system

Abstract

We provide the system. [Solution] Means of collecting biometric data, A means for analyzing the aforementioned biometric data to evaluate the user's mental burden, A means of collecting communication data and analyzing emotional states using natural language processing technology, A means of issuing warnings and providing support based on the aforementioned mental burden and emotional analysis, A means of notifying store managers of the mental stress levels of employees, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a modern remote work environment, it is difficult for companies to grasp the emotions and stress levels of individual employees in real time. As a result, there is a problem that it is difficult to discover employees in a high-stress state at an early stage and take appropriate measures. This may lead to problems such as a decrease in employee satisfaction, a decrease in productivity, and an increase in the turnover rate.

Means for Solving the Problems

[0005] The present invention solves the aforementioned problems by providing a system that includes means for collecting biometric information, means for analyzing the biometric information to evaluate the user's stress level, means for collecting communication data and analyzing emotional states using natural language processing technology, and means for issuing alerts and providing support based on this data. This system comprehensively analyzes vital data acquired from wearable devices and communication data obtained from emails, business chats, voice transcripts, etc., to evaluate employees' emotional states and stress levels in real time and enable the prompt provision of necessary support.

[0006] "Biometric information" refers to data that indicates an individual's physiological state, including indicators such as heart rate, body temperature, and blood pressure.

[0007] "Stress level" is an indicator that assesses the degree of an individual's mental and physical burden, and is measured based on biometric information and emotional data.

[0008] "Communication data" refers to data that includes the content of information exchange between individuals or within an organization, recorded in formats such as email, chat, and voice transcripts.

[0009] "Natural language processing technology" refers to a set of technologies that enable computers to understand, interpret, and generate human language, and are used in areas such as sentiment analysis and keyword extraction.

[0010] A "wearable device" refers to an electronic device that is worn on the body and has the function of collecting and recording biometric information. [Brief explanation of the drawing]

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

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

[0013] First, the terms used in the following description will be explained.

[0014] 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), and the like.

[0015] 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.

[0016] 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.

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

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

[0019] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0032] This invention relates to a system for monitoring employees' emotional states and stress levels in real time and providing support as needed. This system mainly consists of three components: a server, a terminal, and a user.

[0033] The server is responsible for receiving biometric information transmitted from wearable devices. This biometric information includes vital data such as heart rate and body temperature. Based on this data, the server uses pre-set thresholds to assess the employee's stress level.

[0034] Furthermore, the server also collects communication data such as emails, business chats, and meeting audio transcripts. This data is analyzed using natural language processing techniques and used to determine if there are any changes in emotional state or signs of stress.

[0035] The device has the function of acquiring biometric information from wearable devices and sending it to a server. Furthermore, it plays a role in notifying the user of alerts and feedback instructed by the server. This allows the user to instantly understand their own health status and stress level.

[0036] Users wear wearable devices while performing their daily tasks. These devices accurately measure biometric information, and reliable data exchange with a server enables real-time monitoring.

[0037] As a concrete example, consider a case where a user's heart rate is higher than normal during a remote meeting. The server comprehensively analyzes this biometric information along with communication data from the meeting transcript and can immediately issue an alert if it determines that the user's stress level is high. The terminal notifies the user of this alert and, if necessary, provides a link to schedule a counseling session.

[0038] In this way, this system helps create a healthy workplace environment by understanding employees' emotional states and stress levels in real time and providing appropriate support early on.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The terminal acquires biometric information such as heart rate and body temperature in real time from the wearable device. This information is transmitted to the server at predetermined intervals.

[0042] Step 2:

[0043] The server records biometric information received from the terminal in a database. During this process, a timestamp is added to the data, and it is organized and stored for each user.

[0044] Step 3:

[0045] The server evaluates stress levels by comparing them to pre-set thresholds based on biometric information. For example, if the heart rate exceeds the normal range, it flags it as potentially indicating stress.

[0046] Step 4:

[0047] The device sends communication data, such as emails, business chats, and meeting audio transcripts, to the server. This data is collected via APIs from the user's business tools.

[0048] Step 5:

[0049] The server analyzes communication data using natural language processing techniques. It grasps emotional states through stemming and keyword extraction, and analyzes whether there are signs of stress.

[0050] Step 6:

[0051] The server comprehensively evaluates stress levels based on biometric data and sentiment analysis results from communication data, and generates alerts as needed. These alerts indicate that an employee may be experiencing high levels of stress.

[0052] Step 7:

[0053] The device receives alert notifications from the server and notifies the user in real time. The user can view the alert details on the device's display.

[0054] Step 8:

[0055] Users review the support options provided based on the alert (e.g., a link to book a counseling session) and take action as needed. The device also collects user feedback, which the server can use for further analysis.

[0056] (Example 1)

[0057] 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."

[0058] In today's work environment, employee health management is a critical issue. However, traditional methods make it difficult to accurately understand employees' emotional states and stress levels in real time and provide necessary support in a timely manner. This increases the risk of harming employee health and can lead to decreased productivity. Therefore, there is a need for a system that can efficiently and effectively monitor employees' emotional states and stress levels and respond quickly when necessary.

[0059] 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.

[0060] In this invention, the server includes means for acquiring data from a device that collects biometric information, an algorithm for analyzing the biometric information and evaluating the user's stress level, and a program for collecting dialogue data and analyzing emotional states using natural language processing technology. This makes it possible to grasp the health status of employees in real time and provide prompt and appropriate support.

[0061] "Biometric information" refers to data that indicates an individual's physical condition, such as heart rate, body temperature, and activity level.

[0062] "Means of acquiring data from a device" refers to a function for transferring information collected from wearable devices and sensors to a server.

[0063] An "algorithm for evaluating stress levels" is a mathematical or logical method for analyzing biometric information to quantify or determine an individual's psychological and physical stress state.

[0064] "Dialogue data" refers to digital information of a user's communication history, including emails, chat messages, and voice recordings.

[0065] "Natural language processing technology" refers to computer processing techniques used to understand and analyze human language, such as text tokenization and sentiment analysis.

[0066] A "notification method for generating alerts" is a method that has the function of displaying or communicating information in order to give users attention or warnings.

[0067] This invention is a system for monitoring employees' health status and stress levels in real time and responding quickly. Servers, terminals, and users play key roles in implementing the invention.

[0068] The server receives biometric information transmitted from wearable devices via terminals. This biometric information includes multiple state data such as heart rate, body temperature, and activity level. The server has commonly used data analysis tools such as Python and R installed for data acquisition and analysis. This makes it possible to immediately analyze the acquired biometric information and evaluate stress levels by referring to pre-set thresholds.

[0069] The server also collects conversational data such as emails, business chats, and voice recordings. Various APIs are used for this collection, and the data is analyzed using natural language processing techniques. Specifically, the text is tokenized using libraries such as Python's SpaCy library, and sentiment analysis is performed to determine the emotional state.

[0070] The terminal's role is to acquire biometric information from wearable devices in real time and transmit that information to a server. It also immediately notifies the user of alerts and feedback generated by the server. A dedicated application is installed on the terminal, and information is provided to the user through its notification function.

[0071] Users wear wearable devices during their daily work. These devices accurately acquire biometric information, and reliable data exchange between the terminal and the server enables real-time monitoring of the system.

[0072] For example, if a user's heart rate suddenly increases during a remote meeting, the server can detect this as an abnormal condition. The server then analyzes this information along with the meeting transcript, and if it determines that the user is experiencing high stress levels, it immediately generates an alert and sends a notification through the user's device. The device then provides the user with links to schedule counseling sessions or advice on stress reduction, as needed.

[0073] An example of a prompt to be input to the generating AI model would be, "Please tell me what to do if an employee's heart rate data exceeds a threshold." Based on this prompt, the AI ​​model is expected to generate suggestions for appropriate actions.

[0074] Thus, this system protects employee health and contributes to improving workplace productivity.

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

[0076] Step 1:

[0077] The terminal collects biometric information from wearable devices in real time. Inputs include biometric data such as heart rate, body temperature, and activity level. The collected data is temporarily stored within the terminal and prepared for subsequent processing. This process ensures the terminal provides a foundation for obtaining accurate biometric information.

[0078] Step 2:

[0079] The terminal transmits the collected biometric information to the server at regular intervals. The input is the biometric information collected in step 1, and the output is the data transfer to the server. Here, a security protocol is activated during transmission to prevent data leakage. This operation allows the server to receive the latest status data.

[0080] Step 3:

[0081] The server analyzes biometric information received from the terminal. The input is biometric information transmitted from the terminal, and the output is the stress level evaluation result. The server uses data analysis tools such as Python and R to quantify the biometric information and identify the parts that exceed the threshold. This analysis makes it possible to quantify the stress level of employees.

[0082] Step 4:

[0083] The server collects conversational data from sources such as emails, business chats, and voice recordings. The input is conversational data from various communication platforms, and the output is raw data ready for natural language processing. By using an API to import and store the data in a database, the data is prepared for efficient analysis.

[0084] Step 5:

[0085] The server analyzes the collected dialogue data using natural language processing techniques. The input is the dialogue data organized in step 4, and the output is the evaluation result of the emotional state. As a specific example, the SpaCy library is used to perform emotion analysis and determine emotions such as positive and negative. This analysis makes it possible to understand emotional fluctuations in communication.

[0086] Step 6:

[0087] The server integrates biometric information and emotion analysis results to generate alerts. The input is the analysis results from steps 3 and 5, and the output is the generated alert information. The server references thresholds, evaluates stress levels and emotional states, and automatically constructs alerts. This process ensures that necessary information is generated in a timely and accurate manner.

[0088] Step 7:

[0089] The device receives alerts from the server and notifies the user. The input is the alert sent from the server, and the output is the information displayed on the screen as a notification to the user. The device provides this notification via pop-up or audio, and displays a counseling link if necessary. This allows the user to receive immediate feedback on their stress levels.

[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 solve the challenge of not being able to grasp the mental stress and emotional state of employees in physical stores in real time and to provide appropriate support quickly.

[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 for collecting biometric data, means for analyzing the biometric data to evaluate the user's mental burden, and means for collecting communication data and analyzing the emotional state using natural language processing technology. This enables store managers to grasp the mental burden state of employees in real time and quickly provide appropriate support and adjust staffing levels.

[0095] "Biometric data" refers to objective information that indicates the user's physical condition, including vital signs such as heart rate and body temperature.

[0096] "Mental burden" is an indicator that shows the degree of psychological stress and emotional pressure that users experience in their daily lives and work.

[0097] "Communication data" refers to text, audio, and other communication information transmitted by users through electronic communication means.

[0098] "Natural language processing technology" is a technique that enables computers to understand and analyze human language, and is used to extract emotional states and context.

[0099] "Support" refers to the help and assistance provided to reduce the mental burden on users and maintain a healthy state of mind.

[0100] A description of the embodiment for carrying out the invention will be provided.

[0101] The program to implement this system runs on a wearable device, a server, and an administrator's terminal. The wearable device functions as hardware that measures the user's biometric data in real time and transmits it to the server. A multi-functional smart device capable of measuring heart rate and body temperature is used as the wearable device.

[0102] The server uses Python and related machine learning libraries (such as scikit-learn and TENSORFLOW®) to analyze biometric data and run software to assess mental stress. The server also analyzes user communication data (such as emails and business chats) using natural language processing technologies (such as spaCy and NLTK) to analyze emotional states.

[0103] The terminal is used to notify administrators of warnings and support information sent from the server. Smartphones and tablets are suitable terminals. Through this, administrators can understand the mental stress levels of employees in real time and provide appropriate support.

[0104] For example, if a particular employee's heart rate suddenly increases while serving a customer, this is evaluated by the server. If, in conjunction with emotion analysis, it is determined to be a high-stress state, a warning is sent to the administrator's terminal. Upon receiving this notification, the administrator can quickly take countermeasures (e.g., instructing the employee to take a temporary break).

[0105] An example of a prompt message for the generating AI model is: "If an employee's heart rate exceeds 75 BPM, determine that they are under high stress and notify the store manager."

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The user wears a wearable device while performing their daily tasks. The wearable device continuously measures the user's biometric data, such as heart rate and body temperature. The input data obtained from this device is real-time heart rate and body temperature information.

[0109] Step 2:

[0110] Biometric data transmitted from this device arrives at the server. The server receives the input biometric data and analyzes it using Python. Data processing here begins with standardizing the measured values ​​and detecting outliers. Specifically, a trigger is generated when a threshold is exceeded by comparing it with past data.

[0111] Step 3:

[0112] The server uses a machine learning algorithm to assess the user's mental stress based on the received biometric data. The input in this step is normalized biometric data, and the output is the stress level assessment. The data calculation involves classification using a model based on an existing dataset.

[0113] Step 4:

[0114] The server collects user communication data in parallel and analyzes their emotional state using natural language processing techniques. The server processes text-based communication data as input and outputs the results of the emotional state analysis. Specific analysis operations include keyword extraction and emotion scoring.

[0115] Step 5:

[0116] The server determines whether a warning is necessary based on the analysis results of biometric and communication data, and creates an alert if required. The input for processing is the stress level and the result of emotion analysis, and the output is a warning message. Specifically, it prepares to notify the administrator when a threshold is exceeded.

[0117] Step 6:

[0118] The terminal receives warnings and support information from the server and notifies the store manager. The input here is the warning message from the server, and the output is the notification displayed to the manager. Specific actions include real-time notifications to smartphones and dashboard updates.

[0119] Step 7:

[0120] Store managers will review received notifications and take appropriate action. This is intended to reduce the mental burden on users and support the smooth operation of their work. For example, a manager might instruct employees to take breaks.

[0121] 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.

[0122] This invention combines an emotion engine with a system that analyzes a user's biometric information and communication data to grasp their emotional state and stress level in real time, thereby achieving more accurate emotion recognition and stress assessment.

[0123] The server first receives biometric information acquired from the wearable device. This includes vital data indicating the user's physical state, such as heart rate and body temperature. This data is analyzed by an emotion engine installed within the server, which can precisely classify how the user's emotions are changing. The emotion engine uses a pre-trained emotion model to identify abnormal emotional patterns and signs of stress.

[0124] In addition, the device collects communication data recorded in formats such as email, business chat, and conference audio transcripts, and sends it to the server. The server decodes this data using natural language processing technology and evaluates the user's text-based emotional state using an emotion engine. This enables sophisticated emotion recognition that comprehensively combines information obtained from multiple sources, rather than simply making emotion judgments based on text analysis.

[0125] As a concrete example, suppose a user experiences a sharp increase in heart rate after a meeting and frequently uses negative language during the meeting. In this case, the emotion engine within the server analyzes this data comprehensively and determines that the user is in a high-stress state. Based on this result, the server generates an alert and notifies the user through their device. The notification may include suggestions for relaxation or links to book counseling, enabling a quick response.

[0126] In this way, this system supports the creation of a healthier workplace environment by recognizing the user's emotional state with high accuracy and providing prompt and appropriate support.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] The terminal acquires biometric information from the wearable device worn by the user and transmits vital data such as heart rate and body temperature to the server.

[0130] Step 2:

[0131] The server records biometric information received from the terminal in a database. Each piece of data is then associated with a user ID and a timestamp.

[0132] Step 3:

[0133] The emotion engine within the server analyzes biometric information and evaluates the user's real-time emotional state. The emotion engine distinguishes emotional states based on a pre-trained model.

[0134] Step 4:

[0135] The device collects communication data such as emails, business chat messages, and meeting audio transcripts, and sends them to the server.

[0136] Step 5:

[0137] The server uses natural language processing techniques to analyze communication data as text. During this process, it extracts keywords and performs sentiment analysis to infer the user's emotional state from the context.

[0138] Step 6:

[0139] The emotion engine integrates the results of communication data analysis and biometric data analysis to comprehensively evaluate the user's stress level.

[0140] Step 7:

[0141] The server generates an alert if it determines that the stress level exceeds a pre-set threshold. This alert indicates that the user is in a high-stress state.

[0142] Step 8:

[0143] The device receives alerts from the server and notifies the user in real time. The notifications include stress reduction advice and links to book counseling appointments.

[0144] Step 9:

[0145] Based on notifications received through their devices, users can check their health status and, if necessary, initiate procedures to receive support. This allows users to take prompt and appropriate action.

[0146] (Example 2)

[0147] 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".

[0148] Conventional technologies often involved individually analyzing users' physiological states and communication patterns, making it difficult to comprehensively analyze emotional states and stress levels with high accuracy. Furthermore, there was a lack of systems capable of rapidly analyzing this data and providing immediate support to users.

[0149] 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.

[0150] In this invention, the server includes means for receiving and analyzing biometric information, means for classifying the user's emotional changes based on the biometric information using a machine learning algorithm, and means for collecting communication information and comprehensively evaluating the emotional state using natural language processing technology. This makes it possible to comprehensively evaluate the user's emotional state and stress level with high accuracy and to provide appropriate support quickly.

[0151] "Biometric information" refers to data that indicates the user's physical condition, primarily including information that shows vital functions such as heart rate and body temperature.

[0152] "Analysis" refers to the process of classifying and evaluating users' emotional changes and stress levels based on acquired data.

[0153] A "machine learning algorithm" refers to a set of methods that allow computers to learn features from data and make predictions and classifications about unknown data.

[0154] "Communication information" refers to information used by users when communicating with others, and includes electronic communications, dialogue records, and voice recordings.

[0155] "Natural language processing technology" refers to the technology used to process, understand, and generate natural language used by humans using computers.

[0156] "Emotional state" refers to the emotions and psychological state a user is experiencing at a given point in time, and this is used to assess the user's stress levels and well-being.

[0157] An "alert" refers to a notification that alerts users to anomalies or important signs detected during analysis.

[0158] "Support information" refers to advice, suggestions, or links and methods provided to users based on analysis results to encourage action.

[0159] This invention is a system that analyzes a user's emotional state and stress level with high accuracy and provides appropriate support quickly. First, the server acquires vital data such as heart rate and body temperature via a device carried by the user (e.g., smartwatch, fitness band) in order to receive biometric information. This biometric information is analyzed by an emotion engine within the server. The emotion engine incorporates a machine learning algorithm, which classifies the user's emotional changes from the data.

[0160] Next, the terminal collects communication information in various forms, such as the user's emails, business chats, and conference audio. This collected data is then analyzed by a server using natural language processing technology. The server integrates the collected text and audio data and performs a comprehensive emotional state assessment using an emotion engine. This process is crucial for accurately determining the emotional state.

[0161] As a concrete example, suppose a user sends an email during a business meeting that includes an elevated heart rate and negative expressions such as "I'm too busy and tired." In this case, the server integrates the heart rate data with the email content and determines that the user is in a stressed state. Based on this analysis, the server generates a notification that includes relaxation methods and a link to book a counseling session, and suggests these to the user.

[0162] An example of a prompt for a generative AI model might be: "During the meeting, the user's heart rate increased, and they tended to use negative language. Based on this data, assess the user's emotional state and suggest appropriate support."

[0163] This system utilizes advanced emotion analysis technology to provide an effective means of supporting users' healthy lifestyles.

[0164] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0165] Step 1:

[0166] The server receives biometric information from the wearable device, including heart rate and body temperature. The input data is measured in real time by the device, and the server passes it to the emotion engine after receiving it. The emotion engine uses machine learning algorithms to analyze the biometric information and classify the user's emotional changes. As a result, if it detects an anomaly such as a sudden increase in heart rate, it generates an output indicating an abnormal emotional state.

[0167] Step 2:

[0168] The device collects user communication data. Specifically, it acquires information such as emails, business chats, and meeting audio transcripts. This input data is sent from the device to the server. The server analyzes this communication information using natural language processing technology and evaluates the emotional state based on the text and audio data. This process outputs whether the text indicates a positive or negative emotion.

[0169] Step 3:

[0170] The server integrates the results of biometric data analysis and communication data evaluation. This allows for a comprehensive assessment of emotional states obtained from diverse data sources. In this step, the emotional evaluations obtained from the two aforementioned inputs are integrated to determine the user's overall emotional state. The output indicates whether the user's stress level and emotional balance are at a high-risk level.

[0171] Step 4:

[0172] The server generates alerts based on integrated assessment results. These alerts notify users of abnormal emotional states or signs of high stress. The server then provides support information as appropriate countermeasures, specifically including relaxation methods and, if necessary, links to counseling appointments. Finally, it sends a notification to the device, prompting the user to take action based on the information received. The output includes a specific action plan to facilitate the user's prompt response.

[0173] (Application Example 2)

[0174] 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".

[0175] In recent years, there has been a growing need to quickly understand workers' emotional states and stress levels in order to provide an efficient and healthy work environment. However, conventional methods rely on a single data source for emotion recognition, making accurate assessment difficult. Furthermore, mechanisms for visually notifying workers of stress levels immediately are insufficient. Therefore, a system is needed that integrates multiple data sources to perform highly accurate emotion analysis and visually notifies workers of changes in their state.

[0176] 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.

[0177] In this invention, the server includes means for collecting biometric information, means for analyzing the biometric information to evaluate the user's stress level, means for collecting communication data and analyzing the emotional state using natural language processing technology, and means for presenting information to a visual device to notify the user of changes in their state. This makes it possible to perform highly accurate emotion recognition and stress evaluation from multiple data sources while simultaneously providing immediate visual notification to the user.

[0178] "Biometric information" refers to data that indicates the user's physical condition, including vital data such as heart rate and body temperature.

[0179] "Analysis" is the process of breaking down data based on a specific purpose and interpreting its meaning.

[0180] "Stress level" refers to the degree of mental burden a user is experiencing, expressed numerically or using indicators.

[0181] "Communication data" refers to all data generated when users exchange information with others, such as electronic messages, business communications, and voice recordings.

[0182] "Natural language processing technology" is a technique that enables computers to understand and analyze human language, and is used to interpret the meaning of text and speech.

[0183] "Emotional state" refers to the user's current mood and feelings, and is the subject of analysis by the emotion engine.

[0184] "Visual devices" refer to all equipment and devices that allow users to receive information visually by wearing or using them.

[0185] A "notification" is a means of conveying specific information to a user, and may take the form of an alert or message.

[0186] To implement this invention, a system combining multiple hardware and software components is required. Specifically, a smart pair of glasses is used as a wearable device worn by the user, and this functions as a device for acquiring biometric information. The smart glasses collect biometric information such as heart rate and body temperature using sensors and transmit it to a server using wireless communication technology.

[0187] The server runs on a Python program and incorporates specific modules for analyzing acquired biometric information. For example, the aforementioned "biometric_module" has the function of processing biometric information and evaluating stress levels. "communication_analyzer" is a module that analyzes communication data using natural language processing techniques. The server integrates this data and uses the emotion engine "emotion_engine" to accurately evaluate the user's emotional state.

[0188] If the evaluation detects an abnormal stress level, the server sends an alert to the smart glasses, displaying a notification in the user's field of vision. This allows the user to immediately recognize the change in their state and take appropriate action, such as taking a break, if necessary.

[0189] As a concrete example, suppose a security staff member reaches their peak stress level while patrolling the facility at night. At this point, smart glasses would display a message to the user saying, "High stress detected. Take a short break!"

[0190] An example of a prompt message for the AI ​​model in this invention is, "Analyze this staff member's heart rate and body temperature data, as well as their communication logs, and diagnose how their current emotional state is changing." Based on this prompt message, the system provides optimal support.

[0191] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0192] Step 1:

[0193] The server receives biometric information such as heart rate and body temperature from smart glasses. The input is vital data from the smart glasses, and the output is biometric data stored in a database. This data reception uses Bluetooth communication to acquire data from properly paired devices.

[0194] Step 2:

[0195] The server passes biometric information to the "biometric_module" for analysis and evaluates the user's stress level. The input is the biometric data received in step 1, and the output is the stress level evaluation result. This module uses statistical analysis methods and generative AI models to detect abnormal vital patterns.

[0196] Step 3:

[0197] The terminal collects communication data such as emails, business chats, and voice recordings, and sends it to the server. The input is communication data, and the output is text data uploaded to the server. The terminal then prepares text data from various sources.

[0198] Step 4:

[0199] The server analyzes the received communication data using "communication_analyzer" to evaluate the emotional state. The input is the text data from step 3, and the output is the analysis result of the user's emotional state. This analysis uses natural language processing techniques, and the emotion engine classifies the emotional tone of the text.

[0200] Step 5:

[0201] The server integrates the stress level and emotional state assessment results and performs an integrated emotional assessment using a generative AI model. The input is the assessment results from steps 2 and 4, and the output is the final emotional state determination. The generative AI model utilizes supervised learning to detect abnormal emotional states.

[0202] Step 6:

[0203] A warning is displayed as an alert on the user's smart glasses, which are their visual device, if their stress level is high. The input is the result of the emotional state assessment in step 5, and the output is a visual notification to the user. The smart glasses display this warning as a pop-up window, showing a message prompting the user to take a break.

[0204] 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.

[0205] Data generation model 58 is a type of 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.

[0206] 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.

[0207] [Second Embodiment]

[0208] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0209] 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.

[0210] 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).

[0211] 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.

[0212] 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.

[0213] 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).

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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".

[0220] This invention relates to a system for monitoring employees' emotional states and stress levels in real time and providing support as needed. This system mainly consists of three components: a server, a terminal, and a user.

[0221] The server is responsible for receiving biometric information transmitted from wearable devices. This biometric information includes vital data such as heart rate and body temperature. Based on this data, the server uses pre-set thresholds to assess the employee's stress level.

[0222] Furthermore, the server also collects communication data such as emails, business chats, and meeting audio transcripts. This data is analyzed using natural language processing techniques and used to determine if there are any changes in emotional state or signs of stress.

[0223] The device has the function of acquiring biometric information from wearable devices and sending it to a server. Furthermore, it plays a role in notifying the user of alerts and feedback instructed by the server. This allows the user to instantly understand their own health status and stress level.

[0224] Users wear wearable devices while performing their daily tasks. These devices accurately measure biometric information, and reliable data exchange with a server enables real-time monitoring.

[0225] As a concrete example, consider a case where a user's heart rate is higher than normal during a remote meeting. The server comprehensively analyzes this biometric information along with communication data from the meeting transcript and can immediately issue an alert if it determines that the user's stress level is high. The terminal notifies the user of this alert and, if necessary, provides a link to schedule a counseling session.

[0226] In this way, this system helps create a healthy workplace environment by understanding employees' emotional states and stress levels in real time and providing appropriate support early on.

[0227] The following describes the processing flow.

[0228] Step 1:

[0229] The terminal acquires biometric information such as heart rate and body temperature in real time from the wearable device. This information is transmitted to the server at predetermined intervals.

[0230] Step 2:

[0231] The server records biometric information received from the terminal in a database. During this process, a timestamp is added to the data, and it is organized and stored for each user.

[0232] Step 3:

[0233] The server evaluates stress levels by comparing them to pre-set thresholds based on biometric information. For example, if the heart rate exceeds the normal range, it flags it as potentially indicating stress.

[0234] Step 4:

[0235] The device sends communication data, such as emails, business chats, and meeting audio transcripts, to the server. This data is collected via APIs from the user's business tools.

[0236] Step 5:

[0237] The server analyzes communication data using natural language processing techniques. It grasps emotional states through stemming and keyword extraction, and analyzes whether there are signs of stress.

[0238] Step 6:

[0239] The server comprehensively evaluates stress levels based on biometric data and sentiment analysis results from communication data, and generates alerts as needed. These alerts indicate that an employee may be experiencing high levels of stress.

[0240] Step 7:

[0241] The device receives alert notifications from the server and notifies the user in real time. The user can view the alert details on the device's display.

[0242] Step 8:

[0243] Users review the support options provided based on the alert (e.g., a link to book a counseling session) and take action as needed. The device also collects user feedback, which the server can use for further analysis.

[0244] (Example 1)

[0245] 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."

[0246] In today's work environment, employee health management is a critical issue. However, traditional methods make it difficult to accurately understand employees' emotional states and stress levels in real time and provide necessary support in a timely manner. This increases the risk of harming employee health and can lead to decreased productivity. Therefore, there is a need for a system that can efficiently and effectively monitor employees' emotional states and stress levels and respond quickly when necessary.

[0247] 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.

[0248] In this invention, the server includes means for acquiring data from a device that collects biometric information, an algorithm for analyzing the biometric information and evaluating the user's stress level, and a program for collecting dialogue data and analyzing emotional states using natural language processing technology. This makes it possible to grasp the health status of employees in real time and provide prompt and appropriate support.

[0249] "Biometric information" refers to data that indicates an individual's physical condition, such as heart rate, body temperature, and activity level.

[0250] "Means of acquiring data from a device" refers to a function for transferring information collected from wearable devices and sensors to a server.

[0251] An "algorithm for evaluating stress levels" is a mathematical or logical method for analyzing biometric information to quantify or determine an individual's psychological and physical stress state.

[0252] "Dialogue data" refers to digital information of a user's communication history, including emails, chat messages, and voice recordings.

[0253] "Natural language processing technology" refers to computer processing techniques used to understand and analyze human language, such as text tokenization and sentiment analysis.

[0254] A "notification method for generating alerts" is a method that has the function of displaying or communicating information in order to give users attention or warnings.

[0255] This invention is a system for monitoring employees' health status and stress levels in real time and responding quickly. Servers, terminals, and users play key roles in implementing the invention.

[0256] The server receives biometric information transmitted from wearable devices via terminals. This biometric information includes multiple state data such as heart rate, body temperature, and activity level. The server has commonly used data analysis tools such as Python and R installed for data acquisition and analysis. This makes it possible to immediately analyze the acquired biometric information and evaluate stress levels by referring to pre-set thresholds.

[0257] The server also collects conversational data such as emails, business chats, and voice recordings. Various APIs are used for this collection, and the data is analyzed using natural language processing techniques. Specifically, the text is tokenized using libraries such as Python's SpaCy library, and sentiment analysis is performed to determine the emotional state.

[0258] The terminal's role is to acquire biometric information from wearable devices in real time and transmit that information to a server. It also immediately notifies the user of alerts and feedback generated by the server. A dedicated application is installed on the terminal, and information is provided to the user through its notification function.

[0259] Users wear wearable devices during their daily work. These devices accurately acquire biometric information, and reliable data exchange between the terminal and the server enables real-time monitoring of the system.

[0260] For example, if a user's heart rate suddenly increases during a remote meeting, the server can detect this as an abnormal condition. The server then analyzes this information along with the meeting transcript, and if it determines that the user is experiencing high stress levels, it immediately generates an alert and sends a notification through the user's device. The device then provides the user with links to schedule counseling sessions or advice on stress reduction, as needed.

[0261] An example of a prompt to be input to the generating AI model would be, "Please tell me what to do if an employee's heart rate data exceeds a threshold." Based on this prompt, the AI ​​model is expected to generate suggestions for appropriate actions.

[0262] Thus, this system protects employee health and contributes to improving workplace productivity.

[0263] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0264] Step 1:

[0265] The terminal collects biometric information from wearable devices in real time. Inputs include biometric data such as heart rate, body temperature, and activity level. The collected data is temporarily stored within the terminal and prepared for subsequent processing. This process ensures the terminal provides a foundation for obtaining accurate biometric information.

[0266] Step 2:

[0267] The terminal transmits the collected biometric information to the server at regular intervals. The input is the biometric information collected in step 1, and the output is the data transfer to the server. Here, a security protocol is activated during transmission to prevent data leakage. This operation allows the server to receive the latest status data.

[0268] Step 3:

[0269] The server analyzes biometric information received from the terminal. The input is biometric information transmitted from the terminal, and the output is the stress level evaluation result. The server uses data analysis tools such as Python and R to quantify the biometric information and identify the parts that exceed the threshold. This analysis makes it possible to quantify the stress level of employees.

[0270] Step 4:

[0271] The server collects conversational data from sources such as emails, business chats, and voice recordings. The input is conversational data from various communication platforms, and the output is raw data ready for natural language processing. By using an API to import and store the data in a database, the data is prepared for efficient analysis.

[0272] Step 5:

[0273] The server analyzes the collected dialogue data using natural language processing techniques. The input is the dialogue data organized in step 4, and the output is the evaluation result of the emotional state. As a specific example, the SpaCy library is used to perform emotion analysis and determine emotions such as positive and negative. This analysis makes it possible to understand emotional fluctuations in communication.

[0274] Step 6:

[0275] The server integrates biometric information and emotion analysis results to generate alerts. The input is the analysis results from steps 3 and 5, and the output is the generated alert information. The server references thresholds, evaluates stress levels and emotional states, and automatically constructs alerts. This process ensures that necessary information is generated in a timely and accurate manner.

[0276] Step 7:

[0277] The device receives alerts from the server and notifies the user. The input is the alert sent from the server, and the output is the information displayed on the screen as a notification to the user. The device provides this notification via pop-up or audio, and displays a counseling link if necessary. This allows the user to receive immediate feedback on their stress levels.

[0278] (Application Example 1)

[0279] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0280] The aim is to solve the challenge of not being able to grasp the mental stress and emotional state of employees in physical stores in real time and to provide appropriate support quickly.

[0281] 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.

[0282] In this invention, the server includes means for collecting biological data, means for analyzing the biological data to evaluate the mental load of the user, and means for collecting communication data and analyzing the emotional state using natural language processing technology. As a result, the store manager can grasp the mental load state of the employee in real time and can quickly make appropriate support and adjustment of personnel arrangement.

[0283] "Biological data" is objective information indicating the physical state of the user, and includes vital sign data such as heart rate and body temperature.

[0284] "Mental load" is an index indicating the degree of psychological stress and emotional pressure experienced by the user in daily life and work.

[0285] "Communication data" refers to text, voice, and other communication information transmitted by the user through electronic communication means.

[0286] "Natural language processing technology" is a technology for a computer to understand and analyze human language, and is used to extract emotional states and contexts.

[0287] "Support" refers to the help and support provided to reduce the mental load of the user and maintain a healthy state.

[0288] The mode for carrying out the invention will be described.

[0289] The program for realizing this system operates on a wearable device, a server, and the administrator's terminal. The wearable device functions as hardware that measures the biological data of the user in real time and transmits it to the server. As the wearable device, a multifunctional smart device that can measure heart rate and body temperature is used. <000091​​The server uses Python and related machine learning libraries (such as scikit-learn and TensorFlow) to analyze biometric data and run software to assess mental stress. The server also analyzes user communication data (such as emails and business chats) using natural language processing techniques (such as spaCy and NLTK) to analyze emotional states.

[0291] The terminal is used to notify administrators of warnings and support information sent from the server. Smartphones and tablets are suitable terminals. Through this, administrators can understand the mental stress levels of employees in real time and provide appropriate support.

[0292] For example, if a particular employee's heart rate suddenly increases while serving a customer, this is evaluated by the server. If, in conjunction with emotion analysis, it is determined to be a high-stress state, a warning is sent to the administrator's terminal. Upon receiving this notification, the administrator can quickly take countermeasures (e.g., instructing the employee to take a temporary break).

[0293] An example of a prompt message for the generating AI model is: "If an employee's heart rate exceeds 75 BPM, determine that they are under high stress and notify the store manager."

[0294] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0295] Step 1:

[0296] The user wears a wearable device while performing their daily tasks. The wearable device continuously measures the user's biometric data, such as heart rate and body temperature. The input data obtained from this device is real-time heart rate and body temperature information.

[0297] Step 2:

[0298] Biometric data transmitted from this device arrives at the server. The server receives the input biometric data and analyzes it using Python. Data processing here begins with standardizing the measured values ​​and detecting outliers. Specifically, a trigger is generated when a threshold is exceeded by comparing it with past data.

[0299] Step 3:

[0300] The server uses a machine learning algorithm to assess the user's mental stress based on the received biometric data. The input in this step is normalized biometric data, and the output is the stress level assessment. The data calculation involves classification using a model based on an existing dataset.

[0301] Step 4:

[0302] The server collects user communication data in parallel and analyzes their emotional state using natural language processing techniques. The server processes text-based communication data as input and outputs the results of the emotional state analysis. Specific analysis operations include keyword extraction and emotion scoring.

[0303] Step 5:

[0304] The server determines whether a warning is necessary based on the analysis results of biometric and communication data, and creates an alert if required. The input for processing is the stress level and the result of emotion analysis, and the output is a warning message. Specifically, it prepares to notify the administrator when a threshold is exceeded.

[0305] Step 6:

[0306] The terminal receives warnings and support information from the server and notifies the store manager. The input here is the warning message from the server, and the output is the notification displayed to the manager. Specific actions include real-time notifications to smartphones and dashboard updates.

[0307] Step 7:

[0308] The store manager checks the received notification and takes appropriate countermeasures. This aims to reduce the mental burden on the user and support the smooth execution of operations. As a specific example, the manager takes actions such as instructing employees to take breaks.

[0309] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions.

[0310] In the present invention, by combining an emotion engine with a system that analyzes the user's biometric information and communication data and grasps the emotional state and stress level in real time, more accurate emotion recognition and stress evaluation are realized.

[0311] First, the server receives biometric information acquired from the wearable device. This includes vital data indicating the user's physical state, such as heart rate and body temperature. These data are analyzed by an emotion engine installed in the server, and it is possible to finely classify how the user's emotions are changing. The emotion engine uses a pre-learned emotion model to identify abnormal emotion patterns and signs of stress.

[0312] In addition, the terminal collects communication data recorded in the form of e-mails, business chats, conference voice transcripts, etc., and transmits it to the server. The server decodes this data using natural language processing technology and evaluates the user's text-based emotional state using the emotion engine. This enables advanced emotion recognition that comprehensively combines information obtained from multiple sources, rather than emotion judgment based solely on text analysis.

[0313] As a concrete example, suppose a user experiences a sharp increase in heart rate after a meeting and frequently uses negative language during the meeting. In this case, the emotion engine within the server analyzes this data comprehensively and determines that the user is in a high-stress state. Based on this result, the server generates an alert and notifies the user through their device. The notification may include suggestions for relaxation or links to book counseling, enabling a quick response.

[0314] In this way, this system supports the creation of a healthier workplace environment by recognizing the user's emotional state with high accuracy and providing prompt and appropriate support.

[0315] The following describes the processing flow.

[0316] Step 1:

[0317] The terminal acquires biometric information from the wearable device worn by the user and transmits vital data such as heart rate and body temperature to the server.

[0318] Step 2:

[0319] The server records biometric information received from the terminal in a database. Each piece of data is then associated with a user ID and a timestamp.

[0320] Step 3:

[0321] The emotion engine within the server analyzes biometric information and evaluates the user's real-time emotional state. The emotion engine distinguishes emotional states based on a pre-trained model.

[0322] Step 4:

[0323] The device collects communication data such as emails, business chat messages, and meeting audio transcripts, and sends them to the server.

[0324] Step 5:

[0325] The server uses natural language processing techniques to analyze communication data as text. During this process, it extracts keywords and performs sentiment analysis to infer the user's emotional state from the context.

[0326] Step 6:

[0327] The emotion engine integrates the results of communication data analysis and biometric data analysis to comprehensively evaluate the user's stress level.

[0328] Step 7:

[0329] The server generates an alert if it determines that the stress level exceeds a pre-set threshold. This alert indicates that the user is in a high-stress state.

[0330] Step 8:

[0331] The device receives alerts from the server and notifies the user in real time. The notifications include stress reduction advice and links to book counseling appointments.

[0332] Step 9:

[0333] Based on notifications received through their devices, users can check their health status and, if necessary, initiate procedures to receive support. This allows users to take prompt and appropriate action.

[0334] (Example 2)

[0335] 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".

[0336] Conventional technologies often involved individually analyzing users' physiological states and communication patterns, making it difficult to comprehensively analyze emotional states and stress levels with high accuracy. Furthermore, there was a lack of systems capable of rapidly analyzing this data and providing immediate support to users.

[0337] 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.

[0338] In this invention, the server includes means for receiving and analyzing biometric information, means for classifying the user's emotional changes based on the biometric information using a machine learning algorithm, and means for collecting communication information and comprehensively evaluating the emotional state using natural language processing technology. This makes it possible to comprehensively evaluate the user's emotional state and stress level with high accuracy and to provide appropriate support quickly.

[0339] "Biometric information" refers to data that indicates the user's physical condition, primarily including information that shows vital functions such as heart rate and body temperature.

[0340] "Analysis" refers to the process of classifying and evaluating users' emotional changes and stress levels based on acquired data.

[0341] A "machine learning algorithm" refers to a set of methods that allow computers to learn features from data and make predictions and classifications about unknown data.

[0342] "Communication information" refers to information used by users when communicating with others, and includes electronic communications, dialogue records, and voice recordings.

[0343] "Natural language processing technology" refers to the technology used to process, understand, and generate natural language used by humans using computers.

[0344] "Emotional state" refers to the emotions and psychological state a user is experiencing at a given point in time, and this is used to assess the user's stress levels and well-being.

[0345] An "alert" refers to a notification that alerts users to anomalies or important signs detected during analysis.

[0346] "Support information" refers to advice, suggestions, or links and methods provided to users based on analysis results to encourage action.

[0347] This invention is a system that analyzes a user's emotional state and stress level with high accuracy and provides appropriate support quickly. First, the server acquires vital data such as heart rate and body temperature via a device carried by the user (e.g., smartwatch, fitness band) in order to receive biometric information. This biometric information is analyzed by an emotion engine within the server. The emotion engine incorporates a machine learning algorithm, which classifies the user's emotional changes from the data.

[0348] Next, the terminal collects communication information in various forms, such as the user's emails, business chats, and conference audio. This collected data is then analyzed by a server using natural language processing technology. The server integrates the collected text and audio data and performs a comprehensive emotional state assessment using an emotion engine. This process is crucial for accurately determining the emotional state.

[0349] As a concrete example, suppose a user sends an email during a business meeting that includes an elevated heart rate and negative expressions such as "I'm too busy and tired." In this case, the server integrates the heart rate data with the email content and determines that the user is in a stressed state. Based on this analysis, the server generates a notification that includes relaxation methods and a link to book a counseling session, and suggests these to the user.

[0350] An example of a prompt for a generative AI model might be: "During the meeting, the user's heart rate increased, and they tended to use negative language. Based on this data, assess the user's emotional state and suggest appropriate support."

[0351] This system utilizes advanced emotion analysis technology to provide an effective means of supporting users' healthy lifestyles.

[0352] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0353] Step 1:

[0354] The server receives biometric information from the wearable device, including heart rate and body temperature. The input data is measured in real time by the device, and the server passes it to the emotion engine after receiving it. The emotion engine uses machine learning algorithms to analyze the biometric information and classify the user's emotional changes. As a result, if it detects an anomaly such as a sudden increase in heart rate, it generates an output indicating an abnormal emotional state.

[0355] Step 2:

[0356] The device collects user communication data. Specifically, it acquires information such as emails, business chats, and meeting audio transcripts. This input data is sent from the device to the server. The server analyzes this communication information using natural language processing technology and evaluates the emotional state based on the text and audio data. This process outputs whether the text indicates a positive or negative emotion.

[0357] Step 3:

[0358] The server integrates the results of biometric data analysis and communication data evaluation. This allows for a comprehensive assessment of emotional states obtained from diverse data sources. In this step, the emotional evaluations obtained from the two aforementioned inputs are integrated to determine the user's overall emotional state. The output indicates whether the user's stress level and emotional balance are at a high-risk level.

[0359] Step 4:

[0360] The server generates alerts based on integrated assessment results. These alerts notify users of abnormal emotional states or signs of high stress. The server then provides support information as appropriate countermeasures, specifically including relaxation methods and, if necessary, links to counseling appointments. Finally, it sends a notification to the device, prompting the user to take action based on the information received. The output includes a specific action plan to facilitate the user's prompt response.

[0361] (Application Example 2)

[0362] 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."

[0363] In recent years, there has been a growing need to quickly understand workers' emotional states and stress levels in order to provide an efficient and healthy work environment. However, conventional methods rely on a single data source for emotion recognition, making accurate assessment difficult. Furthermore, mechanisms for visually notifying workers of stress levels immediately are insufficient. Therefore, a system is needed that integrates multiple data sources to perform highly accurate emotion analysis and visually notifies workers of changes in their state.

[0364] 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.

[0365] In this invention, the server includes means for collecting biometric information, means for analyzing the biometric information to evaluate the user's stress level, means for collecting communication data and analyzing the emotional state using natural language processing technology, and means for presenting information to a visual device to notify the user of changes in their state. This makes it possible to perform highly accurate emotion recognition and stress evaluation from multiple data sources while simultaneously providing immediate visual notification to the user.

[0366] "Biometric information" refers to data that indicates the user's physical condition, including vital data such as heart rate and body temperature.

[0367] "Analysis" is the process of breaking down data based on a specific purpose and interpreting its meaning.

[0368] "Stress level" refers to the degree of mental burden a user is experiencing, expressed numerically or using indicators.

[0369] "Communication data" refers to all data generated when users exchange information with others, such as electronic messages, business communications, and voice recordings.

[0370] "Natural language processing technology" is a technique that enables computers to understand and analyze human language, and is used to interpret the meaning of text and speech.

[0371] "Emotional state" refers to the user's current mood and feelings, and is the subject of analysis by the emotion engine.

[0372] "Visual devices" refer to all equipment and devices that allow users to receive information visually by wearing or using them.

[0373] A "notification" is a means of conveying specific information to a user, and may take the form of an alert or message.

[0374] To implement this invention, a system combining multiple hardware and software components is required. Specifically, a smart pair of glasses is used as a wearable device worn by the user, and this functions as a device for acquiring biometric information. The smart glasses collect biometric information such as heart rate and body temperature using sensors and transmit it to a server using wireless communication technology.

[0375] The server runs on a Python program and incorporates specific modules for analyzing acquired biometric information. For example, the aforementioned "biometric_module" has the function of processing biometric information and evaluating stress levels. "communication_analyzer" is a module that analyzes communication data using natural language processing techniques. The server integrates this data and uses the emotion engine "emotion_engine" to accurately evaluate the user's emotional state.

[0376] If the evaluation detects an abnormal stress level, the server sends an alert to the smart glasses, displaying a notification in the user's field of vision. This allows the user to immediately recognize the change in their state and take appropriate action, such as taking a break, if necessary.

[0377] As a concrete example, suppose a security staff member reaches their peak stress level while patrolling the facility at night. At this point, smart glasses would display a message to the user saying, "High stress detected. Take a short break!"

[0378] An example of a prompt message for the AI ​​model in this invention is, "Analyze this staff member's heart rate and body temperature data, as well as their communication logs, and diagnose how their current emotional state is changing." Based on this prompt message, the system provides optimal support.

[0379] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0380] Step 1:

[0381] The server receives biometric information such as heart rate and body temperature from smart glasses. The input is vital data from the smart glasses, and the output is biometric data stored in a database. This data reception uses Bluetooth communication to acquire data from properly paired devices.

[0382] Step 2:

[0383] The server passes biometric information to the "biometric_module" for analysis and evaluates the user's stress level. The input is the biometric data received in step 1, and the output is the stress level evaluation result. This module uses statistical analysis methods and generative AI models to detect abnormal vital patterns.

[0384] Step 3:

[0385] The terminal collects communication data such as emails, business chats, and voice recordings, and sends it to the server. The input is communication data, and the output is text data uploaded to the server. The terminal then prepares text data from various sources.

[0386] Step 4:

[0387] The server analyzes the received communication data using "communication_analyzer" to evaluate the emotional state. The input is the text data from step 3, and the output is the analysis result of the user's emotional state. This analysis uses natural language processing techniques, and the emotion engine classifies the emotional tone of the text.

[0388] Step 5:

[0389] The server integrates the stress level and emotional state assessment results and performs an integrated emotional assessment using a generative AI model. The input is the assessment results from steps 2 and 4, and the output is the final emotional state determination. The generative AI model utilizes supervised learning to detect abnormal emotional states.

[0390] Step 6:

[0391] A warning is displayed as an alert on the user's smart glasses, which are their visual device, if their stress level is high. The input is the result of the emotional state assessment in step 5, and the output is a visual notification to the user. The smart glasses display this warning as a pop-up window, showing a message prompting the user to take a break.

[0392] 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.

[0393] 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.

[0394] 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.

[0395] [Third Embodiment]

[0396] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0397] 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.

[0398] 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).

[0399] 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.

[0400] 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.

[0401] 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).

[0402] 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.

[0403] 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.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] 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".

[0408] This invention relates to a system for monitoring employees' emotional states and stress levels in real time and providing support as needed. This system mainly consists of three components: a server, a terminal, and a user.

[0409] The server is responsible for receiving biometric information transmitted from wearable devices. This biometric information includes vital data such as heart rate and body temperature. Based on this data, the server uses pre-set thresholds to assess the employee's stress level.

[0410] Furthermore, the server also collects communication data such as emails, business chats, and meeting audio transcripts. This data is analyzed using natural language processing techniques and used to determine if there are any changes in emotional state or signs of stress.

[0411] The device has the function of acquiring biometric information from wearable devices and sending it to a server. Furthermore, it plays a role in notifying the user of alerts and feedback instructed by the server. This allows the user to instantly understand their own health status and stress level.

[0412] Users wear wearable devices while performing their daily tasks. These devices accurately measure biometric information, and reliable data exchange with a server enables real-time monitoring.

[0413] As a concrete example, consider a case where a user's heart rate is higher than normal during a remote meeting. The server comprehensively analyzes this biometric information along with communication data from the meeting transcript and can immediately issue an alert if it determines that the user's stress level is high. The terminal notifies the user of this alert and, if necessary, provides a link to schedule a counseling session.

[0414] In this way, this system helps create a healthy workplace environment by understanding employees' emotional states and stress levels in real time and providing appropriate support early on.

[0415] The following describes the processing flow.

[0416] Step 1:

[0417] The terminal acquires biometric information such as heart rate and body temperature in real time from the wearable device. This information is transmitted to the server at predetermined intervals.

[0418] Step 2:

[0419] The server records biometric information received from the terminal in a database. During this process, a timestamp is added to the data, and it is organized and stored for each user.

[0420] Step 3:

[0421] The server evaluates stress levels by comparing them to pre-set thresholds based on biometric information. For example, if the heart rate exceeds the normal range, it flags it as potentially indicating stress.

[0422] Step 4:

[0423] The device sends communication data, such as emails, business chats, and meeting audio transcripts, to the server. This data is collected via APIs from the user's business tools.

[0424] Step 5:

[0425] The server analyzes communication data using natural language processing techniques. It grasps emotional states through stemming and keyword extraction, and analyzes whether there are signs of stress.

[0426] Step 6:

[0427] The server comprehensively evaluates stress levels based on biometric data and sentiment analysis results from communication data, and generates alerts as needed. These alerts indicate that an employee may be experiencing high levels of stress.

[0428] Step 7:

[0429] The device receives alert notifications from the server and notifies the user in real time. The user can view the alert details on the device's display.

[0430] Step 8:

[0431] Users review the support options provided based on the alert (e.g., a link to book a counseling session) and take action as needed. The device also collects user feedback, which the server can use for further analysis.

[0432] (Example 1)

[0433] 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."

[0434] In today's work environment, employee health management is a critical issue. However, traditional methods make it difficult to accurately understand employees' emotional states and stress levels in real time and provide necessary support in a timely manner. This increases the risk of harming employee health and can lead to decreased productivity. Therefore, there is a need for a system that can efficiently and effectively monitor employees' emotional states and stress levels and respond quickly when necessary.

[0435] 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.

[0436] In this invention, the server includes means for acquiring data from a device that collects biometric information, an algorithm for analyzing the biometric information and evaluating the user's stress level, and a program for collecting dialogue data and analyzing emotional states using natural language processing technology. This makes it possible to grasp the health status of employees in real time and provide prompt and appropriate support.

[0437] "Biometric information" refers to data that indicates an individual's physical condition, such as heart rate, body temperature, and activity level.

[0438] "Means of acquiring data from a device" refers to a function for transferring information collected from wearable devices and sensors to a server.

[0439] An "algorithm for evaluating stress levels" is a mathematical or logical method for analyzing biometric information to quantify or determine an individual's psychological and physical stress state.

[0440] "Dialogue data" refers to digital information of a user's communication history, including emails, chat messages, and voice recordings.

[0441] "Natural language processing technology" refers to computer processing techniques used to understand and analyze human language, such as text tokenization and sentiment analysis.

[0442] A "notification method for generating alerts" is a method that has the function of displaying or communicating information in order to give users attention or warnings.

[0443] This invention is a system for monitoring employees' health status and stress levels in real time and responding quickly. Servers, terminals, and users play key roles in implementing the invention.

[0444] The server receives biometric information transmitted from wearable devices via terminals. This biometric information includes multiple state data such as heart rate, body temperature, and activity level. The server has commonly used data analysis tools such as Python and R installed for data acquisition and analysis. This makes it possible to immediately analyze the acquired biometric information and evaluate stress levels by referring to pre-set thresholds.

[0445] The server also collects conversational data such as emails, business chats, and voice recordings. Various APIs are used for this collection, and the data is analyzed using natural language processing techniques. Specifically, the text is tokenized using libraries such as Python's SpaCy library, and sentiment analysis is performed to determine the emotional state.

[0446] The terminal's role is to acquire biometric information from wearable devices in real time and transmit that information to a server. It also immediately notifies the user of alerts and feedback generated by the server. A dedicated application is installed on the terminal, and information is provided to the user through its notification function.

[0447] Users wear wearable devices during their daily work. These devices accurately acquire biometric information, and reliable data exchange between the terminal and the server enables real-time monitoring of the system.

[0448] For example, if a user's heart rate suddenly increases during a remote meeting, the server can detect this as an abnormal condition. The server then analyzes this information along with the meeting transcript, and if it determines that the user is experiencing high stress levels, it immediately generates an alert and sends a notification through the user's device. The device then provides the user with links to schedule counseling sessions or advice on stress reduction, as needed.

[0449] An example of a prompt to be input to the generating AI model would be, "Please tell me what to do if an employee's heart rate data exceeds a threshold." Based on this prompt, the AI ​​model is expected to generate suggestions for appropriate actions.

[0450] Thus, this system protects employee health and contributes to improving workplace productivity.

[0451] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0452] Step 1:

[0453] The terminal collects biometric information from wearable devices in real time. Inputs include biometric data such as heart rate, body temperature, and activity level. The collected data is temporarily stored within the terminal and prepared for subsequent processing. This process ensures the terminal provides a foundation for obtaining accurate biometric information.

[0454] Step 2:

[0455] The terminal transmits the collected biometric information to the server at regular intervals. The input is the biometric information collected in step 1, and the output is the data transfer to the server. Here, a security protocol is activated during transmission to prevent data leakage. This operation allows the server to receive the latest status data.

[0456] Step 3:

[0457] The server analyzes biometric information received from the terminal. The input is biometric information transmitted from the terminal, and the output is the stress level evaluation result. The server uses data analysis tools such as Python and R to quantify the biometric information and identify the parts that exceed the threshold. This analysis makes it possible to quantify the stress level of employees.

[0458] Step 4:

[0459] The server collects conversational data from sources such as emails, business chats, and voice recordings. The input is conversational data from various communication platforms, and the output is raw data ready for natural language processing. By using an API to import and store the data in a database, the data is prepared for efficient analysis.

[0460] Step 5:

[0461] The server analyzes the collected dialogue data using natural language processing techniques. The input is the dialogue data organized in step 4, and the output is the evaluation result of the emotional state. As a specific example, the SpaCy library is used to perform emotion analysis and determine emotions such as positive and negative. This analysis makes it possible to understand emotional fluctuations in communication.

[0462] Step 6:

[0463] The server integrates biometric information and emotion analysis results to generate alerts. The input is the analysis results from steps 3 and 5, and the output is the generated alert information. The server references thresholds, evaluates stress levels and emotional states, and automatically constructs alerts. This process ensures that necessary information is generated in a timely and accurate manner.

[0464] Step 7:

[0465] The device receives alerts from the server and notifies the user. The input is the alert sent from the server, and the output is the information displayed on the screen as a notification to the user. The device provides this notification via pop-up or audio, and displays a counseling link if necessary. This allows the user to receive immediate feedback on their stress levels.

[0466] (Application Example 1)

[0467] 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."

[0468] The aim is to solve the challenge of not being able to grasp the mental stress and emotional state of employees in physical stores in real time and to provide appropriate support quickly.

[0469] 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.

[0470] In this invention, the server includes means for collecting biometric data, means for analyzing the biometric data to evaluate the user's mental burden, and means for collecting communication data and analyzing the emotional state using natural language processing technology. This enables store managers to grasp the mental burden state of employees in real time and quickly provide appropriate support and adjust staffing levels.

[0471] "Biometric data" refers to objective information that indicates the user's physical condition, including vital signs such as heart rate and body temperature.

[0472] "Mental burden" is an indicator that shows the degree of psychological stress and emotional pressure that users experience in their daily lives and work.

[0473] "Communication data" refers to text, audio, and other communication information transmitted by users through electronic communication means.

[0474] "Natural language processing technology" is a technique that enables computers to understand and analyze human language, and is used to extract emotional states and context.

[0475] "Support" refers to the help and assistance provided to reduce the mental burden on users and maintain a healthy state of mind.

[0476] A description of the embodiment for carrying out the invention will be provided.

[0477] The program to implement this system runs on a wearable device, a server, and an administrator's terminal. The wearable device functions as hardware that measures the user's biometric data in real time and transmits it to the server. A multi-functional smart device capable of measuring heart rate and body temperature is used as the wearable device.

[0478] The server uses Python and related machine learning libraries (such as scikit-learn and TensorFlow) to analyze biometric data and run software to assess mental stress. The server also analyzes user communication data (such as emails and business chats) using natural language processing techniques (such as spaCy and NLTK) to analyze emotional states.

[0479] The terminal is used to notify administrators of warnings and support information sent from the server. Smartphones and tablets are suitable terminals. Through this, administrators can understand the mental stress levels of employees in real time and provide appropriate support.

[0480] For example, if a particular employee's heart rate suddenly increases while serving a customer, this is evaluated by the server. If, in conjunction with emotion analysis, it is determined to be a high-stress state, a warning is sent to the administrator's terminal. Upon receiving this notification, the administrator can quickly take countermeasures (e.g., instructing the employee to take a temporary break).

[0481] An example of a prompt message for the generating AI model is: "If an employee's heart rate exceeds 75 BPM, determine that they are under high stress and notify the store manager."

[0482] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0483] Step 1:

[0484] The user wears a wearable device while performing their daily tasks. The wearable device continuously measures the user's biometric data, such as heart rate and body temperature. The input data obtained from this device is real-time heart rate and body temperature information.

[0485] Step 2:

[0486] Biometric data transmitted from this device arrives at the server. The server receives the input biometric data and analyzes it using Python. Data processing here begins with standardizing the measured values ​​and detecting outliers. Specifically, a trigger is generated when a threshold is exceeded by comparing it with past data.

[0487] Step 3:

[0488] The server uses a machine learning algorithm to assess the user's mental stress based on the received biometric data. The input in this step is normalized biometric data, and the output is the stress level assessment. The data calculation involves classification using a model based on an existing dataset.

[0489] Step 4:

[0490] The server collects user communication data in parallel and analyzes their emotional state using natural language processing techniques. The server processes text-based communication data as input and outputs the results of the emotional state analysis. Specific analysis operations include keyword extraction and emotion scoring.

[0491] Step 5:

[0492] The server determines whether a warning is necessary based on the analysis results of biometric and communication data, and creates an alert if required. The input for processing is the stress level and the result of emotion analysis, and the output is a warning message. Specifically, it prepares to notify the administrator when a threshold is exceeded.

[0493] Step 6:

[0494] The terminal receives warnings and support information from the server and notifies the store manager. The input here is the warning message from the server, and the output is the notification displayed to the manager. Specific actions include real-time notifications to smartphones and dashboard updates.

[0495] Step 7:

[0496] Store managers will review received notifications and take appropriate action. This is intended to reduce the mental burden on users and support the smooth operation of their work. For example, a manager might instruct employees to take breaks.

[0497] 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.

[0498] This invention combines an emotion engine with a system that analyzes a user's biometric information and communication data to grasp their emotional state and stress level in real time, thereby achieving more accurate emotion recognition and stress assessment.

[0499] The server first receives biometric information acquired from the wearable device. This includes vital data indicating the user's physical state, such as heart rate and body temperature. This data is analyzed by an emotion engine installed within the server, which can precisely classify how the user's emotions are changing. The emotion engine uses a pre-trained emotion model to identify abnormal emotional patterns and signs of stress.

[0500] In addition, the device collects communication data recorded in formats such as email, business chat, and conference audio transcripts, and sends it to the server. The server decodes this data using natural language processing technology and evaluates the user's text-based emotional state using an emotion engine. This enables sophisticated emotion recognition that comprehensively combines information obtained from multiple sources, rather than simply making emotion judgments based on text analysis.

[0501] As a concrete example, suppose a user experiences a sharp increase in heart rate after a meeting and frequently uses negative language during the meeting. In this case, the emotion engine within the server analyzes this data comprehensively and determines that the user is in a high-stress state. Based on this result, the server generates an alert and notifies the user through their device. The notification may include suggestions for relaxation or links to book counseling, enabling a quick response.

[0502] In this way, this system supports the creation of a healthier workplace environment by recognizing the user's emotional state with high accuracy and providing prompt and appropriate support.

[0503] The following describes the processing flow.

[0504] Step 1:

[0505] The terminal acquires biometric information from the wearable device worn by the user and transmits vital data such as heart rate and body temperature to the server.

[0506] Step 2:

[0507] The server records biometric information received from the terminal in a database. Each piece of data is then associated with a user ID and a timestamp.

[0508] Step 3:

[0509] The emotion engine within the server analyzes biometric information and evaluates the user's real-time emotional state. The emotion engine distinguishes emotional states based on a pre-trained model.

[0510] Step 4:

[0511] The device collects communication data such as emails, business chat messages, and meeting audio transcripts, and sends them to the server.

[0512] Step 5:

[0513] The server uses natural language processing techniques to analyze communication data as text. During this process, it extracts keywords and performs sentiment analysis to infer the user's emotional state from the context.

[0514] Step 6:

[0515] The emotion engine integrates the results of communication data analysis and biometric data analysis to comprehensively evaluate the user's stress level.

[0516] Step 7:

[0517] The server generates an alert if it determines that the stress level exceeds a pre-set threshold. This alert indicates that the user is in a high-stress state.

[0518] Step 8:

[0519] The device receives alerts from the server and notifies the user in real time. The notifications include stress reduction advice and links to book counseling appointments.

[0520] Step 9:

[0521] Based on notifications received through their devices, users can check their health status and, if necessary, initiate procedures to receive support. This allows users to take prompt and appropriate action.

[0522] (Example 2)

[0523] 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."

[0524] Conventional technologies often involved individually analyzing users' physiological states and communication patterns, making it difficult to comprehensively analyze emotional states and stress levels with high accuracy. Furthermore, there was a lack of systems capable of rapidly analyzing this data and providing immediate support to users.

[0525] 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.

[0526] In this invention, the server includes means for receiving and analyzing biometric information, means for classifying the user's emotional changes based on the biometric information using a machine learning algorithm, and means for collecting communication information and comprehensively evaluating the emotional state using natural language processing technology. This makes it possible to comprehensively evaluate the user's emotional state and stress level with high accuracy and to provide appropriate support quickly.

[0527] "Biometric information" refers to data that indicates the user's physical condition, primarily including information that shows vital functions such as heart rate and body temperature.

[0528] "Analysis" refers to the process of classifying and evaluating users' emotional changes and stress levels based on acquired data.

[0529] A "machine learning algorithm" refers to a set of methods that allow computers to learn features from data and make predictions and classifications about unknown data.

[0530] "Communication information" refers to information used by users when communicating with others, and includes electronic communications, dialogue records, and voice recordings.

[0531] "Natural language processing technology" refers to the technology used to process, understand, and generate natural language used by humans using computers.

[0532] "Emotional state" refers to the emotions and psychological state a user is experiencing at a given point in time, and this is used to assess the user's stress levels and well-being.

[0533] An "alert" refers to a notification that alerts users to anomalies or important signs detected during analysis.

[0534] "Support information" refers to advice, suggestions, or links and methods provided to users based on analysis results to encourage action.

[0535] This invention is a system that analyzes a user's emotional state and stress level with high accuracy and provides appropriate support quickly. First, the server acquires vital data such as heart rate and body temperature via a device carried by the user (e.g., smartwatch, fitness band) in order to receive biometric information. This biometric information is analyzed by an emotion engine within the server. The emotion engine incorporates a machine learning algorithm, which classifies the user's emotional changes from the data.

[0536] Next, the terminal collects communication information in various forms, such as the user's emails, business chats, and conference audio. This collected data is then analyzed by a server using natural language processing technology. The server integrates the collected text and audio data and performs a comprehensive emotional state assessment using an emotion engine. This process is crucial for accurately determining the emotional state.

[0537] As a concrete example, suppose a user sends an email during a business meeting that includes an elevated heart rate and negative expressions such as "I'm too busy and tired." In this case, the server integrates the heart rate data with the email content and determines that the user is in a stressed state. Based on this analysis, the server generates a notification that includes relaxation methods and a link to book a counseling session, and suggests these to the user.

[0538] An example of a prompt for a generative AI model might be: "During the meeting, the user's heart rate increased, and they tended to use negative language. Based on this data, assess the user's emotional state and suggest appropriate support."

[0539] This system utilizes advanced emotion analysis technology to provide an effective means of supporting users' healthy lifestyles.

[0540] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0541] Step 1:

[0542] The server receives biometric information from the wearable device, including heart rate and body temperature. The input data is measured in real time by the device, and the server passes it to the emotion engine after receiving it. The emotion engine uses machine learning algorithms to analyze the biometric information and classify the user's emotional changes. As a result, if it detects an anomaly such as a sudden increase in heart rate, it generates an output indicating an abnormal emotional state.

[0543] Step 2:

[0544] The device collects user communication data. Specifically, it acquires information such as emails, business chats, and meeting audio transcripts. This input data is sent from the device to the server. The server analyzes this communication information using natural language processing technology and evaluates the emotional state based on the text and audio data. This process outputs whether the text indicates a positive or negative emotion.

[0545] Step 3:

[0546] The server integrates the results of biometric data analysis and communication data evaluation. This allows for a comprehensive assessment of emotional states obtained from diverse data sources. In this step, the emotional evaluations obtained from the two aforementioned inputs are integrated to determine the user's overall emotional state. The output indicates whether the user's stress level and emotional balance are at a high-risk level.

[0547] Step 4:

[0548] The server generates alerts based on integrated assessment results. These alerts notify users of abnormal emotional states or signs of high stress. The server then provides support information as appropriate countermeasures, specifically including relaxation methods and, if necessary, links to counseling appointments. Finally, it sends a notification to the device, prompting the user to take action based on the information received. The output includes a specific action plan to facilitate the user's prompt response.

[0549] (Application Example 2)

[0550] 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."

[0551] In recent years, there has been a growing need to quickly understand workers' emotional states and stress levels in order to provide an efficient and healthy work environment. However, conventional methods rely on a single data source for emotion recognition, making accurate assessment difficult. Furthermore, mechanisms for visually notifying workers of stress levels immediately are insufficient. Therefore, a system is needed that integrates multiple data sources to perform highly accurate emotion analysis and visually notifies workers of changes in their state.

[0552] 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.

[0553] In this invention, the server includes means for collecting biometric information, means for analyzing the biometric information to evaluate the user's stress level, means for collecting communication data and analyzing the emotional state using natural language processing technology, and means for presenting information to a visual device to notify the user of changes in their state. This makes it possible to perform highly accurate emotion recognition and stress evaluation from multiple data sources while simultaneously providing immediate visual notification to the user.

[0554] "Biometric information" refers to data that indicates the user's physical condition, including vital data such as heart rate and body temperature.

[0555] "Analysis" is the process of breaking down data based on a specific purpose and interpreting its meaning.

[0556] "Stress level" refers to the degree of mental burden a user is experiencing, expressed numerically or using indicators.

[0557] "Communication data" refers to all data generated when users exchange information with others, such as electronic messages, business communications, and voice recordings.

[0558] "Natural language processing technology" is a technique that enables computers to understand and analyze human language, and is used to interpret the meaning of text and speech.

[0559] "Emotional state" refers to the user's current mood and feelings, and is the subject of analysis by the emotion engine.

[0560] "Visual devices" refer to all equipment and devices that allow users to receive information visually by wearing or using them.

[0561] A "notification" is a means of conveying specific information to a user, and may take the form of an alert or message.

[0562] To implement this invention, a system combining multiple hardware and software components is required. Specifically, a smart pair of glasses is used as a wearable device worn by the user, and this functions as a device for acquiring biometric information. The smart glasses collect biometric information such as heart rate and body temperature using sensors and transmit it to a server using wireless communication technology.

[0563] The server runs on a Python program and incorporates specific modules for analyzing acquired biometric information. For example, the aforementioned "biometric_module" has the function of processing biometric information and evaluating stress levels. "communication_analyzer" is a module that analyzes communication data using natural language processing techniques. The server integrates this data and uses the emotion engine "emotion_engine" to accurately evaluate the user's emotional state.

[0564] If the evaluation detects an abnormal stress level, the server sends an alert to the smart glasses, displaying a notification in the user's field of vision. This allows the user to immediately recognize the change in their state and take appropriate action, such as taking a break, if necessary.

[0565] As a concrete example, suppose a security staff member reaches their peak stress level while patrolling the facility at night. At this point, smart glasses would display a message to the user saying, "High stress detected. Take a short break!"

[0566] An example of a prompt message for the AI ​​model in this invention is, "Analyze this staff member's heart rate and body temperature data, as well as their communication logs, and diagnose how their current emotional state is changing." Based on this prompt message, the system provides optimal support.

[0567] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0568] Step 1:

[0569] The server receives biometric information such as heart rate and body temperature from smart glasses. The input is vital data from the smart glasses, and the output is biometric data stored in a database. This data reception uses Bluetooth communication to acquire data from properly paired devices.

[0570] Step 2:

[0571] The server passes biometric information to the "biometric_module" for analysis and evaluates the user's stress level. The input is the biometric data received in step 1, and the output is the stress level evaluation result. This module uses statistical analysis methods and generative AI models to detect abnormal vital patterns.

[0572] Step 3:

[0573] The terminal collects communication data such as emails, business chats, and voice recordings, and sends it to the server. The input is communication data, and the output is text data uploaded to the server. The terminal then prepares text data from various sources.

[0574] Step 4:

[0575] The server analyzes the received communication data using "communication_analyzer" to evaluate the emotional state. The input is the text data from step 3, and the output is the analysis result of the user's emotional state. This analysis uses natural language processing techniques, and the emotion engine classifies the emotional tone of the text.

[0576] Step 5:

[0577] The server integrates the stress level and emotional state assessment results and performs an integrated emotional assessment using a generative AI model. The input is the assessment results from steps 2 and 4, and the output is the final emotional state determination. The generative AI model utilizes supervised learning to detect abnormal emotional states.

[0578] Step 6:

[0579] A warning is displayed as an alert on the user's smart glasses, which are their visual device, if their stress level is high. The input is the result of the emotional state assessment in step 5, and the output is a visual notification to the user. The smart glasses display this warning as a pop-up window, showing a message prompting the user to take a break.

[0580] 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.

[0581] 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.

[0582] 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.

[0583] [Fourth Embodiment]

[0584] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0585] 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.

[0586] 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).

[0587] 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.

[0588] 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.

[0589] 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).

[0590] 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.

[0591] 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.

[0592] 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.

[0593] 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.

[0594] 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.

[0595] 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.

[0596] 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".

[0597] This invention relates to a system for monitoring employees' emotional states and stress levels in real time and providing support as needed. This system mainly consists of three components: a server, a terminal, and a user.

[0598] The server is responsible for receiving biometric information transmitted from wearable devices. This biometric information includes vital data such as heart rate and body temperature. Based on this data, the server uses pre-set thresholds to assess the employee's stress level.

[0599] Furthermore, the server also collects communication data such as emails, business chats, and meeting audio transcripts. This data is analyzed using natural language processing techniques and used to determine if there are any changes in emotional state or signs of stress.

[0600] The device has the function of acquiring biometric information from wearable devices and sending it to a server. Furthermore, it plays a role in notifying the user of alerts and feedback instructed by the server. This allows the user to instantly understand their own health status and stress level.

[0601] Users wear wearable devices while performing their daily tasks. These devices accurately measure biometric information, and reliable data exchange with a server enables real-time monitoring.

[0602] As a concrete example, consider a case where a user's heart rate is higher than normal during a remote meeting. The server comprehensively analyzes this biometric information along with communication data from the meeting transcript and can immediately issue an alert if it determines that the user's stress level is high. The terminal notifies the user of this alert and, if necessary, provides a link to schedule a counseling session.

[0603] In this way, this system helps create a healthy workplace environment by understanding employees' emotional states and stress levels in real time and providing appropriate support early on.

[0604] The following describes the processing flow.

[0605] Step 1:

[0606] The terminal acquires biometric information such as heart rate and body temperature in real time from the wearable device. This information is transmitted to the server at predetermined intervals.

[0607] Step 2:

[0608] The server records biometric information received from the terminal in a database. During this process, a timestamp is added to the data, and it is organized and stored for each user.

[0609] Step 3:

[0610] The server evaluates stress levels by comparing them to pre-set thresholds based on biometric information. For example, if the heart rate exceeds the normal range, it flags it as potentially indicating stress.

[0611] Step 4:

[0612] The device sends communication data, such as emails, business chats, and meeting audio transcripts, to the server. This data is collected via APIs from the user's business tools.

[0613] Step 5:

[0614] The server analyzes communication data using natural language processing techniques. It grasps emotional states through stemming and keyword extraction, and analyzes whether there are signs of stress.

[0615] Step 6:

[0616] The server comprehensively evaluates stress levels based on biometric data and sentiment analysis results from communication data, and generates alerts as needed. These alerts indicate that an employee may be experiencing high levels of stress.

[0617] Step 7:

[0618] The device receives alert notifications from the server and notifies the user in real time. The user can view the alert details on the device's display.

[0619] Step 8:

[0620] Users review the support options provided based on the alert (e.g., a link to book a counseling session) and take action as needed. The device also collects user feedback, which the server can use for further analysis.

[0621] (Example 1)

[0622] 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".

[0623] In today's work environment, employee health management is a critical issue. However, traditional methods make it difficult to accurately understand employees' emotional states and stress levels in real time and provide necessary support in a timely manner. This increases the risk of harming employee health and can lead to decreased productivity. Therefore, there is a need for a system that can efficiently and effectively monitor employees' emotional states and stress levels and respond quickly when necessary.

[0624] 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.

[0625] In this invention, the server includes means for acquiring data from a device that collects biometric information, an algorithm for analyzing the biometric information and evaluating the user's stress level, and a program for collecting dialogue data and analyzing emotional states using natural language processing technology. This makes it possible to grasp the health status of employees in real time and provide prompt and appropriate support.

[0626] "Biometric information" refers to data that indicates an individual's physical condition, such as heart rate, body temperature, and activity level.

[0627] "Means of acquiring data from a device" refers to a function for transferring information collected from wearable devices and sensors to a server.

[0628] An "algorithm for evaluating stress levels" is a mathematical or logical method for analyzing biometric information to quantify or determine an individual's psychological and physical stress state.

[0629] "Dialogue data" refers to digital information of a user's communication history, including emails, chat messages, and voice recordings.

[0630] "Natural language processing technology" refers to computer processing techniques used to understand and analyze human language, such as text tokenization and sentiment analysis.

[0631] A "notification method for generating alerts" is a method that has the function of displaying or communicating information in order to give users attention or warnings.

[0632] This invention is a system for monitoring employees' health status and stress levels in real time and responding quickly. Servers, terminals, and users play key roles in implementing the invention.

[0633] The server receives biometric information transmitted from wearable devices via terminals. This biometric information includes multiple state data such as heart rate, body temperature, and activity level. The server has commonly used data analysis tools such as Python and R installed for data acquisition and analysis. This makes it possible to immediately analyze the acquired biometric information and evaluate stress levels by referring to pre-set thresholds.

[0634] The server also collects conversational data such as emails, business chats, and voice recordings. Various APIs are used for this collection, and the data is analyzed using natural language processing techniques. Specifically, the text is tokenized using libraries such as Python's SpaCy library, and sentiment analysis is performed to determine the emotional state.

[0635] The terminal's role is to acquire biometric information from wearable devices in real time and transmit that information to a server. It also immediately notifies the user of alerts and feedback generated by the server. A dedicated application is installed on the terminal, and information is provided to the user through its notification function.

[0636] Users wear wearable devices during their daily work. These devices accurately acquire biometric information, and reliable data exchange between the terminal and the server enables real-time monitoring of the system.

[0637] For example, if a user's heart rate suddenly increases during a remote meeting, the server can detect this as an abnormal condition. The server then analyzes this information along with the meeting transcript, and if it determines that the user is experiencing high stress levels, it immediately generates an alert and sends a notification through the user's device. The device then provides the user with links to schedule counseling sessions or advice on stress reduction, as needed.

[0638] An example of a prompt to be input to the generating AI model would be, "Please tell me what to do if an employee's heart rate data exceeds a threshold." Based on this prompt, the AI ​​model is expected to generate suggestions for appropriate actions.

[0639] Thus, this system protects employee health and contributes to improving workplace productivity.

[0640] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0641] Step 1:

[0642] The terminal collects biometric information from wearable devices in real time. Inputs include biometric data such as heart rate, body temperature, and activity level. The collected data is temporarily stored within the terminal and prepared for subsequent processing. This process ensures the terminal provides a foundation for obtaining accurate biometric information.

[0643] Step 2:

[0644] The terminal transmits the collected biometric information to the server at regular intervals. The input is the biometric information collected in step 1, and the output is the data transfer to the server. Here, a security protocol is activated during transmission to prevent data leakage. This operation allows the server to receive the latest status data.

[0645] Step 3:

[0646] The server analyzes biometric information received from the terminal. The input is biometric information transmitted from the terminal, and the output is the stress level evaluation result. The server uses data analysis tools such as Python and R to quantify the biometric information and identify the parts that exceed the threshold. This analysis makes it possible to quantify the stress level of employees.

[0647] Step 4:

[0648] The server collects conversational data from sources such as emails, business chats, and voice recordings. The input is conversational data from various communication platforms, and the output is raw data ready for natural language processing. By using an API to import and store the data in a database, the data is prepared for efficient analysis.

[0649] Step 5:

[0650] The server analyzes the collected dialogue data using natural language processing techniques. The input is the dialogue data organized in step 4, and the output is the evaluation result of the emotional state. As a specific example, the SpaCy library is used to perform emotion analysis and determine emotions such as positive and negative. This analysis makes it possible to understand emotional fluctuations in communication.

[0651] Step 6:

[0652] The server integrates biometric information and emotion analysis results to generate alerts. The input is the analysis results from steps 3 and 5, and the output is the generated alert information. The server references thresholds, evaluates stress levels and emotional states, and automatically constructs alerts. This process ensures that necessary information is generated in a timely and accurate manner.

[0653] Step 7:

[0654] The device receives alerts from the server and notifies the user. The input is the alert sent from the server, and the output is the information displayed on the screen as a notification to the user. The device provides this notification via pop-up or audio, and displays a counseling link if necessary. This allows the user to receive immediate feedback on their stress levels.

[0655] (Application Example 1)

[0656] 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".

[0657] The aim is to solve the challenge of not being able to grasp the mental stress and emotional state of employees in physical stores in real time and to provide appropriate support quickly.

[0658] 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.

[0659] In this invention, the server includes means for collecting biometric data, means for analyzing the biometric data to evaluate the user's mental burden, and means for collecting communication data and analyzing the emotional state using natural language processing technology. This enables store managers to grasp the mental burden state of employees in real time and quickly provide appropriate support and adjust staffing levels.

[0660] "Biometric data" refers to objective information that indicates the user's physical condition, including vital signs such as heart rate and body temperature.

[0661] "Mental burden" is an indicator that shows the degree of psychological stress and emotional pressure that users experience in their daily lives and work.

[0662] "Communication data" refers to text, audio, and other communication information transmitted by users through electronic communication means.

[0663] "Natural language processing technology" is a technique that enables computers to understand and analyze human language, and is used to extract emotional states and context.

[0664] "Support" refers to the help and assistance provided to reduce the mental burden on users and maintain a healthy state of mind.

[0665] A description of the embodiment for carrying out the invention will be provided.

[0666] The program to implement this system runs on a wearable device, a server, and an administrator's terminal. The wearable device functions as hardware that measures the user's biometric data in real time and transmits it to the server. A multi-functional smart device capable of measuring heart rate and body temperature is used as the wearable device.

[0667] The server uses Python and related machine learning libraries (such as scikit-learn and TensorFlow) to analyze biometric data and run software to assess mental stress. The server also analyzes user communication data (such as emails and business chats) using natural language processing techniques (such as spaCy and NLTK) to analyze emotional states.

[0668] The terminal is used to notify administrators of warnings and support information sent from the server. Smartphones and tablets are suitable terminals. Through this, administrators can understand the mental stress levels of employees in real time and provide appropriate support.

[0669] For example, if a particular employee's heart rate suddenly increases while serving a customer, this is evaluated by the server. If, in conjunction with emotion analysis, it is determined to be a high-stress state, a warning is sent to the administrator's terminal. Upon receiving this notification, the administrator can quickly take countermeasures (e.g., instructing the employee to take a temporary break).

[0670] An example of a prompt message for the generating AI model is: "If an employee's heart rate exceeds 75 BPM, determine that they are under high stress and notify the store manager."

[0671] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0672] Step 1:

[0673] The user wears a wearable device while performing their daily tasks. The wearable device continuously measures the user's biometric data, such as heart rate and body temperature. The input data obtained from this device is real-time heart rate and body temperature information.

[0674] Step 2:

[0675] Biometric data transmitted from this device arrives at the server. The server receives the input biometric data and analyzes it using Python. Data processing here begins with standardizing the measured values ​​and detecting outliers. Specifically, a trigger is generated when a threshold is exceeded by comparing it with past data.

[0676] Step 3:

[0677] The server uses a machine learning algorithm to assess the user's mental stress based on the received biometric data. The input in this step is normalized biometric data, and the output is the stress level assessment. The data calculation involves classification using a model based on an existing dataset.

[0678] Step 4:

[0679] The server collects user communication data in parallel and analyzes their emotional state using natural language processing techniques. The server processes text-based communication data as input and outputs the results of the emotional state analysis. Specific analysis operations include keyword extraction and emotion scoring.

[0680] Step 5:

[0681] The server determines whether a warning is necessary based on the analysis results of biometric and communication data, and creates an alert if required. The input for processing is the stress level and the result of emotion analysis, and the output is a warning message. Specifically, it prepares to notify the administrator when a threshold is exceeded.

[0682] Step 6:

[0683] The terminal receives warnings and support information from the server and notifies the store manager. The input here is the warning message from the server, and the output is the notification displayed to the manager. Specific actions include real-time notifications to smartphones and dashboard updates.

[0684] Step 7:

[0685] Store managers will review received notifications and take appropriate action. This is intended to reduce the mental burden on users and support the smooth operation of their work. For example, a manager might instruct employees to take breaks.

[0686] 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.

[0687] This invention combines an emotion engine with a system that analyzes a user's biometric information and communication data to grasp their emotional state and stress level in real time, thereby achieving more accurate emotion recognition and stress assessment.

[0688] The server first receives biometric information acquired from the wearable device. This includes vital data indicating the user's physical state, such as heart rate and body temperature. This data is analyzed by an emotion engine installed within the server, which can precisely classify how the user's emotions are changing. The emotion engine uses a pre-trained emotion model to identify abnormal emotional patterns and signs of stress.

[0689] In addition, the device collects communication data recorded in formats such as email, business chat, and conference audio transcripts, and sends it to the server. The server decodes this data using natural language processing technology and evaluates the user's text-based emotional state using an emotion engine. This enables sophisticated emotion recognition that comprehensively combines information obtained from multiple sources, rather than simply making emotion judgments based on text analysis.

[0690] As a concrete example, suppose a user experiences a sharp increase in heart rate after a meeting and frequently uses negative language during the meeting. In this case, the emotion engine within the server analyzes this data comprehensively and determines that the user is in a high-stress state. Based on this result, the server generates an alert and notifies the user through their device. The notification may include suggestions for relaxation or links to book counseling, enabling a quick response.

[0691] In this way, this system supports the creation of a healthier workplace environment by recognizing the user's emotional state with high accuracy and providing prompt and appropriate support.

[0692] The following describes the processing flow.

[0693] Step 1:

[0694] The terminal acquires biometric information from the wearable device worn by the user and transmits vital data such as heart rate and body temperature to the server.

[0695] Step 2:

[0696] The server records biometric information received from the terminal in a database. Each piece of data is then associated with a user ID and a timestamp.

[0697] Step 3:

[0698] The emotion engine within the server analyzes biometric information and evaluates the user's real-time emotional state. The emotion engine distinguishes emotional states based on a pre-trained model.

[0699] Step 4:

[0700] The device collects communication data such as emails, business chat messages, and meeting audio transcripts, and sends them to the server.

[0701] Step 5:

[0702] The server uses natural language processing techniques to analyze communication data as text. During this process, it extracts keywords and performs sentiment analysis to infer the user's emotional state from the context.

[0703] Step 6:

[0704] The emotion engine integrates the results of communication data analysis and biometric data analysis to comprehensively evaluate the user's stress level.

[0705] Step 7:

[0706] The server generates an alert if it determines that the stress level exceeds a pre-set threshold. This alert indicates that the user is in a high-stress state.

[0707] Step 8:

[0708] The device receives alerts from the server and notifies the user in real time. The notifications include stress reduction advice and links to book counseling appointments.

[0709] Step 9:

[0710] Based on notifications received through their devices, users can check their health status and, if necessary, initiate procedures to receive support. This allows users to take prompt and appropriate action.

[0711] (Example 2)

[0712] 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".

[0713] Conventional technologies often involved individually analyzing users' physiological states and communication patterns, making it difficult to comprehensively analyze emotional states and stress levels with high accuracy. Furthermore, there was a lack of systems capable of rapidly analyzing this data and providing immediate support to users.

[0714] 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.

[0715] In this invention, the server includes means for receiving and analyzing biometric information, means for classifying the user's emotional changes based on the biometric information using a machine learning algorithm, and means for collecting communication information and comprehensively evaluating the emotional state using natural language processing technology. This makes it possible to comprehensively evaluate the user's emotional state and stress level with high accuracy and to provide appropriate support quickly.

[0716] "Biometric information" refers to data that indicates the user's physical condition, primarily including information that shows vital functions such as heart rate and body temperature.

[0717] "Analysis" refers to the process of classifying and evaluating users' emotional changes and stress levels based on acquired data.

[0718] A "machine learning algorithm" refers to a set of methods that allow computers to learn features from data and make predictions and classifications about unknown data.

[0719] "Communication information" refers to information used by users when communicating with others, and includes electronic communications, dialogue records, and voice recordings.

[0720] "Natural language processing technology" refers to the technology used to process, understand, and generate natural language used by humans using computers.

[0721] "Emotional state" refers to the emotions and psychological state a user is experiencing at a given point in time, and this is used to assess the user's stress levels and well-being.

[0722] An "alert" refers to a notification that alerts users to anomalies or important signs detected during analysis.

[0723] "Support information" refers to advice, suggestions, or links and methods provided to users based on analysis results to encourage action.

[0724] This invention is a system that analyzes a user's emotional state and stress level with high accuracy and provides appropriate support quickly. First, the server acquires vital data such as heart rate and body temperature via a device carried by the user (e.g., smartwatch, fitness band) in order to receive biometric information. This biometric information is analyzed by an emotion engine within the server. The emotion engine incorporates a machine learning algorithm, which classifies the user's emotional changes from the data.

[0725] Next, the terminal collects communication information in various forms, such as the user's emails, business chats, and conference audio. This collected data is then analyzed by a server using natural language processing technology. The server integrates the collected text and audio data and performs a comprehensive emotional state assessment using an emotion engine. This process is crucial for accurately determining the emotional state.

[0726] As a concrete example, suppose a user sends an email during a business meeting that includes an elevated heart rate and negative expressions such as "I'm too busy and tired." In this case, the server integrates the heart rate data with the email content and determines that the user is in a stressed state. Based on this analysis, the server generates a notification that includes relaxation methods and a link to book a counseling session, and suggests these to the user.

[0727] An example of a prompt for a generative AI model might be: "During the meeting, the user's heart rate increased, and they tended to use negative language. Based on this data, assess the user's emotional state and suggest appropriate support."

[0728] This system utilizes advanced emotion analysis technology to provide an effective means of supporting users' healthy lifestyles.

[0729] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0730] Step 1:

[0731] The server receives biometric information from the wearable device, including heart rate and body temperature. The input data is measured in real time by the device, and the server passes it to the emotion engine after receiving it. The emotion engine uses machine learning algorithms to analyze the biometric information and classify the user's emotional changes. As a result, if it detects an anomaly such as a sudden increase in heart rate, it generates an output indicating an abnormal emotional state.

[0732] Step 2:

[0733] The device collects user communication data. Specifically, it acquires information such as emails, business chats, and meeting audio transcripts. This input data is sent from the device to the server. The server analyzes this communication information using natural language processing technology and evaluates the emotional state based on the text and audio data. This process outputs whether the text indicates a positive or negative emotion.

[0734] Step 3:

[0735] The server integrates the results of biometric data analysis and communication data evaluation. This allows for a comprehensive assessment of emotional states obtained from diverse data sources. In this step, the emotional evaluations obtained from the two aforementioned inputs are integrated to determine the user's overall emotional state. The output indicates whether the user's stress level and emotional balance are at a high-risk level.

[0736] Step 4:

[0737] The server generates alerts based on integrated assessment results. These alerts notify users of abnormal emotional states or signs of high stress. The server then provides support information as appropriate countermeasures, specifically including relaxation methods and, if necessary, links to counseling appointments. Finally, it sends a notification to the device, prompting the user to take action based on the information received. The output includes a specific action plan to facilitate the user's prompt response.

[0738] (Application Example 2)

[0739] 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".

[0740] In recent years, there has been a growing need to quickly understand workers' emotional states and stress levels in order to provide an efficient and healthy work environment. However, conventional methods rely on a single data source for emotion recognition, making accurate assessment difficult. Furthermore, mechanisms for visually notifying workers of stress levels immediately are insufficient. Therefore, a system is needed that integrates multiple data sources to perform highly accurate emotion analysis and visually notifies workers of changes in their state.

[0741] 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.

[0742] In this invention, the server includes means for collecting biometric information, means for analyzing the biometric information to evaluate the user's stress level, means for collecting communication data and analyzing the emotional state using natural language processing technology, and means for presenting information to a visual device to notify the user of changes in their state. This makes it possible to perform highly accurate emotion recognition and stress evaluation from multiple data sources while simultaneously providing immediate visual notification to the user.

[0743] "Biometric information" refers to data that indicates the user's physical condition, including vital data such as heart rate and body temperature.

[0744] "Analysis" is the process of breaking down data based on a specific purpose and interpreting its meaning.

[0745] "Stress level" refers to the degree of mental burden a user is experiencing, expressed numerically or using indicators.

[0746] "Communication data" refers to all data generated when users exchange information with others, such as electronic messages, business communications, and voice recordings.

[0747] "Natural language processing technology" is a technique that enables computers to understand and analyze human language, and is used to interpret the meaning of text and speech.

[0748] "Emotional state" refers to the user's current mood and feelings, and is the subject of analysis by the emotion engine.

[0749] "Visual devices" refer to all equipment and devices that allow users to receive information visually by wearing or using them.

[0750] A "notification" is a means of conveying specific information to a user, and may take the form of an alert or message.

[0751] To implement this invention, a system combining multiple hardware and software components is required. Specifically, a smart pair of glasses is used as a wearable device worn by the user, and this functions as a device for acquiring biometric information. The smart glasses collect biometric information such as heart rate and body temperature using sensors and transmit it to a server using wireless communication technology.

[0752] The server runs on a Python program and incorporates specific modules for analyzing acquired biometric information. For example, the aforementioned "biometric_module" has the function of processing biometric information and evaluating stress levels. "communication_analyzer" is a module that analyzes communication data using natural language processing techniques. The server integrates this data and uses the emotion engine "emotion_engine" to accurately evaluate the user's emotional state.

[0753] If the evaluation detects an abnormal stress level, the server sends an alert to the smart glasses, displaying a notification in the user's field of vision. This allows the user to immediately recognize the change in their state and take appropriate action, such as taking a break, if necessary.

[0754] As a concrete example, suppose a security staff member reaches their peak stress level while patrolling the facility at night. At this point, smart glasses would display a message to the user saying, "High stress detected. Take a short break!"

[0755] An example of a prompt message for the AI ​​model in this invention is, "Analyze this staff member's heart rate and body temperature data, as well as their communication logs, and diagnose how their current emotional state is changing." Based on this prompt message, the system provides optimal support.

[0756] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0757] Step 1:

[0758] The server receives biometric information such as heart rate and body temperature from smart glasses. The input is vital data from the smart glasses, and the output is biometric data stored in a database. This data reception uses Bluetooth communication to acquire data from properly paired devices.

[0759] Step 2:

[0760] The server passes biometric information to the "biometric_module" for analysis and evaluates the user's stress level. The input is the biometric data received in step 1, and the output is the stress level evaluation result. This module uses statistical analysis methods and generative AI models to detect abnormal vital patterns.

[0761] Step 3:

[0762] The terminal collects communication data such as emails, business chats, and voice recordings, and sends it to the server. The input is communication data, and the output is text data uploaded to the server. The terminal then prepares text data from various sources.

[0763] Step 4:

[0764] The server analyzes the received communication data using "communication_analyzer" to evaluate the emotional state. The input is the text data from step 3, and the output is the analysis result of the user's emotional state. This analysis uses natural language processing techniques, and the emotion engine classifies the emotional tone of the text.

[0765] Step 5:

[0766] The server integrates the stress level and emotional state assessment results and performs an integrated emotional assessment using a generative AI model. The input is the assessment results from steps 2 and 4, and the output is the final emotional state determination. The generative AI model utilizes supervised learning to detect abnormal emotional states.

[0767] Step 6:

[0768] A warning is displayed as an alert on the user's smart glasses, which are their visual device, if their stress level is high. The input is the result of the emotional state assessment in step 5, and the output is a visual notification to the user. The smart glasses display this warning as a pop-up window, showing a message prompting the user to take a break.

[0769] 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.

[0770] 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.

[0771] 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 robot 414.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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."

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0790] The following is further disclosed regarding the embodiments described above.

[0791] (Claim 1)

[0792] Means for collecting biometric information,

[0793] A means for analyzing the aforementioned biometric information to evaluate the user's stress level,

[0794] A means of collecting communication data and analyzing emotional states using natural language processing technology,

[0795] A means of issuing alerts and providing support based on the aforementioned stress level and emotion analysis,

[0796] A system that includes this.

[0797] (Claim 2)

[0798] The system according to claim 1, which includes multiple vital data obtained from a wearable device as biometric information.

[0799] (Claim 3)

[0800] The system according to claim 1, which analyzes emails, business chats, and voice transcripts as user communication data.

[0801] "Example 1"

[0802] (Claim 1)

[0803] A means of acquiring data from a device that collects biometric information,

[0804] An algorithm that analyzes the aforementioned biometric information to evaluate the user's stress level,

[0805] A program that collects dialogue data and analyzes emotional states using natural language processing technology,

[0806] A notification means for generating alerts and providing support based on the stress level and emotion analysis,

[0807] A system that includes this.

[0808] (Claim 2)

[0809] The system according to claim 1, which includes multiple state data obtained from a wearable electronic device as biometric information.

[0810] (Claim 3)

[0811] The system according to claim 1, which analyzes electronic communications, business chats, and voice recordings as user dialogue data.

[0812] "Application Example 1"

[0813] (Claim 1)

[0814] Means of collecting biometric data,

[0815] A means for analyzing the aforementioned biometric data to evaluate the user's mental burden,

[0816] A means of collecting communication data and analyzing emotional states using natural language processing technology,

[0817] A means of issuing warnings and providing support based on the aforementioned mental burden and emotional analysis,

[0818] A means of notifying store managers of the mental stress levels of employees,

[0819] A system that includes this.

[0820] (Claim 2)

[0821] The system according to claim 1, comprising multiple life sign data obtained from a wearable device as biometric data.

[0822] (Claim 3)

[0823] The system according to claim 1, which analyzes electronic communication means as user communication data.

[0824] "Example 2 of combining an emotion engine"

[0825] (Claim 1)

[0826] A means of receiving and analyzing biological information,

[0827] A means for classifying the user's emotional changes using a machine learning algorithm based on the aforementioned biometric information,

[0828] A means of collecting communication information and comprehensively evaluating emotional states using natural language processing technology,

[0829] A means for generating alerts and providing support information based on the aforementioned emotional state and stress assessment,

[0830] A system that includes this.

[0831] (Claim 2)

[0832] The system according to claim 1, which uses multiple physical condition data acquired from a portable device as biometric information.

[0833] (Claim 3)

[0834] The system according to claim 1, which analyzes electronic communications, dialogue records, and voice records as user communication information.

[0835] "Application example 2 when combining with an emotional engine"

[0836] (Claim 1)

[0837] Means for collecting biometric information,

[0838] A means for analyzing the aforementioned biometric information to evaluate the user's stress level,

[0839] A means of collecting communication data and analyzing emotional states using natural language processing technology,

[0840] A means of issuing alerts and providing support based on the aforementioned stress level and emotion analysis,

[0841] A means of presenting information to a visual device to notify the user of changes in their state,

[0842] A system that includes this.

[0843] (Claim 2)

[0844] The system according to claim 1, which includes multiple vital data obtained from an information processing device as biological information.

[0845] (Claim 3)

[0846] The system according to claim 1, which analyzes electronic messages, business communications, and voice recordings as user communication data. [Explanation of Symbols]

[0847] 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. Means of collecting biometric data, A means for analyzing the aforementioned biometric data to evaluate the user's mental burden, A means of collecting communication data and analyzing emotional states using natural language processing technology, A means of issuing warnings and providing support based on the aforementioned mental burden and emotional analysis, A means of notifying store managers of the mental stress levels of employees, A system that includes this.

2. The system according to claim 1, which includes multiple life sign data obtained from a wearable device as biometric data.

3. The system according to claim 1, which analyzes electronic communication means as user communication data.

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