels and psychological states, and generation means for providing security workers with personalized rest notifications and relaxation methods. This makes it possible to grasp the stress levels of security workers in real time and provide individually optimized mental support.

[0187] A "measuring device" is a device used to acquire various data related to the user's health. Its primary role is to collect data such as heart rate, exercise level, and voice tone.

[0188] "Analysis methods" refer to the processes and techniques used to analyze collected health data and estimate the user's stress level and psychological state.

[0189] "Generation method" refers to the process and technology of creating mental support information, including recommended information and relaxation methods, for users based on analysis results.

[0190] "Transmission means" refers to the methods and devices used to transfer generated information to terminal devices, and is responsible for delivering notifications and alerts to users.

[0191] "Report generation means" refers to the processes and technologies for creating and providing appropriate mental health reports to organizational managers based on analysis results.

[0192] "Personalized break notifications" refer to customized information that informs users of the timing and method of taking breaks based on analysis results and tailored to their individual circumstances.

[0193] "Relaxation methods" refer to specific behavioral suggestions and activities provided with the aim of reducing stress for users.

[0194] The system that implements this application collects data using a smartwatch or similar measuring device worn by the user and temporarily stores it on a smartphone. The smartphone then transmits this data to a server in the cloud. The server organizes the received data and performs analysis using an emotion engine. This emotion engine uses services such as Microsoft® Azure® Emotion API to recognize emotions, including voice and activity data, and accurately estimates stress levels and psychological states. Based on the analysis results, the server automatically generates appropriate rest notifications and relaxation information for security workers. This generated information is sent to the security worker's smartphone or smartwatch, allowing the user to refer to it and select optimized activities.

[0195] Furthermore, the server generates detailed reports for administrators based on the aggregated data. These reports can be used to monitor employee health and improve work efficiency. As a specific example, a security guard's smartwatch detected an increase in heart rate during work and sent a notification recommending a break. By following this notification and taking a short break, the guard was able to continue working efficiently.

[0196] An example of a prompt for a generating AI model would be: "Describe the design of a system that analyzes heart rate data from a smartwatch, identifies patterns indicating high stress levels, and provides users with rest notifications and relaxation advice."

[0197] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0198] Step 1:

[0199] The device (smartwatch) collects data related to the user's health (heart rate, activity level, voice tone, etc.) in real time. This data is acquired by sensors and stored inside the device in an appropriate format. This data serves as input for subsequent processing.

[0200] Step 2:

[0201] The device (smartphone) receives health data collected from the smartwatch at regular intervals. The received data is temporarily stored in the smartphone. At this point, the integrity of the data is checked, and if there is any incomplete data, an attempt is made to collect it again.

[0202] Step 3:

[0203] The device (smartphone) sends temporarily stored health data to a server in the cloud. The data is transmitted using a secure communication protocol and used as input for processing on the server.

[0204] Step 4:

[0205] The server stores the received health data in a database and analyzes the data using an emotion engine. Using tools such as the Microsoft Azure Emotion API, it estimates stress levels and psychological states from voice and activity data. This analysis yields output that includes insights into emotional states.

[0206] Step 5:

[0207] Based on the analysis results, the server uses a generative AI model to generate personalized break and relaxation suggestions for security workers. It utilizes prompts to create suggestions best suited to specific situations. These generated suggestions then serve as input for the next processing step.

[0208] Step 6:

[0209] The server sends the generated personalized suggestions to the device. The device displays them to the user as a notification, prompting them to take specific action. The output here is the specific notification content presented to the user.

[0210] Step 7:

[0211] Based on the notifications provided, users engage in specific relaxation activities such as exercise or rest. Data from these activities is collected again in the next cycle, and the process is repeated. This processing cycle allows for continuous monitoring and improvement of the user's mental health.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] [Second Embodiment]

[0216] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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).

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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".

[0228] This invention is implemented as a system to support the mental health of employees in a remote work environment. This system consists of multiple components that work together to analyze the user's mental health status and provide appropriate feedback based on the results.

[0229] First, the smartwatch worn by the user collects health data such as heart rate, activity level, and voice tone in real time. This data is sent to the user's smartphone, where it is temporarily stored. The smartphone then periodically sends the collected data to a server in the cloud.

[0230] The server stores the received data in a cloud database and inputs it into an AI analysis model. This AI analysis model estimates the user's stress level and psychological state and generates specific stress indicators. Based on the results, it automatically generates advice and nudges, i.e., suggestions to encourage action, tailored to each user.

[0231] For example, if a user experiences a consistently high heart rate for several days, the server may interpret this as a sign of stress and send a notification to their smartphone suggesting actions such as "Try taking a deep breath" or "Take a 5-minute break." The user can then receive this notification and take the suggested action.

[0232] Furthermore, the server aggregates the analysis results and creates a report that visualizes the overall mental health status of the organization. This report is provided to administrators and the human resources department and used to build individual support systems and consider improvement measures for the entire organization.

[0233] In this way, this system aims to improve communication in remote environments, maintain productivity, and provide stress-free mental health care.

[0234] The following describes the processing flow.

[0235] Step 1:

[0236] The device measures health data such as heart rate, activity level, and voice tone through the user's smartwatch and transfers this data to the smartphone.

[0237] Step 2:

[0238] The device is configured to periodically upload health data stored on the smartphone to a cloud server. Data transmission occurs automatically at regular intervals.

[0239] Step 3:

[0240] The server records the received health data in a database and manages it as stored data. This data is then prepared for analysis by AI analysis models.

[0241] Step 4:

[0242] The server inputs health data from the database into an AI analysis model, which then analyzes the user's stress level and psychological state. The AI ​​analysis model uses past patterns and learning algorithms to determine the current state.

[0243] Step 5:

[0244] The server generates personalized advice and nudges for the user based on the analysis results. Examples of generated content include "reminders to rest" and "suggestions for simple stretches."

[0245] Step 6:

[0246] The server issues instructions to send the generated advice and nudges to the user's smartphone. This information is delivered as a notification so that the user can take immediate action.

[0247] Step 7:

[0248] The device receives notifications sent to the user's smartphone and displays an alert to the user. This allows the user to take the recommended action immediately.

[0249] Step 8:

[0250] The server analyzes the organization's mental health trends and generates a detailed report for managers or the human resources department. This report can be used to plan the organization's health initiatives.

[0251] (Example 1)

[0252] 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."

[0253] In recent years, as remote work has become commonplace, individual psychological stress and mental health are impacting the overall performance of organizations. However, effectively monitoring employees' mental health and taking timely and appropriate action is difficult. Therefore, there is a growing need for methods to accurately assess the mental health of individual employees and provide personalized support.

[0254] 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.

[0255] In this invention, the server includes means having an electronic device for acquiring biometric information, computation means for aggregating biometric information and evaluating psychological state, and generation means for creating specific advice based on the evaluation results. This makes it possible to monitor an individual's mental health in real time and provide necessary support quickly and appropriately.

[0256] "Biometric information" refers to information that indicates the user's physical condition, and includes data such as heart rate, activity level, and voice tone.

[0257] "Electronic device" refers to a hardware device for acquiring biological information, specifically a measuring instrument that can be worn on the user's body.

[0258] "Computational means" refers to processes and algorithms for analyzing aggregated biometric information and evaluating the user's psychological state.

[0259] "Generation means" refers to a function that creates specific advice and action-promoting messages for users based on analyzed data.

[0260] "Communication means" refers to methods or technologies for transmitting generated information to a user's mobile device, such as wireless communication or the use of Internet protocols.

[0261] "Reporting methods" refer to the processes and tools used to aggregate and visualize analytical results for presentation to decision-makers within an organization.

[0262] This invention is a system for supporting the mental health of users in a remote environment, with each component working in coordination. First, the user wears a smartwatch, an electronic device, which acquires biometric information such as heart rate, activity level, and voice tone in real time. This smartwatch uses sensor technology to collect this data. The collected data is transmitted to the user's smartphone via Bluetooth.

[0263] Smartphones play a role in temporarily storing received biometric information and sending it to a cloud server. HTTPS, a secure communication protocol, is used for data transmission. The cloud server stores the transmitted data in a database and has the computational means to analyze it. Specifically, it uses an AI analysis model to evaluate the user's psychological state and stress level, and calculates stress indicators through a generative AI model.

[0264] Based on the analysis results, the server generates information aimed at providing useful advice and encouraging actions to the user. The generated information is written in natural language and sent as a push notification to the smartphone. The user can check the notification on their smartphone and take action on the suggested methods (e.g., "Try taking a deep breath," "Take a 5-minute break," etc.).

[0265] For example, if a user's heart rate remains higher than normal, the server will consider this a sign of stress and notify them of suggestions such as deep breathing. Furthermore, the server will compile the analysis results and provide a visualized report to the organization's administrators. This report will serve as important guidance for improving the overall mental health situation of the organization.

[0266] Examples of prompts include, "If the user's heart rate has been elevated for a week, suggest specific advice for stress reduction," and "Based on the collected health data, list suggestions for actions the user should take."

[0267] In this way, the present invention provides mental health support tailored to the individual circumstances of users and promotes healthy working styles in remote work.

[0268] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0269] Step 1:

[0270] The user wears a smartwatch, which acquires biometric information such as heart rate, activity level, and voice tone in real time. Specifically, sensors within the smartwatch collect this data. The inputs are the user's heart rate, voice, and movement, and the output is a dataset of these data.

[0271] Step 2:

[0272] The user's smartwatch transmits collected biometric information to their smartphone via Bluetooth. The smartphone receives the data and temporarily stores it in its storage. In this process, the input is data from the smartwatch, and the output is data stored in the smartphone.

[0273] Step 3:

[0274] The smartphone, acting as the terminal, periodically sends collected data to a cloud server. The secure HTTPS protocol is used for data transmission. The input is biometric data stored on the smartphone, and the output is data transmission to the cloud server.

[0275] Step 4:

[0276] The server receives biometric data on the cloud and stores it in a database. Since the stored data is analyzed by an AI analysis model, the server preprocesses the data for the AI ​​model and performs data cleansing. The input is data sent from a smartphone, and the output is an analyzable dataset.

[0277] Step 5:

[0278] The server uses an AI analysis model to assess the user's stress level and psychological state. By analyzing the data, it generates stress indicators and psychological state evaluation points. The input is pre-processed data, and the output is the analysis result.

[0279] Step 6:

[0280] Based on the analysis results, the server generates specific advice for the user using an AI model. This generated information is then converted into an easily understandable form using natural language. The input is the analysis results, and the output is the generated advice message.

[0281] Step 7:

[0282] The server pushes the generated advice to the user's smartphone. The user receives the notification on their smartphone and can then take the suggested action. The input is the generated message, and the output is the notification to the smartphone.

[0283] (Application Example 1)

[0284] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0285] In a remote environment, the purpose is to improve the working environment by appropriately evaluating the individual mental load and psychological state by utilizing the biometric information of employees and quickly providing action proposals based on the results. In addition, it visualizes the mental health status of all employees for the administrator and provides a basis for more effective operation of the organization.

[0286] 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.

[0287] In this invention, the server includes means having a measuring device for collecting the biometric information of the user, analysis means for analyzing the biometric information and estimating the mental load and psychological state, and generation means for generating information for making appropriate action proposals or promotions to the user based on the analysis result. As a result, it becomes possible to grasp the stress and psychological state of each employee in real time and provide appropriate support.

[0288] "Biometric information" refers to data related to the physical state and functions of the user, such as the user's heart rate, amount of exercise, voice tone, etc.

[0289] "Measuring device" refers to a device or a group of devices for collecting biometric information, and is a device including a smart watch or the like.

[0290] "Analysis means" refers to a process or function for analyzing the collected biometric information and estimating the mental load and psychological state of the user.

[0291] "Generation means" refers to a procedure or process for generating information for making action proposals or promotions based on the analysis result.

[0292] "Transmission means" refers to the communication path and protocol used to notify the user's information terminal of the generated information.

[0293] "Output generation means" refers to methods and functions for creating materials and reports to inform the person in charge of the organization of the user's analysis results.

[0294] "Response mechanisms" refer to interfaces or tools designed to encourage users to take the suggested actions.

[0295] This system is designed to support the mental health of employees in remote work environments. Its main components include a measurement device worn by the user, a server that processes and analyzes the information, and a terminal device that provides feedback to the user.

[0296] As a measuring device, a smartwatch collects biometric information such as heart rate, activity level, and voice tone in real time. This data is transmitted to the user's smartphone and uploaded to a cloud server via the internet.

[0297] The server uses software such as Python and TensorFlow to estimate mental stress and psychological state using an AI analysis model based on collected data. Based on the analysis results, personalized action suggestions are generated for the user. The suggestions are notified to the user through an application installed on their smartphone. For example, this might suggest actions such as "take a 5-minute break to relax."

[0298] Furthermore, the server aggregates the data for organizational leaders and creates a report outlining the overall mental health status. This report is used as a reference for organizational improvement measures.

[0299] As a specific example, when the AI model determines that the stress level in the morning is very high, the server generates a nudge saying "Let's practice deep breathing." The user can receive this notification via a smartphone and take stress-reducing actions according to the instructions.

[0300] Example of a prompt sentence for the AI model:

[0301] "Analyze the mental load associated with changes in heart rate, and if the stress level is above a certain threshold, please propose specific relaxation measures."

[0302] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0303] Step 1:

[0304] The terminal acquires biometric information such as heart rate, amount of exercise, and voice tone from a smartwatch as a measuring device. These data are temporarily stored in the terminal. The input data is biometric information, and the output is the temporarily stored data in the terminal.

[0305] Step 2:

[0306] The terminal transmits the temporarily stored biometric information to the cloud server via the Internet. Here, as the specific operation of the terminal, it checks the network connection and uploads the data to the cloud using a secure communication protocol. The input is the biometric information data in the terminal, and the output is the data stored in the cloud server.

[0307] Step 3:

[0308] The server inputs the biometric information received on the cloud into the AI analysis model to estimate the mental load and psychological state. Specifically, it uses Python and TensorFlow to extract the features of the data and calculate the stress level. The input is the data stored in the cloud server, and the output is the estimation result of the psychological state.

[0309] Step 4:

[0310] The server automatically generates appropriate action suggestions for the user based on the analysis results. Specifically, it uses an AI model to generate suggestions such as "promote relaxation" based on the evaluation of the analysis results. The input is the estimated result of the user's psychological state, and the output is the text of the action suggestion.

[0311] Step 5:

[0312] The generated action suggestions are notified to the device. The device presents the suggestions to the user through on-screen pop-up notifications, audio notifications, etc. Specific actions such as vibration or alarm sounds may be used to attract the user's attention. The input is the text of the action suggestion, and the output is the notification to the user.

[0313] Step 6:

[0314] The user chooses whether to perform the action suggested through their device. Based on this choice, the response is recorded and may be sent to the server as feedback. Specifically, the record is updated according to the user's actions. The input is the user's response, and the output is feedback data.

[0315] Step 7:

[0316] The server continuously improves the AI ​​analysis model by incorporating user feedback and additional biometric information. Specifically, it accumulates feedback data and retrains the model to improve its performance. The input is the feedback data, and the output is the improved analysis model.

[0317] 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.

[0318] This invention is implemented as a system for monitoring the mental health of users in a remote work environment and prompting appropriate responses. This system has multiple main components that work together to analyze the user's psychological and emotional state.

[0319] The smartwatch worn by the user collects basic health data such as heart rate, activity level, and voice tone. This data is temporarily stored on the user's smartphone. The smartphone then periodically sends the collected data to a server in the cloud.

[0320] The server organizes the data received on the cloud and performs analysis using an emotion engine. This emotion engine recognizes emotions from the user's voice and facial expression data and has the technology to estimate stress levels and psychological states with greater precision. The analyzed data is evaluated while taking into account the user's individual history data, making it possible to gain insights into the user's current emotional state.

[0321] Based on the analysis results, the server automatically generates personalized advice and nudges for the user. These nudges may include mental support information tailored to the user's emotional state. For example, if the server determines that the user is experiencing excessive stress, it will create a notification suggesting relaxation methods or recommending a short break.

[0322] Next, the server sends the generated notification to the smartphone, delivering it to the user as an alert through the device. Upon receiving this alert, the user can then take specific action.

[0323] Furthermore, the server manages the mental health of the entire organization by generating reports for administrators based on aggregated data. These reports can be used to develop individual care plans for employees and to plan organizational healthcare initiatives.

[0324] This invention aims to efficiently understand the emotional state of users working remotely by combining emotion recognition technology, thereby facilitating timely countermeasures.

[0325] The following describes the processing flow.

[0326] Step 1:

[0327] The device uses the user's smartwatch to measure health data such as heart rate, activity level, and voice tone, and transfers this data to the smartphone.

[0328] Step 2:

[0329] The device uploads health data stored on the smartphone to a cloud server at regular intervals, making it easier to collect data in real time.

[0330] Step 3:

[0331] The server retrieves health data received from the cloud and records it in a database. This recorded data is then prepared for further detailed analysis.

[0332] Step 4:

[0333] The server uses an emotion engine to analyze health data and recognize the user's emotional state from their voice and facial expression data. This allows for highly accurate estimation of stress levels and psychological state.

[0334] Step 5:

[0335] The server generates personalized advice and nudges for the user based on their perceived emotional state. This may include suggestions for relaxation techniques or ways to improve work-life balance.

[0336] Step 6:

[0337] The server issues instructions to send the generated advice and nudges to the user's smartphone. The device then delivers this to the user as a notification.

[0338] Step 7:

[0339] By receiving notifications on their smartphones and taking specific actions based on their content, users can reduce stress and improve their mental health.

[0340] Step 8:

[0341] The server aggregates mental health data across the entire organization and generates detailed reports for administrators and the human resources department. These reports are used to develop the organization's health initiatives.

[0342] (Example 2)

[0343] 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".

[0344] The aim is to improve the current situation where there is insufficient infrastructure to efficiently monitor the mental health of employees working remotely and to automatically prompt appropriate responses. In particular, there is a need for a method that analyzes employees' emotional states and stress levels in real time and provides individually customized advice, thereby contributing to the maintenance of individual health while improving the mental health of the entire organization.

[0345] 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.

[0346] In this invention, the server includes information collection means for collecting the user's biometric information, analysis means for analyzing the biometric information and estimating the emotional state and mental health, and generation means for generating information that provides individually tailored recommendations to the user based on the analysis results. This enables accurate understanding of the user's psychological state and appropriate responses in real time.

[0347] "User" refers to an individual who provides their biometric information using the system.

[0348] "Biometric information" refers to all data that indicates a user's health status, such as heart rate, activity level, and voice tone.

[0349] "Information gathering means" refers to devices and methods for acquiring biometric information from users.

[0350] "Analysis methods" refer to methods and processes for evaluating a user's emotional state and mental health based on collected biometric information.

[0351] "Generation method" refers to the process of creating tailored advice and recommendations for users based on the analysis results.

[0352] "Means of communication" refers to methods for notifying users of generated advice and information on their devices.

[0353] "Report creation method" refers to a method for providing users' analysis results to organizational leaders in an easily understandable format.

[0354] This invention is a system that collects biometric information from users working in a remote environment and provides personalized health advice based on that information. This system has the following specific components, which work together to analyze the user's emotional state and stress level.

[0355] Body devices such as smartwatches worn by users acquire biometric information such as heart rate, activity level, and voice tone. This information is temporarily stored on the user's smartphone. This smartphone receives biometric information via Bluetooth and periodically transmits it to a server in the cloud. Standard Wi-Fi or mobile data communication is used for this purpose.

[0356] The server receives data in the cloud and performs data organization and analysis. The analysis utilizes emotion recognition technologies, including voice data analysis and facial expression estimation. This allows for an assessment of the user's emotional state and stress level. General cloud computing services are used for the analysis.

[0357] Furthermore, the server uses a generative AI model to create prompt messages based on the analysis results. This generation method takes individual historical data into consideration to provide personalized advice. For example, a prompt such as, "Based on the observed data, your stress level appears to be high. We recommend incorporating 10 minutes of meditation," might be generated.

[0358] The server sends the generated prompt to the smartphone, displaying an alert to the user via the device. Upon receiving this alert, the user can take the suggested action, which is expected to improve their health.

[0359] This system also provides organizational administrators with reports that assess the overall mental health status of employees based on aggregated data. These reports can be used to plan for maintaining employee well-being.

[0360] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0361] Step 1:

[0362] The user wears a smartwatch, which acquires biometric information (heart rate, activity level, voice tone) in real time. Specifically, the smartwatch uses sensors to continuously measure this data and transmits the acquired data to the user's smartphone via Bluetooth. The input here is the user's biological behavior, and the output is digitized biometric data.

[0363] Step 2:

[0364] The device, i.e., the user's smartphone, temporarily stores the received biometric data in its storage. Furthermore, it periodically sends this data to a server in the cloud. Specifically, it sends the data at regular intervals using Wi-Fi or a mobile data network. The input is biometric data from the smartwatch, and the output is the data sent to the server.

[0365] Step 3:

[0366] The server receives data in the cloud and performs organization and preprocessing. Specifically, it classifies the received data by user, completes missing data, and corrects outliers. The input is biometric data sent from smartphones, and the output is data prepared for analysis.

[0367] Step 4:

[0368] The server uses emotion recognition technology to evaluate emotional states and stress levels based on organized data. Specifically, it employs methods such as analyzing voice data and comparing it with historical data. The input is pre-processed biometric data, and the output is evaluation data regarding emotional states and stress levels.

[0369] Step 5:

[0370] The server utilizes a generative AI model to generate prompt messages based on the analysis results. These prompts include advice tailored to the user's psychological state. Specifically, prompts such as, "Based on the observed data, your stress level appears to be high. We recommend incorporating 10 minutes of meditation," are generated. The input is emotion evaluation data, and the output is a prompt message containing specific advice for the user.

[0371] Step 6:

[0372] The server sends the generated prompt to the terminal, and the terminal displays the received information as a notification to the user. Specifically, the prompt is shown to the user using the smartphone's notification function. The input is the generated prompt, and the output is a notification that the user can visually confirm.

[0373] Step 7:

[0374] The server evaluates the overall mental health status of the organization based on aggregated user data and generates a report. Specifically, it generates a report in a format viewable by administrators, which includes trends in the organization's stress levels and suggested countermeasures. The input is the analysis results of the users, and the output is a report for administrators.

[0375] (Application Example 2)

[0376] 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."

[0377] In occupations involving long working hours, such as security guards, it is not easy to quickly identify employee stress and mental burden and take appropriate measures. In particular, the inability to offer specific rest and relaxation methods tailored to individual needs can lead to a deterioration of employee mental health and a decrease in work efficiency. Therefore, there is a need for a system that can effectively monitor the mental health of security guards and provide individually tailored mental support.

[0378] 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.

[0379] In this invention, the server includes means having a measuring device for collecting user health data, analysis means for analyzing the health data and estimating stress levels and psychological states, and generation means for providing security workers with personalized rest notifications and relaxation methods. This makes it possible to grasp the stress levels of security workers in real time and provide individually optimized mental support.

[0380] A "measuring device" is a device used to acquire various data related to the user's health. Its primary role is to collect data such as heart rate, exercise level, and voice tone.

[0381] "Analysis methods" refer to the processes and techniques used to analyze collected health data and estimate the user's stress level and psychological state.

[0382] "Generation method" refers to the process and technology of creating mental support information, including recommended information and relaxation methods, for users based on the analysis results.

[0383] "Transmission means" refers to the methods and devices used to transfer generated information to terminal devices, and is responsible for delivering notifications and alerts to users.

[0384] "Report generation means" refers to the processes and technologies for creating and providing appropriate mental health reports to organizational managers based on analysis results.

[0385] "Personalized break notifications" refer to customized information that informs users of the timing and method of breaks tailored to their individual circumstances, based on analysis results.

[0386] "Relaxation methods" refer to specific behavioral suggestions and activities provided with the aim of reducing stress for users.

[0387] The system that implements this application collects data using a smartwatch or similar measuring device worn by the user and temporarily stores it on a smartphone. The smartphone then transmits this data to a server in the cloud. The server organizes the received data and performs analysis using an emotion engine. This emotion engine uses services such as the Microsoft Azure Emotion API to recognize emotions, including voice and activity data, and accurately estimates stress levels and psychological states. Based on the analysis results, the server automatically generates appropriate rest notifications and relaxation information for security workers. This generated information is sent to the security worker's smartphone or smartwatch, allowing the user to refer to it and select optimized activities.

[0388] Furthermore, the server generates detailed reports for administrators based on the aggregated data. These reports can be used to monitor employee health and improve work efficiency. As a specific example, a security guard's smartwatch detected an increase in heart rate during work and sent a notification recommending a break. By following this notification and taking a short break, the guard was able to continue working efficiently.

[0389] An example of a prompt for a generating AI model would be: "Describe the design of a system that analyzes heart rate data from a smartwatch, identifies patterns indicating high stress levels, and provides users with rest notifications and relaxation advice."

[0390] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0391] Step 1:

[0392] The device (smartwatch) collects data related to the user's health (heart rate, activity level, voice tone, etc.) in real time. This data is acquired by sensors and stored inside the device in an appropriate format. This data serves as input for subsequent processing.

[0393] Step 2:

[0394] The device (smartphone) receives health data collected from the smartwatch at regular intervals. The received data is temporarily stored in the smartphone. At this point, the integrity of the data is checked, and if there is any incomplete data, an attempt is made to collect it again.

[0395] Step 3:

[0396] The device (smartphone) sends temporarily stored health data to a server in the cloud. The data is transmitted using a secure communication protocol and used as input for processing on the server.

[0397] Step 4:

[0398] The server stores the received health data in a database and analyzes the data using an emotion engine. It estimates stress levels and psychological states from voice and activity data using tools such as the Microsoft Azure Emotion API. This analysis yields output that includes insights into emotional states.

[0399] Step 5:

[0400] Based on the analysis results, the server uses a generative AI model to generate personalized break and relaxation suggestions for security workers. It utilizes prompts to create suggestions best suited to specific situations. These generated suggestions then serve as input for the next processing step.

[0401] Step 6:

[0402] The server sends the generated personalized suggestions to the device. The device displays them to the user as a notification, prompting them to take specific action. The output here is the specific notification content presented to the user.

[0403] Step 7:

[0404] Based on the notifications provided, users engage in specific relaxation activities such as exercise or rest. Data from these activities is collected again in the next cycle, and the process is repeated. This processing cycle allows for continuous monitoring and improvement of the user's mental health.

[0405] 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.

[0406] 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.

[0407] 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.

[0408] [Third Embodiment]

[0409] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0410] 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.

[0411] 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).

[0412] 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.

[0413] 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.

[0414] 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).

[0415] 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.

[0416] 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.

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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".

[0421] This invention is implemented as a system to support the mental health of employees in a remote work environment. This system consists of multiple components that work together to analyze the user's mental health status and provide appropriate feedback based on the results.

[0422] First, the smartwatch worn by the user collects health data such as heart rate, activity level, and voice tone in real time. This data is sent to the user's smartphone, where it is temporarily stored. The smartphone then periodically sends the collected data to a server in the cloud.

[0423] The server stores the received data in a cloud database and inputs it into an AI analysis model. This AI analysis model estimates the user's stress level and psychological state and generates specific stress indicators. Based on the results, it automatically generates advice and nudges, i.e., suggestions to encourage action, tailored to each user.

[0424] For example, if a user experiences a consistently high heart rate for several days, the server may interpret this as a sign of stress and send a notification to their smartphone suggesting actions such as "Try taking a deep breath" or "Take a 5-minute break." The user can then receive this notification and take the suggested action.

[0425] Furthermore, the server aggregates the analysis results and creates a report that visualizes the overall mental health status of the organization. This report is provided to administrators and the human resources department and used to build individual support systems and consider improvement measures for the entire organization.

[0426] In this way, this system aims to improve communication in remote environments, maintain productivity, and provide stress-free mental health care.

[0427] The following describes the processing flow.

[0428] Step 1:

[0429] The device measures health data such as heart rate, activity level, and voice tone through the user's smartwatch and transfers this data to the smartphone.

[0430] Step 2:

[0431] The device is configured to periodically upload health data stored on the smartphone to a cloud server. Data transmission occurs automatically at regular intervals.

[0432] Step 3:

[0433] The server records the received health data in a database and manages it as stored data. This data is then prepared for analysis by AI analysis models.

[0434] Step 4:

[0435] The server inputs health data from the database into an AI analysis model, which then analyzes the user's stress level and psychological state. The AI ​​analysis model uses past patterns and learning algorithms to determine the current state.

[0436] Step 5:

[0437] The server generates personalized advice and nudges for the user based on the analysis results. Examples of generated content include "reminders to rest" and "suggestions for simple stretches."

[0438] Step 6:

[0439] The server issues instructions to send the generated advice and nudges to the user's smartphone. This information is delivered as a notification so that the user can take immediate action.

[0440] Step 7:

[0441] The device receives notifications sent to the user's smartphone and displays an alert to the user. This allows the user to take the recommended action immediately.

[0442] Step 8:

[0443] The server analyzes the organization's mental health trends and generates a detailed report for managers or the human resources department. This report can be used to plan the organization's health initiatives.

[0444] (Example 1)

[0445] 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."

[0446] In recent years, as remote work has become commonplace, individual psychological stress and mental health are impacting the overall performance of organizations. However, effectively monitoring employees' mental health and taking timely and appropriate action is difficult. Therefore, there is a growing need for methods to accurately assess the mental health of individual employees and provide personalized support.

[0447] 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.

[0448] In this invention, the server includes means having an electronic device for acquiring biometric information, computation means for aggregating biometric information and evaluating psychological state, and generation means for creating specific advice based on the evaluation results. This makes it possible to monitor an individual's mental health in real time and provide necessary support quickly and appropriately.

[0449] "Biometric information" refers to information that indicates the user's physical condition, and includes data such as heart rate, activity level, and voice tone.

[0450] "Electronic device" refers to a hardware device for acquiring biological information, specifically a measuring instrument that can be worn on the user's body.

[0451] "Computational means" refers to processes and algorithms for analyzing aggregated biometric information and evaluating the user's psychological state.

[0452] "Generation means" refers to a function that creates specific advice and action-promoting messages for users based on analyzed data.

[0453] "Communication means" refers to methods or technologies for transmitting generated information to a user's mobile device, such as wireless communication or the use of Internet protocols.

[0454] "Reporting methods" refer to the processes and tools used to aggregate and visualize analytical results for presentation to decision-makers within an organization.

[0455] This invention is a system for supporting the mental health of users in a remote environment, with each component working in coordination. First, the user wears a smartwatch, an electronic device, which acquires biometric information such as heart rate, activity level, and voice tone in real time. This smartwatch uses sensor technology to collect this data. The collected data is transmitted to the user's smartphone via Bluetooth.

[0456] Smartphones play a role in temporarily storing received biometric information and sending it to a cloud server. HTTPS, a secure communication protocol, is used for data transmission. The cloud server stores the transmitted data in a database and has the computational means to analyze it. Specifically, it uses an AI analysis model to evaluate the user's psychological state and stress level, and calculates stress indicators through a generative AI model.

[0457] Based on the analysis results, the server generates information aimed at providing useful advice and encouraging actions to the user. The generated information is written in natural language and sent as a push notification to the smartphone. The user can check the notification on their smartphone and take action on the suggested methods (e.g., "Try taking a deep breath," "Take a 5-minute break," etc.).

[0458] For example, if a user's heart rate remains higher than normal, the server will consider this a sign of stress and notify them of suggestions such as deep breathing. Furthermore, the server will compile the analysis results and provide a visualized report to the organization's administrators. This report will serve as important guidance for improving the overall mental health situation of the organization.

[0459] Examples of prompts include, "If the user's heart rate has been elevated for a week, suggest specific advice for stress reduction," and "Based on the collected health data, list suggestions for actions the user should take."

[0460] In this way, the present invention provides mental health support tailored to the individual circumstances of users and promotes healthy working styles in remote work.

[0461] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0462] Step 1:

[0463] The user wears a smartwatch, which acquires biometric information such as heart rate, activity level, and voice tone in real time. Specifically, sensors within the smartwatch collect this data. The inputs are the user's heart rate, voice, and movement, and the output is a dataset of these data.

[0464] Step 2:

[0465] The user's smartwatch transmits collected biometric information to their smartphone via Bluetooth. The smartphone receives the data and temporarily stores it in its storage. In this process, the input is data from the smartwatch, and the output is data stored in the smartphone.

[0466] Step 3:

[0467] The smartphone, acting as the terminal, periodically sends collected data to a cloud server. The secure HTTPS protocol is used for data transmission. The input is biometric data stored on the smartphone, and the output is data transmission to the cloud server.

[0468] Step 4:

[0469] The server receives biometric data on the cloud and stores it in a database. Since the stored data is analyzed by an AI analysis model, the server preprocesses the data for the AI ​​model and performs data cleansing. The input is data sent from a smartphone, and the output is an analyzable dataset.

[0470] Step 5:

[0471] The server uses an AI analysis model to assess the user's stress level and psychological state. By analyzing the data, it generates stress indicators and psychological state evaluation points. The input is pre-processed data, and the output is the analysis result.

[0472] Step 6:

[0473] Based on the analysis results, the server generates specific advice for the user using an AI model. This generated information is then converted into an easily understandable form using natural language. The input is the analysis results, and the output is the generated advice message.

[0474] Step 7:

[0475] The server pushes the generated advice to the user's smartphone. The user receives the notification on their smartphone and can then take the suggested action. The input is the generated message, and the output is the notification to the smartphone.

[0476] (Application Example 1)

[0477] 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."

[0478] The aim is to improve the work environment in a remote setting by utilizing employees' biometric information to appropriately assess individual mental stress and psychological states, and by promptly providing actionable suggestions based on the results. Furthermore, it provides managers with a foundation for more effective organizational management by visualizing the overall mental health status of employees.

[0479] 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.

[0480] In this invention, the server includes means having a measuring device for collecting the user's biometric information, analysis means for analyzing the biometric information and estimating mental stress and psychological state, and generation means for generating information to provide appropriate action suggestions or encouragement to the user based on the analysis results. This makes it possible to grasp the stress and psychological state of individual employees in real time and provide appropriate support.

[0481] "Biometric information" refers to data related to the user's physical condition and function, such as heart rate, exercise level, and voice tone.

[0482] A "measuring device" is a device or group of devices used to collect biological information, and includes devices such as smartwatches.

[0483] "Analysis means" refers to the process or function of analyzing collected biometric information to estimate the user's mental burden and psychological state.

[0484] "Generative means" refers to the procedures and processes for generating information that provides action suggestions or promotions based on the analysis results.

[0485] "Transmission means" refers to the communication path and protocol used to notify the user's information terminal of the generated information.

[0486] "Output generation means" refers to methods and functions for creating materials and reports to inform the person in charge of the organization of the user's analysis results.

[0487] "Response mechanisms" refer to interfaces or tools designed to encourage users to take the suggested actions.

[0488] This system is designed to support the mental health of employees in remote work environments. Its main components include a measurement device worn by the user, a server that processes and analyzes the information, and a terminal device that provides feedback to the user.

[0489] As a measuring device, a smartwatch collects biometric information such as heart rate, activity level, and voice tone in real time. This data is transmitted to the user's smartphone and uploaded to a cloud server via the internet.

[0490] The server uses software such as Python and TensorFlow to estimate mental stress and psychological state using an AI analysis model based on collected data. Based on the analysis results, personalized action suggestions are generated for the user. The suggestions are notified to the user through an application installed on their smartphone. For example, this might suggest actions such as "take a 5-minute break to relax."

[0491] Furthermore, the server aggregates the data for organizational leaders and creates a report outlining the overall mental health status. This report is used as a reference for organizational improvement measures.

[0492] For example, if the AI ​​model determines that the user's stress level is very high in the morning, the server will generate a nudge suggesting, "Let's practice deep breathing." The user receives this notification via their smartphone and can take stress-reducing action by following the instructions.

[0493] Examples of prompts for generative AI models:

[0494] "Analyze the mental stress associated with changes in heart rate, and if the stress level exceeds a certain level, propose specific mitigation measures."

[0495] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0496] Step 1:

[0497] The device acquires biometric information such as heart rate, activity level, and voice tone from the smartwatch, which acts as a measuring device. This data is temporarily stored on the device. The input data is biometric information, and the output is temporarily stored data on the device.

[0498] Step 2:

[0499] The device transmits temporarily stored biometric information to a cloud server via the internet. Specifically, the device verifies the network connection and uploads the data to the cloud using a secure communication protocol. The input is the biometric data stored on the device, and the output is the data stored on the cloud server.

[0500] Step 3:

[0501] The server inputs biometric information received on the cloud into an AI analysis model to estimate mental stress and psychological state. Specifically, it uses Python and TensorFlow to extract data features and calculate stress levels. The input is data stored on the cloud server, and the output is the estimated result of the psychological state.

[0502] Step 4:

[0503] The server automatically generates appropriate action suggestions for the user based on the analysis results. Specifically, it uses an AI model to generate suggestions such as "promote relaxation" based on the evaluation of the analysis results. The input is the estimated result of the user's psychological state, and the output is the text of the action suggestion.

[0504] Step 5:

[0505] The generated action suggestions are notified to the device. The device presents the suggestions to the user through on-screen pop-up notifications, audio notifications, etc. Specific actions such as vibration or alarm sounds may be used to attract the user's attention. The input is the text of the action suggestion, and the output is the notification to the user.

[0506] Step 6:

[0507] The user chooses whether to perform the action suggested through their device. Based on this choice, the response is recorded and may be sent to the server as feedback. Specifically, the record is updated according to the user's actions. The input is the user's response, and the output is feedback data.

[0508] Step 7:

[0509] The server continuously improves the AI ​​analysis model by incorporating user feedback and additional biometric information. Specifically, it accumulates feedback data and retrains the model to improve its performance. The input is the feedback data, and the output is the improved analysis model.

[0510] 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.

[0511] This invention is implemented as a system for monitoring the mental health of users in a remote work environment and prompting appropriate responses. This system has multiple main components that work together to analyze the user's psychological and emotional state.

[0512] The smartwatch worn by the user collects basic health data such as heart rate, activity level, and voice tone. This data is temporarily stored on the user's smartphone. The smartphone then periodically sends the collected data to a server in the cloud.

[0513] The server organizes the data received on the cloud and performs analysis using an emotion engine. This emotion engine recognizes emotions from the user's voice and facial expression data and has the technology to estimate stress levels and psychological states with greater precision. The analyzed data is evaluated while taking into account the user's individual history data, making it possible to gain insights into the user's current emotional state.

[0514] Based on the analysis results, the server automatically generates personalized advice and nudges for the user. These nudges may include mental support information tailored to the user's emotional state. For example, if the server determines that the user is experiencing excessive stress, it will create a notification suggesting relaxation methods or recommending a short break.

[0515] Next, the server sends the generated notification to the smartphone, delivering it to the user as an alert via the device. Upon receiving this alert, the user can then take specific action.

[0516] Furthermore, the server manages the mental health of the entire organization by generating reports for administrators based on aggregated data. These reports can be used to develop individual care plans for employees and to plan organizational healthcare initiatives.

[0517] This invention aims to efficiently understand the emotional state of users working remotely by combining emotion recognition technology, thereby facilitating timely countermeasures.

[0518] The following describes the processing flow.

[0519] Step 1:

[0520] The device uses the user's smartwatch to measure health data such as heart rate, activity level, and voice tone, and transfers this data to the smartphone.

[0521] Step 2:

[0522] The device uploads health data stored on the smartphone to a cloud server at regular intervals, making it easier to collect data in real time.

[0523] Step 3:

[0524] The server retrieves health data received from the cloud and records it in a database. This recorded data is then prepared for further detailed analysis.

[0525] Step 4:

[0526] The server uses an emotion engine to analyze health data and recognize the user's emotional state from their voice and facial expression data. This allows for highly accurate estimation of stress levels and psychological state.

[0527] Step 5:

[0528] The server generates personalized advice and nudges for the user based on their perceived emotional state. This may include suggestions for relaxation techniques or ways to improve work-life balance.

[0529] Step 6:

[0530] The server issues instructions to send the generated advice and nudges to the user's smartphone. The device then delivers this to the user as a notification.

[0531] Step 7:

[0532] By receiving notifications on their smartphones and taking specific actions based on their content, users can reduce stress and improve their mental health.

[0533] Step 8:

[0534] The server aggregates mental health data across the entire organization and generates detailed reports for administrators and the human resources department. These reports are used to develop the organization's health initiatives.

[0535] (Example 2)

[0536] 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."

[0537] The aim is to improve the current situation where there is insufficient infrastructure to efficiently monitor the mental health of employees working remotely and to automatically prompt appropriate responses. In particular, there is a need for a method that analyzes employees' emotional states and stress levels in real time and provides individually customized advice, thereby contributing to the maintenance of individual health while improving the mental health of the entire organization.

[0538] 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.

[0539] In this invention, the server includes information collection means for collecting the user's biometric information, analysis means for analyzing the biometric information and estimating the emotional state and mental health, and generation means for generating information that provides individually tailored recommendations to the user based on the analysis results. This enables accurate understanding of the user's psychological state and appropriate responses in real time.

[0540] "User" refers to an individual who provides their biometric information using the system.

[0541] "Biometric information" refers to all data that indicates a user's health status, such as heart rate, activity level, and voice tone.

[0542] "Information gathering means" refers to devices and methods for acquiring biometric information from users.

[0543] "Analysis methods" refer to methods and processes for evaluating a user's emotional state and mental health based on collected biometric information.

[0544] "Generation method" refers to the process of creating tailored advice and recommendations for users based on the analysis results.

[0545] "Means of communication" refers to methods for notifying users of generated advice and information on their devices.

[0546] "Report creation method" refers to a method for providing users with their analysis results in an easily understandable format for the organization's responsible person.

[0547] This invention is a system that collects biometric information from users working in a remote environment and provides personalized health advice based on that information. This system has the following specific components, which work together to analyze the user's emotional state and stress level.

[0548] Body devices such as smartwatches worn by users acquire biometric information such as heart rate, activity level, and voice tone. This information is temporarily stored on the user's smartphone. This smartphone receives biometric information via Bluetooth and periodically transmits it to a server in the cloud. Standard Wi-Fi or mobile data communication is used for this purpose.

[0549] The server receives data in the cloud and performs data organization and analysis. The analysis utilizes emotion recognition technologies, including voice data analysis and facial expression estimation. This allows for an assessment of the user's emotional state and stress level. General cloud computing services are used for the analysis.

[0550] Furthermore, the server uses a generative AI model to create prompt messages based on the analysis results. This generation method takes individual historical data into consideration to provide personalized advice. For example, a prompt such as, "Based on the observed data, your stress level appears to be high. We recommend incorporating 10 minutes of meditation," might be generated.

[0551] The server sends the generated prompt to the smartphone, displaying an alert to the user via the device. Upon receiving this alert, the user can take the suggested action, which is expected to improve their health.

[0552] This system also provides organizational administrators with reports that assess the overall mental health status of employees based on aggregated data. These reports can be used to plan for maintaining employee well-being.

[0553] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0554] Step 1:

[0555] The user wears a smartwatch, which acquires biometric information (heart rate, activity level, voice tone) in real time. Specifically, the smartwatch uses sensors to continuously measure this data and transmits the acquired data to the user's smartphone via Bluetooth. The input here is the user's biological behavior, and the output is digitized biometric data.

[0556] Step 2:

[0557] The device, i.e., the user's smartphone, temporarily stores the received biometric data in its storage. Furthermore, it periodically sends this data to a server in the cloud. Specifically, it sends the data at regular intervals using Wi-Fi or a mobile data network. The input is biometric data from the smartwatch, and the output is the data sent to the server.

[0558] Step 3:

[0559] The server receives data in the cloud and performs organization and preprocessing. Specifically, it classifies the received data by user, completes missing data, and corrects outliers. The input is biometric data sent from smartphones, and the output is data prepared for analysis.

[0560] Step 4:

[0561] The server uses emotion recognition technology to evaluate emotional states and stress levels based on organized data. Specifically, it employs methods such as analyzing voice data and comparing it with historical data. The input is pre-processed biometric data, and the output is evaluation data regarding emotional states and stress levels.

[0562] Step 5:

[0563] The server utilizes a generative AI model to generate prompt messages based on the analysis results. These prompts include advice tailored to the user's psychological state. Specifically, prompts such as, "Based on the observed data, your stress level appears to be high. We recommend incorporating 10 minutes of meditation," are generated. The input is emotion evaluation data, and the output is a prompt message containing specific advice for the user.

[0564] Step 6:

[0565] The server sends the generated prompt to the terminal, and the terminal displays the received information as a notification to the user. Specifically, the prompt is shown to the user using the smartphone's notification function. The input is the generated prompt, and the output is a notification that the user can visually confirm.

[0566] Step 7:

[0567] The server evaluates the overall mental health status of the organization based on aggregated user data and generates a report. Specifically, it generates a report in a format viewable by administrators, which includes trends in the organization's stress levels and suggested countermeasures. The input is the analysis results of the users, and the output is a report for administrators.

[0568] (Application Example 2)

[0569] 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."

[0570] In occupations involving long working hours, such as security guards, it is not easy to quickly identify employee stress and mental burden and take appropriate measures. In particular, the inability to offer specific rest and relaxation methods tailored to individual needs can lead to a deterioration of employee mental health and a decrease in work efficiency. Therefore, there is a need for a system that can effectively monitor the mental health of security guards and provide individually tailored mental support.

[0571] 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.

[0572] In this invention, the server includes means having a measuring device for collecting user health data, analysis means for analyzing the health data and estimating stress levels and psychological states, and generation means for providing security workers with personalized rest notifications and relaxation methods. This makes it possible to grasp the stress levels of security workers in real time and provide individually optimized mental support.

[0573] A "measuring device" is a device used to acquire various data related to the user's health. Its primary role is to collect data such as heart rate, exercise level, and voice tone.

[0574] "Analysis methods" refer to the processes and techniques used to analyze collected health data and estimate the user's stress level and psychological state.

[0575] "Generation method" refers to the process and technology of creating mental support information, including recommended information and relaxation methods, for users based on analysis results.

[0576] "Transmission means" refers to the methods and devices used to transfer generated information to terminal devices, and is responsible for delivering notifications and alerts to users.

[0577] "Report generation means" refers to the processes and technologies for creating and providing appropriate mental health reports to organizational managers based on analysis results.

[0578] "Personalized break notifications" refer to customized information that informs users of the timing and method of taking breaks based on analysis results and tailored to their individual circumstances.

[0579] "Relaxation methods" refer to specific behavioral suggestions and activities provided with the aim of reducing stress for users.

[0580] The system that implements this application collects data using a smartwatch or similar measuring device worn by the user and temporarily stores it on a smartphone. The smartphone then transmits this data to a server in the cloud. The server organizes the received data and performs analysis using an emotion engine. This emotion engine uses services such as the Microsoft Azure Emotion API to recognize emotions, including voice and activity data, and accurately estimates stress levels and psychological states. Based on the analysis results, the server automatically generates appropriate rest notifications and relaxation information for security workers. This generated information is sent to the security worker's smartphone or smartwatch, allowing the user to refer to it and select optimized activities.

[0581] Furthermore, the server generates detailed reports for administrators based on the aggregated data. These reports can be used to monitor employee health and improve work efficiency. As a specific example, a security guard's smartwatch detected an increase in heart rate during work and sent a notification recommending a break. By following this notification and taking a short break, the guard was able to continue working efficiently.

[0582] An example of a prompt for a generating AI model would be: "Describe the design of a system that analyzes heart rate data from a smartwatch, identifies patterns indicating high stress levels, and provides users with rest notifications and relaxation advice."

[0583] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0584] Step 1:

[0585] The device (smartwatch) collects data related to the user's health (heart rate, activity level, voice tone, etc.) in real time. This data is acquired by sensors and stored inside the device in an appropriate format. This data serves as input for subsequent processing.

[0586] Step 2:

[0587] The device (smartphone) receives health data collected from the smartwatch at regular intervals. The received data is temporarily stored in the smartphone. At this point, the integrity of the data is checked, and if there is any incomplete data, an attempt is made to collect it again.

[0588] Step 3:

[0589] The device (smartphone) sends temporarily stored health data to a server in the cloud. The data is transmitted using a secure communication protocol and used as input for processing on the server.

[0590] Step 4:

[0591] The server stores the received health data in a database and analyzes the data using an emotion engine. Using tools such as the Microsoft Azure Emotion API, it estimates stress levels and psychological states from voice and activity data. This analysis yields output that includes insights into emotional states.

[0592] Step 5:

[0593] Based on the analysis results, the server uses a generative AI model to generate personalized break and relaxation suggestions for security workers. It utilizes prompts to create suggestions best suited to specific situations. These generated suggestions then serve as input for the next processing step.

[0594] Step 6:

[0595] The server sends the generated personalized suggestions to the device. The device displays them to the user as a notification, prompting them to take specific action. The output here is the specific notification content presented to the user.

[0596] Step 7:

[0597] Based on the notifications provided, users engage in specific relaxation activities such as exercise or rest. Data from these activities is collected again in the next cycle, and the process is repeated. This processing cycle allows for continuous monitoring and improvement of the user's mental health.

[0598] 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.

[0599] 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.

[0600] 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.

[0601] [Fourth Embodiment]

[0602] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0603] 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.

[0604] 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).

[0605] 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.

[0606] 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.

[0607] 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).

[0608] 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.

[0609] 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.

[0610] 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.

[0611] 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.

[0612] 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.

[0613] 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.

[0614] 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".

[0615] This invention is implemented as a system to support the mental health of employees in a remote work environment. This system consists of multiple components that work together to analyze the user's mental health status and provide appropriate feedback based on the results.

[0616] First, the smartwatch worn by the user collects health data such as heart rate, activity level, and voice tone in real time. This data is sent to the user's smartphone, where it is temporarily stored. The smartphone then periodically sends the collected data to a server in the cloud.

[0617] The server stores the received data in a cloud database and inputs it into an AI analysis model. This AI analysis model estimates the user's stress level and psychological state and generates specific stress indicators. Based on the results, it automatically generates advice and nudges, i.e., suggestions to encourage action, tailored to each user.

[0618] For example, if a user experiences a consistently high heart rate for several days, the server may interpret this as a sign of stress and send a notification to their smartphone suggesting actions such as "Try taking a deep breath" or "Take a 5-minute break." The user can then receive this notification and take the suggested action.

[0619] Furthermore, the server aggregates the analysis results and creates a report that visualizes the overall mental health status of the organization. This report is provided to administrators and the human resources department and used to build individual support systems and consider improvement measures for the entire organization.

[0620] In this way, this system aims to improve communication in remote environments, maintain productivity, and provide stress-free mental health care.

[0621] The following describes the processing flow.

[0622] Step 1:

[0623] The device measures health data such as heart rate, activity level, and voice tone through the user's smartwatch and transfers this data to the smartphone.

[0624] Step 2:

[0625] The device is configured to periodically upload health data stored on the smartphone to a cloud server. Data transmission occurs automatically at regular intervals.

[0626] Step 3:

[0627] The server records the received health data in a database and manages it as stored data. This data is then prepared for analysis by AI analysis models.

[0628] Step 4:

[0629] The server inputs health data from the database into an AI analysis model, which then analyzes the user's stress level and psychological state. The AI ​​analysis model uses past patterns and learning algorithms to determine the current state.

[0630] Step 5:

[0631] The server generates personalized advice and nudges for the user based on the analysis results. Examples of generated content include "reminders to rest" and "suggestions for simple stretches."

[0632] Step 6:

[0633] The server issues instructions to send the generated advice and nudges to the user's smartphone. This information is delivered as a notification so that the user can take immediate action.

[0634] Step 7:

[0635] The device receives notifications sent to the user's smartphone and displays an alert to the user. This allows the user to take the recommended action immediately.

[0636] Step 8:

[0637] The server analyzes the organization's mental health trends and generates a detailed report for managers or the human resources department. This report can be used to plan the organization's health initiatives.

[0638] (Example 1)

[0639] 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".

[0640] In recent years, as remote work has become commonplace, individual psychological stress and mental health are impacting the overall performance of organizations. However, effectively monitoring employees' mental health and taking timely and appropriate action is difficult. Therefore, there is a growing need for methods to accurately assess the mental health of individual employees and provide personalized support.

[0641] 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.

[0642] In this invention, the server includes means having an electronic device for acquiring biometric information, computation means for aggregating biometric information and evaluating psychological state, and generation means for creating specific advice based on the evaluation results. This makes it possible to monitor an individual's mental health in real time and provide necessary support quickly and appropriately.

[0643] "Biometric information" refers to information that indicates the user's physical condition, and includes data such as heart rate, activity level, and voice tone.

[0644] "Electronic device" refers to a hardware device for acquiring biological information, specifically a measuring instrument that can be worn on the user's body.

[0645] "Computational means" refers to processes and algorithms for analyzing aggregated biometric information and evaluating the user's psychological state.

[0646] "Generation means" refers to a function that creates specific advice and action-promoting messages for users based on analyzed data.

[0647] "Communication means" refers to methods or technologies for transmitting generated information to a user's mobile device, such as wireless communication or the use of Internet protocols.

[0648] "Reporting methods" refer to the processes and tools used to aggregate and visualize analytical results for presentation to decision-makers within an organization.

[0649] This invention is a system for supporting the mental health of users in a remote environment, with each component working in coordination. First, the user wears a smartwatch, an electronic device, which acquires biometric information such as heart rate, activity level, and voice tone in real time. This smartwatch uses sensor technology to collect this data. The collected data is transmitted to the user's smartphone via Bluetooth.

[0650] Smartphones play a role in temporarily storing received biometric information and sending it to a cloud server. HTTPS, a secure communication protocol, is used for data transmission. The cloud server stores the transmitted data in a database and has the computational means to analyze it. Specifically, it uses an AI analysis model to evaluate the user's psychological state and stress level, and calculates stress indicators through a generative AI model.

[0651] Based on the analysis results, the server generates information aimed at providing useful advice and encouraging actions to the user. The generated information is written in natural language and sent as a push notification to the smartphone. The user can check the notification on their smartphone and take action on the suggested methods (e.g., "Try taking a deep breath," "Take a 5-minute break," etc.).

[0652] For example, if a user's heart rate remains higher than normal, the server will consider this a sign of stress and notify them of suggestions such as deep breathing. Furthermore, the server will compile the analysis results and provide a visualized report to the organization's administrators. This report will serve as important guidance for improving the overall mental health situation of the organization.

[0653] Examples of prompts include, "If the user's heart rate has been elevated for a week, suggest specific advice for stress reduction," and "Based on the collected health data, list suggestions for actions the user should take."

[0654] In this way, the present invention provides mental health support tailored to the individual circumstances of users and promotes healthy working styles in remote work.

[0655] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0656] Step 1:

[0657] The user wears a smartwatch, which acquires biometric information such as heart rate, activity level, and voice tone in real time. Specifically, sensors within the smartwatch collect this data. The inputs are the user's heart rate, voice, and movement, and the output is a dataset of these data.

[0658] Step 2:

[0659] The user's smartwatch transmits collected biometric information to their smartphone via Bluetooth. The smartphone receives the data and temporarily stores it in its storage. In this process, the input is data from the smartwatch, and the output is data stored in the smartphone.

[0660] Step 3:

[0661] The smartphone, acting as the terminal, periodically sends collected data to a cloud server. The secure HTTPS protocol is used for data transmission. The input is biometric data stored on the smartphone, and the output is data transmission to the cloud server.

[0662] Step 4:

[0663] The server receives biometric data on the cloud and stores it in a database. Since the stored data is analyzed by an AI analysis model, the server preprocesses the data for the AI ​​model and performs data cleansing. The input is data sent from a smartphone, and the output is an analyzable dataset.

[0664] Step 5:

[0665] The server uses an AI analysis model to assess the user's stress level and psychological state. By analyzing the data, it generates stress indicators and psychological state evaluation points. The input is pre-processed data, and the output is the analysis result.

[0666] Step 6:

[0667] Based on the analysis results, the server generates specific advice for the user using an AI model. This generated information is then converted into an easily understandable form using natural language. The input is the analysis results, and the output is the generated advice message.

[0668] Step 7:

[0669] The server pushes the generated advice to the user's smartphone. The user receives the notification on their smartphone and can then take the suggested action. The input is the generated message, and the output is the notification to the smartphone.

[0670] (Application Example 1)

[0671] 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".

[0672] The aim is to improve the work environment in a remote setting by utilizing employees' biometric information to appropriately assess individual mental stress and psychological states, and by promptly providing actionable suggestions based on the results. Furthermore, it provides managers with a foundation for more effective organizational management by visualizing the overall mental health status of employees.

[0673] 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.

[0674] In this invention, the server includes means having a measuring device for collecting the user's biometric information, analysis means for analyzing the biometric information and estimating mental stress and psychological state, and generation means for generating information to provide appropriate action suggestions or encouragement to the user based on the analysis results. This makes it possible to grasp the stress and psychological state of individual employees in real time and provide appropriate support.

[0675] "Biometric information" refers to data related to the user's physical condition and function, such as heart rate, exercise level, and voice tone.

[0676] A "measuring device" is a device or group of devices used to collect biological information, and includes devices such as smartwatches.

[0677] "Analysis means" refers to the process or function of analyzing collected biometric information to estimate the user's mental burden and psychological state.

[0678] "Generative means" refers to the procedures and processes for generating information that provides action suggestions or promotions based on the analysis results.

[0679] "Transmission means" refers to the communication path and protocol used to notify the user's information terminal of the generated information.

[0680] "Output generation means" refers to methods and functions for creating materials and reports to inform the person in charge of the organization of the user's analysis results.

[0681] "Response mechanisms" refer to interfaces or tools designed to encourage users to take the suggested actions.

[0682] This system is designed to support the mental health of employees in remote work environments. Its main components include a measurement device worn by the user, a server that processes and analyzes the information, and a terminal device that provides feedback to the user.

[0683] As a measuring device, a smartwatch collects biometric information such as heart rate, activity level, and voice tone in real time. This data is transmitted to the user's smartphone and uploaded to a cloud server via the internet.

[0684] The server uses software such as Python and TensorFlow to estimate mental stress and psychological state using an AI analysis model based on collected data. Based on the analysis results, personalized action suggestions are generated for the user. The suggestions are notified to the user through an application installed on their smartphone. For example, this might suggest actions such as "take a 5-minute break to relax."

[0685] Furthermore, the server aggregates the data for organizational leaders and creates a report outlining the overall mental health status. This report is used as a reference for organizational improvement measures.

[0686] For example, if the AI ​​model determines that the user's stress level is very high in the morning, the server will generate a nudge suggesting, "Let's practice deep breathing." The user receives this notification via their smartphone and can take stress-reducing action by following the instructions.

[0687] Examples of prompts for generative AI models:

[0688] "Analyze the mental stress associated with changes in heart rate, and if the stress level exceeds a certain level, propose specific mitigation measures."

[0689] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0690] Step 1:

[0691] The device acquires biometric information such as heart rate, activity level, and voice tone from the smartwatch, which acts as a measuring device. This data is temporarily stored on the device. The input data is biometric information, and the output is temporarily stored data on the device.

[0692] Step 2:

[0693] The device transmits temporarily stored biometric information to a cloud server via the internet. Specifically, the device verifies the network connection and uploads the data to the cloud using a secure communication protocol. The input is the biometric data stored on the device, and the output is the data stored on the cloud server.

[0694] Step 3:

[0695] The server inputs biometric information received on the cloud into an AI analysis model to estimate mental stress and psychological state. Specifically, it uses Python and TensorFlow to extract data features and calculate stress levels. The input is data stored on the cloud server, and the output is the estimated result of the psychological state.

[0696] Step 4:

[0697] The server automatically generates appropriate action suggestions for the user based on the analysis results. Specifically, it uses an AI model to generate suggestions such as "promote relaxation" based on the evaluation of the analysis results. The input is the estimated result of the user's psychological state, and the output is the text of the action suggestion.

[0698] Step 5:

[0699] The generated action suggestions are notified to the device. The device presents the suggestions to the user through on-screen pop-up notifications, audio notifications, etc. Specific actions such as vibration or alarm sounds may be used to attract the user's attention. The input is the text of the action suggestion, and the output is the notification to the user.

[0700] Step 6:

[0701] The user chooses whether to perform the action suggested through their device. Based on this choice, the response is recorded and may be sent to the server as feedback. Specifically, the record is updated according to the user's actions. The input is the user's response, and the output is feedback data.

[0702] Step 7:

[0703] The server continuously improves the AI ​​analysis model by incorporating user feedback and additional biometric information. Specifically, it accumulates feedback data and retrains the model to improve its performance. The input is the feedback data, and the output is the improved analysis model.

[0704] 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.

[0705] This invention is implemented as a system for monitoring the mental health of users in a remote work environment and prompting appropriate responses. This system has multiple main components that work together to analyze the user's psychological and emotional state.

[0706] The smartwatch worn by the user collects basic health data such as heart rate, activity level, and voice tone. This data is temporarily stored on the user's smartphone. The smartphone then periodically sends the collected data to a server in the cloud.

[0707] The server organizes the data received on the cloud and performs analysis using an emotion engine. This emotion engine recognizes emotions from the user's voice and facial expression data and has the technology to estimate stress levels and psychological states with greater precision. The analyzed data is evaluated while taking into account the user's individual history data, making it possible to gain insights into the user's current emotional state.

[0708] Based on the analysis results, the server automatically generates personalized advice and nudges for the user. These nudges may include mental support information tailored to the user's emotional state. For example, if the server determines that the user is experiencing excessive stress, it will create a notification suggesting relaxation methods or recommending a short break.

[0709] Next, the server sends the generated notification to the smartphone, delivering it to the user as an alert via the device. Upon receiving this alert, the user can then take specific action.

[0710] Furthermore, the server manages the mental health of the entire organization by generating reports for administrators based on aggregated data. These reports can be used to develop individual care plans for employees and to plan organizational healthcare initiatives.

[0711] This invention aims to efficiently understand the emotional state of users working remotely by combining emotion recognition technology, thereby facilitating timely countermeasures.

[0712] The following describes the processing flow.

[0713] Step 1:

[0714] The device uses the user's smartwatch to measure health data such as heart rate, activity level, and voice tone, and transfers this data to the smartphone.

[0715] Step 2:

[0716] The device uploads health data stored on the smartphone to a cloud server at regular intervals, making it easier to collect data in real time.

[0717] Step 3:

[0718] The server retrieves health data received from the cloud and records it in a database. This recorded data is then prepared for further detailed analysis.

[0719] Step 4:

[0720] The server uses an emotion engine to analyze health data and recognize the user's emotional state from their voice and facial expression data. This allows for highly accurate estimation of stress levels and psychological state.

[0721] Step 5:

[0722] The server generates personalized advice and nudges for the user based on their perceived emotional state. This may include suggestions for relaxation techniques or ways to improve work-life balance.

[0723] Step 6:

[0724] The server issues instructions to send the generated advice and nudges to the user's smartphone. The device then delivers this to the user as a notification.

[0725] Step 7:

[0726] By receiving notifications on their smartphones and taking specific actions based on their content, users can reduce stress and improve their mental health.

[0727] Step 8:

[0728] The server aggregates mental health data across the entire organization and generates detailed reports for administrators and the human resources department. These reports are used to develop the organization's health initiatives.

[0729] (Example 2)

[0730] 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".

[0731] The aim is to improve the current situation where there is insufficient infrastructure to efficiently monitor the mental health of employees working remotely and to automatically prompt appropriate responses. In particular, there is a need for a method that analyzes employees' emotional states and stress levels in real time and provides individually customized advice, thereby contributing to the maintenance of individual health while improving the mental health of the entire organization.

[0732] 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.

[0733] In this invention, the server includes information collection means for collecting the user's biometric information, analysis means for analyzing the biometric information and estimating the emotional state and mental health, and generation means for generating information that provides individually tailored recommendations to the user based on the analysis results. This enables accurate understanding of the user's psychological state and appropriate responses in real time.

[0734] "User" refers to an individual who provides their biometric information using the system.

[0735] "Biometric information" refers to all data that indicates a user's health status, such as heart rate, activity level, and voice tone.

[0736] "Information gathering means" refers to devices and methods for acquiring biometric information from users.

[0737] "Analysis methods" refer to methods and processes for evaluating a user's emotional state and mental health based on collected biometric information.

[0738] "Generation method" refers to the process of creating tailored advice and recommendations for users based on the analysis results.

[0739] "Means of communication" refers to methods for notifying users of generated advice and information on their devices.

[0740] "Report creation method" refers to a method for providing users with their analysis results in an easily understandable format for the organization's responsible person.

[0741] This invention is a system that collects biometric information from users working in a remote environment and provides personalized health advice based on that information. This system has the following specific components, which work together to analyze the user's emotional state and stress level.

[0742] Body devices such as smartwatches worn by users acquire biometric information such as heart rate, activity level, and voice tone. This information is temporarily stored on the user's smartphone. This smartphone receives biometric information via Bluetooth and periodically transmits it to a server in the cloud. Standard Wi-Fi or mobile data communication is used for this purpose.

[0743] The server receives data in the cloud and performs data organization and analysis. The analysis utilizes emotion recognition technologies, including voice data analysis and facial expression estimation. This allows for an assessment of the user's emotional state and stress level. General cloud computing services are used for the analysis.

[0744] Furthermore, the server uses a generative AI model to create prompt messages based on the analysis results. This generation method takes individual historical data into consideration to provide personalized advice. For example, a prompt such as, "Based on the observed data, your stress level appears to be high. We recommend incorporating 10 minutes of meditation," might be generated.

[0745] The server sends the generated prompt to the smartphone, displaying an alert to the user via the device. Upon receiving this alert, the user can take the suggested action, which is expected to improve their health.

[0746] This system also provides organizational administrators with reports that assess the overall mental health status of employees based on aggregated data. These reports can be used to plan for maintaining employee well-being.

[0747] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0748] Step 1:

[0749] The user wears a smartwatch, which acquires biometric information (heart rate, activity level, voice tone) in real time. Specifically, the smartwatch uses sensors to continuously measure this data and transmits the acquired data to the user's smartphone via Bluetooth. The input here is the user's biological behavior, and the output is digitized biometric data.

[0750] Step 2:

[0751] The device, i.e., the user's smartphone, temporarily stores the received biometric data in its storage. Furthermore, it periodically sends this data to a server in the cloud. Specifically, it sends the data at regular intervals using Wi-Fi or a mobile data network. The input is biometric data from the smartwatch, and the output is the data sent to the server.

[0752] Step 3:

[0753] The server receives data in the cloud and performs organization and preprocessing. Specifically, it classifies the received data by user, completes missing data, and corrects outliers. The input is biometric data sent from smartphones, and the output is data prepared for analysis.

[0754] Step 4:

[0755] The server uses emotion recognition technology to evaluate emotional states and stress levels based on organized data. Specifically, it employs methods such as analyzing voice data and comparing it with historical data. The input is pre-processed biometric data, and the output is evaluation data regarding emotional states and stress levels.

[0756] Step 5:

[0757] The server utilizes a generative AI model to generate prompt messages based on the analysis results. These prompts include advice tailored to the user's psychological state. Specifically, prompts such as, "Based on the observed data, your stress level appears to be high. We recommend incorporating 10 minutes of meditation," are generated. The input is emotion evaluation data, and the output is a prompt message containing specific advice for the user.

[0758] Step 6:

[0759] The server sends the generated prompt to the terminal, and the terminal displays the received information as a notification to the user. Specifically, the prompt is shown to the user using the smartphone's notification function. The input is the generated prompt, and the output is a notification that the user can visually confirm.

[0760] Step 7:

[0761] The server evaluates the overall mental health status of the organization based on aggregated user data and generates a report. Specifically, it generates a report in a format viewable by administrators, which includes trends in the organization's stress levels and suggested countermeasures. The input is the analysis results of the users, and the output is a report for administrators.

[0762] (Application Example 2)

[0763] 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".

[0764] In occupations involving long working hours, such as security guards, it is not easy to quickly identify employee stress and mental burden and take appropriate measures. In particular, the inability to offer specific rest and relaxation methods tailored to individual needs can lead to a deterioration of employee mental health and a decrease in work efficiency. Therefore, there is a need for a system that can effectively monitor the mental health of security guards and provide individually tailored mental support.

[0765] 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.

[0766] In this invention, the server includes means having a measuring device for collecting user health data, analysis means for analyzing the health data and estimating stress levels and psychological states, and generation means for providing security workers with personalized rest notifications and relaxation methods. This makes it possible to grasp the stress levels of security workers in real time and provide individually optimized mental support.

[0767] A "measuring device" is a device used to acquire various data related to the user's health. Its primary role is to collect data such as heart rate, exercise level, and voice tone.

[0768] "Analysis methods" refer to the processes and techniques used to analyze collected health data and estimate the user's stress level and psychological state.

[0769] "Generation method" refers to the process and technology of creating mental support information, including recommended information and relaxation methods, for users based on analysis results.

[0770] "Transmission means" refers to the methods and devices used to transfer generated information to terminal devices, and is responsible for delivering notifications and alerts to users.

[0771] "Report generation means" refers to the processes and technologies for creating and providing appropriate mental health reports to organizational managers based on analysis results.

[0772] "Personalized break notifications" refer to customized information that informs users of the timing and method of taking breaks based on analysis results and tailored to their individual circumstances.

[0773] "Relaxation methods" refer to specific behavioral suggestions and activities provided with the aim of reducing stress for users.

[0774] The system that implements this application collects data using a smartwatch or similar measuring device worn by the user and temporarily stores it on a smartphone. The smartphone then transmits this data to a server in the cloud. The server organizes the received data and performs analysis using an emotion engine. This emotion engine uses services such as the Microsoft Azure Emotion API to recognize emotions, including voice and activity data, and accurately estimates stress levels and psychological states. Based on the analysis results, the server automatically generates appropriate rest notifications and relaxation information for security workers. This generated information is sent to the security worker's smartphone or smartwatch, allowing the user to refer to it and select optimized activities.

[0775] Furthermore, the server generates detailed reports for administrators based on the aggregated data. These reports can be used to monitor employee health and improve work efficiency. As a specific example, a security guard's smartwatch detected an increase in heart rate during work and sent a notification recommending a break. By following this notification and taking a short break, the guard was able to continue working efficiently.

[0776] An example of a prompt for a generating AI model would be: "Describe the design of a system that analyzes heart rate data from a smartwatch, identifies patterns indicating high stress levels, and provides users with rest notifications and relaxation advice."

[0777] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0778] Step 1:

[0779] The device (smartwatch) collects data related to the user's health (heart rate, activity level, voice tone, etc.) in real time. This data is acquired by sensors and stored inside the device in an appropriate format. This data serves as input for subsequent processing.

[0780] Step 2:

[0781] The device (smartphone) receives health data collected from the smartwatch at regular intervals. The received data is temporarily stored in the smartphone. At this point, the integrity of the data is checked, and if there is any incomplete data, an attempt is made to collect it again.

[0782] Step 3:

[0783] The device (smartphone) sends temporarily stored health data to a server in the cloud. The data is transmitted using a secure communication protocol and used as input for processing on the server.

[0784] Step 4:

[0785] The server stores the received health data in a database and analyzes the data using an emotion engine. Using tools such as the Microsoft Azure Emotion API, it estimates stress levels and psychological states from voice and activity data. This analysis yields output that includes insights into emotional states.

[0786] Step 5:

[0787] Based on the analysis results, the server uses a generative AI model to generate personalized break and relaxation suggestions for security workers. It utilizes prompts to create suggestions best suited to specific situations. These generated suggestions then serve as input for the next processing step.

[0788] Step 6:

[0789] The server sends the generated personalized suggestions to the device. The device displays them to the user as a notification, prompting them to take specific action. The output here is the specific notification content presented to the user.

[0790] Step 7:

[0791] Based on the notifications provided, users engage in specific relaxation activities such as exercise or rest. Data from these activities is collected again in the next cycle, and the process is repeated. This processing cycle allows for continuous monitoring and improvement of the user's mental health.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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."

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] The following is further disclosed regarding the embodiments described above.

[0814] (Claim 1)

[0815] A means having a measuring device for collecting user health data,

[0816] An analytical means for analyzing the aforementioned health data and estimating stress levels and psychological state,

[0817] A generation means for generating information to provide appropriate recommendations or promotions to users based on the analysis results,

[0818] A transmission means for notifying the user's terminal device of the generated information,

[0819] A report generation means for reporting the analysis results of the user to the administrator of the organization,

[0820] A system that includes this.

[0821] (Claim 2)

[0822] The system according to claim 1, characterized in that the analysis means also performs analysis including the user's voice data and activity data.

[0823] (Claim 3)

[0824] The system according to claim 1, characterized in that the generation means generates personalized information based on the user's individual history data.

[0825] "Example 1"

[0826] (Claim 1)

[0827] Means having an electronic device for acquiring the user's biometric information,

[0828] A calculation means for aggregating the aforementioned biological information and evaluating the psychological state,

[0829] A generation means for creating information to encourage specific advice and actions from users based on the evaluation results,

[0830] A communication means for transmitting the created information to the user's mobile device,

[0831] A means for creating reports that provides organizational decision-makers with a record of the aggregated evaluation results of the aforementioned users,

[0832] A system that includes this.

[0833] (Claim 2)

[0834] The system according to claim 1, wherein the calculation means performs analysis including user voice data and motion data.

[0835] (Claim 3)

[0836] The system according to claim 1, wherein the generation means creates individually tailored information based on the user's individual history information.

[0837] "Application Example 1"

[0838] (Claim 1)

[0839] A means having a measuring device for collecting the user's biometric information,

[0840] An analytical means for analyzing the aforementioned biological information and estimating mental stress and psychological state,

[0841] A generation means for generating information to provide appropriate action suggestions or encouragement to users based on the analysis results,

[0842] A transmission means for notifying the user's information terminal of the generated information,

[0843] An output generation means for reporting the user's analysis results to the person in charge of the organization,

[0844] A response mechanism to encourage users to take the suggested action,

[0845] A system that includes this.

[0846] (Claim 2)

[0847] The system according to claim 1, characterized in that the analysis means performs analysis including the user's voice information and movement information.

[0848] (Claim 3)

[0849] The system according to claim 1, characterized in that the generation means generates personalized information based on the user's individual history information.

[0850] "Example 2 of combining an emotion engine"

[0851] (Claim 1)

[0852] Information collection means for collecting users' biometric information,

[0853] Analysis means for analyzing the aforementioned biological information and estimating emotional state and mental health,

[0854] A generation means that generates information to provide individually tailored recommendations to users based on the analysis results,

[0855] A means for notifying the user's mobile device of the generated information,

[0856] A means for creating a report summarizing the analysis results of the aforementioned users for the person in charge of the organization,

[0857] A system that includes this.

[0858] (Claim 2)

[0859] The system according to claim 1, characterized in that the analysis means performs emotion analysis using the user's voice information and behavioral information.

[0860] (Claim 3)

[0861] The system according to claim 1, characterized in that the generation means generates customized information based on the user's past information.

[0862] "Application example 2 when combining with an emotional engine"

[0863] (Claim 1)

[0864] A means having a measuring device for collecting user health data,

[0865] An analytical means for analyzing the aforementioned health data and estimating stress levels and psychological state,

[0866] A generation means for generating information to provide appropriate recommendations or promotions to users based on the analysis results,

[0867] A generation means for providing security workers with personalized break notifications and relaxation methods,

[0868] A transmission means for notifying the user's terminal device of the generated information,

[0869] A report generation means for reporting the analysis results of the user to the administrator of the organization,

[0870] A system that includes this.

[0871] (Claim 2)

[0872] The system according to claim 1, characterized in that the analysis means also performs analysis including the user's voice data and activity data.

[0873] (Claim 3)

[0874] The system according to claim 1, characterized in that the generation means generates personalized information based on the user's individual history data. [Explanation of Symbols]

[0875] 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 having a measuring device for collecting user health data, An analytical means for analyzing the aforementioned health data and estimating stress levels and psychological state, A generation means for generating information to provide appropriate recommendations or promotions to users based on the analysis results, A transmission means for notifying the user's terminal device of the generated information, A report generation means for reporting the analysis results of the user to the administrator of the organization, A system that includes this.

2. The system according to claim 1, characterized in that the analysis means also performs analysis including the user's voice data and activity data.

3. The system according to claim 1, characterized in that the generation means generates personalized information based on the user's individual history data.

Images