system

Abstract

Provide a system. 【Solution means】 Means for acquiring user voice data, Means for analyzing the user's emotion from the acquired voice data, Means for acquiring user facial expression data, Means for analyzing the user's emotion from the acquired facial expression data, Means for evaluating the user's stress level based on the analyzed emotion data, Means for selecting a relaxation method based on the stress level, Means for providing the selected relaxation method to the user, Means for receiving feedback on the relaxation method from the user, Means for optimizing the next relaxation method based on the feedback, A system including means for presenting the user's emotional situation to caregivers.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, many individuals are daily burdened with stress, and such stress may have an adverse impact on mental health and overall quality of life. However, many people cannot accurately recognize their emotional state and it is difficult to find effective stress reduction means. Therefore, there is a need for a means to simply and effectively monitor one's own emotions in daily life and propose appropriate relaxation methods based on it.

Means for Solving the Problems

[0005] This invention provides a system that acquires user voice and facial expression data and analyzes emotions and stress levels based on that data. This system utilizes voice data to determine the user's emotions and further analyzes emotions from facial expression data using facial recognition technology. Based on the analysis results, it evaluates the stress level and automatically selects and provides the optimal relaxation method to reduce the user's stress. Furthermore, by collecting user feedback and continuously optimizing the relaxation methods suggested by the system, it provides care tailored to each individual user.

[0006] A "user" is an individual who provides voice data and facial expression data using the system.

[0007] "Audio data" refers to digitized sound information that includes the characteristics of the user's voice.

[0008] "Facial expression data" refers to digitized visual information that includes the user's facial movements and facial features.

[0009] "Means of analyzing emotions" refer to algorithms and programs that identify a user's emotional state from voice data and facial expression data.

[0010] "Stress level" is an indicator that quantifies the degree of mental tension a user is experiencing.

[0011] "Relaxation techniques" refer to methods such as music, meditation, and deep breathing exercises that are provided to reduce the user's stress.

[0012] "Feedback" refers to the impressions and evaluations that users provide after experiencing a relaxation method. [Brief explanation of the drawing]

[0013] [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] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a labeled 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.

[0017] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, a labeled 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), and the like.

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] The present invention's system collects user voice and facial expression data, analyzes emotions based on this data, and proposes an appropriate relaxation method. The implemented form of the system is described below.

[0035] System Configuration

[0036] This system consists of two main components: a terminal and a server. The terminal is primarily responsible for data collection and providing feedback to the user, while the server is responsible for data analysis and selecting relaxation methods. Users utilize the services provided by the system to manage their stress.

[0037] Operation Description

[0038] The device functions as an interface for interacting with the user. Equipped with a voice input device and a camera, it captures the user's voice and facial expressions in real time. This data is encrypted to ensure user privacy before being sent to the server.

[0039] The server analyzes the voice tone from the received audio data to determine the user's emotions. In addition, it uses facial recognition technology to identify emotions from facial expression data. This allows for a comprehensive evaluation of the user's emotional state.

[0040] Based on these evaluation results, the server estimates the user's stress level and selects the optimal relaxation method. This selection also takes into account feedback previously provided by the user, allowing for suggestions tailored to individual needs.

[0041] The device implements specific relaxation methods for the user, such as playing music and providing meditation guidance, based on the selected relaxation technique. Through these relaxation methods, the user can reduce stress.

[0042] Specific example

[0043] For example, the system is activated when a user is experiencing stress at work. The device detects that the user's voice tone is low, their speaking speed is slow, and their facial expression is tense. Based on this data, the server determines that the user's stress levels are high. The device then starts playing relaxing music in the background and suggests deep breathing exercises to the user. By following these instructions, the user can calm down and reduce stress.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The device records the user's voice and captures their facial expressions with its camera. The acquired data is encrypted for security purposes.

[0047] Step 2:

[0048] The device sends encrypted audio data and facial expression data to the server.

[0049] Step 3:

[0050] The server analyzes the received audio data. It applies an audio analysis algorithm to analyze the tone and rhythm of the voice and estimate the emotion from it.

[0051] Step 4:

[0052] The server analyzes facial data using a facial recognition algorithm and identifies emotional states from facial movements.

[0053] Step 5:

[0054] The server integrates emotional data from voice and facial expressions and compares it with past data to assess the overall stress level.

[0055] Step 6:

[0056] The server selects an appropriate relaxation method based on the user's stress level. This selection takes into account the suggested relaxation options and the user's past feedback.

[0057] Step 7:

[0058] The device notifies the user of the selected relaxation method and initiates specific actions such as playing music or providing meditation instructions.

[0059] Step 8:

[0060] Users experience the provided relaxation methods and provide feedback on their effects.

[0061] Step 9:

[0062] The server collects user feedback and uses it to optimize the selection of relaxation methods in the future.

[0063] (Example 1)

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

[0065] Accurately understanding a user's emotional state and proposing appropriate relaxation techniques tailored to their individual circumstances is not yet fully achieved with current technology. Existing systems are limited to superficial data such as voice and facial expressions, making it difficult to comprehensively evaluate the stress and tension a user experiences. This invention aims to overcome these challenges and provide more accurate emotional analysis and individually optimized relaxation techniques.

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

[0067] In this invention, the server includes means for acquiring the user's voice signal, means for analyzing the user's emotions from the voice signal, and means for analyzing the user's emotions from facial expression information. This makes it possible to accurately assess the user's state of tension and propose appropriate relaxation techniques based on the results.

[0068] An "audio signal" is a digital or analog signal obtained from a user, including characteristics such as frequency, volume, and tone of the voice.

[0069] "Emotion" refers to the user's emotional state or mental response, which often means a psychological state inferred from external cues such as voice and facial expressions.

[0070] "Facial expression information" refers to data that captures the movements and characteristics of a user's face, providing visual clues for understanding their emotions and attitudes.

[0071] "State of tension" refers to the psychological and physiological state, such as stress and anxiety, that the user experiences, and this is particularly evaluated through emotion analysis.

[0072] "Relaxation techniques" refer to specific approaches and methods offered to users with the aim of reducing stress and promoting mental and physical relaxation.

[0073] "Response" refers to data showing user feedback and physiological / psychological changes after receiving relaxation techniques.

[0074] "Adaptation" refers to the process of adjusting the relaxation techniques offered next time based on user feedback, making them more suitable for individual needs.

[0075] The present invention will now be described in terms of embodiments. This system analyzes the user's voice and facial expression data and provides a relaxation technique that is optimally suited to each individual. Its specific configuration and operation are shown below.

[0076] The device is responsible for capturing the user's voice and facial expressions using an audio input device and a camera. A standard microphone is used for audio input, enabling the acquisition of high-quality audio signals. The camera is used to capture the user's facial movements, continuously capturing facial information. This acquired data is processed using encryption technology to protect the user's privacy.

[0077] The server analyzes the voice and facial expression data transmitted from the terminal. Speech recognition software is used to analyze the voice signal and evaluate the user's emotions. For example, emotional states can be estimated from the tone, speed, and volume of the voice. Facial expression information is processed using image analysis software to extract the user's facial characteristics and make a specific emotional judgment.

[0078] Based on the analyzed emotional data, the server uses a generative AI model to assess the user's tension level and select appropriate relaxation techniques. By taking into account the user's past usage history and feedback, more personalized suggestions can be provided. These suggestions may be delivered, for example, as playback of specific relaxation music through a music playback application or as guidance using a meditation application.

[0079] The device provides the user with a selected relaxation technique, thereby allowing the user to alleviate tension. In one example, when the user activates the system, the device analyzes the user's voice and facial expressions and initiates a relaxation technique accordingly. An example of a prompt message would be, "Design a system that analyzes voice tone and facial expression data to suggest an appropriate relaxation method in real time."

[0080] In this way, the present invention aims to support the psychological health of users by comprehensively analyzing the stress and tension they experience and providing relaxation techniques tailored to each individual user.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] The user activates the system. This causes the terminal to simultaneously activate the voice input device and camera, and begin collecting data on the user's voice and facial expressions. Real-time voice and facial images of the user are provided as input. The terminal encrypts this data and prepares it for transmission. Voice signals and facial expression information are obtained as the output of the process.

[0084] Step 2:

[0085] The terminal transmits encrypted voice and facial expression data to the server using a secure communication protocol. The input is encrypted data from the terminal, which the server receives and decrypts. As a result, the voice signal and facial expression information are output in preparation for analysis on the server.

[0086] Step 3:

[0087] The server analyzes the received audio signal using speech recognition software to evaluate speech tone, speed, and volume. The input is an audio signal, and data processing includes extraction of acoustic features and emotion estimation. The output is the result of the emotion evaluation based on the speech.

[0088] Step 4:

[0089] The server analyzes the received facial expression information using image analysis software. The input is the user's facial expression information, and as a specific action, it analyzes facial features and changes in the eyes and mouth. This yields the results of an emotional analysis based on facial expressions.

[0090] Step 5:

[0091] The server combines emotional assessments obtained from voice and facial expressions, and uses a generative AI model to evaluate the user's stress level. Emotional assessments of voice and facial expressions are used as input, and the stress state is estimated through data calculation. The output is an evaluation result indicating the user's level of tension.

[0092] Step 6:

[0093] The server selects the optimal relaxation technique based on the stress assessment. It considers past feedback and history to choose a technique tailored to individual needs. Inputs are stress assessment and historical data, and output is instructions for the selected relaxation technique.

[0094] Step 7:

[0095] The terminal executes selected relaxation techniques according to instructions from the server. Specifically, this may involve launching applications such as music playback or meditation guidance. The input is instructions from the server, and the output is the relaxation technique that is executed.

[0096] Step 8:

[0097] Users experience the provided relaxation techniques and input feedback on their effects into the device. This feedback is important for improving future suggestions. The input is the result of experiencing the relaxation technique and is output as feedback.

[0098] (Application Example 1)

[0099] 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."

[0100] In mental healthcare for the elderly, there is a need to appropriately assess emotional changes in real time and provide relaxation methods based on individual needs. However, conventional systems have the challenge of not being able to quickly grasp changes in an individual's emotional state and effectively provide feedback to caregivers.

[0101] 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.

[0102] In this invention, the server includes means for acquiring user voice data, means for analyzing emotions from the voice data, means for acquiring and analyzing user facial expression data, and means for presenting the user's emotional state to the caregiver. This enables real-time evaluation of the user's emotional state, allowing caregivers to quickly and effectively support the mental health care of the elderly.

[0103] "User" refers to an individual or user who uses the system.

[0104] "Means of acquiring voice data" refers to the devices and processes used to record the user's voice.

[0105] "Methods for analyzing emotions" refer to technologies and algorithms that evaluate a person's emotional state based on voice and facial expression data.

[0106] "Means of acquiring facial expression data" refers to devices and processes for capturing a user's face with a camera or similar device and collecting the data as image data.

[0107] "Means for evaluating stress levels" refers to technologies and methods for measuring how much stress a user is experiencing, based on analyzed emotional data.

[0108] "Means of selecting relaxation methods" refers to the process or system for determining the most suitable relaxation method according to the user's stress level.

[0109] "Means of providing relaxation methods" refers to devices or techniques that present selected relaxation methods to users in an actionable format.

[0110] "Means of receiving feedback" refers to the process or method for users to communicate their opinions and impressions about the effectiveness of relaxation techniques to the system.

[0111] "Optimization methods" refer to ways of adjusting future relaxation methods to better suit users, based on user feedback.

[0112] "Means of presenting emotional status to caregivers" refers to methods of displaying or notifying caregivers of analyzed user emotional data in a way that is easy for them to understand.

[0113] This invention is a system that analyzes user emotions and supports stress management. It mainly consists of terminals and servers, which work together in cooperation.

[0114] The server is equipped with programs for processing voice and facial expression data. Specifically, it utilizes the open-source facial recognition library "dlib" and "Google® Cloud Speech-to-Text API" to precisely analyze the user's emotions from their voice and facial expressions. It analyzes tone and speaking speed from the voice data and reads the movement of facial muscles from the facial expression data. This makes it possible to grasp the user's emotions in real time.

[0115] The device uses a wearable device, such as smart glasses, to capture the user's voice and facial expressions. The data acquired is encrypted and sent to a server. Based on the analyzed data, the device controls and provides the user with selected relaxation methods. These relaxation methods include playing background music and providing real-time notifications to caregivers.

[0116] Users can use the system in their daily lives. For example, they can activate the system while taking a walk and enjoy music that suits their mood that day. If the user's mental health changes, the device will notify their caregiver, allowing for immediate adjustments to care.

[0117] A concrete example of a prompt using a generative AI model is: "How can the system automatically suggest relaxing music when an elderly person is experiencing stress in their daily life?" By using prompts like this, it is possible to provide the most suitable relaxation method for each individual user.

[0118] This system allows for proactive management of the mental health of elderly individuals in care settings, enabling caregivers to provide more effective support.

[0119] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0120] Step 1:

[0121] The device acquires voice and facial expression data from the user. The user's voice is collected via a voice input device, and their facial expressions are captured using a camera. This data is encrypted as input and sent to the server. The output consists of encrypted voice and facial expression data.

[0122] Step 2:

[0123] The server converts the received audio data into text using the Google Cloud Speech-to-Text API and analyzes the tone and speaking speed. This analysis allows the server to infer the user's emotional state. The input is encrypted audio data, and the output is analyzed audio tone and speaking speed data.

[0124] Step 3:

[0125] The server uses "dlib" to extract features from facial expression data and determine the user's emotions. It analyzes the movement of facial muscles to clarify the characteristics of emotions. The input is encrypted facial expression data, and the output is the analyzed facial features.

[0126] Step 4:

[0127] The server integrates the results of voice and facial expression analysis to comprehensively evaluate the emotional state and calculate the stress level. This calculation is performed using ambiguous inference and AI models. The input is the analyzed voice tone, speech rate, and facial features, and the output is the estimated stress level.

[0128] Step 5:

[0129] The server selects the optimal relaxation method based on the user's stress level, taking into account their past feedback. This selection utilizes a generative AI model. The input is the estimated stress level and past feedback, while the output is the selected relaxation method.

[0130] Step 6:

[0131] The device performs actions such as playing music or sending notifications to caregivers based on the selected relaxation method. The device provides an interface for receiving appropriate feedback. The input is the relaxation method sent from the server, and the output is the specific relaxation method provided to the user.

[0132] Step 7:

[0133] The user provides feedback on the relaxation method offered, and the device sends this information to the server. This feedback is used to optimize the next relaxation method. The input is the user's feedback, and the output is the feedback data from the device to the server.

[0134] 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.

[0135] This invention provides a system that incorporates an emotion engine utilizing the user's voice and facial expression data to precisely recognize the user's emotions and propose relaxation methods that lead to stress reduction. Specific embodiments are described below.

[0136] System Configuration

[0137] The system consists of a terminal, a server, and an emotion engine. The terminal is responsible for collecting data from the user and sending it to the server and emotion engine. The server works with the emotion engine to analyze the data and select a relaxation method.

[0138] Operation Description

[0139] The device acquires the user's voice and facial expression data. It captures the data in real time and sends it to the server in an encrypted state. Simultaneously, it sends voice tone and facial features to the emotion engine.

[0140] The emotion engine analyzes the user's emotional state using voice and facial expression data. It analyzes tone and rhythm in voice, and facial movements and microexpressions in facial expressions, to comprehensively determine the user's emotions. Furthermore, this analysis is performed in real time, allowing for immediate recognition of emotional changes.

[0141] The server assesses the user's stress level based on information from the emotion engine. This assessment also takes into account past stress data and feedback to make a more accurate judgment. Next, it selects an appropriate relaxation method (music, meditation, breathing exercises, etc.) and prepares it for delivery.

[0142] The device provides the user with selected relaxation techniques, delivered through voice guidance and a visual interface. The user reduces their own stress by accepting and implementing these techniques.

[0143] Specific example

[0144] For example, suppose a user is under pressure from a sudden work deadline, and a tense voice tone and rigid facial expression are detected. The emotion engine recognizes this as a high-stress emotional state and notifies the server. Based on this information, the server selects a relaxation audio guide that encourages deep breathing. The device immediately provides this guide to the user, allowing the user to reduce stress by repeatedly taking deep breaths.

[0145] The following describes the processing flow.

[0146] Step 1:

[0147] The device uses a microphone and camera to capture the user's voice and facial expressions in real time. The voice data includes the tone of the user's voice, and the facial expression data captures the user's facial movements.

[0148] Step 2:

[0149] The device encrypts the captured audio and facial expression data and sends it to the server. A secure communication protocol is used to ensure data privacy.

[0150] Step 3:

[0151] The server uses an emotion engine to analyze the tone and rhythm of the received audio data, thereby identifying the emotions derived from the speech.

[0152] Step 4:

[0153] The server also analyzes facial expression data via an emotion engine, determining emotions by analyzing subtle facial movements and changes in expression. This determination is then integrated with emotion data obtained from speech.

[0154] Step 5:

[0155] The server assesses the user's stress level based on emotional data integrated from voice and facial expressions. Past user data and feedback are also used in this assessment.

[0156] Step 6:

[0157] The server selects an appropriate relaxation method, taking into account the stress level already assessed. Options include music, meditation guidance, and deep breathing exercises.

[0158] Step 7:

[0159] The device presents the user with selected relaxation methods and encourages them to perform them. This is done by providing information to the user through a voice assistant and a display screen.

[0160] Step 8:

[0161] Users practice the provided relaxation techniques and provide feedback on their experience and impressions via their device.

[0162] Step 9:

[0163] The server analyzes user feedback and updates its database to make the next relaxation method suggestions more suitable for the user.

[0164] (Example 2)

[0165] 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 will be referred to as the "terminal."

[0166] In modern society, many people are exposed to various forms of stress, which negatively impacts their health and quality of life. However, emotional states and stress levels differ among individuals, and uniform stress relief measures are often insufficient. Therefore, there is a need for a system that analyzes users' emotional states in real time and provides optimal stress relief measures accordingly.

[0167] 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.

[0168] In this invention, the server includes means for acquiring user voice information and facial expression information, means for analyzing the user's emotional state in real time from the acquired information, and means for evaluating the stress level based on the analysis results and selecting appropriate mental and physical relief measures. This makes it possible to provide mental and physical relief measures optimized for each individual user and to reduce stress more effectively.

[0169] "Audio information" refers to data acquired through voice, including the tone and rhythm of the user's voice.

[0170] "Facial expression information" refers to data obtained through the user's face, including facial movements and micro-expressions.

[0171] "Emotional state" refers to the user's current mental and psychological state, and is analyzed based on voice and facial expression information.

[0172] "Stress level" refers to a state that indicates the degree and nature of stress experienced by the user, and is evaluated based on the results of an analysis of their emotional state.

[0173] "Mental and physical relaxation measures" refer to measures and methods aimed at reducing user stress and promoting mental calmness and relaxation.

[0174] "Real-time" refers to a time frame in which data acquisition and analysis are performed immediately, enabling a quick response to the user's state.

[0175] "Analysis results" refer to the results of the emotion engine's assessment of the user's emotional state and stress level, based on the acquired audio and facial expression information.

[0176] "Means of selection" refers to the process and methods for selecting the most suitable mental and physical relief measures based on the analysis results.

[0177] This system collects user voice and facial expression information in real time, analyzes their emotional state, and provides optimal mental and physical relief measures based on their stress levels. The following outlines the specific implementation of this system.

[0178] The device is equipped with a microphone to capture the user's voice information and a camera to capture facial expression information. This allows for real-time capture of the user's voice tone and changes in facial expression, which are then transmitted to a server and emotion analysis device. The collected data is sent to the server in an encrypted format to protect the user's privacy.

[0179] The server uses an emotion analysis device to analyze the acquired voice and facial expression information. This analysis includes various data processing techniques to more accurately determine the user's emotions, such as tone, rhythm, and facial muscle movements. This data is then analyzed using a generative AI model to identify the user's emotional state.

[0180] Based on the analysis results, the server evaluates the user's stress level and selects appropriate mental and physical relaxation measures from a pre-programmed database. These measures may include playing meditation music or providing breathing exercises, tailored to the user's preferences and past experiences.

[0181] The device provides users with selected mental and physical relaxation measures. Specifically, it supports users in reducing stress through voice guidance and visual interfaces utilizing speakers and displays. User feedback is also collected during this process and used to optimize future relaxation measures.

[0182] For example, if a user is nervous before a presentation, the device detects this state from the user's rapid breathing and eyebrow movements. The server analyzes this using a generated AI model and selects a deep breathing guide. Following the audio guidance from the device, the user can relax.

[0183] An example of a prompt message would be, "Analyze the voice tone and facial expression data and suggest a relaxation method suitable for a user experiencing stress." In this way, the system provides optimized support tailored to the user's current emotional state.

[0184] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0185] Step 1:

[0186] The device acquires the user's voice and facial expression information. Specifically, it captures voice in real time using a microphone and monitors facial expressions using a camera. It acquires raw audio and video data as input, encrypts it, and prepares it for transmission to the server. This process includes encryption operations to ensure data privacy.

[0187] Step 2:

[0188] The terminal transmits acquired voice and facial expression information to the server in real time. The input here is encrypted voice and video data, and the output is data transmission via a secure communication channel. The data is transferred securely using a security protocol (e.g., HTTPS).

[0189] Step 3:

[0190] The server decrypts the received encrypted data and provides it to the emotion analysis device. The encrypted data, as input, is converted back into the original voice and facial expression information using a decryption algorithm and passed to the emotion analysis device. Data decryption and verification are performed during this process.

[0191] Step 4:

[0192] The server uses an emotion analysis device to analyze the user's voice and facial expressions. The analysis utilizes a generative AI model to analyze the tone and rhythm of the input voice and the movement of facial muscles to identify the user's emotional state. The output is the analysis result indicating the user's emotional state.

[0193] Step 5:

[0194] The server evaluates the user's stress level based on the analysis results. Past data is also considered, and the results of the emotion analysis are used as input. Data calculations are performed, and an evaluation result showing the user's stress level as a numerical value or category is output.

[0195] Step 6:

[0196] The server selects the most appropriate mental and physical relief measures based on the stress level assessment. It takes the stress assessment results as input and selects appropriate relief measures from a database. This process uses an algorithm that considers the user's preferences and past performance to select specific relief measures as output.

[0197] Step 7:

[0198] The device provides users with selected mental and physical relaxation techniques. The input is the selected relaxation technique, and based on this, it provides users with specific methods through voice guidance and a visual interface. Specifically, guided voices are played from the speaker, and visuals of meditation and breathing exercises are displayed on the screen.

[0199] (Application Example 2)

[0200] 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".

[0201] Conventional stress reduction systems often struggle to accurately recognize users' emotions, resulting in relaxation techniques that don't always align with the user's actual state. Furthermore, in home environments, there's a need for autonomous devices that can understand the user's emotional state in real time through natural dialogue and provide appropriate support.

[0202] 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.

[0203] In this invention, the server includes means for acquiring user voice data, means for analyzing the user's emotions from the acquired voice data, and means for acquiring user facial expression data. This makes it possible to accurately grasp the user's emotions and provide appropriate relaxation methods in real time according to each individual's stress level.

[0204] "Voice data" refers to audio information obtained from users and is used for sentiment analysis.

[0205] "Facial expression data" refers to information that represents the movements and expressions of a user's face, and is used to analyze their emotional state.

[0206] "Means of analyzing emotions" refers to processes and technologies for identifying and evaluating a user's emotional state using the user's voice data and facial expression data.

[0207] A "means for evaluating stress levels" refers to a system that determines the degree of stress a user is experiencing based on analyzed emotional data.

[0208] "Relaxation techniques" refer to a series of methods and techniques proposed to reduce user stress.

[0209] "Feedback" refers to information such as comments and evaluations that users provide regarding the relaxation methods offered.

[0210] An "autonomous mobile device equipped with artificial intelligence" refers to an autonomous mechanical device that operates spontaneously through interaction with and support from the user, and provides voice and visual assistance.

[0211] This invention is a system that precisely analyzes emotions using the user's voice and facial expression data and proposes relaxation methods tailored to their individual stress levels. The specific implementation of this system will now be described.

[0212] Hardware configuration

[0213] The user's device is equipped with a microphone and camera. This device has the capability to acquire the user's voice and facial data in real time. It also includes an autonomous mobile device with artificial intelligence that perceives the user's surroundings and provides voice and visual feedback. The server houses a processor that receives and analyzes voice and facial data, and is equipped with an emotion engine.

[0214] Software Configuration

[0215] The emotion engine implements algorithms that analyze the user's voice tone and facial features. This allows for accurate determination of emotional state and assessment of the user's stress level. Based on the analysis results, the server selects the most suitable relaxation method and sends instructions to the user's device.

[0216] Examples

[0217] When a user speaks to you while working in the living room, if their voice sounds tense or their facial expression shows signs of fatigue, the emotional engine recognizes this as a high-stress state. The system then recommends appropriate meditation techniques and music, and an AI-powered autonomous mobile device creates a relaxing environment with soft lighting and guides you through relaxation methods with a gentle voice.

[0218] Example of a prompt

[0219] "Please tell me about the design of a life support robot that can analyze a user's emotions in real time based on their voice and facial expression data, and suggest relaxation methods that help reduce stress."

[0220] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0221] Step 1:

[0222] The device acquires the user's voice and facial expression data in real time. Voice data is collected via a microphone, and facial expression data is captured via a camera. It receives voice and image data as input, encrypts them, and sends them to the server.

[0223] Step 2:

[0224] The server receives audio and facial expression data transmitted from the terminal and decodes each for analysis. From the decoded audio data, speech tone and rhythm information are extracted, and from the decoded facial expression data, facial features are extracted. This data is then input into the emotion engine.

[0225] Step 3:

[0226] The emotion engine analyzes the user's emotional state based on extracted voice tone, rhythm, and facial features. Based on the analysis, the emotion engine outputs the user's current emotions and stress level. This provides a foundation for determining what kind of relaxation method is needed.

[0227] Step 4:

[0228] The server selects the optimal relaxation method from the database based on the user's emotional state and stress level obtained from the emotion engine. It then generates information about the selected relaxation method and converts it into a format for transmission to the terminal.

[0229] Step 5:

[0230] The device receives information about relaxation techniques transmitted from the server and prepares to provide it to the user. Specifically, it presents the user with details of the selected relaxation technique through audio guidance and a visual interface.

[0231] Step 6:

[0232] Users perform the suggested relaxation techniques and provide feedback on their impressions and the effects to the system via their device. This feedback is collected and processed to optimize future suggestions.

[0233] 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.

[0234] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0235] 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.

[0236] [Second Embodiment]

[0237] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0238] 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.

[0239] 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).

[0240] 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.

[0241] 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.

[0242] 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).

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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".

[0249] The present invention's system collects user voice and facial expression data, analyzes emotions based on this data, and proposes an appropriate relaxation method. The implemented form of the system is described below.

[0250] System Configuration

[0251] This system consists of two main components: a terminal and a server. The terminal is primarily responsible for data collection and providing feedback to the user, while the server is responsible for data analysis and selecting relaxation methods. Users utilize the services provided by the system to manage their stress.

[0252] Operation Description

[0253] The device functions as an interface for interacting with the user. Equipped with a voice input device and a camera, it captures the user's voice and facial expressions in real time. This data is encrypted to ensure user privacy before being sent to the server.

[0254] The server analyzes the voice tone from the received audio data to determine the user's emotions. In addition, it uses facial recognition technology to identify emotions from facial expression data. This allows for a comprehensive evaluation of the user's emotional state.

[0255] Based on these evaluation results, the server estimates the user's stress level and selects the optimal relaxation method. This selection also takes into account feedback previously provided by the user, allowing for suggestions tailored to individual needs.

[0256] The device implements specific relaxation methods for the user, such as playing music and providing meditation guidance, based on the selected relaxation technique. Through these relaxation methods, the user can reduce stress.

[0257] Specific example

[0258] For example, the system is activated when a user is experiencing stress at work. The device detects that the user's voice tone is low, their speaking speed is slow, and their facial expression is tense. Based on this data, the server determines that the user's stress levels are high. The device then starts playing relaxing music in the background and suggests deep breathing exercises to the user. By following these instructions, the user can calm down and reduce stress.

[0259] The following describes the processing flow.

[0260] Step 1:

[0261] The device records the user's voice and captures their facial expressions with its camera. The acquired data is encrypted for security purposes.

[0262] Step 2:

[0263] The device sends encrypted audio data and facial expression data to the server.

[0264] Step 3:

[0265] The server analyzes the received audio data. It applies an audio analysis algorithm to analyze the tone and rhythm of the voice and estimate the emotion from it.

[0266] Step 4:

[0267] The server analyzes facial data using a facial recognition algorithm and identifies emotional states from facial movements.

[0268] Step 5:

[0269] The server integrates emotional data from voice and facial expressions and compares it with past data to assess the overall stress level.

[0270] Step 6:

[0271] The server selects an appropriate relaxation method based on the user's stress level. This selection takes into account the suggested relaxation options and the user's past feedback.

[0272] Step 7:

[0273] The device notifies the user of the selected relaxation method and initiates specific actions such as playing music or providing meditation instructions.

[0274] Step 8:

[0275] Users experience the provided relaxation methods and provide feedback on their effects.

[0276] Step 9:

[0277] The server collects user feedback and uses it to optimize the selection of relaxation methods in the future.

[0278] (Example 1)

[0279] 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."

[0280] Accurately understanding a user's emotional state and proposing appropriate relaxation techniques tailored to their individual circumstances is not yet fully achieved with current technology. Existing systems are limited to superficial data such as voice and facial expressions, making it difficult to comprehensively evaluate the stress and tension a user experiences. This invention aims to overcome these challenges and provide more accurate emotional analysis and individually optimized relaxation techniques.

[0281] 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.

[0282] In this invention, the server includes means for acquiring the user's voice signal, means for analyzing the user's emotions from the voice signal, and means for analyzing the user's emotions from facial expression information. This makes it possible to accurately assess the user's state of tension and propose appropriate relaxation techniques based on the results.

[0283] An "audio signal" is a digital or analog signal obtained from a user, including characteristics such as frequency, volume, and tone of the voice.

[0284] "Emotion" refers to the emotional state and mental reaction of the user, which often means a psychological state inferred from external cues such as voice and expression.

[0285] "Facial expression information" is data that captures the movements and features of the user's face, providing a visual cue for understanding emotions and attitudes.

[0286] "Tension state" refers to the psychological and physiological states such as stress and anxiety experienced by the user, which is particularly evaluated by emotion analysis.

[0287] "Relaxation technique" refers to specific approaches and methods aimed at reducing stress and relaxing the mind and body provided to the user.

[0288] "Reaction" means data indicating the user's feedback and physiological and psychological changes after receiving a relaxation technique.

[0289] "Adaptation" is the process of adjusting the relaxation technique provided next time based on the reaction from the user and adapting it to individual needs.

[0290] The embodiments for implementing the present invention will be described. This system is a system that analyzes the voice and facial expression data of the user and provides an optimal relaxation technique for each individual. The specific configuration and operation are shown below.

[0291] The terminal plays the role of acquiring the voice and facial expressions of the user using a voice input device and a camera. A general microphone is used for voice input, enabling the acquisition of high-quality voice signals. The camera is used to capture the movements of the user's face and continuously capture facial expression information. This acquired data is processed using encryption technology to protect the user's privacy.

[0292] The server analyzes the voice and facial expression data transmitted from the terminal. Speech recognition software is used to analyze the voice signal and evaluate the user's emotions. For example, emotional states can be estimated from the tone, speed, and volume of the voice. Facial expression information is processed using image analysis software to extract the user's facial characteristics and make a specific emotional judgment.

[0293] Based on the analyzed emotional data, the server uses a generative AI model to assess the user's tension level and select appropriate relaxation techniques. By taking into account the user's past usage history and feedback, more personalized suggestions can be provided. These suggestions may be delivered, for example, as playback of specific relaxation music through a music playback application or as guidance using a meditation application.

[0294] The device provides the user with a selected relaxation technique, thereby allowing the user to alleviate tension. In one example, when the user activates the system, the device analyzes the user's voice and facial expressions and initiates a relaxation technique accordingly. An example of a prompt message would be, "Design a system that analyzes voice tone and facial expression data to suggest an appropriate relaxation method in real time."

[0295] In this way, the present invention aims to support the psychological health of users by comprehensively analyzing the stress and tension they experience and providing relaxation techniques tailored to each individual user.

[0296] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0297] Step 1:

[0298] The user activates the system. Thereby, the terminal simultaneously activates the voice input device and the camera, and starts collecting data on the user's voice and expressions. As input, the user's real-time voice and face images are provided. The terminal encrypts this data and prepares it for transmission. The voice signal and expression information are obtained as the output of the process.

[0299] Step 2:

[0300] The terminal transmits the encrypted voice and expression data to the server using a secure communication protocol. The input is the data encrypted at the terminal, and the server receives and decrypts it. Thereby, the voice signal and expression information are output in preparation for analysis at the server.

[0301] Step 3:

[0302] The server analyzes the received voice signal with voice recognition software and evaluates the voice tone, speed, and volume. The input is the voice signal, and as data processing, acoustic feature extraction and emotion estimation are performed. The output is the result of the emotion evaluation by voice.

[0303] Step 4:

[0304] The server analyzes the received expression information with image analysis software. The input is the user's expression information, and as a specific operation, face feature extraction and analysis of changes around the eyes and mouth are performed. Thereby, the result of the emotion analysis based on the expression is obtained.

[0305] Step 5:

[0306] The server combines the emotion evaluations obtained from the voice and expression, and evaluates the user's stress level using a generated AI model. As input, the emotion evaluations of the voice and expression are used, and the stress state is estimated by data calculation. The output is the evaluation result indicating the user's state of tension.

[0307] Step 6:

[0308] The server selects the optimal relaxation technique based on the stress assessment. It considers past feedback and history to choose a technique tailored to individual needs. Inputs are stress assessment and historical data, and output is instructions for the selected relaxation technique.

[0309] Step 7:

[0310] The terminal executes selected relaxation techniques according to instructions from the server. Specifically, this may involve launching applications such as music playback or meditation guidance. The input is instructions from the server, and the output is the relaxation technique that is executed.

[0311] Step 8:

[0312] Users experience the provided relaxation techniques and input feedback on their effects into the device. This feedback is important for improving future suggestions. The input is the result of experiencing the relaxation technique and is output as feedback.

[0313] (Application Example 1)

[0314] 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."

[0315] In mental healthcare for the elderly, there is a need to appropriately assess emotional changes in real time and provide relaxation methods based on individual needs. However, conventional systems have the challenge of not being able to quickly grasp changes in an individual's emotional state and effectively provide feedback to caregivers.

[0316] 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.

[0317] In this invention, the server includes means for acquiring user voice data, means for analyzing emotions from the voice data, means for acquiring and analyzing user facial expression data, and means for presenting the user's emotional state to the caregiver. This enables real-time evaluation of the user's emotional state, allowing caregivers to quickly and effectively support the mental health care of the elderly.

[0318] "User" refers to an individual or user who uses the system.

[0319] "Means of acquiring voice data" refers to the devices and processes used to record the user's voice.

[0320] "Methods for analyzing emotions" refer to technologies and algorithms that evaluate a person's emotional state based on voice and facial expression data.

[0321] "Means of acquiring facial expression data" refers to devices and processes for capturing a user's face with a camera or similar device and collecting the data as image data.

[0322] "Means for evaluating stress levels" refers to technologies and methods for measuring how much stress a user is experiencing, based on analyzed emotional data.

[0323] "Means of selecting relaxation methods" refers to the process or system for determining the most suitable relaxation method according to the user's stress level.

[0324] "Means of providing relaxation methods" refers to devices or techniques that present selected relaxation methods to users in an actionable format.

[0325] "Means of receiving feedback" refers to the process or method for users to communicate their opinions and impressions about the effectiveness of relaxation techniques to the system.

[0326] "Optimization methods" refer to ways of adjusting future relaxation methods to better suit users, based on user feedback.

[0327] "Means of presenting emotional status to caregivers" refers to methods of displaying or notifying caregivers of analyzed user emotional data in a way that is easy for them to understand.

[0328] This invention is a system that analyzes user emotions and supports stress management. It mainly consists of terminals and servers, which work together in cooperation.

[0329] The server is equipped with programs for processing voice and facial expression data. Specifically, it utilizes the open-source facial recognition library "dlib" and the "Google Cloud Speech-to-Text API" to precisely analyze the user's emotions from their voice and facial expressions. It analyzes tone and speech rate from the voice data and reads the movement of facial muscles from the facial expression data. This makes it possible to understand the user's emotions in real time.

[0330] The device uses a wearable device, such as smart glasses, to capture the user's voice and facial expressions. The data acquired is encrypted and sent to a server. Based on the analyzed data, the device controls and provides the user with selected relaxation methods. These relaxation methods include playing background music and providing real-time notifications to caregivers.

[0331] Users can use the system in their daily lives. For example, they can activate the system while taking a walk and enjoy music that suits their mood that day. If the user's mental health changes, the device will notify their caregiver, allowing for immediate adjustments to care.

[0332] A concrete example of a prompt using a generative AI model is: "How can the system automatically suggest relaxing music when an elderly person is experiencing stress in their daily life?" By using prompts like this, it is possible to provide the most suitable relaxation method for each individual user.

[0333] This system allows for proactive management of the mental health of elderly individuals in care settings, enabling caregivers to provide more effective support.

[0334] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0335] Step 1:

[0336] The device acquires voice and facial expression data from the user. The user's voice is collected via a voice input device, and their facial expressions are captured using a camera. This data is encrypted as input and sent to the server. The output consists of encrypted voice and facial expression data.

[0337] Step 2:

[0338] The server converts the received audio data into text using the Google Cloud Speech-to-Text API and analyzes the tone and speaking speed. This analysis allows the server to infer the user's emotional state. The input is encrypted audio data, and the output is analyzed audio tone and speaking speed data.

[0339] Step 3:

[0340] The server uses "dlib" to extract features from facial expression data and determine the user's emotions. It analyzes the movement of facial muscles to clarify the characteristics of emotions. The input is encrypted facial expression data, and the output is the analyzed facial features.

[0341] Step 4:

[0342] The server integrates the results of voice and facial expression analysis to comprehensively evaluate the emotional state and calculate the stress level. This calculation is performed using ambiguous inference and AI models. The input is the analyzed voice tone, speech rate, and facial features, and the output is the estimated stress level.

[0343] Step 5:

[0344] The server selects the optimal relaxation method based on the user's stress level, taking into account their past feedback. This selection utilizes a generative AI model. The input is the estimated stress level and past feedback, while the output is the selected relaxation method.

[0345] Step 6:

[0346] The device performs actions such as playing music or sending notifications to caregivers based on the selected relaxation method. The device provides an interface for receiving appropriate feedback. The input is the relaxation method sent from the server, and the output is the specific relaxation method provided to the user.

[0347] Step 7:

[0348] The user provides feedback on the relaxation method offered, and the device sends this information to the server. This feedback is used to optimize the next relaxation method. The input is the user's feedback, and the output is the feedback data from the device to the server.

[0349] 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.

[0350] This invention provides a system that incorporates an emotion engine utilizing the user's voice and facial expression data to precisely recognize the user's emotions and propose relaxation methods that lead to stress reduction. Specific embodiments are described below.

[0351] System Configuration

[0352] The system consists of a terminal, a server, and an emotion engine. The terminal is responsible for collecting data from the user and sending it to the server and emotion engine. The server works with the emotion engine to analyze the data and select a relaxation method.

[0353] Operation Description

[0354] The device acquires the user's voice and facial expression data. It captures the data in real time and sends it to the server in an encrypted state. Simultaneously, it sends voice tone and facial features to the emotion engine.

[0355] The emotion engine analyzes the user's emotional state using voice and facial expression data. It analyzes tone and rhythm in voice, and facial movements and microexpressions in facial expressions, to comprehensively determine the user's emotions. Furthermore, this analysis is performed in real time, allowing for immediate recognition of emotional changes.

[0356] The server assesses the user's stress level based on information from the emotion engine. This assessment also takes into account past stress data and feedback to make a more accurate judgment. Next, it selects an appropriate relaxation method (music, meditation, breathing exercises, etc.) and prepares it for delivery.

[0357] The device provides the user with selected relaxation techniques, delivered through voice guidance and a visual interface. The user reduces their own stress by accepting and implementing these techniques.

[0358] Specific example

[0359] For example, suppose a user is under pressure from a sudden work deadline, and a tense voice tone and rigid facial expression are detected. The emotion engine recognizes this as a high-stress emotional state and notifies the server. Based on this information, the server selects a relaxation audio guide that encourages deep breathing. The device immediately provides this guide to the user, allowing the user to reduce stress by repeatedly taking deep breaths.

[0360] The following describes the processing flow.

[0361] Step 1:

[0362] The device uses a microphone and camera to capture the user's voice and facial expressions in real time. The voice data includes the tone of the user's voice, and the facial expression data captures the user's facial movements.

[0363] Step 2:

[0364] The device encrypts the captured audio and facial expression data and sends it to the server. A secure communication protocol is used to ensure data privacy.

[0365] Step 3:

[0366] The server uses an emotion engine to analyze the tone and rhythm of the received audio data, thereby identifying the emotions derived from the speech.

[0367] Step 4:

[0368] The server also analyzes facial expression data via an emotion engine, determining emotions by analyzing subtle facial movements and changes in expression. This determination is then integrated with emotion data obtained from speech.

[0369] Step 5:

[0370] The server assesses the user's stress level based on emotional data integrated from voice and facial expressions. Past user data and feedback are also used in this assessment.

[0371] Step 6:

[0372] The server selects an appropriate relaxation method, taking into account the stress level already assessed. Options include music, meditation guidance, and deep breathing exercises.

[0373] Step 7:

[0374] The device presents the user with selected relaxation methods and encourages them to perform them. This is done by providing information to the user through a voice assistant and a display screen.

[0375] Step 8:

[0376] Users practice the provided relaxation techniques and provide feedback on their experience and impressions via their device.

[0377] Step 9:

[0378] The server analyzes user feedback and updates its database to make the next relaxation method suggestions more suitable for the user.

[0379] (Example 2)

[0380] 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".

[0381] In modern society, many people are exposed to various forms of stress, which negatively impacts their health and quality of life. However, emotional states and stress levels differ among individuals, and uniform stress relief measures are often insufficient. Therefore, there is a need for a system that analyzes users' emotional states in real time and provides optimal stress relief measures accordingly.

[0382] 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.

[0383] In this invention, the server includes means for acquiring user voice information and facial expression information, means for analyzing the user's emotional state in real time from the acquired information, and means for evaluating the stress level based on the analysis results and selecting appropriate mental and physical relief measures. This makes it possible to provide mental and physical relief measures optimized for each individual user and to reduce stress more effectively.

[0384] "Audio information" refers to data acquired through voice, including the tone and rhythm of the user's voice.

[0385] "Facial expression information" refers to data obtained through the user's face, including facial movements and micro-expressions.

[0386] "Emotional state" refers to the user's current mental and psychological state, and is analyzed based on voice and facial expression information.

[0387] "Stress level" refers to a state that indicates the degree and nature of stress experienced by the user, and is evaluated based on the results of an analysis of their emotional state.

[0388] "Mental and physical relaxation measures" refer to measures and methods aimed at reducing user stress and promoting mental calmness and relaxation.

[0389] "Real-time" refers to a time frame in which data acquisition and analysis are performed immediately, enabling a quick response to the user's state.

[0390] "Analysis results" refer to the results of the emotion engine's assessment of the user's emotional state and stress level, based on the acquired audio and facial expression information.

[0391] "Means of selection" refers to the process and methods for selecting the most suitable mental and physical relief measures based on the analysis results.

[0392] This system collects user voice and facial expression information in real time, analyzes their emotional state, and provides optimal mental and physical relief measures based on their stress levels. The following outlines the specific implementation of this system.

[0393] The device is equipped with a microphone to capture the user's voice information and a camera to capture facial expression information. This allows for real-time capture of the user's voice tone and changes in facial expression, which are then transmitted to a server and emotion analysis device. The collected data is sent to the server in an encrypted format to protect the user's privacy.

[0394] The server uses an emotion analysis device to analyze the acquired voice and facial expression information. This analysis includes various data processing techniques to more accurately determine the user's emotions, such as tone, rhythm, and facial muscle movements. This data is then analyzed using a generative AI model to identify the user's emotional state.

[0395] Based on the analysis results, the server evaluates the user's stress level and selects appropriate mental and physical relaxation measures from a pre-programmed database. These measures may include playing meditation music or providing breathing exercises, tailored to the user's preferences and past experiences.

[0396] The device provides users with selected mental and physical relaxation measures. Specifically, it supports users in reducing stress through voice guidance and visual interfaces utilizing speakers and displays. User feedback is also collected during this process and used to optimize future relaxation measures.

[0397] For example, if a user is nervous before a presentation, the device detects this state from the user's rapid breathing and eyebrow movements. The server analyzes this using a generated AI model and selects a deep breathing guide. Following the audio guidance from the device, the user can relax.

[0398] An example of a prompt message would be, "Analyze the voice tone and facial expression data and suggest a relaxation method suitable for a user experiencing stress." In this way, the system provides optimized support tailored to the user's current emotional state.

[0399] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0400] Step 1:

[0401] The device acquires the user's voice and facial expression information. Specifically, it captures voice in real time using a microphone and monitors facial expressions using a camera. It acquires raw audio and video data as input, encrypts it, and prepares it for transmission to the server. This process includes encryption operations to ensure data privacy.

[0402] Step 2:

[0403] The terminal transmits acquired voice and facial expression information to the server in real time. The input here is encrypted voice and video data, and the output is data transmission via a secure communication channel. The data is transferred securely using a security protocol (e.g., HTTPS).

[0404] Step 3:

[0405] The server decrypts the received encrypted data and provides it to the emotion analysis device. The encrypted data, as input, is converted back into the original voice and facial expression information using a decryption algorithm and passed to the emotion analysis device. Data decryption and verification are performed during this process.

[0406] Step 4:

[0407] The server uses an emotion analysis device to analyze the user's voice and facial expressions. The analysis utilizes a generative AI model to analyze the tone and rhythm of the input voice and the movement of facial muscles to identify the user's emotional state. The output is the analysis result indicating the user's emotional state.

[0408] Step 5:

[0409] The server evaluates the user's stress level based on the analysis results. Past data is also considered, and the results of the emotion analysis are used as input. Data calculations are performed, and an evaluation result showing the user's stress level as a numerical value or category is output.

[0410] Step 6:

[0411] The server selects the most appropriate mental and physical relief measures based on the stress level assessment. It takes the stress assessment results as input and selects appropriate relief measures from a database. This process uses an algorithm that considers the user's preferences and past performance to select specific relief measures as output.

[0412] Step 7:

[0413] The device provides users with selected mental and physical relaxation techniques. The input is the selected relaxation technique, and based on this, it provides users with specific methods through voice guidance and a visual interface. Specifically, guided voices are played from the speaker, and visuals of meditation and breathing exercises are displayed on the screen.

[0414] (Application Example 2)

[0415] 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."

[0416] Conventional stress reduction systems often struggle to accurately recognize users' emotions, resulting in relaxation techniques that don't always align with the user's actual state. Furthermore, in home environments, there's a need for autonomous devices that can understand the user's emotional state in real time through natural dialogue and provide appropriate support.

[0417] 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.

[0418] In this invention, the server includes means for acquiring user voice data, means for analyzing the user's emotions from the acquired voice data, and means for acquiring user facial expression data. This makes it possible to accurately grasp the user's emotions and provide appropriate relaxation methods in real time according to each individual's stress level.

[0419] "Voice data" refers to audio information obtained from users and is used for sentiment analysis.

[0420] "Facial expression data" refers to information that represents the movements and expressions of a user's face, and is used to analyze their emotional state.

[0421] "Means of analyzing emotions" refers to processes and technologies for identifying and evaluating a user's emotional state using the user's voice data and facial expression data.

[0422] A "means for evaluating stress levels" refers to a system that determines the degree of stress a user is experiencing based on analyzed emotional data.

[0423] "Relaxation techniques" refer to a series of methods and techniques proposed to reduce user stress.

[0424] "Feedback" refers to information such as comments and evaluations that users provide regarding the relaxation methods offered.

[0425] An "autonomous mobile device equipped with artificial intelligence" refers to an autonomous mechanical device that operates spontaneously through interaction with and support from the user, and provides voice and visual assistance.

[0426] This invention is a system that precisely analyzes emotions using the user's voice and facial expression data and proposes relaxation methods tailored to their individual stress levels. The specific implementation of this system will now be described.

[0427] Hardware configuration

[0428] The user's device is equipped with a microphone and camera. This device has the capability to acquire the user's voice and facial data in real time. It also includes an autonomous mobile device with artificial intelligence that perceives the user's surroundings and provides voice and visual feedback. The server houses a processor that receives and analyzes voice and facial data, and is equipped with an emotion engine.

[0429] Software Configuration

[0430] The emotion engine implements algorithms that analyze the user's voice tone and facial features. This allows for accurate determination of emotional state and assessment of the user's stress level. Based on the analysis results, the server selects the most suitable relaxation method and sends instructions to the user's device.

[0431] Examples

[0432] When a user speaks to you while working in the living room, if their voice sounds tense or their facial expression shows signs of fatigue, the emotional engine recognizes this as a high-stress state. The system then recommends appropriate meditation techniques and music, and an AI-powered autonomous mobile device creates a relaxing environment with soft lighting and guides you through relaxation methods with a gentle voice.

[0433] Example of a prompt

[0434] "Please tell me about the design of a life support robot that can analyze a user's emotions in real time based on their voice and facial expression data, and suggest relaxation methods that help reduce stress."

[0435] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0436] Step 1:

[0437] The device acquires the user's voice and facial expression data in real time. Voice data is collected via a microphone, and facial expression data is captured via a camera. It receives voice and image data as input, encrypts them, and sends them to the server.

[0438] Step 2:

[0439] The server receives audio and facial expression data transmitted from the terminal and decodes each for analysis. From the decoded audio data, speech tone and rhythm information are extracted, and from the decoded facial expression data, facial features are extracted. This data is then input into the emotion engine.

[0440] Step 3:

[0441] The emotion engine analyzes the user's emotional state based on extracted voice tone, rhythm, and facial features. Based on the analysis, the emotion engine outputs the user's current emotions and stress level. This provides a foundation for determining what kind of relaxation method is needed.

[0442] Step 4:

[0443] The server selects the optimal relaxation method from the database based on the user's emotional state and stress level obtained from the emotion engine. It then generates information about the selected relaxation method and converts it into a format for transmission to the terminal.

[0444] Step 5:

[0445] The device receives information about relaxation techniques transmitted from the server and prepares to provide it to the user. Specifically, it presents the user with details of the selected relaxation technique through audio guidance and a visual interface.

[0446] Step 6:

[0447] Users perform the suggested relaxation techniques and provide feedback on their impressions and the effects to the system via their device. This feedback is collected and processed to optimize future suggestions.

[0448] 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.

[0449] 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.

[0450] 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.

[0451] [Third Embodiment]

[0452] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0453] 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.

[0454] 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).

[0455] 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.

[0456] 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.

[0457] 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).

[0458] 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.

[0459] 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.

[0460] 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.

[0461] 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.

[0462] 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.

[0463] 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".

[0464] The present invention's system collects user voice and facial expression data, analyzes emotions based on this data, and proposes an appropriate relaxation method. The implemented form of the system is described below.

[0465] System Configuration

[0466] This system consists of two main components: a terminal and a server. The terminal is primarily responsible for data collection and providing feedback to the user, while the server is responsible for data analysis and selecting relaxation methods. Users utilize the services provided by the system to manage their stress.

[0467] Operation Description

[0468] The device functions as an interface for interacting with the user. Equipped with a voice input device and a camera, it captures the user's voice and facial expressions in real time. This data is encrypted to ensure user privacy before being sent to the server.

[0469] The server analyzes the voice tone from the received audio data to determine the user's emotions. In addition, it uses facial recognition technology to identify emotions from facial expression data. This allows for a comprehensive evaluation of the user's emotional state.

[0470] Based on these evaluation results, the server estimates the user's stress level and selects the optimal relaxation method. This selection also takes into account feedback previously provided by the user, allowing for suggestions tailored to individual needs.

[0471] The device implements specific relaxation methods for the user, such as playing music and providing meditation guidance, based on the selected relaxation technique. Through these relaxation methods, the user can reduce stress.

[0472] Specific example

[0473] For example, the system is activated when a user is experiencing stress at work. The device detects that the user's voice tone is low, their speaking speed is slow, and their facial expression is tense. Based on this data, the server determines that the user's stress levels are high. The device then starts playing relaxing music in the background and suggests deep breathing exercises to the user. By following these instructions, the user can calm down and reduce stress.

[0474] The following describes the processing flow.

[0475] Step 1:

[0476] The device records the user's voice and captures their facial expressions with its camera. The acquired data is encrypted for security purposes.

[0477] Step 2:

[0478] The device sends encrypted audio data and facial expression data to the server.

[0479] Step 3:

[0480] The server analyzes the received audio data. It applies an audio analysis algorithm to analyze the tone and rhythm of the voice and estimate the emotion from it.

[0481] Step 4:

[0482] The server analyzes facial data using a facial recognition algorithm and identifies emotional states from facial movements.

[0483] Step 5:

[0484] The server integrates emotional data from voice and facial expressions and compares it with past data to assess the overall stress level.

[0485] Step 6:

[0486] The server selects an appropriate relaxation method based on the user's stress level. This selection takes into account the suggested relaxation options and the user's past feedback.

[0487] Step 7:

[0488] The device notifies the user of the selected relaxation method and initiates specific actions such as playing music or providing meditation instructions.

[0489] Step 8:

[0490] Users experience the provided relaxation methods and provide feedback on their effects.

[0491] Step 9:

[0492] The server collects user feedback and uses it to optimize the selection of relaxation methods in the future.

[0493] (Example 1)

[0494] 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."

[0495] Accurately understanding a user's emotional state and proposing appropriate relaxation techniques tailored to their individual circumstances is not yet fully achieved with current technology. Existing systems are limited to superficial data such as voice and facial expressions, making it difficult to comprehensively evaluate the stress and tension a user experiences. This invention aims to overcome these challenges and provide more accurate emotional analysis and individually optimized relaxation techniques.

[0496] 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.

[0497] In this invention, the server includes means for acquiring the user's voice signal, means for analyzing the user's emotions from the voice signal, and means for analyzing the user's emotions from facial expression information. This makes it possible to accurately assess the user's state of tension and propose appropriate relaxation techniques based on the results.

[0498] An "audio signal" is a digital or analog signal obtained from a user, including characteristics such as frequency, volume, and tone of the voice.

[0499] "Emotion" refers to the user's emotional state or mental response, which often means a psychological state inferred from external cues such as voice and facial expressions.

[0500] "Facial expression information" refers to data that captures the movements and characteristics of a user's face, providing visual clues for understanding their emotions and attitudes.

[0501] "State of tension" refers to the psychological and physiological state, such as stress and anxiety, that the user experiences, and this is particularly evaluated through emotion analysis.

[0502] "Relaxation techniques" refer to specific approaches and methods offered to users with the aim of reducing stress and promoting mental and physical relaxation.

[0503] "Response" refers to data showing user feedback and physiological / psychological changes after receiving relaxation techniques.

[0504] "Adaptation" refers to the process of adjusting the relaxation techniques offered next time based on user feedback, making them more suitable for individual needs.

[0505] The present invention will now be described in terms of embodiments. This system analyzes the user's voice and facial expression data and provides a relaxation technique that is optimally suited to each individual. Its specific configuration and operation are shown below.

[0506] The device is responsible for capturing the user's voice and facial expressions using an audio input device and a camera. A standard microphone is used for audio input, enabling the acquisition of high-quality audio signals. The camera is used to capture the user's facial movements, continuously capturing facial information. This acquired data is processed using encryption technology to protect the user's privacy.

[0507] The server analyzes the voice and facial expression data transmitted from the terminal. Speech recognition software is used to analyze the voice signal and evaluate the user's emotions. For example, emotional states can be estimated from the tone, speed, and volume of the voice. Facial expression information is processed using image analysis software to extract the user's facial characteristics and make a specific emotional judgment.

[0508] Based on the analyzed emotional data, the server uses a generative AI model to assess the user's tension level and select appropriate relaxation techniques. By taking into account the user's past usage history and feedback, more personalized suggestions can be provided. These suggestions may be delivered, for example, as playback of specific relaxation music through a music playback application or as guidance using a meditation application.

[0509] The device provides the user with a selected relaxation technique, thereby allowing the user to alleviate tension. In one example, when the user activates the system, the device analyzes the user's voice and facial expressions and initiates a relaxation technique accordingly. An example of a prompt message would be, "Design a system that analyzes voice tone and facial expression data to suggest an appropriate relaxation method in real time."

[0510] In this way, the present invention aims to support the psychological health of users by comprehensively analyzing the stress and tension they experience and providing relaxation techniques tailored to each individual user.

[0511] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0512] Step 1:

[0513] The user activates the system. This causes the terminal to simultaneously activate the voice input device and camera, and begin collecting data on the user's voice and facial expressions. Real-time voice and facial images of the user are provided as input. The terminal encrypts this data and prepares it for transmission. Voice signals and facial expression information are obtained as the output of the process.

[0514] Step 2:

[0515] The terminal transmits encrypted voice and facial expression data to the server using a secure communication protocol. The input is encrypted data from the terminal, which the server receives and decrypts. As a result, the voice signal and facial expression information are output in preparation for analysis on the server.

[0516] Step 3:

[0517] The server analyzes the received audio signal using speech recognition software to evaluate speech tone, speed, and volume. The input is an audio signal, and data processing includes extraction of acoustic features and emotion estimation. The output is the result of the emotion evaluation based on the speech.

[0518] Step 4:

[0519] The server analyzes the received facial expression information using image analysis software. The input is the user's facial expression information, and as a specific action, it analyzes facial features and changes in the eyes and mouth. This yields the results of an emotional analysis based on facial expressions.

[0520] Step 5:

[0521] The server combines emotional assessments obtained from voice and facial expressions, and uses a generative AI model to evaluate the user's stress level. Emotional assessments of voice and facial expressions are used as input, and the stress state is estimated through data calculation. The output is an evaluation result indicating the user's level of tension.

[0522] Step 6:

[0523] The server selects the optimal relaxation technique based on the stress assessment. It considers past feedback and history to choose a technique tailored to individual needs. Inputs are stress assessment and historical data, and output is instructions for the selected relaxation technique.

[0524] Step 7:

[0525] The terminal executes selected relaxation techniques according to instructions from the server. Specifically, this may involve launching applications such as music playback or meditation guidance. The input is instructions from the server, and the output is the relaxation technique that is executed.

[0526] Step 8:

[0527] Users experience the provided relaxation techniques and input feedback on their effects into the device. This feedback is important for improving future suggestions. The input is the result of experiencing the relaxation technique and is output as feedback.

[0528] (Application Example 1)

[0529] 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."

[0530] In mental healthcare for the elderly, there is a need to appropriately assess emotional changes in real time and provide relaxation methods based on individual needs. However, conventional systems have the challenge of not being able to quickly grasp changes in an individual's emotional state and effectively provide feedback to caregivers.

[0531] 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.

[0532] In this invention, the server includes means for acquiring user voice data, means for analyzing emotions from the voice data, means for acquiring and analyzing user facial expression data, and means for presenting the user's emotional state to the caregiver. This enables real-time evaluation of the user's emotional state, allowing caregivers to quickly and effectively support the mental health care of the elderly.

[0533] "User" refers to an individual or user who uses the system.

[0534] "Means of acquiring voice data" refers to the devices and processes used to record the user's voice.

[0535] "Methods for analyzing emotions" refer to technologies and algorithms that evaluate a person's emotional state based on voice and facial expression data.

[0536] "Means of acquiring facial expression data" refers to devices and processes for capturing a user's face with a camera or similar device and collecting the data as image data.

[0537] "Means for evaluating stress levels" refers to technologies and methods for measuring how much stress a user is experiencing, based on analyzed emotional data.

[0538] "Means of selecting relaxation methods" refers to the process or system for determining the most suitable relaxation method according to the user's stress level.

[0539] "Means of providing relaxation methods" refers to devices or techniques that present selected relaxation methods to users in an actionable format.

[0540] "Means of receiving feedback" refers to the process or method for users to communicate their opinions and impressions about the effectiveness of relaxation techniques to the system.

[0541] "Optimization methods" refer to ways of adjusting future relaxation methods to better suit users, based on user feedback.

[0542] "Means of presenting emotional status to caregivers" refers to methods of displaying or notifying caregivers of analyzed user emotional data in a way that is easy for them to understand.

[0543] This invention is a system that analyzes user emotions and supports stress management. It mainly consists of terminals and servers, which work together in cooperation.

[0544] The server is equipped with programs for processing voice and facial expression data. Specifically, it utilizes the open-source facial recognition library "dlib" and the "Google Cloud Speech-to-Text API" to precisely analyze the user's emotions from their voice and facial expressions. It analyzes tone and speech rate from the voice data and reads the movement of facial muscles from the facial expression data. This makes it possible to understand the user's emotions in real time.

[0545] The device uses a wearable device, such as smart glasses, to capture the user's voice and facial expressions. The data acquired is encrypted and sent to a server. Based on the analyzed data, the device controls and provides the user with selected relaxation methods. These relaxation methods include playing background music and providing real-time notifications to caregivers.

[0546] Users can use the system in their daily lives. For example, they can activate the system while taking a walk and enjoy music that suits their mood that day. If the user's mental health changes, the device will notify their caregiver, allowing for immediate adjustments to care.

[0547] A concrete example of a prompt using a generative AI model is: "How can the system automatically suggest relaxing music when an elderly person is experiencing stress in their daily life?" By using prompts like this, it is possible to provide the most suitable relaxation method for each individual user.

[0548] This system allows for proactive management of the mental health of elderly individuals in care settings, enabling caregivers to provide more effective support.

[0549] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0550] Step 1:

[0551] The device acquires voice and facial expression data from the user. The user's voice is collected via a voice input device, and their facial expressions are captured using a camera. This data is encrypted as input and sent to the server. The output consists of encrypted voice and facial expression data.

[0552] Step 2:

[0553] The server converts the received audio data into text using the Google Cloud Speech-to-Text API and analyzes the tone and speaking speed. This analysis allows the server to infer the user's emotional state. The input is encrypted audio data, and the output is analyzed audio tone and speaking speed data.

[0554] Step 3:

[0555] The server uses "dlib" to extract features from facial expression data and determine the user's emotions. It analyzes the movement of facial muscles to clarify the characteristics of emotions. The input is encrypted facial expression data, and the output is the analyzed facial features.

[0556] Step 4:

[0557] The server integrates the results of voice and facial expression analysis to comprehensively evaluate the emotional state and calculate the stress level. This calculation is performed using ambiguous inference and AI models. The input is the analyzed voice tone, speech rate, and facial features, and the output is the estimated stress level.

[0558] Step 5:

[0559] The server selects the optimal relaxation method based on the user's stress level, taking into account their past feedback. This selection utilizes a generative AI model. The input is the estimated stress level and past feedback, while the output is the selected relaxation method.

[0560] Step 6:

[0561] The device performs actions such as playing music or sending notifications to caregivers based on the selected relaxation method. The device provides an interface for receiving appropriate feedback. The input is the relaxation method sent from the server, and the output is the specific relaxation method provided to the user.

[0562] Step 7:

[0563] The user provides feedback on the relaxation method offered, and the device sends this information to the server. This feedback is used to optimize the next relaxation method. The input is the user's feedback, and the output is the feedback data from the device to the server.

[0564] 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.

[0565] This invention provides a system that incorporates an emotion engine utilizing the user's voice and facial expression data to precisely recognize the user's emotions and propose relaxation methods that lead to stress reduction. Specific embodiments are described below.

[0566] System Configuration

[0567] The system consists of a terminal, a server, and an emotion engine. The terminal is responsible for collecting data from the user and sending it to the server and emotion engine. The server works with the emotion engine to analyze the data and select a relaxation method.

[0568] Operation Description

[0569] The device acquires the user's voice and facial expression data. It captures the data in real time and sends it to the server in an encrypted state. Simultaneously, it sends voice tone and facial features to the emotion engine.

[0570] The emotion engine analyzes the user's emotional state using voice and facial expression data. It analyzes tone and rhythm in voice, and facial movements and microexpressions in facial expressions, to comprehensively determine the user's emotions. Furthermore, this analysis is performed in real time, allowing for immediate recognition of emotional changes.

[0571] The server assesses the user's stress level based on information from the emotion engine. This assessment also takes into account past stress data and feedback to make a more accurate judgment. Next, it selects an appropriate relaxation method (music, meditation, breathing exercises, etc.) and prepares it for delivery.

[0572] The device provides the user with selected relaxation techniques, delivered through voice guidance and a visual interface. The user reduces their own stress by accepting and implementing these techniques.

[0573] Specific example

[0574] For example, suppose a user is under pressure from a sudden work deadline, and a tense voice tone and rigid facial expression are detected. The emotion engine recognizes this as a high-stress emotional state and notifies the server. Based on this information, the server selects a relaxation audio guide that encourages deep breathing. The device immediately provides this guide to the user, allowing the user to reduce stress by repeatedly taking deep breaths.

[0575] The following describes the processing flow.

[0576] Step 1:

[0577] The device uses a microphone and camera to capture the user's voice and facial expressions in real time. The voice data includes the tone of the user's voice, and the facial expression data captures the user's facial movements.

[0578] Step 2:

[0579] The device encrypts the captured audio and facial expression data and sends it to the server. A secure communication protocol is used to ensure data privacy.

[0580] Step 3:

[0581] The server uses an emotion engine to analyze the tone and rhythm of the received audio data, thereby identifying the emotions derived from the speech.

[0582] Step 4:

[0583] The server also analyzes facial expression data via an emotion engine, determining emotions by analyzing subtle facial movements and changes in expression. This determination is then integrated with emotion data obtained from speech.

[0584] Step 5:

[0585] The server assesses the user's stress level based on emotional data integrated from voice and facial expressions. Past user data and feedback are also used in this assessment.

[0586] Step 6:

[0587] The server selects an appropriate relaxation method, taking into account the stress level already assessed. Options include music, meditation guidance, and deep breathing exercises.

[0588] Step 7:

[0589] The device presents the user with selected relaxation methods and encourages them to perform them. This is done by providing information to the user through a voice assistant and a display screen.

[0590] Step 8:

[0591] Users practice the provided relaxation techniques and provide feedback on their experience and impressions via their device.

[0592] Step 9:

[0593] The server analyzes user feedback and updates its database to make the next relaxation method suggestions more suitable for the user.

[0594] (Example 2)

[0595] 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."

[0596] In modern society, many people are exposed to various forms of stress, which negatively impacts their health and quality of life. However, emotional states and stress levels differ among individuals, and uniform stress relief measures are often insufficient. Therefore, there is a need for a system that analyzes users' emotional states in real time and provides optimal stress relief measures accordingly.

[0597] 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.

[0598] In this invention, the server includes means for acquiring user voice information and facial expression information, means for analyzing the user's emotional state in real time from the acquired information, and means for evaluating the stress level based on the analysis results and selecting appropriate mental and physical relief measures. This makes it possible to provide mental and physical relief measures optimized for each individual user and to reduce stress more effectively.

[0599] "Audio information" refers to data acquired through voice, including the tone and rhythm of the user's voice.

[0600] "Facial expression information" refers to data obtained through the user's face, including facial movements and micro-expressions.

[0601] "Emotional state" refers to the user's current mental and psychological state, and is analyzed based on voice and facial expression information.

[0602] "Stress level" refers to a state that indicates the degree and nature of stress experienced by the user, and is evaluated based on the results of an analysis of their emotional state.

[0603] "Mental and physical relaxation measures" refer to measures and methods aimed at reducing user stress and promoting mental calmness and relaxation.

[0604] "Real-time" refers to a time frame in which data acquisition and analysis are performed immediately, enabling a quick response to the user's state.

[0605] "Analysis results" refer to the results of the emotion engine's assessment of the user's emotional state and stress level, based on the acquired audio and facial expression information.

[0606] "Means of selection" refers to the process and methods for selecting the most suitable mental and physical relief measures based on the analysis results.

[0607] This system collects user voice and facial expression information in real time, analyzes their emotional state, and provides optimal mental and physical relief measures based on their stress levels. The following outlines the specific implementation of this system.

[0608] The device is equipped with a microphone to capture the user's voice information and a camera to capture facial expression information. This allows for real-time capture of the user's voice tone and changes in facial expression, which are then transmitted to a server and emotion analysis device. The collected data is sent to the server in an encrypted format to protect the user's privacy.

[0609] The server uses an emotion analysis device to analyze the acquired voice and facial expression information. This analysis includes various data processing techniques to more accurately determine the user's emotions, such as tone, rhythm, and facial muscle movements. This data is then analyzed using a generative AI model to identify the user's emotional state.

[0610] Based on the analysis results, the server evaluates the user's stress level and selects appropriate mental and physical relaxation measures from a pre-programmed database. These measures may include playing meditation music or providing breathing exercises, tailored to the user's preferences and past experiences.

[0611] The device provides users with selected mental and physical relaxation measures. Specifically, it supports users in reducing stress through voice guidance and visual interfaces utilizing speakers and displays. User feedback is also collected during this process and used to optimize future relaxation measures.

[0612] For example, if a user is nervous before a presentation, the device detects this state from the user's rapid breathing and eyebrow movements. The server analyzes this using a generated AI model and selects a deep breathing guide. Following the audio guidance from the device, the user can relax.

[0613] An example of a prompt message would be, "Analyze the voice tone and facial expression data and suggest a relaxation method suitable for a user experiencing stress." In this way, the system provides optimized support tailored to the user's current emotional state.

[0614] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0615] Step 1:

[0616] The device acquires the user's voice and facial expression information. Specifically, it captures voice in real time using a microphone and monitors facial expressions using a camera. It acquires raw audio and video data as input, encrypts it, and prepares it for transmission to the server. This process includes encryption operations to ensure data privacy.

[0617] Step 2:

[0618] The terminal transmits acquired voice and facial expression information to the server in real time. The input here is encrypted voice and video data, and the output is data transmission via a secure communication channel. The data is transferred securely using a security protocol (e.g., HTTPS).

[0619] Step 3:

[0620] The server decrypts the received encrypted data and provides it to the emotion analysis device. The encrypted data, as input, is converted back into the original voice and facial expression information using a decryption algorithm and passed to the emotion analysis device. Data decryption and verification are performed during this process.

[0621] Step 4:

[0622] The server uses an emotion analysis device to analyze the user's voice and facial expressions. The analysis utilizes a generative AI model to analyze the tone and rhythm of the input voice and the movement of facial muscles to identify the user's emotional state. The output is the analysis result indicating the user's emotional state.

[0623] Step 5:

[0624] The server evaluates the user's stress level based on the analysis results. Past data is also considered, and the results of the emotion analysis are used as input. Data calculations are performed, and an evaluation result showing the user's stress level as a numerical value or category is output.

[0625] Step 6:

[0626] The server selects the most appropriate mental and physical relief measures based on the stress level assessment. It takes the stress assessment results as input and selects appropriate relief measures from a database. This process uses an algorithm that considers the user's preferences and past performance to select specific relief measures as output.

[0627] Step 7:

[0628] The device provides users with selected mental and physical relaxation techniques. The input is the selected relaxation technique, and based on this, it provides users with specific methods through voice guidance and a visual interface. Specifically, guided voices are played from the speaker, and visuals of meditation and breathing exercises are displayed on the screen.

[0629] (Application Example 2)

[0630] 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."

[0631] Conventional stress reduction systems often struggle to accurately recognize users' emotions, resulting in relaxation techniques that don't always align with the user's actual state. Furthermore, in home environments, there's a need for autonomous devices that can understand the user's emotional state in real time through natural dialogue and provide appropriate support.

[0632] 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.

[0633] In this invention, the server includes means for acquiring user voice data, means for analyzing the user's emotions from the acquired voice data, and means for acquiring user facial expression data. This makes it possible to accurately grasp the user's emotions and provide appropriate relaxation methods in real time according to each individual's stress level.

[0634] "Voice data" refers to audio information obtained from users and is used for sentiment analysis.

[0635] "Facial expression data" refers to information that represents the movements and expressions of a user's face, and is used to analyze their emotional state.

[0636] "Means of analyzing emotions" refers to processes and technologies for identifying and evaluating a user's emotional state using the user's voice data and facial expression data.

[0637] A "means for evaluating stress levels" refers to a system that determines the degree of stress a user is experiencing based on analyzed emotional data.

[0638] "Relaxation techniques" refer to a series of methods and techniques proposed to reduce user stress.

[0639] "Feedback" refers to information such as comments and evaluations that users provide regarding the relaxation methods offered.

[0640] An "autonomous mobile device equipped with artificial intelligence" refers to an autonomous mechanical device that operates spontaneously through interaction with and support from the user, and provides voice and visual assistance.

[0641] This invention is a system that precisely analyzes emotions using the user's voice and facial expression data and proposes relaxation methods tailored to their individual stress levels. The specific implementation of this system will now be described.

[0642] Hardware configuration

[0643] The user's device is equipped with a microphone and camera. This device has the capability to acquire the user's voice and facial data in real time. It also includes an autonomous mobile device with artificial intelligence that perceives the user's surroundings and provides voice and visual feedback. The server houses a processor that receives and analyzes voice and facial data, and is equipped with an emotion engine.

[0644] Software Configuration

[0645] The emotion engine implements algorithms that analyze the user's voice tone and facial features. This allows for accurate determination of emotional state and assessment of the user's stress level. Based on the analysis results, the server selects the most suitable relaxation method and sends instructions to the user's device.

[0646] Examples

[0647] When a user speaks to you while working in the living room, if their voice sounds tense or their facial expression shows signs of fatigue, the emotional engine recognizes this as a high-stress state. The system then recommends appropriate meditation techniques and music, and an AI-powered autonomous mobile device creates a relaxing environment with soft lighting and guides you through relaxation methods with a gentle voice.

[0648] Example of a prompt

[0649] "Please tell me about the design of a life support robot that can analyze a user's emotions in real time based on their voice and facial expression data, and suggest relaxation methods that help reduce stress."

[0650] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0651] Step 1:

[0652] The device acquires the user's voice and facial expression data in real time. Voice data is collected via a microphone, and facial expression data is captured via a camera. It receives voice and image data as input, encrypts them, and sends them to the server.

[0653] Step 2:

[0654] The server receives audio and facial expression data transmitted from the terminal and decodes each for analysis. From the decoded audio data, speech tone and rhythm information are extracted, and from the decoded facial expression data, facial features are extracted. This data is then input into the emotion engine.

[0655] Step 3:

[0656] The emotion engine analyzes the user's emotional state based on extracted voice tone, rhythm, and facial features. Based on the analysis, the emotion engine outputs the user's current emotions and stress level. This provides a foundation for determining what kind of relaxation method is needed.

[0657] Step 4:

[0658] The server selects the optimal relaxation method from the database based on the user's emotional state and stress level obtained from the emotion engine. It then generates information about the selected relaxation method and converts it into a format for transmission to the terminal.

[0659] Step 5:

[0660] The device receives information about relaxation techniques transmitted from the server and prepares to provide it to the user. Specifically, it presents the user with details of the selected relaxation technique through audio guidance and a visual interface.

[0661] Step 6:

[0662] Users perform the suggested relaxation techniques and provide feedback on their impressions and the effects to the system via their device. This feedback is collected and processed to optimize future suggestions.

[0663] 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.

[0664] 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.

[0665] 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.

[0666] [Fourth Embodiment]

[0667] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0668] 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.

[0669] 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).

[0670] 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.

[0671] 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.

[0672] 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).

[0673] 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.

[0674] 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.

[0675] 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.

[0676] 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.

[0677] 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.

[0678] 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.

[0679] 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".

[0680] The present invention's system collects user voice and facial expression data, analyzes emotions based on this data, and proposes an appropriate relaxation method. The implemented form of the system is described below.

[0681] System Configuration

[0682] This system consists of two main components: a terminal and a server. The terminal is primarily responsible for data collection and providing feedback to the user, while the server is responsible for data analysis and selecting relaxation methods. Users utilize the services provided by the system to manage their stress.

[0683] Operation Description

[0684] The device functions as an interface for interacting with the user. Equipped with a voice input device and a camera, it captures the user's voice and facial expressions in real time. This data is encrypted to ensure user privacy before being sent to the server.

[0685] The server analyzes the voice tone from the received audio data to determine the user's emotions. In addition, it uses facial recognition technology to identify emotions from facial expression data. This allows for a comprehensive evaluation of the user's emotional state.

[0686] Based on these evaluation results, the server estimates the user's stress level and selects the optimal relaxation method. This selection also takes into account feedback previously provided by the user, allowing for suggestions tailored to individual needs.

[0687] The device implements specific relaxation methods for the user, such as playing music and providing meditation guidance, based on the selected relaxation technique. Through these relaxation methods, the user can reduce stress.

[0688] Specific example

[0689] For example, the system is activated when a user is experiencing stress at work. The device detects that the user's voice tone is low, their speaking speed is slow, and their facial expression is tense. Based on this data, the server determines that the user's stress levels are high. The device then starts playing relaxing music in the background and suggests deep breathing exercises to the user. By following these instructions, the user can calm down and reduce stress.

[0690] The following describes the processing flow.

[0691] Step 1:

[0692] The device records the user's voice and captures their facial expressions with its camera. The acquired data is encrypted for security purposes.

[0693] Step 2:

[0694] The device sends encrypted audio data and facial expression data to the server.

[0695] Step 3:

[0696] The server analyzes the received audio data. It applies an audio analysis algorithm to analyze the tone and rhythm of the voice and estimate the emotion from it.

[0697] Step 4:

[0698] The server analyzes facial data using a facial recognition algorithm and identifies emotional states from facial movements.

[0699] Step 5:

[0700] The server integrates emotional data from voice and facial expressions and compares it with past data to assess the overall stress level.

[0701] Step 6:

[0702] The server selects an appropriate relaxation method based on the user's stress level. This selection takes into account the suggested relaxation options and the user's past feedback.

[0703] Step 7:

[0704] The device notifies the user of the selected relaxation method and initiates specific actions such as playing music or providing meditation instructions.

[0705] Step 8:

[0706] Users experience the provided relaxation methods and provide feedback on their effects.

[0707] Step 9:

[0708] The server collects user feedback and uses it to optimize the selection of relaxation methods in the future.

[0709] (Example 1)

[0710] 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".

[0711] Accurately understanding a user's emotional state and proposing appropriate relaxation techniques tailored to their individual circumstances is not yet fully achieved with current technology. Existing systems are limited to superficial data such as voice and facial expressions, making it difficult to comprehensively evaluate the stress and tension a user experiences. This invention aims to overcome these challenges and provide more accurate emotional analysis and individually optimized relaxation techniques.

[0712] 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.

[0713] In this invention, the server includes means for acquiring the user's voice signal, means for analyzing the user's emotions from the voice signal, and means for analyzing the user's emotions from facial expression information. This makes it possible to accurately assess the user's state of tension and propose appropriate relaxation techniques based on the results.

[0714] An "audio signal" is a digital or analog signal obtained from a user, including characteristics such as frequency, volume, and tone of the voice.

[0715] "Emotion" refers to the user's emotional state or mental response, which often means a psychological state inferred from external cues such as voice and facial expressions.

[0716] "Facial expression information" refers to data that captures the movements and characteristics of a user's face, providing visual clues for understanding their emotions and attitudes.

[0717] "State of tension" refers to the psychological and physiological state, such as stress and anxiety, that the user experiences, and this is particularly evaluated through emotion analysis.

[0718] "Relaxation techniques" refer to specific approaches and methods offered to users with the aim of reducing stress and promoting mental and physical relaxation.

[0719] "Response" refers to data showing user feedback and physiological / psychological changes after receiving relaxation techniques.

[0720] "Adaptation" refers to the process of adjusting the relaxation techniques offered next time based on user feedback, making them more suitable for individual needs.

[0721] The present invention will now be described in terms of embodiments. This system analyzes the user's voice and facial expression data and provides a relaxation technique that is optimally suited to each individual. Its specific configuration and operation are shown below.

[0722] The device is responsible for capturing the user's voice and facial expressions using an audio input device and a camera. A standard microphone is used for audio input, enabling the acquisition of high-quality audio signals. The camera is used to capture the user's facial movements, continuously capturing facial information. This acquired data is processed using encryption technology to protect the user's privacy.

[0723] The server analyzes the voice and facial expression data transmitted from the terminal. Speech recognition software is used to analyze the voice signal and evaluate the user's emotions. For example, emotional states can be estimated from the tone, speed, and volume of the voice. Facial expression information is processed using image analysis software to extract the user's facial characteristics and make a specific emotional judgment.

[0724] Based on the analyzed emotional data, the server uses a generative AI model to assess the user's tension level and select appropriate relaxation techniques. By taking into account the user's past usage history and feedback, more personalized suggestions can be provided. These suggestions may be delivered, for example, as playback of specific relaxation music through a music playback application or as guidance using a meditation application.

[0725] The device provides the user with a selected relaxation technique, thereby allowing the user to alleviate tension. In one example, when the user activates the system, the device analyzes the user's voice and facial expressions and initiates a relaxation technique accordingly. An example of a prompt message would be, "Design a system that analyzes voice tone and facial expression data to suggest an appropriate relaxation method in real time."

[0726] In this way, the present invention aims to support the psychological health of users by comprehensively analyzing the stress and tension they experience and providing relaxation techniques tailored to each individual user.

[0727] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0728] Step 1:

[0729] The user activates the system. This causes the terminal to simultaneously activate the voice input device and camera, and begin collecting data on the user's voice and facial expressions. Real-time voice and facial images of the user are provided as input. The terminal encrypts this data and prepares it for transmission. Voice signals and facial expression information are obtained as the output of the process.

[0730] Step 2:

[0731] The terminal transmits encrypted voice and facial expression data to the server using a secure communication protocol. The input is encrypted data from the terminal, which the server receives and decrypts. As a result, the voice signal and facial expression information are output in preparation for analysis on the server.

[0732] Step 3:

[0733] The server analyzes the received audio signal using speech recognition software to evaluate speech tone, speed, and volume. The input is an audio signal, and data processing includes extraction of acoustic features and emotion estimation. The output is the result of the emotion evaluation based on the speech.

[0734] Step 4:

[0735] The server analyzes the received facial expression information using image analysis software. The input is the user's facial expression information, and as a specific action, it analyzes facial features and changes in the eyes and mouth. This yields the results of an emotional analysis based on facial expressions.

[0736] Step 5:

[0737] The server combines emotional assessments obtained from voice and facial expressions, and uses a generative AI model to evaluate the user's stress level. Emotional assessments of voice and facial expressions are used as input, and the stress state is estimated through data calculation. The output is an evaluation result indicating the user's level of tension.

[0738] Step 6:

[0739] The server selects the optimal relaxation technique based on the stress assessment. It considers past feedback and history to choose a technique tailored to individual needs. Inputs are stress assessment and historical data, and output is instructions for the selected relaxation technique.

[0740] Step 7:

[0741] The terminal executes selected relaxation techniques according to instructions from the server. Specifically, this may involve launching applications such as music playback or meditation guidance. The input is instructions from the server, and the output is the relaxation technique that is executed.

[0742] Step 8:

[0743] Users experience the provided relaxation techniques and input feedback on their effects into the device. This feedback is important for improving future suggestions. The input is the result of experiencing the relaxation technique and is output as feedback.

[0744] (Application Example 1)

[0745] 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".

[0746] In mental healthcare for the elderly, there is a need to appropriately assess emotional changes in real time and provide relaxation methods based on individual needs. However, conventional systems have the challenge of not being able to quickly grasp changes in an individual's emotional state and effectively provide feedback to caregivers.

[0747] 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.

[0748] In this invention, the server includes means for acquiring user voice data, means for analyzing emotions from the voice data, means for acquiring and analyzing user facial expression data, and means for presenting the user's emotional state to the caregiver. This enables real-time evaluation of the user's emotional state, allowing caregivers to quickly and effectively support the mental health care of the elderly.

[0749] "User" refers to an individual or user who uses the system.

[0750] "Means of acquiring voice data" refers to the devices and processes used to record the user's voice.

[0751] "Methods for analyzing emotions" refer to technologies and algorithms that evaluate a person's emotional state based on voice and facial expression data.

[0752] "Means of acquiring facial expression data" refers to devices and processes for capturing a user's face with a camera or similar device and collecting the data as image data.

[0753] "Means for evaluating stress levels" refers to technologies and methods for measuring how much stress a user is experiencing, based on analyzed emotional data.

[0754] "Means of selecting relaxation methods" refers to the process or system for determining the most suitable relaxation method according to the user's stress level.

[0755] "Means of providing relaxation methods" refers to devices or techniques that present selected relaxation methods to users in an actionable format.

[0756] "Means of receiving feedback" refers to the process or method for users to communicate their opinions and impressions about the effectiveness of relaxation techniques to the system.

[0757] "Optimization methods" refer to ways of adjusting future relaxation methods to better suit users, based on user feedback.

[0758] "Means of presenting emotional status to caregivers" refers to methods of displaying or notifying caregivers of analyzed user emotional data in a way that is easy for them to understand.

[0759] This invention is a system that analyzes user emotions and supports stress management. It mainly consists of terminals and servers, which work together in cooperation.

[0760] The server is equipped with programs for processing voice and facial expression data. Specifically, it utilizes the open-source facial recognition library "dlib" and the "Google Cloud Speech-to-Text API" to precisely analyze the user's emotions from their voice and facial expressions. It analyzes tone and speech rate from the voice data and reads the movement of facial muscles from the facial expression data. This makes it possible to understand the user's emotions in real time.

[0761] The device uses a wearable device, such as smart glasses, to capture the user's voice and facial expressions. The data acquired is encrypted and sent to a server. Based on the analyzed data, the device controls and provides the user with selected relaxation methods. These relaxation methods include playing background music and providing real-time notifications to caregivers.

[0762] Users can use the system in their daily lives. For example, they can activate the system while taking a walk and enjoy music that suits their mood that day. If the user's mental health changes, the device will notify their caregiver, allowing for immediate adjustments to care.

[0763] A concrete example of a prompt using a generative AI model is: "How can the system automatically suggest relaxing music when an elderly person is experiencing stress in their daily life?" By using prompts like this, it is possible to provide the most suitable relaxation method for each individual user.

[0764] This system allows for proactive management of the mental health of elderly individuals in care settings, enabling caregivers to provide more effective support.

[0765] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0766] Step 1:

[0767] The device acquires voice and facial expression data from the user. The user's voice is collected via a voice input device, and their facial expressions are captured using a camera. This data is encrypted as input and sent to the server. The output consists of encrypted voice and facial expression data.

[0768] Step 2:

[0769] The server converts the received audio data into text using the Google Cloud Speech-to-Text API and analyzes the tone and speaking speed. This analysis allows the server to infer the user's emotional state. The input is encrypted audio data, and the output is analyzed audio tone and speaking speed data.

[0770] Step 3:

[0771] The server uses "dlib" to extract features from facial expression data and determine the user's emotions. It analyzes the movement of facial muscles to clarify the characteristics of emotions. The input is encrypted facial expression data, and the output is the analyzed facial features.

[0772] Step 4:

[0773] The server integrates the results of voice and facial expression analysis to comprehensively evaluate the emotional state and calculate the stress level. This calculation is performed using ambiguous inference and AI models. The input is the analyzed voice tone, speech rate, and facial features, and the output is the estimated stress level.

[0774] Step 5:

[0775] The server selects the optimal relaxation method based on the user's stress level, taking into account their past feedback. This selection utilizes a generative AI model. The input is the estimated stress level and past feedback, while the output is the selected relaxation method.

[0776] Step 6:

[0777] The device performs actions such as playing music or sending notifications to caregivers based on the selected relaxation method. The device provides an interface for receiving appropriate feedback. The input is the relaxation method sent from the server, and the output is the specific relaxation method provided to the user.

[0778] Step 7:

[0779] The user provides feedback on the relaxation method offered, and the device sends this information to the server. This feedback is used to optimize the next relaxation method. The input is the user's feedback, and the output is the feedback data from the device to the server.

[0780] 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.

[0781] This invention provides a system that incorporates an emotion engine utilizing the user's voice and facial expression data to precisely recognize the user's emotions and propose relaxation methods that lead to stress reduction. Specific embodiments are described below.

[0782] System Configuration

[0783] The system consists of a terminal, a server, and an emotion engine. The terminal is responsible for collecting data from the user and sending it to the server and emotion engine. The server works with the emotion engine to analyze the data and select a relaxation method.

[0784] Operation Description

[0785] The device acquires the user's voice and facial expression data. It captures the data in real time and sends it to the server in an encrypted state. Simultaneously, it sends voice tone and facial features to the emotion engine.

[0786] The emotion engine analyzes the user's emotional state using voice and facial expression data. It analyzes tone and rhythm in voice, and facial movements and microexpressions in facial expressions, to comprehensively determine the user's emotions. Furthermore, this analysis is performed in real time, allowing for immediate recognition of emotional changes.

[0787] The server assesses the user's stress level based on information from the emotion engine. This assessment also takes into account past stress data and feedback to make a more accurate judgment. Next, it selects an appropriate relaxation method (music, meditation, breathing exercises, etc.) and prepares it for delivery.

[0788] The device provides the user with selected relaxation techniques, delivered through voice guidance and a visual interface. The user reduces their own stress by accepting and implementing these techniques.

[0789] Specific example

[0790] For example, suppose a user is under pressure from a sudden work deadline, and a tense voice tone and rigid facial expression are detected. The emotion engine recognizes this as a high-stress emotional state and notifies the server. Based on this information, the server selects a relaxation audio guide that encourages deep breathing. The device immediately provides this guide to the user, allowing the user to reduce stress by repeatedly taking deep breaths.

[0791] The following describes the processing flow.

[0792] Step 1:

[0793] The device uses a microphone and camera to capture the user's voice and facial expressions in real time. The voice data includes the tone of the user's voice, and the facial expression data captures the user's facial movements.

[0794] Step 2:

[0795] The device encrypts the captured audio and facial expression data and sends it to the server. A secure communication protocol is used to ensure data privacy.

[0796] Step 3:

[0797] The server uses an emotion engine to analyze the tone and rhythm of the received audio data, thereby identifying the emotions derived from the speech.

[0798] Step 4:

[0799] The server also analyzes facial expression data via an emotion engine, determining emotions by analyzing subtle facial movements and changes in expression. This determination is then integrated with emotion data obtained from speech.

[0800] Step 5:

[0801] The server assesses the user's stress level based on emotional data integrated from voice and facial expressions. Past user data and feedback are also used in this assessment.

[0802] Step 6:

[0803] The server selects an appropriate relaxation method, taking into account the stress level already assessed. Options include music, meditation guidance, and deep breathing exercises.

[0804] Step 7:

[0805] The device presents the user with selected relaxation methods and encourages them to perform them. This is done by providing information to the user through a voice assistant and a display screen.

[0806] Step 8:

[0807] Users practice the provided relaxation techniques and provide feedback on their experience and impressions via their device.

[0808] Step 9:

[0809] The server analyzes user feedback and updates its database to make the next relaxation method suggestions more suitable for the user.

[0810] (Example 2)

[0811] 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".

[0812] In modern society, many people are exposed to various forms of stress, which negatively impacts their health and quality of life. However, emotional states and stress levels differ among individuals, and uniform stress relief measures are often insufficient. Therefore, there is a need for a system that analyzes users' emotional states in real time and provides optimal stress relief measures accordingly.

[0813] 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.

[0814] In this invention, the server includes means for acquiring user voice information and facial expression information, means for analyzing the user's emotional state in real time from the acquired information, and means for evaluating the stress level based on the analysis results and selecting appropriate mental and physical relief measures. This makes it possible to provide mental and physical relief measures optimized for each individual user and to reduce stress more effectively.

[0815] "Audio information" refers to data acquired through voice, including the tone and rhythm of the user's voice.

[0816] "Facial expression information" refers to data obtained through the user's face, including facial movements and micro-expressions.

[0817] "Emotional state" refers to the user's current mental and psychological state, and is analyzed based on voice and facial expression information.

[0818] "Stress level" refers to a state that indicates the degree and nature of stress experienced by the user, and is evaluated based on the results of an analysis of their emotional state.

[0819] "Mental and physical relaxation measures" refer to measures and methods aimed at reducing user stress and promoting mental calmness and relaxation.

[0820] "Real-time" refers to a time frame in which data acquisition and analysis are performed immediately, enabling a quick response to the user's state.

[0821] "Analysis results" refer to the results of the emotion engine's assessment of the user's emotional state and stress level, based on the acquired audio and facial expression information.

[0822] "Means of selection" refers to the process and methods for selecting the most suitable mental and physical relief measures based on the analysis results.

[0823] This system collects user voice and facial expression information in real time, analyzes their emotional state, and provides optimal mental and physical relief measures based on their stress levels. The following outlines the specific implementation of this system.

[0824] The device is equipped with a microphone to capture the user's voice information and a camera to capture facial expression information. This allows for real-time capture of the user's voice tone and changes in facial expression, which are then transmitted to a server and emotion analysis device. The collected data is sent to the server in an encrypted format to protect the user's privacy.

[0825] The server uses an emotion analysis device to analyze the acquired voice and facial expression information. This analysis includes various data processing techniques to more accurately determine the user's emotions, such as tone, rhythm, and facial muscle movements. This data is then analyzed using a generative AI model to identify the user's emotional state.

[0826] Based on the analysis results, the server evaluates the user's stress level and selects appropriate mental and physical relaxation measures from a pre-programmed database. These measures may include playing meditation music or providing breathing exercises, tailored to the user's preferences and past experiences.

[0827] The device provides users with selected mental and physical relaxation measures. Specifically, it supports users in reducing stress through voice guidance and visual interfaces utilizing speakers and displays. User feedback is also collected during this process and used to optimize future relaxation measures.

[0828] For example, if a user is nervous before a presentation, the device detects this state from the user's rapid breathing and eyebrow movements. The server analyzes this using a generated AI model and selects a deep breathing guide. Following the audio guidance from the device, the user can relax.

[0829] An example of a prompt message would be, "Analyze the voice tone and facial expression data and suggest a relaxation method suitable for a user experiencing stress." In this way, the system provides optimized support tailored to the user's current emotional state.

[0830] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0831] Step 1:

[0832] The device acquires the user's voice and facial expression information. Specifically, it captures voice in real time using a microphone and monitors facial expressions using a camera. It acquires raw audio and video data as input, encrypts it, and prepares it for transmission to the server. This process includes encryption operations to ensure data privacy.

[0833] Step 2:

[0834] The terminal transmits acquired voice and facial expression information to the server in real time. The input here is encrypted voice and video data, and the output is data transmission via a secure communication channel. The data is transferred securely using a security protocol (e.g., HTTPS).

[0835] Step 3:

[0836] The server decrypts the received encrypted data and provides it to the emotion analysis device. The encrypted data, as input, is converted back into the original voice and facial expression information using a decryption algorithm and passed to the emotion analysis device. Data decryption and verification are performed during this process.

[0837] Step 4:

[0838] The server uses an emotion analysis device to analyze the user's voice and facial expressions. The analysis utilizes a generative AI model to analyze the tone and rhythm of the input voice and the movement of facial muscles to identify the user's emotional state. The output is the analysis result indicating the user's emotional state.

[0839] Step 5:

[0840] The server evaluates the user's stress level based on the analysis results. Past data is also considered, and the results of the emotion analysis are used as input. Data calculations are performed, and an evaluation result showing the user's stress level as a numerical value or category is output.

[0841] Step 6:

[0842] The server selects the most appropriate mental and physical relief measures based on the stress level assessment. It takes the stress assessment results as input and selects appropriate relief measures from a database. This process uses an algorithm that considers the user's preferences and past performance to select specific relief measures as output.

[0843] Step 7:

[0844] The device provides users with selected mental and physical relaxation techniques. The input is the selected relaxation technique, and based on this, it provides users with specific methods through voice guidance and a visual interface. Specifically, guided voices are played from the speaker, and visuals of meditation and breathing exercises are displayed on the screen.

[0845] (Application Example 2)

[0846] 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".

[0847] Conventional stress reduction systems often struggle to accurately recognize users' emotions, resulting in relaxation techniques that don't always align with the user's actual state. Furthermore, in home environments, there's a need for autonomous devices that can understand the user's emotional state in real time through natural dialogue and provide appropriate support.

[0848] 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.

[0849] In this invention, the server includes means for acquiring user voice data, means for analyzing the user's emotions from the acquired voice data, and means for acquiring user facial expression data. This makes it possible to accurately grasp the user's emotions and provide appropriate relaxation methods in real time according to each individual's stress level.

[0850] "Voice data" refers to audio information obtained from users and is used for sentiment analysis.

[0851] "Facial expression data" refers to information that represents the movements and expressions of a user's face, and is used to analyze their emotional state.

[0852] "Means of analyzing emotions" refers to processes and technologies for identifying and evaluating a user's emotional state using the user's voice data and facial expression data.

[0853] A "means for evaluating stress levels" refers to a system that determines the degree of stress a user is experiencing based on analyzed emotional data.

[0854] "Relaxation techniques" refer to a series of methods and techniques proposed to reduce user stress.

[0855] "Feedback" refers to information such as comments and evaluations that users provide regarding the relaxation methods offered.

[0856] An "autonomous mobile device equipped with artificial intelligence" refers to an autonomous mechanical device that operates spontaneously through interaction with and support from the user, and provides voice and visual assistance.

[0857] This invention is a system that precisely analyzes emotions using the user's voice and facial expression data and proposes relaxation methods tailored to their individual stress levels. The specific implementation of this system will now be described.

[0858] Hardware configuration

[0859] The user's device is equipped with a microphone and camera. This device has the capability to acquire the user's voice and facial data in real time. It also includes an autonomous mobile device with artificial intelligence that perceives the user's surroundings and provides voice and visual feedback. The server houses a processor that receives and analyzes voice and facial data, and is equipped with an emotion engine.

[0860] Software Configuration

[0861] The emotion engine implements algorithms that analyze the user's voice tone and facial features. This allows for accurate determination of emotional state and assessment of the user's stress level. Based on the analysis results, the server selects the most suitable relaxation method and sends instructions to the user's device.

[0862] Examples

[0863] When a user speaks to you while working in the living room, if their voice sounds tense or their facial expression shows signs of fatigue, the emotional engine recognizes this as a high-stress state. The system then recommends appropriate meditation techniques and music, and an AI-powered autonomous mobile device creates a relaxing environment with soft lighting and guides you through relaxation methods with a gentle voice.

[0864] Example of a prompt

[0865] "Please tell me about the design of a life support robot that can analyze a user's emotions in real time based on their voice and facial expression data, and suggest relaxation methods that help reduce stress."

[0866] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0867] Step 1:

[0868] The device acquires the user's voice and facial expression data in real time. Voice data is collected via a microphone, and facial expression data is captured via a camera. It receives voice and image data as input, encrypts them, and sends them to the server.

[0869] Step 2:

[0870] The server receives audio and facial expression data transmitted from the terminal and decodes each for analysis. From the decoded audio data, speech tone and rhythm information are extracted, and from the decoded facial expression data, facial features are extracted. This data is then input into the emotion engine.

[0871] Step 3:

[0872] The emotion engine analyzes the user's emotional state based on extracted voice tone, rhythm, and facial features. Based on the analysis, the emotion engine outputs the user's current emotions and stress level. This provides a foundation for determining what kind of relaxation method is needed.

[0873] Step 4:

[0874] The server selects the optimal relaxation method from the database based on the user's emotional state and stress level obtained from the emotion engine. It then generates information about the selected relaxation method and converts it into a format for transmission to the terminal.

[0875] Step 5:

[0876] The device receives information about relaxation techniques transmitted from the server and prepares to provide it to the user. Specifically, it presents the user with details of the selected relaxation technique through audio guidance and a visual interface.

[0877] Step 6:

[0878] Users perform the suggested relaxation techniques and provide feedback on their impressions and the effects to the system via their device. This feedback is collected and processed to optimize future suggestions.

[0879] 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.

[0880] 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.

[0881] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0882] 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.

[0883] 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.

[0884] 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.

[0885] 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.

[0886] 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.

[0887] 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."

[0888] 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.

[0889] 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.

[0890] 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.

[0891] 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.

[0892] 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.

[0893] 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.

[0894] 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.

[0895] 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.

[0896] 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.

[0897] 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.

[0898] 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.

[0899] 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.

[0900] The following is further disclosed regarding the embodiments described above.

[0901] (Claim 1)

[0902] Means for obtaining user voice data,

[0903] A method for analyzing user emotions from acquired audio data,

[0904] A means of acquiring user facial expression data,

[0905] A method for analyzing user emotions from acquired facial expression data,

[0906] A means of evaluating a user's stress level based on analyzed emotional data,

[0907] A means of selecting relaxation methods based on stress levels,

[0908] Means of providing users with selected relaxation methods,

[0909] A means of receiving feedback from users on relaxation methods,

[0910] A system that includes a means to optimize the next relaxation method based on feedback.

[0911] (Claim 2)

[0912] The system according to claim 1, further comprising means for analyzing the user's voice tone.

[0913] (Claim 3)

[0914] The system according to claim 1, further comprising means for extracting the user's facial features.

[0915] "Example 1"

[0916] (Claim 1)

[0917] A means of acquiring the user's voice signal,

[0918] A means of analyzing the user's emotions from acquired audio signals,

[0919] A means of acquiring user facial expression information,

[0920] A means of analyzing the user's emotions from acquired facial expression information,

[0921] A means for evaluating the user's tension level based on analyzed emotional data,

[0922] A means of selecting relaxation techniques based on the state of tension,

[0923] A means of providing users with selected relaxation techniques,

[0924] A means of receiving feedback from users regarding relaxation techniques,

[0925] A system that includes a means of adapting the next relaxation technique based on the response.

[0926] (Claim 2)

[0927] The system according to claim 1, further comprising means for analyzing the user's voice tone.

[0928] (Claim 3)

[0929] The system according to claim 1, further comprising means for extracting the user's facial expression characteristics.

[0930] "Application Example 1"

[0931] (Claim 1)

[0932] Means for obtaining user voice data,

[0933] A method for analyzing user emotions from acquired audio data,

[0934] A means of acquiring user facial expression data,

[0935] A method for analyzing user emotions from acquired facial expression data,

[0936] A means of evaluating a user's stress level based on analyzed emotional data,

[0937] A means of selecting relaxation methods based on stress levels,

[0938] Means of providing users with selected relaxation methods,

[0939] A means of receiving feedback from users on relaxation methods,

[0940] A means to optimize the next relaxation method based on feedback,

[0941] A system that includes means of presenting the user's emotional state to caregivers.

[0942] (Claim 2)

[0943] The system according to claim 1, further comprising means for analyzing the user's voice tone.

[0944] (Claim 3)

[0945] The system according to claim 1, further comprising means for extracting the user's facial features.

[0946] "Example 2 of combining an emotion engine"

[0947] (Claim 1)

[0948] Means for obtaining user voice information,

[0949] A means of analyzing the user's emotional state from acquired audio information,

[0950] A means of acquiring user facial expression information,

[0951] A means of analyzing the user's emotional state from acquired facial expression information,

[0952] A means of evaluating the user's stress level based on the analyzed emotional state,

[0953] A means of selecting mental and physical relief measures based on the stress level,

[0954] A means of providing users with selected mental and physical relief measures,

[0955] A means for transmitting voice information and facial expression information in real time to an emotion analysis device and notifying a server of the analysis results,

[0956] A means of collecting opinions from users on measures to alleviate physical and mental stress,

[0957] A system that includes means to optimize the next mental and physical relaxation measures based on feedback.

[0958] (Claim 2)

[0959] The system according to claim 1, further comprising means for analyzing the tone and rhythm of the user's voice.

[0960] (Claim 3)

[0961] The system according to claim 1, further comprising means for extracting the characteristics of the user's facial expressions and instantly recognizing changes in emotion.

[0962] "Application example 2 when combining with an emotional engine"

[0963] (Claim 1)

[0964] Means for obtaining user voice data,

[0965] A method for analyzing user emotions from acquired audio data,

[0966] A means of acquiring user facial expression data,

[0967] A method for analyzing user emotions from acquired facial expression data,

[0968] A means of evaluating a user's stress level based on analyzed emotional data,

[0969] A means of selecting relaxation methods based on stress levels,

[0970] Means of providing users with selected relaxation methods,

[0971] A means of receiving feedback from users on relaxation methods,

[0972] A means to optimize the next relaxation method based on feedback,

[0973] A system including an autonomous mobile device equipped with artificial intelligence that provides voice and visual assistance to perform the selected relaxation method.

[0974] (Claim 2)

[0975] The system according to claim 1, further comprising means for analyzing the user's voice tone.

[0976] (Claim 3)

[0977] The system according to claim 1, further comprising means for extracting the user's facial features. [Explanation of Symbols]

[0978] 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 for obtaining user voice data, A method for analyzing user emotions from acquired audio data, A means of acquiring user facial expression data, A method for analyzing user emotions from acquired facial expression data, A means of evaluating a user's stress level based on analyzed emotional data, A means of selecting relaxation methods based on stress levels, Means of providing users with selected relaxation methods, A means of receiving feedback from users on relaxation methods, A means to optimize the next relaxation method based on feedback, A system that includes means of presenting the user's emotional state to caregivers.

2. The system according to claim 1, further comprising means for analyzing the user's voice tone.

3. The system according to claim 1, further comprising means for extracting the user's facial features.

Images