system

Abstract

We provide the system. [Solution] A means of collecting biometric information, An analytical means for analyzing the user's emotional state based on collected biometric information, A means for generating visual information corresponding to the user's emotional state, A display means for displaying the generated visual information, A system that includes this.

Description

Technical Field

[0004] , , ,

[0005] , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, the mental health of many people is often damaged by stress and emotional instability. In such a situation, there is a need for a method to achieve mental stability by detecting the emotional state in real time through a device that users carry around daily and presenting appropriate visual information accordingly. However, with conventional methods, it has been difficult to immediately grasp the emotional state and appropriately provide visual information corresponding thereto.

Means for Solving the Problems

[0005] ​This invention provides a system that includes a collection means for collecting biometric information, an analysis means for analyzing the user's emotional state based on the collected biometric information, a generation means for generating visual information corresponding to the user's emotional state based on the analysis results, and a display means for displaying the generated visual information. By evaluating the user's emotional state in real time and dynamically responding to the visual information based on the results, it becomes possible to stabilize the user's emotions and guide them in a positive direction.

[0006] "Biometric information" refers to data that indicates the user's physical condition, and includes heart rate, steps taken, body temperature, blood oxygen saturation, and facial expression data.

[0007] "Means of data collection" refer to sensors and devices used to acquire a user's biometric information, which are worn or used on a daily basis.

[0008] "Analysis means" refers to algorithms or computer systems that process collected biometric information and evaluate the user's emotional state.

[0009] "Generation means" refers to technologies and processes for creating visual information appropriate to the user's state based on an evaluation of the emotional state obtained by the analysis means.

[0010] "Display means" refers to monitors or display devices used to present visual information created by the generation means to the user. [Brief explanation of the drawing]

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

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

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface that includes 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.

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

[0019] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0032] This invention provides a system configuration and embodiments for a system that analyzes a user's emotional state and provides appropriate visual information in order to stabilize the user's mind. The system includes a process for analyzing the user's emotional state in real time using biometric information obtained from the user's device.

[0033] This system first uses the user's device to acquire biometric information such as heart rate, body temperature, applications used, and voice data. This information is collected unconsciously during the user's daily activities and transmitted from the device to the server.

[0034] The server uses AI analysis algorithms to analyze the user's emotional state based on the received biometric information. The emotional state is categorized into stress, joy, sadness, anger, etc., and evaluated using specific indicators.

[0035] Next, the server generates visual information to improve or stabilize the user's emotional state based on the analysis results. This generated visual information may be selected from artworks stored in a database, or it may be newly generated using an AI model.

[0036] The generated visual information is sent to the device and displayed on the screen to help the user relax. The timing and method of display of the visual information are adjusted according to the user's preferences and environment.

[0037] For example, if the server determines that a user's heart rate is elevated due to work stress, it will select artwork depicting a calming natural landscape and display it on the device's screen. In this way, the user can have a visually calming experience, helping them regain a positive mental state.

[0038] This system features the automatic generation and display of artwork in response to changes in emotions, and aims to continuously improve the user's emotional state.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] Users wear devices such as smartwatches and smartphones to collect biometric information during their daily activities. These devices acquire data in real time, including heart rate, body temperature, steps taken, and information from applications being used.

[0042] Step 2:

[0043] The device transmits acquired biometric information to the server at regular intervals. This data transmission is securely performed using an encrypted protocol.

[0044] Step 3:

[0045] The server analyzes the received biometric information. Using an AI algorithm, it estimates the user's emotional state from this data. In this process, it infers from heart rate fluctuations and the applications being used to determine emotional categories such as stress, anger, and relaxation.

[0046] Step 4:

[0047] The server generates appropriate visual information based on the analysis results. Depending on the user's emotional state, it either selects an existing artwork from the database or uses AI to generate new artwork.

[0048] Step 5:

[0049] The server sends selected or generated visual information to the terminal. This transmission includes suggestions that guide the user's emotions towards a positive state.

[0050] Step 6:

[0051] The device displays received visual information on its screen. The display is visually adjusted according to the user's lighting environment and the device's position. The user can appreciate this artwork and achieve emotional stability.

[0052] Step 7:

[0053] Users can input their impressions and emotional changes after viewing visual information into the system as feedback. This feedback will be used to improve the accuracy of future emotion analysis.

[0054] (Example 1)

[0055] 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."

[0056] In modern society, users frequently encounter situations that cause stress on a daily basis. Therefore, there is an urgent need for technology that can analyze users' emotional states in real time and stabilize them in an appropriate manner. However, current technology lacks personalized information delivery methods to visually promote relaxation according to the user's emotional state. Consequently, a system that provides effective visual information tailored to each user's individual emotional state is required.

[0057] 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.

[0058] In this invention, the server includes a collection means for collecting the user's biometric information, an analysis device for analyzing the user's emotional state based on the collected biometric information, and a generation device for generating visual data corresponding to the user's emotional state. This makes it possible to individually provide information that visually promotes relaxation based on the user's individual emotional state.

[0059] "Collection means" refers to a function or device for sensing and acquiring biometric information such as heart rate, body temperature, application data being used, and voice data obtained from the user.

[0060] An "analysis device" is a function or device that uses biometric information acquired through collection methods to classify a user's emotional state in real time into categories such as stress, joy, sadness, and anger, utilizing machine learning and AI algorithms.

[0061] A "generation device" is a function or device that generates or selects visual data to improve the user's emotional state based on analysis results. This may involve generating new visual content using a generation AI model.

[0062] A "display device" is a function or device that visually displays generated visual data so that users can confirm it. The timing and method of display are adjusted according to the user's preferences and environment.

[0063] This system aims to stabilize emotions by analyzing the user's emotional state in real time and providing appropriate visual information. Specifically, it is implemented using the following hardware and software.

[0064] First, the user's device collects biometric information. Specifically, it uses a smartwatch or smartphone to acquire data such as heart rate, body temperature, applications used, and voice data. This information is detected by sensors on the device and automatically recorded. The information collected by the device is transmitted to the server in real time.

[0065] Next, the server uses an AI analysis algorithm to analyze the received biometric information. This analysis uses a machine learning model to process the information and classify the user's emotional state into categories such as stress, joy, sadness, and anger. This process utilizes programming languages ​​such as Python and machine learning libraries such as TENSORFLOW® and PyTorch. Through this analysis, the user's current emotional state can be objectively determined.

[0066] Furthermore, the server uses a generative AI model to generate visual information tailored to the user based on the analyzed emotional state. This visual information can be created by selecting existing artwork stored in a database or by generating new visual data using prompts. Potential generative AI models include Stable Diffusion and DALL-E. A concrete example of a prompt might be text such as, "Generate a relaxing natural landscape."

[0067] Finally, the generated or selected visual information is sent to the device. The device displays this information on its screen, visually promoting relaxation for the user. This display allows the user to calm their mind and more easily maintain a positive mental state.

[0068] This system allows users to reduce daily stress and obtain a visual experience tailored to their individual needs.

[0069] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0070] Step 1:

[0071] The user's device collects biometric information. Inputs include heart rate, body temperature, applications used, and voice data, obtained from sensors on smartwatches and smartphones. It also collects information about the applications running on the device. This information is temporarily stored in a database within the device.

[0072] Step 2:

[0073] The device sends the collected biometric information to the server. The input is the biometric information obtained in step 1, and the output is a data packet destined for the server. The device encrypts and transmits the data using a secure communication protocol. A low-latency network is used to ensure the information reaches the server in real time.

[0074] Step 3:

[0075] The server analyzes the biometric information it receives. The input is data sent from the terminal, and an AI analysis algorithm is used based on this information to determine the user's emotional state. For data processing, a machine learning model is used to extract features and classify emotional states into categories such as stress, joy, sadness, and anger. The output is data showing the user's emotional category and its level.

[0076] Step 4:

[0077] The server generates visual information using a generative AI model. The input consists of data about the user's emotional state obtained in step 3 and a prompt message for the generative AI model. An example of a prompt message is, "Generate a relaxing natural landscape." The AI ​​model generates a new visual image based on this. The output is the visual data to be provided to the user.

[0078] Step 5:

[0079] The server sends generated visual information to the user's terminal, which then displays it. The input is the visual data sent from the server, and the output is the image displayed on the terminal's screen. The terminal adjusts the timing and method of display according to the user's environment and preferences. This allows the user to feel visually calm.

[0080] (Application Example 1)

[0081] 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."

[0082] Traditional systems have limited ability to provide personalized information based on the user's emotional state, and have faced challenges in applying immediate emotional feedback, particularly in virtual environments. As a result, especially in virtual stores, it has been difficult to quickly provide appropriate visual information to encourage relaxation when users are feeling stressed.

[0083] 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.

[0084] In this invention, the server includes means for collecting biometric indicators, means for analyzing the user's emotional state, and means for generating visual information corresponding to the emotional state. This makes it possible to analyze the user's emotional state in real time, dynamically change the displayed content in the virtual environment, and provide visual information appropriate to each individual's emotions.

[0085] "Means for collecting biometric indicators" refers to devices or methods for acquiring physiological data such as heart rate and body temperature from users.

[0086] "Means for analyzing the emotional state of users" refers to a program or algorithm for identifying and classifying users' emotions based on collected biometric indicators.

[0087] "Means for generating visual information corresponding to emotional state" refers to a function that generates visual data such as images and videos to alleviate the user's mental state based on the analyzed emotional state.

[0088] "Means for displaying generated visual information" refers to display devices or display technologies for showing generated visual information to users.

[0089] "Means for dynamically changing the displayed content in a virtual environment based on the user's emotional state" refers to a mechanism that adapts the appearance and arrangement of information in the virtual environment in real time according to the user's current emotions.

[0090] The embodiments for carrying out the invention are as follows:

[0091] This system collects user biometric data using smart devices. Specifically, it uses wearable devices such as smart glasses to acquire data such as heart rate and body temperature in real time. These devices transmit information to a cloud server via Bluetooth or Wi-Fi.

[0092] The server receives biometric data in the cloud and then uses an AI analysis algorithm to analyze the user's emotional state. Machine learning services such as Amazon SageMaker are used for this AI analysis. Emotional states are categorized into different categories such as stress, relaxation, and excitement. Based on the analysis results, a generative AI model is used to generate visual information to alleviate the user's mental state. Image generation models such as DALL-E are used for this generation.

[0093] The generated visual information is then sent back to the device and displayed on smart glasses or a head-mounted display. This allows users to receive visual information tailored to their emotional state, making their experience in the virtual environment more comfortable.

[0094] For example, if the system determines that a user is stressed during work, it will generate a calming natural landscape and display it on their smart device. This can provide a visually relaxing effect that helps alleviate stress. An example of a prompt for the generating AI model would be, "The user needs to relax, so please generate artwork of a sunset over the sea in soft colors."

[0095] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0096] Step 1:

[0097] The terminal uses wearable devices such as smart glasses to collect biometric indicators such as heart rate and body temperature from the user in real time. Physiological data obtained from the body is used as input, and formatted digital data is obtained as output. This data is transmitted to a server via Bluetooth or Wi-Fi.

[0098] Step 2:

[0099] The server receives biometric data transmitted from the terminal. The input here is data from the terminal, and the output is data converted into an analyzable format. Based on this data, AI analysis algorithms such as Amazon SageMaker are used to analyze the user's emotional state and evaluate factors such as stress level and relaxation level.

[0100] Step 3:

[0101] The server uses a generative AI model based on the analyzed emotional state to generate visual information optimized for the user. The input here is the analysis result of the user's emotional state, and the output is the generated visual information. The output is generated as image data using tools like DALL-E, and prompts are used during this process. For example, a prompt such as "The user needs relaxation, so please generate artwork of a sunset over the sea with soft colors" might be used.

[0102] Step 4:

[0103] The server transmits the generated visual information to the terminal. The input is the generated visual information, and the output is visual data ready for display. This data is transmitted to the user's smart glasses or head-mounted display.

[0104] Step 5:

[0105] The terminal displays information on its screen based on the visual information it receives. The input is visual data received from the server, and the output is an image or video visually presented to the user. This allows the user to receive visual information optimized according to their emotions, enabling them to enjoy a more relaxed and comfortable experience in the virtual environment.

[0106] 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.

[0107] This invention is a system that incorporates an emotion engine to analyze a user's emotions from multiple perspectives and provide appropriate visual information. This system acquires biometric information and voice data through devices that the user uses on a daily basis and recognizes the user's emotions in real time based on this information.

[0108] First, the user's device collects biometric information such as heart rate and body temperature, as well as voice data and facial expression data. This data is acquired through devices such as sensors, cameras, and microphones, and stored by the device in real time.

[0109] Next, the device sends this data to the server. The server uses an emotion engine to analyze the received information. The emotion engine performs linguistic analysis on the user's voice data and recognizes the emotional tone from it. It also obtains visual emotional cues from changes in facial expressions. Furthermore, it comprehensively considers all of this information to make a detailed assessment of the user's emotional state.

[0110] The analysis mechanism within the server classifies the user's emotional state into categories such as stress, joy, anger, and sadness, based on feedback from the emotion engine. Based on these results, the generation mechanism selects or generates visual information to improve or stabilize the user's emotions.

[0111] The generated visual information is sent to the device and displayed on the screen. This display is adjusted according to the user's emotional state and is set to evoke relaxation or positive emotions.

[0112] For example, if the server detects that a user is in a high-stress state, it selects artwork depicting a calm and peaceful natural landscape and displays it through the terminal. This artwork helps the user reduce stress and relax. Thus, the present invention functions as a system that supports mental health by multidimensionally analyzing the user's emotional state and providing appropriate visual information.

[0113] The following describes the processing flow.

[0114] Step 1:

[0115] Users wear smartphones or smartwatches to collect biometric information in real time during their daily activities, including heart rate, body temperature, voice, and facial expression data. The devices automatically acquire this data through sensors, microphones, and cameras.

[0116] Step 2:

[0117] The device transmits collected biometric and voice data to the server at regular intervals. The data is anonymized for privacy protection and transmitted via a secure communication protocol.

[0118] Step 3:

[0119] The server passes the received data to the emotion engine. The emotion engine analyzes the user's speech content, tone, and speed from the audio data, and extracts emotional characteristics from the facial expression data. Through these processes, the server comprehensively evaluates the user's emotional state.

[0120] Step 4:

[0121] Based on the analysis results provided by the emotion engine, the server classifies the user's emotional state into specific categories such as stress, joy, anger, and sadness. Furthermore, it also takes into account changes and intensity of the emotions.

[0122] Step 5:

[0123] The server generates visual information optimized for the user based on their classified emotional state. The generation method involves selecting existing artwork from a database or generating new artwork using an AI model. This selection is based on the user's preferences and past feedback.

[0124] Step 6:

[0125] The server sends selected or generated visual information to the terminal. The terminal displays this information on its screen and simultaneously provides audio or music tailored to the user's current environment to enhance the effect of the visual information.

[0126] Step 7:

[0127] Users can view the displayed visual information and provide feedback to the system regarding their resulting emotional changes. This feedback is used by the system to improve the accuracy of future analysis and visual information generation.

[0128] (Example 2)

[0129] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0130] Conventional emotion analysis systems rely solely on the user's biometric information to determine their emotional state, resulting in insufficient accuracy and difficulty in providing appropriate feedback. Furthermore, there is a lack of effective means to grasp the user's multifaceted emotional state in real time and provide corresponding visual information.

[0131] 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.

[0132] In this invention, the server includes data collection means for collecting biometric information and voice data, emotion analysis means for comprehensively analyzing the user's emotional state based on the collected biometric information and voice data, and facial expression analysis means for acquiring visual emotional cues of the user. This makes it possible to analyze the user's emotional state with higher accuracy and provide appropriate visual information.

[0133] "Biometric information" refers to data that reflects the user's physical condition, such as heart rate and body temperature.

[0134] "Data collection means" refers to means for acquiring biometric information and voice data using sensors, cameras, and microphones.

[0135] "Emotional analysis tools" are means of comprehensively analyzing a user's emotional state from collected data and recognizing emotional tone and visual cues.

[0136] A "facial expression analysis method" is a means for extracting visual emotional cues from a user's facial expressions.

[0137] A "classification method" is a means of categorizing a user's emotional state based on information obtained through emotion analysis methods.

[0138] A "generative AI model" is an artificial intelligence model that selects or generates visual information in accordance with the user's emotional state.

[0139] "Visual information generation means" refers to a means of implementing a process that uses a generation AI model to generate visual information that is appropriate to the user's emotions.

[0140] "Display means" refers to means including displays and monitors for presenting generated visual information to the user.

[0141] This invention is a system that analyzes a user's emotions from multiple perspectives and provides appropriate visual information. This system utilizes a terminal that the user uses daily to acquire biometric information and voice data, thereby recognizing the user's emotions in real time. This invention is implemented using components such as a server, terminals, and a generative AI model.

[0142] The device collects biometric information, including heart rate and body temperature, as well as voice data and facial expression data. Hardware such as sensors, cameras, and microphones are used for this purpose. For example, if the device is a wearable device, sensors will directly touch the skin to measure heart rate, and microphones will listen to the user's speech and record it as voice data. In addition, cameras will capture the user's face and detect subtle changes in facial expressions.

[0143] The collected data is sent to a server where it is analyzed using sentiment analysis techniques. This sentiment analysis incorporates natural language processing on the collected audio data to analyze the tone and emotion of the voice. In addition, facial expression data is analyzed using image analysis technology to extract visual emotional cues. This allows the server to evaluate the user's emotional state in detail.

[0144] Based on the analysis results, the server uses a generative AI model to generate visual information that improves the user's emotions. For example, by inputting a prompt such as "Create a relaxing landscape that soothes the user's feelings," the generative AI model can generate a calming natural landscape painting.

[0145] The generated visual information is transmitted to the terminal and displayed on the screen. This display is optimized for the user's emotional state and is set to induce relaxation and positive emotions in the user. For example, a user experiencing stress might be shown a tranquil lakeside landscape. This invention functions as a groundbreaking system that supports mental health through technological advancements.

[0146] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0147] Step 1:

[0148] The user's device collects biometric and audio data. Sensors, microphones, and cameras are used as inputs to acquire heart rate, body temperature, audio data, and facial expression data. Data processing is performed in real time, collecting biometric information from sensors and converting audio data into digital data. A buffer of the collected data is formed as output. Specifically, the device records audio data and activates the camera to capture facial expressions.

[0149] Step 2:

[0150] The terminal sends the collected data to the server. Inputs include biometric information, voice data, and facial expression data stored in a buffer. Data transmission is secure using an encrypted, secure protocol. The server receives this data as output and prepares it for the next analysis step. Specifically, the terminal assembles data packets at regular time intervals and sends them to the server over the network.

[0151] Step 3:

[0152] The server analyzes incoming data and estimates emotional states. The server takes biometric information, voice data, and facial expression data as input. Data processing involves converting voice data to text using natural language processing, and analyzing the tone of the text using an emotion analysis model. The output is an emotional state, such as categories like joy, anger, or sadness. In its specific operation, the server extracts emotional nuances from the voice using a voice analysis algorithm and categorizes them.

[0153] Step 4:

[0154] The server uses a generative AI model to generate appropriate visual information. The input is the user's emotional state category, obtained through emotion analysis. Data processing involves inputting prompts into the generative AI model, which then generates visual data based on those prompts. The output is visual data appropriate to the user's emotion, such as relaxing soundscapes or landscape images. Specifically, the server sends a prompt to the generative AI model such as, "Generate an image that will relax the user."

[0155] Step 5:

[0156] The generated visual information is sent from the server to the terminal. The input is the visual data generated by the server. Data transmission is optimized for efficiency while reducing the amount of data using a compression algorithm. The output is the completion of the visual information on the terminal. Specifically, the terminal receives the data sent by the server and decodes it into a format that can be displayed on the screen.

[0157] Step 6:

[0158] The device displays received visual information on its screen. The input is the visual data received by the device. The visual information is displayed in a format optimized for the user's emotional state. The output is images that evoke relaxation and positive emotions. Specifically, the device presents the visual data on the display screen while adjusting the image quality.

[0159] (Application Example 2)

[0160] 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".

[0161] In physical stores, it is difficult to consistently provide an appropriate environment tailored to the emotional state of each customer. This problem stems from the limitations of traditional manual responses, which cannot immediately address the comfort needs of individual customers.

[0162] 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.

[0163] In this invention, the server includes input means for collecting biometric and environmental information, processing means for analyzing an individual's emotional state based on the collected information, and management means for controlling predetermined environmental elements based on the analysis results. This makes it possible to provide customers with a comfortable environment that responds to their emotions in real time and to create a purchasing experience that suits their individual needs.

[0164] "Biometric information" refers to data that indicates an individual's health status and physical responses, including heart rate, body temperature, and facial expressions.

[0165] "Environmental information" refers to data that indicates the physical conditions of a place where an individual is located, and includes temperature, sound level, illuminance, etc.

[0166] "Input means" refers to equipment or devices used to detect and collect biometric and environmental information.

[0167] "Processing means" refers to software or algorithms used to analyze collected information and identify an individual's emotional state.

[0168] "Management means" refers to equipment or systems that adjust environmental elements based on the analysis results obtained by processing means.

[0169] "Output means" refers to devices or mechanisms that present environmental elements tailored to an individual and provide emotional care.

[0170] "Emotional care" refers to environmental adjustments or support aimed at promoting relaxation and stress reduction, taking into account an individual's emotional state.

[0171] The system that realizes this application consists of a combination of multiple hardware and software components. The server is responsible for collecting the user's biometric and environmental information in real time and analyzing their emotional state. Sensors and cameras are used as input means in this process. Specifically, it uses a face recognition camera and voice capture device in conjunction with a Raspberry Pi, as well as various sensors to detect the environment. This allows for the continuous acquisition of the user's heart rate, voice tone, and indoor environmental conditions.

[0172] Next, the collected data is sent to a server. On the server side, the received data is analyzed in detail using analysis software based on Python and libraries such as DeepFace and TensorFlow. This analysis detects the emotional state of each user and determines the optimal environmental elements according to that state. The management system then generates recommendations based on the analysis results and controls the store's lighting and sound equipment to present the user with the optimal environment.

[0173] For example, if the server analyzes that a user's stress level is high, a system controlled by a Raspberry Pi could deploy soft music and lighting in the store to enhance relaxation. This would allow individual users to naturally experience comfort in the space, and improve overall customer satisfaction for the store.

[0174] As an example of a prompt, it is possible to instruct the AI ​​model using a format such as, "I want to create a Python program that performs real-time emotion analysis from facial images captured using DeepFace, and then integrates the control of the music and lighting systems in the store based on the acquired emotion data."

[0175] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0176] Step 1:

[0177] The terminal collects the user's biometric and environmental information. Input here includes biometric information acquired by a facial recognition camera and voice capture device, as well as temperature, volume, and illuminance information obtained by environmental sensors. This data is then processed and prepared for transmission to the server.

[0178] Step 2:

[0179] The server analyzes biometric and environmental information received from the terminal. The input consists of collected biometric and environmental data. The DeepFace library is used to analyze emotional data from facial images, and TensorFlow is used to analyze tone from audio data. As a data processing step, each piece of information is output as a numerical representation of the emotional state.

[0180] Step 3:

[0181] The server evaluates the user's emotional state based on the analysis results and determines appropriate environmental adjustments. The input here is a quantified emotional state, and data processing is performed to generate control information such as lighting brightness and music selection based on this evaluation. The output is a control command for adjusting the environment.

[0182] Step 4:

[0183] The terminal executes control commands received from the server, adjusting the lighting and sound equipment within the store. The input consists of control commands to adjust the environment, and based on these, the terminal operates the actual hardware to generate output that provides comfortable emotional care for the user.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] [Second Embodiment]

[0188] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0189] 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.

[0190] 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).

[0191] 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.

[0192] 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.

[0193] 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).

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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".

[0200] This invention provides a system configuration and embodiments for a system that analyzes a user's emotional state and provides appropriate visual information in order to stabilize the user's mind. The system includes a process for analyzing the user's emotional state in real time using biometric information obtained from the user's device.

[0201] This system first uses the user's device to acquire biometric information such as heart rate, body temperature, applications used, and voice data. This information is collected unconsciously during the user's daily activities and transmitted from the device to the server.

[0202] The server uses AI analysis algorithms to analyze the user's emotional state based on the received biometric information. The emotional state is categorized into stress, joy, sadness, anger, etc., and evaluated using specific indicators.

[0203] Next, the server generates visual information to improve or stabilize the user's emotional state based on the analysis results. This generated visual information may be selected from artworks stored in a database, or it may be newly generated using an AI model.

[0204] The generated visual information is sent to the device and displayed on the screen to help the user relax. The timing and method of display of the visual information are adjusted according to the user's preferences and environment.

[0205] For example, if the server determines that a user's heart rate is elevated due to work stress, it will select artwork depicting a calming natural landscape and display it on the device's screen. In this way, the user can have a visually calming experience, helping them regain a positive mental state.

[0206] This system features the automatic generation and display of artwork in response to changes in emotions, and aims to continuously improve the user's emotional state.

[0207] The following describes the processing flow.

[0208] Step 1:

[0209] Users wear devices such as smartwatches and smartphones to collect biometric information during their daily activities. These devices acquire data in real time, including heart rate, body temperature, steps taken, and information from applications being used.

[0210] Step 2:

[0211] The device transmits acquired biometric information to the server at regular intervals. This data transmission is securely performed using an encrypted protocol.

[0212] Step 3:

[0213] The server analyzes the received biometric information. Using an AI algorithm, it estimates the user's emotional state from this data. In this process, it infers from heart rate fluctuations and the applications being used to determine emotional categories such as stress, anger, and relaxation.

[0214] Step 4:

[0215] The server generates appropriate visual information based on the analysis results. Depending on the user's emotional state, it either selects an existing artwork from the database or uses AI to generate new artwork.

[0216] Step 5:

[0217] The server sends selected or generated visual information to the terminal. This transmission includes suggestions that guide the user's emotions towards a positive state.

[0218] Step 6:

[0219] The device displays received visual information on its screen. The display is visually adjusted according to the user's lighting environment and the device's position. The user can appreciate this artwork and achieve emotional stability.

[0220] Step 7:

[0221] Users can input their impressions and emotional changes after viewing visual information into the system as feedback. This feedback will be used to improve the accuracy of future emotion analysis.

[0222] (Example 1)

[0223] 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."

[0224] In modern society, users frequently encounter situations that cause stress on a daily basis. Therefore, there is an urgent need for technology that can analyze users' emotional states in real time and stabilize them in an appropriate manner. However, current technology lacks personalized information delivery methods to visually promote relaxation according to the user's emotional state. Consequently, a system that provides effective visual information tailored to each user's individual emotional state is required.

[0225] 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.

[0226] In this invention, the server includes a collection means for collecting the user's biometric information, an analysis device for analyzing the user's emotional state based on the collected biometric information, and a generation device for generating visual data corresponding to the user's emotional state. This makes it possible to individually provide information that visually promotes relaxation based on the user's individual emotional state.

[0227] "Collection means" refers to a function or device for sensing and acquiring biometric information such as heart rate, body temperature, application data being used, and voice data obtained from the user.

[0228] An "analysis device" is a function or device that uses biometric information acquired through collection methods to classify a user's emotional state in real time into categories such as stress, joy, sadness, and anger, utilizing machine learning and AI algorithms.

[0229] A "generation device" is a function or device that generates or selects visual data to improve the user's emotional state based on analysis results. This may involve generating new visual content using a generation AI model.

[0230] A "display device" is a function or device that visually displays generated visual data so that users can confirm it. The timing and method of display are adjusted according to the user's preferences and environment.

[0231] This system aims to stabilize emotions by analyzing the user's emotional state in real time and providing appropriate visual information. Specifically, it is implemented using the following hardware and software.

[0232] First, the user's device collects biometric information. Specifically, it uses a smartwatch or smartphone to acquire data such as heart rate, body temperature, applications used, and voice data. This information is detected by sensors on the device and automatically recorded. The information collected by the device is transmitted to the server in real time.

[0233] Next, the server uses an AI analysis algorithm to analyze the received biometric information. This analysis uses a machine learning model to process the information and classify the user's emotional state into categories such as stress, joy, sadness, and anger. This process utilizes programming languages ​​such as Python and machine learning libraries such as TensorFlow and PyTorch. Through this analysis, the user's current emotional state can be objectively determined.

[0234] Furthermore, the server uses a generative AI model to generate visual information tailored to the user based on the analyzed emotional state. This visual information can be created by selecting existing artwork stored in a database or by generating new visual data using prompts. Potential generative AI models include Stable Diffusion and DALL-E. A concrete example of a prompt might be text such as, "Generate a relaxing natural landscape."

[0235] Finally, the generated or selected visual information is sent to the device. The device displays this information on its screen, visually promoting relaxation for the user. This display allows the user to calm their mind and more easily maintain a positive mental state.

[0236] This system allows users to reduce daily stress and obtain a visual experience tailored to their individual needs.

[0237] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0238] Step 1:

[0239] The user's device collects biometric information. Inputs include heart rate, body temperature, applications used, and voice data, obtained from sensors on smartwatches and smartphones. It also collects information about the applications running on the device. This information is temporarily stored in a database within the device.

[0240] Step 2:

[0241] The device sends the collected biometric information to the server. The input is the biometric information obtained in step 1, and the output is a data packet destined for the server. The device encrypts and transmits the data using a secure communication protocol. A low-latency network is used to ensure the information reaches the server in real time.

[0242] Step 3:

[0243] The server analyzes the biometric information it receives. The input is data sent from the terminal, and an AI analysis algorithm is used based on this information to determine the user's emotional state. For data processing, a machine learning model is used to extract features and classify emotional states into categories such as stress, joy, sadness, and anger. The output is data showing the user's emotional category and its level.

[0244] Step 4:

[0245] The server generates visual information using a generative AI model. The input consists of data about the user's emotional state obtained in step 3 and a prompt message for the generative AI model. An example of a prompt message is, "Generate a relaxing natural landscape." The AI ​​model generates a new visual image based on this. The output is the visual data to be provided to the user.

[0246] Step 5:

[0247] The server sends generated visual information to the user's terminal, which then displays it. The input is the visual data sent from the server, and the output is the image displayed on the terminal's screen. The terminal adjusts the timing and method of display according to the user's environment and preferences. This allows the user to feel visually calm.

[0248] (Application Example 1)

[0249] 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."

[0250] Traditional systems have limited ability to provide personalized information based on the user's emotional state, and have faced challenges in applying immediate emotional feedback, particularly in virtual environments. As a result, especially in virtual stores, it has been difficult to quickly provide appropriate visual information to encourage relaxation when users are feeling stressed.

[0251] 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.

[0252] In this invention, the server includes means for collecting biometric indicators, means for analyzing the user's emotional state, and means for generating visual information corresponding to the emotional state. This makes it possible to analyze the user's emotional state in real time, dynamically change the displayed content in the virtual environment, and provide visual information appropriate to each individual's emotions.

[0253] "Means for collecting biometric indicators" refers to devices or methods for acquiring physiological data such as heart rate and body temperature from users.

[0254] "Means for analyzing the emotional state of users" refers to a program or algorithm for identifying and classifying users' emotions based on collected biometric indicators.

[0255] "Means for generating visual information corresponding to emotional state" refers to a function that generates visual data such as images and videos to alleviate the user's mental state based on the analyzed emotional state.

[0256] "Means for displaying generated visual information" refers to display devices or display technologies for showing generated visual information to users.

[0257] "Means for dynamically changing the displayed content in a virtual environment based on the user's emotional state" refers to a mechanism that adapts the appearance and arrangement of information in the virtual environment in real time according to the user's current emotions.

[0258] The embodiments for carrying out the invention are as follows:

[0259] This system collects user biometric data using smart devices. Specifically, it uses wearable devices such as smart glasses to acquire data such as heart rate and body temperature in real time. These devices transmit information to a cloud server via Bluetooth or Wi-Fi.

[0260] The server receives biometric data in the cloud and then uses an AI analysis algorithm to analyze the user's emotional state. Machine learning services such as Amazon SageMaker are used for this AI analysis. Emotional states are categorized into different categories such as stress, relaxation, and excitement. Based on the analysis results, a generative AI model is used to generate visual information to alleviate the user's mental state. Image generation models such as DALL-E are used for this generation.

[0261] The generated visual information is then sent back to the device and displayed on smart glasses or a head-mounted display. This allows users to receive visual information tailored to their emotional state, making their experience in the virtual environment more comfortable.

[0262] For example, if the system determines that a user is stressed during work, it will generate a calming natural landscape and display it on their smart device. This can provide a visually relaxing effect that helps alleviate stress. An example of a prompt for the generating AI model would be, "The user needs to relax, so please generate artwork of a sunset over the sea in soft colors."

[0263] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0264] Step 1:

[0265] The terminal uses wearable devices such as smart glasses to collect biometric indicators such as heart rate and body temperature from the user in real time. Physiological data obtained from the body is used as input, and formatted digital data is obtained as output. This data is transmitted to a server via Bluetooth or Wi-Fi.

[0266] Step 2:

[0267] The server receives biometric data transmitted from the terminal. The input here is data from the terminal, and the output is data converted into an analyzable format. Based on this data, AI analysis algorithms such as Amazon SageMaker are used to analyze the user's emotional state and evaluate factors such as stress level and relaxation level.

[0268] Step 3:

[0269] The server uses a generative AI model based on the analyzed emotional state to generate visual information optimized for the user. The input here is the analysis result of the user's emotional state, and the output is the generated visual information. The output is generated as image data using tools like DALL-E, and prompts are used during this process. For example, a prompt such as "The user needs relaxation, so please generate artwork of a sunset over the sea with soft colors" might be used.

[0270] Step 4:

[0271] The server transmits the generated visual information to the terminal. The input is the generated visual information, and the output is visual data ready for display. This data is transmitted to the user's smart glasses or head-mounted display.

[0272] Step 5:

[0273] The terminal displays information on its screen based on the visual information it receives. The input is visual data received from the server, and the output is an image or video visually presented to the user. This allows the user to receive visual information optimized according to their emotions, enabling them to enjoy a more relaxed and comfortable experience in the virtual environment.

[0274] 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.

[0275] This invention is a system that incorporates an emotion engine to analyze a user's emotions from multiple perspectives and provide appropriate visual information. This system acquires biometric information and voice data through devices that the user uses on a daily basis and recognizes the user's emotions in real time based on this information.

[0276] First, the user's device collects biometric information such as heart rate and body temperature, as well as voice data and facial expression data. This data is acquired through devices such as sensors, cameras, and microphones, and stored by the device in real time.

[0277] Next, the device sends this data to the server. The server uses an emotion engine to analyze the received information. The emotion engine performs linguistic analysis on the user's voice data and recognizes the emotional tone from it. It also obtains visual emotional cues from changes in facial expressions. Furthermore, it comprehensively considers all of this information to make a detailed assessment of the user's emotional state.

[0278] The analysis mechanism within the server classifies the user's emotional state into categories such as stress, joy, anger, and sadness, based on feedback from the emotion engine. Based on these results, the generation mechanism selects or generates visual information to improve or stabilize the user's emotions.

[0279] The generated visual information is sent to the device and displayed on the screen. This display is adjusted according to the user's emotional state and is set to evoke relaxation or positive emotions.

[0280] For example, if the server detects that a user is in a high-stress state, it selects artwork depicting a calm and peaceful natural landscape and displays it through the terminal. This artwork helps the user reduce stress and relax. Thus, the present invention functions as a system that supports mental health by multidimensionally analyzing the user's emotional state and providing appropriate visual information.

[0281] The processing flow will be described below.

[0282] Step 1:

[0283] The user wears a smartphone or smartwatch and collects biometric information including heart rate, body temperature, voice, and facial expression data in real-time during daily activities. The terminal automatically acquires these data through sensors, microphones, and cameras.

[0284] Step 2:

[0285] The terminal transmits the collected biometric information and voice data to the server at regular intervals. The data is anonymized for privacy protection and sent through a secure communication protocol.

[0286] Step 3:

[0287] The server passes the received data to the emotion engine. The emotion engine analyzes the user's speech content, tone, and speed from the voice data, and extracts emotional features from the facial expression data. Through these processes, the server comprehensively evaluates the user's emotional state.

[0288] Step 4:

[0289] The server classifies the user's emotional state into specific categories such as stress, joy, anger, sadness, etc. based on the analysis results provided by the emotion engine. Additionally, changes and intensities of emotions are also taken into consideration.

[0290] Step 5:

[0291] The server generates optimal visual information for the user based on the classified emotional state. The generation means selects existing artworks from the database or generates new art using an AI model. This selection is based on the user's preferences and past feedback.

[0292] Step 6:

[0293] The server sends selected or generated visual information to the terminal. The terminal displays this information on its screen and simultaneously provides audio or music tailored to the user's current environment to enhance the effect of the visual information.

[0294] Step 7:

[0295] Users can view the displayed visual information and provide feedback to the system regarding their resulting emotional changes. This feedback is used by the system to improve the accuracy of future analysis and visual information generation.

[0296] (Example 2)

[0297] 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".

[0298] Conventional emotion analysis systems rely solely on the user's biometric information to determine their emotional state, resulting in insufficient accuracy and difficulty in providing appropriate feedback. Furthermore, there is a lack of effective means to grasp the user's multifaceted emotional state in real time and provide corresponding visual information.

[0299] 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.

[0300] In this invention, the server includes data collection means for collecting biometric information and voice data, emotion analysis means for comprehensively analyzing the user's emotional state based on the collected biometric information and voice data, and facial expression analysis means for acquiring visual emotional cues of the user. This makes it possible to analyze the user's emotional state with higher accuracy and provide appropriate visual information.

[0301] "Biometric information" refers to data that reflects the physical state of a user, such as heart rate and body temperature.

[0302] "Data collection means" refers to means for acquiring biometric information and voice data using sensors, cameras, microphones, etc.

[0303] "Emotion analysis means" refers to means for comprehensively analyzing the emotional state of a user from the collected data and recognizing emotional tones and visual cues.

[0304] "Facial expression analysis means" is means for extracting visual emotional cues from the facial expressions of a user.

[0305] "Classification means" is means for categorizing the emotional state of a user based on the information obtained by the emotion analysis means.

[0306] "Generated AI model" is an artificial intelligence model for selecting or generating visual information according to the emotional state of a user.

[0307] "Visual information generation means" is means for implementing a process of generating visual information suitable for the emotion of a user using the generated AI model.

[0308] "Display means" is means including displays and monitors for presenting the generated visual information to the user.

[0309] The present invention is a system that comprehensively analyzes the emotions of a user and provides appropriate visual information. This system utilizes the terminal that a user uses daily, acquires biometric information and voice data, and recognizes the emotions of the user in real time. This invention is implemented using components such as a server, a terminal, and a generated AI model.

[0310] The device collects biometric information, including heart rate and body temperature, as well as voice data and facial expression data. Hardware such as sensors, cameras, and microphones are used for this purpose. For example, if the device is a wearable device, sensors will directly touch the skin to measure heart rate, and microphones will listen to the user's speech and record it as voice data. In addition, cameras will capture the user's face and detect subtle changes in facial expressions.

[0311] The collected data is sent to a server where it is analyzed using sentiment analysis techniques. This sentiment analysis incorporates natural language processing on the collected audio data to analyze the tone and emotion of the voice. In addition, facial expression data is analyzed using image analysis technology to extract visual emotional cues. This allows the server to evaluate the user's emotional state in detail.

[0312] Based on the analysis results, the server uses a generative AI model to generate visual information that improves the user's emotions. For example, by inputting a prompt such as "Create a relaxing landscape that soothes the user's feelings," the generative AI model can generate a calming natural landscape painting.

[0313] The generated visual information is transmitted to the terminal and displayed on the screen. This display is optimized for the user's emotional state and is set to induce relaxation and positive emotions in the user. For example, a user experiencing stress might be shown a tranquil lakeside landscape. This invention functions as a groundbreaking system that supports mental health through technological advancements.

[0314] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0315] Step 1:

[0316] The user's device collects biometric and audio data. Sensors, microphones, and cameras are used as inputs to acquire heart rate, body temperature, audio data, and facial expression data. Data processing is performed in real time, collecting biometric information from sensors and converting audio data into digital data. A buffer of the collected data is formed as output. Specifically, the device records audio data and activates the camera to capture facial expressions.

[0317] Step 2:

[0318] The terminal sends the collected data to the server. Inputs include biometric information, voice data, and facial expression data stored in a buffer. Data transmission is secure using an encrypted, secure protocol. The server receives this data as output and prepares it for the next analysis step. Specifically, the terminal assembles data packets at regular time intervals and sends them to the server over the network.

[0319] Step 3:

[0320] The server analyzes incoming data and estimates emotional states. The server takes biometric information, voice data, and facial expression data as input. Data processing involves converting voice data to text using natural language processing, and analyzing the tone of the text using an emotion analysis model. The output is an emotional state, such as categories like joy, anger, or sadness. In its specific operation, the server extracts emotional nuances from the voice using a voice analysis algorithm and categorizes them.

[0321] Step 4:

[0322] The server uses a generative AI model to generate appropriate visual information. The input is the user's emotional state category, obtained through emotion analysis. Data processing involves inputting prompts into the generative AI model, which then generates visual data based on those prompts. The output is visual data appropriate to the user's emotion, such as relaxing soundscapes or landscape images. Specifically, the server sends a prompt to the generative AI model such as, "Generate an image that will relax the user."

[0323] Step 5:

[0324] The generated visual information is sent from the server to the terminal. The input is the visual data generated by the server. Data transmission is optimized for efficiency while reducing the amount of data using a compression algorithm. The output is the completion of the visual information on the terminal. Specifically, the terminal receives the data sent by the server and decodes it into a format that can be displayed on the screen.

[0325] Step 6:

[0326] The device displays received visual information on its screen. The input is the visual data received by the device. The visual information is displayed in a format optimized for the user's emotional state. The output is images that evoke relaxation and positive emotions. Specifically, the device presents the visual data on the display screen while adjusting the image quality.

[0327] (Application Example 2)

[0328] 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."

[0329] In physical stores, it is difficult to consistently provide an appropriate environment tailored to the emotional state of each customer. This problem stems from the limitations of traditional manual responses, which cannot immediately address the comfort needs of individual customers.

[0330] 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.

[0331] In this invention, the server includes input means for collecting biometric and environmental information, processing means for analyzing an individual's emotional state based on the collected information, and management means for controlling predetermined environmental elements based on the analysis results. This makes it possible to provide customers with a comfortable environment that responds to their emotions in real time and to create a purchasing experience that suits their individual needs.

[0332] "Biometric information" refers to data that indicates an individual's health status and physical responses, including heart rate, body temperature, and facial expressions.

[0333] "Environmental information" refers to data that indicates the physical conditions of a place where an individual is located, and includes temperature, sound level, illuminance, etc.

[0334] "Input means" refers to equipment or devices used to detect and collect biometric and environmental information.

[0335] "Processing means" refers to software or algorithms used to analyze collected information and identify an individual's emotional state.

[0336] "Management means" refers to equipment or systems that adjust environmental elements based on the analysis results obtained by processing means.

[0337] "Output means" refers to devices or mechanisms that present environmental elements tailored to an individual and provide emotional care.

[0338] "Emotional care" refers to environmental adjustments or support aimed at promoting relaxation and stress reduction, taking into account an individual's emotional state.

[0339] The system that realizes this application consists of a combination of multiple hardware and software components. The server is responsible for collecting the user's biometric and environmental information in real time and analyzing their emotional state. Sensors and cameras are used as input means in this process. Specifically, it uses a face recognition camera and voice capture device in conjunction with a Raspberry Pi, as well as various sensors to detect the environment. This allows for the continuous acquisition of the user's heart rate, voice tone, and indoor environmental conditions.

[0340] Next, the collected data is sent to a server. On the server side, the received data is analyzed in detail using analysis software based on Python and libraries such as DeepFace and TensorFlow. This analysis detects the emotional state of each user and determines the optimal environmental elements according to that state. The management system then generates recommendations based on the analysis results and controls the store's lighting and sound equipment to present the user with the optimal environment.

[0341] For example, if the server analyzes that a user's stress level is high, a system controlled by a Raspberry Pi could deploy soft music and lighting in the store to enhance relaxation. This would allow individual users to naturally experience comfort in the space, and improve overall customer satisfaction for the store.

[0342] As an example of a prompt, it is possible to instruct the AI ​​model using a format such as, "I want to create a Python program that performs real-time emotion analysis from facial images captured using DeepFace, and then integrates the control of the music and lighting systems in the store based on the acquired emotion data."

[0343] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0344] Step 1:

[0345] The terminal collects the user's biometric and environmental information. Input here includes biometric information acquired by a facial recognition camera and voice capture device, as well as temperature, volume, and illuminance information obtained by environmental sensors. This data is then processed and prepared for transmission to the server.

[0346] Step 2:

[0347] The server analyzes biometric and environmental information received from the terminal. The input consists of collected biometric and environmental data. The DeepFace library is used to analyze emotional data from facial images, and TensorFlow is used to analyze tone from audio data. As a data processing step, each piece of information is output as a numerical representation of the emotional state.

[0348] Step 3:

[0349] The server evaluates the user's emotional state based on the analysis results and determines appropriate environmental adjustments. The input here is a quantified emotional state, and data processing is performed to generate control information such as lighting brightness and music selection based on this evaluation. The output is a control command for adjusting the environment.

[0350] Step 4:

[0351] The terminal executes control commands received from the server, adjusting the lighting and sound equipment within the store. The input consists of control commands to adjust the environment, and based on these, the terminal operates the actual hardware to generate output that provides comfortable emotional care for the user.

[0352] 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.

[0353] 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.

[0354] 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.

[0355] [Third Embodiment]

[0356] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0357] 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.

[0358] 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).

[0359] 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.

[0360] 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.

[0361] 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).

[0362] 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.

[0363] 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.

[0364] 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.

[0365] 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.

[0366] 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.

[0367] 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".

[0368] This invention provides a system configuration and embodiments for a system that analyzes a user's emotional state and provides appropriate visual information in order to stabilize the user's mind. The system includes a process for analyzing the user's emotional state in real time using biometric information obtained from the user's device.

[0369] This system first uses the user's device to acquire biometric information such as heart rate, body temperature, applications used, and voice data. This information is collected unconsciously during the user's daily activities and transmitted from the device to the server.

[0370] The server uses AI analysis algorithms to analyze the user's emotional state based on the received biometric information. The emotional state is categorized into stress, joy, sadness, anger, etc., and evaluated using specific indicators.

[0371] Next, the server generates visual information to improve or stabilize the user's emotional state based on the analysis results. This generated visual information may be selected from artworks stored in a database, or it may be newly generated using an AI model.

[0372] The generated visual information is sent to the device and displayed on the screen to help the user relax. The timing and method of display of the visual information are adjusted according to the user's preferences and environment.

[0373] For example, if the server determines that a user's heart rate is elevated due to work stress, it will select artwork depicting a calming natural landscape and display it on the device's screen. In this way, the user can have a visually calming experience, helping them regain a positive mental state.

[0374] This system features the automatic generation and display of artwork in response to changes in emotions, and aims to continuously improve the user's emotional state.

[0375] The following describes the processing flow.

[0376] Step 1:

[0377] Users wear devices such as smartwatches and smartphones to collect biometric information during their daily activities. These devices acquire data in real time, including heart rate, body temperature, steps taken, and information from applications being used.

[0378] Step 2:

[0379] The device transmits acquired biometric information to the server at regular intervals. This data transmission is securely performed using an encrypted protocol.

[0380] Step 3:

[0381] The server analyzes the received biometric information. Using an AI algorithm, it estimates the user's emotional state from this data. In this process, it infers from heart rate fluctuations and the applications being used to determine emotional categories such as stress, anger, and relaxation.

[0382] Step 4:

[0383] The server generates appropriate visual information based on the analysis results. Depending on the user's emotional state, it either selects an existing artwork from the database or uses AI to generate new artwork.

[0384] Step 5:

[0385] The server sends selected or generated visual information to the terminal. This transmission includes suggestions that guide the user's emotions towards a positive state.

[0386] Step 6:

[0387] The device displays received visual information on its screen. The display is visually adjusted according to the user's lighting environment and the device's position. The user can appreciate this artwork and achieve emotional stability.

[0388] Step 7:

[0389] Users can input their impressions and emotional changes after viewing visual information into the system as feedback. This feedback will be used to improve the accuracy of future emotion analysis.

[0390] (Example 1)

[0391] 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."

[0392] In modern society, users frequently encounter situations that cause stress on a daily basis. Therefore, there is an urgent need for technology that can analyze users' emotional states in real time and stabilize them in an appropriate manner. However, current technology lacks personalized information delivery methods to visually promote relaxation according to the user's emotional state. Consequently, a system that provides effective visual information tailored to each user's individual emotional state is required.

[0393] 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.

[0394] In this invention, the server includes a collection means for collecting the user's biometric information, an analysis device for analyzing the user's emotional state based on the collected biometric information, and a generation device for generating visual data corresponding to the user's emotional state. This makes it possible to individually provide information that visually promotes relaxation based on the user's individual emotional state.

[0395] "Collection means" refers to a function or device for sensing and acquiring biometric information such as heart rate, body temperature, application data being used, and voice data obtained from the user.

[0396] An "analysis device" is a function or device that uses biometric information acquired through collection methods to classify a user's emotional state in real time into categories such as stress, joy, sadness, and anger, utilizing machine learning and AI algorithms.

[0397] A "generation device" is a function or device that generates or selects visual data to improve the user's emotional state based on analysis results. This may involve generating new visual content using a generation AI model.

[0398] A "display device" is a function or device that visually displays generated visual data so that users can confirm it. The timing and method of display are adjusted according to the user's preferences and environment.

[0399] This system aims to stabilize emotions by analyzing the user's emotional state in real time and providing appropriate visual information. Specifically, it is implemented using the following hardware and software.

[0400] First, the user's device collects biometric information. Specifically, it uses a smartwatch or smartphone to acquire data such as heart rate, body temperature, applications used, and voice data. This information is detected by sensors on the device and automatically recorded. The information collected by the device is transmitted to the server in real time.

[0401] Next, the server uses an AI analysis algorithm to analyze the received biometric information. This analysis uses a machine learning model to process the information and classify the user's emotional state into categories such as stress, joy, sadness, and anger. This process utilizes programming languages ​​such as Python and machine learning libraries such as TensorFlow and PyTorch. Through this analysis, the user's current emotional state can be objectively determined.

[0402] Furthermore, the server uses a generative AI model to generate visual information tailored to the user based on the analyzed emotional state. This visual information can be created by selecting existing artwork stored in a database or by generating new visual data using prompts. Potential generative AI models include Stable Diffusion and DALL-E. A concrete example of a prompt might be text such as, "Generate a relaxing natural landscape."

[0403] Finally, the generated or selected visual information is sent to the device. The device displays this information on its screen, visually promoting relaxation for the user. This display allows the user to calm their mind and more easily maintain a positive mental state.

[0404] This system allows users to reduce daily stress and obtain a visual experience tailored to their individual needs.

[0405] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0406] Step 1:

[0407] The user's device collects biometric information. Inputs include heart rate, body temperature, applications used, and voice data, obtained from sensors on smartwatches and smartphones. It also collects information about the applications running on the device. This information is temporarily stored in a database within the device.

[0408] Step 2:

[0409] The device sends the collected biometric information to the server. The input is the biometric information obtained in step 1, and the output is a data packet destined for the server. The device encrypts and transmits the data using a secure communication protocol. A low-latency network is used to ensure the information reaches the server in real time.

[0410] Step 3:

[0411] The server analyzes the biometric information it receives. The input is data sent from the terminal, and an AI analysis algorithm is used based on this information to determine the user's emotional state. For data processing, a machine learning model is used to extract features and classify emotional states into categories such as stress, joy, sadness, and anger. The output is data showing the user's emotional category and its level.

[0412] Step 4:

[0413] The server generates visual information using a generative AI model. The input consists of data about the user's emotional state obtained in step 3 and a prompt message for the generative AI model. An example of a prompt message is, "Generate a relaxing natural landscape." The AI ​​model generates a new visual image based on this. The output is the visual data to be provided to the user.

[0414] Step 5:

[0415] The server sends generated visual information to the user's terminal, which then displays it. The input is the visual data sent from the server, and the output is the image displayed on the terminal's screen. The terminal adjusts the timing and method of display according to the user's environment and preferences. This allows the user to feel visually calm.

[0416] (Application Example 1)

[0417] 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."

[0418] Traditional systems have limited ability to provide personalized information based on the user's emotional state, and have faced challenges in applying immediate emotional feedback, particularly in virtual environments. As a result, especially in virtual stores, it has been difficult to quickly provide appropriate visual information to encourage relaxation when users are feeling stressed.

[0419] 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.

[0420] In this invention, the server includes means for collecting biometric indicators, means for analyzing the user's emotional state, and means for generating visual information corresponding to the emotional state. This makes it possible to analyze the user's emotional state in real time, dynamically change the displayed content in the virtual environment, and provide visual information appropriate to each individual's emotions.

[0421] "Means for collecting biometric indicators" refers to devices or methods for acquiring physiological data such as heart rate and body temperature from users.

[0422] "Means for analyzing the emotional state of users" refers to a program or algorithm for identifying and classifying users' emotions based on collected biometric indicators.

[0423] "Means for generating visual information corresponding to emotional state" refers to a function that generates visual data such as images and videos to alleviate the user's mental state based on the analyzed emotional state.

[0424] "Means for displaying generated visual information" refers to display devices or display technologies for showing generated visual information to users.

[0425] "Means for dynamically changing the displayed content in a virtual environment based on the user's emotional state" refers to a mechanism that adapts the appearance and arrangement of information in the virtual environment in real time according to the user's current emotions.

[0426] The embodiments for carrying out the invention are as follows:

[0427] This system collects user biometric data using smart devices. Specifically, it uses wearable devices such as smart glasses to acquire data such as heart rate and body temperature in real time. These devices transmit information to a cloud server via Bluetooth or Wi-Fi.

[0428] The server receives biometric data in the cloud and then uses an AI analysis algorithm to analyze the user's emotional state. Machine learning services such as Amazon SageMaker are used for this AI analysis. Emotional states are categorized into different categories such as stress, relaxation, and excitement. Based on the analysis results, a generative AI model is used to generate visual information to alleviate the user's mental state. Image generation models such as DALL-E are used for this generation.

[0429] The generated visual information is then sent back to the device and displayed on smart glasses or a head-mounted display. This allows users to receive visual information tailored to their emotional state, making their experience in the virtual environment more comfortable.

[0430] For example, if the system determines that a user is stressed during work, it will generate a calming natural landscape and display it on their smart device. This can provide a visually relaxing effect that helps alleviate stress. An example of a prompt for the generating AI model would be, "The user needs to relax, so please generate artwork of a sunset over the sea in soft colors."

[0431] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0432] Step 1:

[0433] The terminal uses wearable devices such as smart glasses to collect biometric indicators such as heart rate and body temperature from the user in real time. Physiological data obtained from the body is used as input, and formatted digital data is obtained as output. This data is transmitted to a server via Bluetooth or Wi-Fi.

[0434] Step 2:

[0435] The server receives biometric data transmitted from the terminal. The input here is data from the terminal, and the output is data converted into an analyzable format. Based on this data, AI analysis algorithms such as Amazon SageMaker are used to analyze the user's emotional state and evaluate factors such as stress level and relaxation level.

[0436] Step 3:

[0437] The server uses a generative AI model based on the analyzed emotional state to generate visual information optimized for the user. The input here is the analysis result of the user's emotional state, and the output is the generated visual information. The output is generated as image data using tools like DALL-E, and prompts are used during this process. For example, a prompt such as "The user needs relaxation, so please generate artwork of a sunset over the sea with soft colors" might be used.

[0438] Step 4:

[0439] The server transmits the generated visual information to the terminal. The input is the generated visual information, and the output is visual data ready for display. This data is transmitted to the user's smart glasses or head-mounted display.

[0440] Step 5:

[0441] The terminal displays information on its screen based on the visual information it receives. The input is visual data received from the server, and the output is an image or video visually presented to the user. This allows the user to receive visual information optimized according to their emotions, enabling them to enjoy a more relaxed and comfortable experience in the virtual environment.

[0442] 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.

[0443] This invention is a system that incorporates an emotion engine to analyze a user's emotions from multiple perspectives and provide appropriate visual information. This system acquires biometric information and voice data through devices that the user uses on a daily basis and recognizes the user's emotions in real time based on this information.

[0444] First, the user's device collects biometric information such as heart rate and body temperature, as well as voice data and facial expression data. This data is acquired through devices such as sensors, cameras, and microphones, and stored by the device in real time.

[0445] Next, the device sends this data to the server. The server uses an emotion engine to analyze the received information. The emotion engine performs linguistic analysis on the user's voice data and recognizes the emotional tone from it. It also obtains visual emotional cues from changes in facial expressions. Furthermore, it comprehensively considers all of this information to make a detailed assessment of the user's emotional state.

[0446] The analysis mechanism within the server classifies the user's emotional state into categories such as stress, joy, anger, and sadness, based on feedback from the emotion engine. Based on these results, the generation mechanism selects or generates visual information to improve or stabilize the user's emotions.

[0447] The generated visual information is sent to the device and displayed on the screen. This display is adjusted according to the user's emotional state and is set to evoke relaxation or positive emotions.

[0448] For example, if the server detects that a user is in a high-stress state, it selects artwork depicting a calm and peaceful natural landscape and displays it through the terminal. This artwork helps the user reduce stress and relax. Thus, the present invention functions as a system that supports mental health by multidimensionally analyzing the user's emotional state and providing appropriate visual information.

[0449] The following describes the processing flow.

[0450] Step 1:

[0451] Users wear smartphones or smartwatches to collect biometric information in real time during their daily activities, including heart rate, body temperature, voice, and facial expression data. The devices automatically acquire this data through sensors, microphones, and cameras.

[0452] Step 2:

[0453] The device transmits collected biometric and voice data to the server at regular intervals. The data is anonymized for privacy protection and transmitted via a secure communication protocol.

[0454] Step 3:

[0455] The server passes the received data to the emotion engine. The emotion engine analyzes the user's speech content, tone, and speed from the audio data, and extracts emotional characteristics from the facial expression data. Through these processes, the server comprehensively evaluates the user's emotional state.

[0456] Step 4:

[0457] Based on the analysis results provided by the emotion engine, the server classifies the user's emotional state into specific categories such as stress, joy, anger, and sadness. Furthermore, it also takes into account changes and intensity of the emotions.

[0458] Step 5:

[0459] The server generates visual information optimized for the user based on their classified emotional state. The generation method involves selecting existing artwork from a database or generating new artwork using an AI model. This selection is based on the user's preferences and past feedback.

[0460] Step 6:

[0461] The server sends selected or generated visual information to the terminal. The terminal displays this information on its screen and simultaneously provides audio or music tailored to the user's current environment to enhance the effect of the visual information.

[0462] Step 7:

[0463] Users can view the displayed visual information and provide feedback to the system regarding their resulting emotional changes. This feedback is used by the system to improve the accuracy of future analysis and visual information generation.

[0464] (Example 2)

[0465] 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."

[0466] Conventional emotion analysis systems rely solely on the user's biometric information to determine their emotional state, resulting in insufficient accuracy and difficulty in providing appropriate feedback. Furthermore, there is a lack of effective means to grasp the user's multifaceted emotional state in real time and provide corresponding visual information.

[0467] 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.

[0468] In this invention, the server includes data collection means for collecting biometric information and voice data, emotion analysis means for comprehensively analyzing the user's emotional state based on the collected biometric information and voice data, and facial expression analysis means for acquiring visual emotional cues of the user. This makes it possible to analyze the user's emotional state with higher accuracy and provide appropriate visual information.

[0469] "Biometric information" refers to data that reflects the user's physical condition, such as heart rate and body temperature.

[0470] "Data collection means" refers to means for acquiring biometric information and voice data using sensors, cameras, and microphones.

[0471] "Emotional analysis tools" are means of comprehensively analyzing a user's emotional state from collected data and recognizing emotional tone and visual cues.

[0472] A "facial expression analysis method" is a means for extracting visual emotional cues from a user's facial expressions.

[0473] A "classification method" is a means of categorizing a user's emotional state based on information obtained through emotion analysis methods.

[0474] A "generative AI model" is an artificial intelligence model that selects or generates visual information in accordance with the user's emotional state.

[0475] "Visual information generation means" refers to a means of implementing a process that uses a generation AI model to generate visual information that is appropriate to the user's emotions.

[0476] "Display means" refers to means including displays and monitors for presenting generated visual information to the user.

[0477] This invention is a system that analyzes a user's emotions from multiple perspectives and provides appropriate visual information. This system utilizes a terminal that the user uses daily to acquire biometric information and voice data, thereby recognizing the user's emotions in real time. This invention is implemented using components such as a server, terminals, and a generative AI model.

[0478] The device collects biometric information, including heart rate and body temperature, as well as voice data and facial expression data. Hardware such as sensors, cameras, and microphones are used for this purpose. For example, if the device is a wearable device, sensors will directly touch the skin to measure heart rate, and microphones will listen to the user's speech and record it as voice data. In addition, cameras will capture the user's face and detect subtle changes in facial expressions.

[0479] The collected data is sent to a server where it is analyzed using sentiment analysis techniques. This sentiment analysis incorporates natural language processing on the collected audio data to analyze the tone and emotion of the voice. In addition, facial expression data is analyzed using image analysis technology to extract visual emotional cues. This allows the server to evaluate the user's emotional state in detail.

[0480] Based on the analysis results, the server uses a generative AI model to generate visual information that improves the user's emotions. For example, by inputting a prompt such as "Create a relaxing landscape that soothes the user's feelings," the generative AI model can generate a calming natural landscape painting.

[0481] The generated visual information is transmitted to the terminal and displayed on the screen. This display is optimized for the user's emotional state and is set to induce relaxation and positive emotions in the user. For example, a user experiencing stress might be shown a tranquil lakeside landscape. This invention functions as a groundbreaking system that supports mental health through technological advancements.

[0482] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0483] Step 1:

[0484] The user's device collects biometric and audio data. Sensors, microphones, and cameras are used as inputs to acquire heart rate, body temperature, audio data, and facial expression data. Data processing is performed in real time, collecting biometric information from sensors and converting audio data into digital data. A buffer of the collected data is formed as output. Specifically, the device records audio data and activates the camera to capture facial expressions.

[0485] Step 2:

[0486] The terminal sends the collected data to the server. Inputs include biometric information, voice data, and facial expression data stored in a buffer. Data transmission is secure using an encrypted, secure protocol. The server receives this data as output and prepares it for the next analysis step. Specifically, the terminal assembles data packets at regular time intervals and sends them to the server over the network.

[0487] Step 3:

[0488] The server analyzes incoming data and estimates emotional states. The server takes biometric information, voice data, and facial expression data as input. Data processing involves converting voice data to text using natural language processing, and analyzing the tone of the text using an emotion analysis model. The output is an emotional state, such as categories like joy, anger, or sadness. In its specific operation, the server extracts emotional nuances from the voice using a voice analysis algorithm and categorizes them.

[0489] Step 4:

[0490] The server uses a generative AI model to generate appropriate visual information. The input is the user's emotional state category, obtained through emotion analysis. Data processing involves inputting prompts into the generative AI model, which then generates visual data based on those prompts. The output is visual data appropriate to the user's emotion, such as relaxing soundscapes or landscape images. Specifically, the server sends a prompt to the generative AI model such as, "Generate an image that will relax the user."

[0491] Step 5:

[0492] The generated visual information is sent from the server to the terminal. The input is the visual data generated by the server. Data transmission is optimized for efficiency while reducing the amount of data using a compression algorithm. The output is the completion of the visual information on the terminal. Specifically, the terminal receives the data sent by the server and decodes it into a format that can be displayed on the screen.

[0493] Step 6:

[0494] The device displays received visual information on its screen. The input is the visual data received by the device. The visual information is displayed in a format optimized for the user's emotional state. The output is images that evoke relaxation and positive emotions. Specifically, the device presents the visual data on the display screen while adjusting the image quality.

[0495] (Application Example 2)

[0496] 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."

[0497] In physical stores, it is difficult to consistently provide an appropriate environment tailored to the emotional state of each customer. This problem stems from the limitations of traditional manual responses, which cannot immediately address the comfort needs of individual customers.

[0498] 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.

[0499] In this invention, the server includes input means for collecting biometric and environmental information, processing means for analyzing an individual's emotional state based on the collected information, and management means for controlling predetermined environmental elements based on the analysis results. This makes it possible to provide customers with a comfortable environment that responds to their emotions in real time and to create a purchasing experience that suits their individual needs.

[0500] "Biometric information" refers to data that indicates an individual's health status and physical responses, including heart rate, body temperature, and facial expressions.

[0501] "Environmental information" refers to data that indicates the physical conditions of a place where an individual is located, and includes temperature, sound level, illuminance, etc.

[0502] "Input means" refers to equipment or devices used to detect and collect biometric and environmental information.

[0503] "Processing means" refers to software or algorithms used to analyze collected information and identify an individual's emotional state.

[0504] "Management means" refers to equipment or systems that adjust environmental elements based on the analysis results obtained by processing means.

[0505] "Output means" refers to devices or mechanisms that present environmental elements tailored to an individual and provide emotional care.

[0506] "Emotional care" refers to environmental adjustments or support aimed at promoting relaxation and stress reduction, taking into account an individual's emotional state.

[0507] The system that realizes this application consists of a combination of multiple hardware and software components. The server is responsible for collecting the user's biometric and environmental information in real time and analyzing their emotional state. Sensors and cameras are used as input means in this process. Specifically, it uses a face recognition camera and voice capture device in conjunction with a Raspberry Pi, as well as various sensors to detect the environment. This allows for the continuous acquisition of the user's heart rate, voice tone, and indoor environmental conditions.

[0508] Next, the collected data is sent to a server. On the server side, the received data is analyzed in detail using analysis software based on Python and libraries such as DeepFace and TensorFlow. This analysis detects the emotional state of each user and determines the optimal environmental elements according to that state. The management system then generates recommendations based on the analysis results and controls the store's lighting and sound equipment to present the user with the optimal environment.

[0509] For example, if the server analyzes that a user's stress level is high, a system controlled by a Raspberry Pi could deploy soft music and lighting in the store to enhance relaxation. This would allow individual users to naturally experience comfort in the space, and improve overall customer satisfaction for the store.

[0510] As an example of a prompt, it is possible to instruct the AI ​​model using a format such as, "I want to create a Python program that performs real-time emotion analysis from facial images captured using DeepFace, and then integrates the control of the music and lighting systems in the store based on the acquired emotion data."

[0511] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0512] Step 1:

[0513] The terminal collects the user's biometric and environmental information. Input here includes biometric information acquired by a facial recognition camera and voice capture device, as well as temperature, volume, and illuminance information obtained by environmental sensors. This data is then processed and prepared for transmission to the server.

[0514] Step 2:

[0515] The server analyzes biometric and environmental information received from the terminal. The input consists of collected biometric and environmental data. The DeepFace library is used to analyze emotional data from facial images, and TensorFlow is used to analyze tone from audio data. As a data processing step, each piece of information is output as a numerical representation of the emotional state.

[0516] Step 3:

[0517] The server evaluates the user's emotional state based on the analysis results and determines appropriate environmental adjustments. The input here is a quantified emotional state, and data processing is performed to generate control information such as lighting brightness and music selection based on this evaluation. The output is a control command for adjusting the environment.

[0518] Step 4:

[0519] The terminal executes control commands received from the server, adjusting the lighting and sound equipment within the store. The input consists of control commands to adjust the environment, and based on these, the terminal operates the actual hardware to generate output that provides comfortable emotional care for the user.

[0520] 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.

[0521] 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.

[0522] 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.

[0523] [Fourth Embodiment]

[0524] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0525] 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.

[0526] 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).

[0527] 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.

[0528] 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.

[0529] 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).

[0530] 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.

[0531] 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.

[0532] 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.

[0533] 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.

[0534] 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.

[0535] 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.

[0536] 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".

[0537] This invention provides a system configuration and embodiments for a system that analyzes a user's emotional state and provides appropriate visual information in order to stabilize the user's mind. The system includes a process for analyzing the user's emotional state in real time using biometric information obtained from the user's device.

[0538] This system first uses the user's device to acquire biometric information such as heart rate, body temperature, applications used, and voice data. This information is collected unconsciously during the user's daily activities and transmitted from the device to the server.

[0539] The server uses AI analysis algorithms to analyze the user's emotional state based on the received biometric information. The emotional state is categorized into stress, joy, sadness, anger, etc., and evaluated using specific indicators.

[0540] Next, the server generates visual information to improve or stabilize the user's emotional state based on the analysis results. This generated visual information may be selected from artworks stored in a database, or it may be newly generated using an AI model.

[0541] The generated visual information is sent to the device and displayed on the screen to help the user relax. The timing and method of display of the visual information are adjusted according to the user's preferences and environment.

[0542] For example, if the server determines that a user's heart rate is elevated due to work stress, it will select artwork depicting a calming natural landscape and display it on the device's screen. In this way, the user can have a visually calming experience, helping them regain a positive mental state.

[0543] This system features the automatic generation and display of artwork in response to changes in emotions, and aims to continuously improve the user's emotional state.

[0544] The following describes the processing flow.

[0545] Step 1:

[0546] Users wear devices such as smartwatches and smartphones to collect biometric information during their daily activities. These devices acquire data in real time, including heart rate, body temperature, steps taken, and information from applications being used.

[0547] Step 2:

[0548] The device transmits acquired biometric information to the server at regular intervals. This data transmission is securely performed using an encrypted protocol.

[0549] Step 3:

[0550] The server analyzes the received biometric information. Using an AI algorithm, it estimates the user's emotional state from this data. In this process, it infers from heart rate fluctuations and the applications being used to determine emotional categories such as stress, anger, and relaxation.

[0551] Step 4:

[0552] The server generates appropriate visual information based on the analysis results. Depending on the user's emotional state, it either selects an existing artwork from the database or uses AI to generate new artwork.

[0553] Step 5:

[0554] The server sends selected or generated visual information to the terminal. This transmission includes suggestions that guide the user's emotions towards a positive state.

[0555] Step 6:

[0556] The device displays received visual information on its screen. The display is visually adjusted according to the user's lighting environment and the device's position. The user can appreciate this artwork and achieve emotional stability.

[0557] Step 7:

[0558] Users can input their impressions and emotional changes after viewing visual information into the system as feedback. This feedback will be used to improve the accuracy of future emotion analysis.

[0559] (Example 1)

[0560] 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".

[0561] In modern society, users frequently encounter situations that cause stress on a daily basis. Therefore, there is an urgent need for technology that can analyze users' emotional states in real time and stabilize them in an appropriate manner. However, current technology lacks personalized information delivery methods to visually promote relaxation according to the user's emotional state. Consequently, a system that provides effective visual information tailored to each user's individual emotional state is required.

[0562] 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.

[0563] In this invention, the server includes a collection means for collecting the user's biometric information, an analysis device for analyzing the user's emotional state based on the collected biometric information, and a generation device for generating visual data corresponding to the user's emotional state. This makes it possible to individually provide information that visually promotes relaxation based on the user's individual emotional state.

[0564] "Collection means" refers to a function or device for sensing and acquiring biometric information such as heart rate, body temperature, application data being used, and voice data obtained from the user.

[0565] An "analysis device" is a function or device that uses biometric information acquired through collection methods to classify a user's emotional state in real time into categories such as stress, joy, sadness, and anger, utilizing machine learning and AI algorithms.

[0566] A "generation device" is a function or device that generates or selects visual data to improve the user's emotional state based on analysis results. This may involve generating new visual content using a generation AI model.

[0567] A "display device" is a function or device that visually displays generated visual data so that users can confirm it. The timing and method of display are adjusted according to the user's preferences and environment.

[0568] This system aims to stabilize emotions by analyzing the user's emotional state in real time and providing appropriate visual information. Specifically, it is implemented using the following hardware and software.

[0569] First, the user's device collects biometric information. Specifically, it uses a smartwatch or smartphone to acquire data such as heart rate, body temperature, applications used, and voice data. This information is detected by sensors on the device and automatically recorded. The information collected by the device is transmitted to the server in real time.

[0570] Next, the server uses an AI analysis algorithm to analyze the received biometric information. This analysis uses a machine learning model to process the information and classify the user's emotional state into categories such as stress, joy, sadness, and anger. This process utilizes programming languages ​​such as Python and machine learning libraries such as TensorFlow and PyTorch. Through this analysis, the user's current emotional state can be objectively determined.

[0571] Furthermore, the server uses a generative AI model to generate visual information tailored to the user based on the analyzed emotional state. This visual information can be created by selecting existing artwork stored in a database or by generating new visual data using prompts. Potential generative AI models include Stable Diffusion and DALL-E. A concrete example of a prompt might be text such as, "Generate a relaxing natural landscape."

[0572] Finally, the generated or selected visual information is sent to the device. The device displays this information on its screen, visually promoting relaxation for the user. This display allows the user to calm their mind and more easily maintain a positive mental state.

[0573] This system allows users to reduce daily stress and obtain a visual experience tailored to their individual needs.

[0574] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0575] Step 1:

[0576] The user's device collects biometric information. Inputs include heart rate, body temperature, applications used, and voice data, obtained from sensors on smartwatches and smartphones. It also collects information about the applications running on the device. This information is temporarily stored in a database within the device.

[0577] Step 2:

[0578] The device sends the collected biometric information to the server. The input is the biometric information obtained in step 1, and the output is a data packet destined for the server. The device encrypts and transmits the data using a secure communication protocol. A low-latency network is used to ensure the information reaches the server in real time.

[0579] Step 3:

[0580] The server analyzes the biometric information it receives. The input is data sent from the terminal, and an AI analysis algorithm is used based on this information to determine the user's emotional state. For data processing, a machine learning model is used to extract features and classify emotional states into categories such as stress, joy, sadness, and anger. The output is data showing the user's emotional category and its level.

[0581] Step 4:

[0582] The server generates visual information using a generative AI model. The input consists of data about the user's emotional state obtained in step 3 and a prompt message for the generative AI model. An example of a prompt message is, "Generate a relaxing natural landscape." The AI ​​model generates a new visual image based on this. The output is the visual data to be provided to the user.

[0583] Step 5:

[0584] The server sends generated visual information to the user's terminal, which then displays it. The input is the visual data sent from the server, and the output is the image displayed on the terminal's screen. The terminal adjusts the timing and method of display according to the user's environment and preferences. This allows the user to feel visually calm.

[0585] (Application Example 1)

[0586] 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".

[0587] Traditional systems have limited ability to provide personalized information based on the user's emotional state, and have faced challenges in applying immediate emotional feedback, particularly in virtual environments. As a result, especially in virtual stores, it has been difficult to quickly provide appropriate visual information to encourage relaxation when users are feeling stressed.

[0588] 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.

[0589] In this invention, the server includes means for collecting biometric indicators, means for analyzing the user's emotional state, and means for generating visual information corresponding to the emotional state. This makes it possible to analyze the user's emotional state in real time, dynamically change the displayed content in the virtual environment, and provide visual information appropriate to each individual's emotions.

[0590] "Means for collecting biometric indicators" refers to devices or methods for acquiring physiological data such as heart rate and body temperature from users.

[0591] "Means for analyzing the emotional state of users" refers to a program or algorithm for identifying and classifying users' emotions based on collected biometric indicators.

[0592] "Means for generating visual information corresponding to emotional state" refers to a function that generates visual data such as images and videos to alleviate the user's mental state based on the analyzed emotional state.

[0593] "Means for displaying generated visual information" refers to display devices or display technologies for showing generated visual information to users.

[0594] "Means for dynamically changing the displayed content in a virtual environment based on the user's emotional state" refers to a mechanism that adapts the appearance and arrangement of information in the virtual environment in real time according to the user's current emotions.

[0595] The embodiments for carrying out the invention are as follows:

[0596] This system collects user biometric data using smart devices. Specifically, it uses wearable devices such as smart glasses to acquire data such as heart rate and body temperature in real time. These devices transmit information to a cloud server via Bluetooth or Wi-Fi.

[0597] The server receives biometric data in the cloud and then uses an AI analysis algorithm to analyze the user's emotional state. Machine learning services such as Amazon SageMaker are used for this AI analysis. Emotional states are categorized into different categories such as stress, relaxation, and excitement. Based on the analysis results, a generative AI model is used to generate visual information to alleviate the user's mental state. Image generation models such as DALL-E are used for this generation.

[0598] The generated visual information is then sent back to the device and displayed on smart glasses or a head-mounted display. This allows users to receive visual information tailored to their emotional state, making their experience in the virtual environment more comfortable.

[0599] For example, if the system determines that a user is stressed during work, it will generate a calming natural landscape and display it on their smart device. This can provide a visually relaxing effect that helps alleviate stress. An example of a prompt for the generating AI model would be, "The user needs to relax, so please generate artwork of a sunset over the sea in soft colors."

[0600] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0601] Step 1:

[0602] The terminal uses wearable devices such as smart glasses to collect biometric indicators such as heart rate and body temperature from the user in real time. Physiological data obtained from the body is used as input, and formatted digital data is obtained as output. This data is transmitted to a server via Bluetooth or Wi-Fi.

[0603] Step 2:

[0604] The server receives biometric data transmitted from the terminal. The input here is data from the terminal, and the output is data converted into an analyzable format. Based on this data, AI analysis algorithms such as Amazon SageMaker are used to analyze the user's emotional state and evaluate factors such as stress level and relaxation level.

[0605] Step 3:

[0606] The server uses a generative AI model based on the analyzed emotional state to generate visual information optimized for the user. The input here is the analysis result of the user's emotional state, and the output is the generated visual information. The output is generated as image data using tools like DALL-E, and prompts are used during this process. For example, a prompt such as "The user needs relaxation, so please generate artwork of a sunset over the sea with soft colors" might be used.

[0607] Step 4:

[0608] The server transmits the generated visual information to the terminal. The input is the generated visual information, and the output is visual data ready for display. This data is transmitted to the user's smart glasses or head-mounted display.

[0609] Step 5:

[0610] The terminal displays information on its screen based on the visual information it receives. The input is visual data received from the server, and the output is an image or video visually presented to the user. This allows the user to receive visual information optimized according to their emotions, enabling them to enjoy a more relaxed and comfortable experience in the virtual environment.

[0611] 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.

[0612] This invention is a system that incorporates an emotion engine to analyze a user's emotions from multiple perspectives and provide appropriate visual information. This system acquires biometric information and voice data through devices that the user uses on a daily basis and recognizes the user's emotions in real time based on this information.

[0613] First, the user's device collects biometric information such as heart rate and body temperature, as well as voice data and facial expression data. This data is acquired through devices such as sensors, cameras, and microphones, and stored by the device in real time.

[0614] Next, the device sends this data to the server. The server uses an emotion engine to analyze the received information. The emotion engine performs linguistic analysis on the user's voice data and recognizes the emotional tone from it. It also obtains visual emotional cues from changes in facial expressions. Furthermore, it comprehensively considers all of this information to make a detailed assessment of the user's emotional state.

[0615] The analysis mechanism within the server classifies the user's emotional state into categories such as stress, joy, anger, and sadness, based on feedback from the emotion engine. Based on these results, the generation mechanism selects or generates visual information to improve or stabilize the user's emotions.

[0616] The generated visual information is sent to the device and displayed on the screen. This display is adjusted according to the user's emotional state and is set to evoke relaxation or positive emotions.

[0617] For example, if the server detects that a user is in a high-stress state, it selects artwork depicting a calm and peaceful natural landscape and displays it through the terminal. This artwork helps the user reduce stress and relax. Thus, the present invention functions as a system that supports mental health by multidimensionally analyzing the user's emotional state and providing appropriate visual information.

[0618] The following describes the processing flow.

[0619] Step 1:

[0620] Users wear smartphones or smartwatches to collect biometric information in real time during their daily activities, including heart rate, body temperature, voice, and facial expression data. The devices automatically acquire this data through sensors, microphones, and cameras.

[0621] Step 2:

[0622] The device transmits collected biometric and voice data to the server at regular intervals. The data is anonymized for privacy protection and transmitted via a secure communication protocol.

[0623] Step 3:

[0624] The server passes the received data to the emotion engine. The emotion engine analyzes the user's speech content, tone, and speed from the audio data, and extracts emotional characteristics from the facial expression data. Through these processes, the server comprehensively evaluates the user's emotional state.

[0625] Step 4:

[0626] Based on the analysis results provided by the emotion engine, the server classifies the user's emotional state into specific categories such as stress, joy, anger, and sadness. Furthermore, it also takes into account changes and intensity of the emotions.

[0627] Step 5:

[0628] The server generates visual information optimized for the user based on their classified emotional state. The generation method involves selecting existing artwork from a database or generating new artwork using an AI model. This selection is based on the user's preferences and past feedback.

[0629] Step 6:

[0630] The server sends selected or generated visual information to the terminal. The terminal displays this information on its screen and simultaneously provides audio or music tailored to the user's current environment to enhance the effect of the visual information.

[0631] Step 7:

[0632] Users can view the displayed visual information and provide feedback to the system regarding their resulting emotional changes. This feedback is used by the system to improve the accuracy of future analysis and visual information generation.

[0633] (Example 2)

[0634] 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".

[0635] Conventional emotion analysis systems rely solely on the user's biometric information to determine their emotional state, resulting in insufficient accuracy and difficulty in providing appropriate feedback. Furthermore, there is a lack of effective means to grasp the user's multifaceted emotional state in real time and provide corresponding visual information.

[0636] 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.

[0637] In this invention, the server includes data collection means for collecting biometric information and voice data, emotion analysis means for comprehensively analyzing the user's emotional state based on the collected biometric information and voice data, and facial expression analysis means for acquiring visual emotional cues of the user. This makes it possible to analyze the user's emotional state with higher accuracy and provide appropriate visual information.

[0638] "Biometric information" refers to data that reflects the user's physical condition, such as heart rate and body temperature.

[0639] "Data collection means" refers to means for acquiring biometric information and voice data using sensors, cameras, and microphones.

[0640] "Emotional analysis tools" are means of comprehensively analyzing a user's emotional state from collected data and recognizing emotional tone and visual cues.

[0641] A "facial expression analysis method" is a means for extracting visual emotional cues from a user's facial expressions.

[0642] A "classification method" is a means of categorizing a user's emotional state based on information obtained through emotion analysis methods.

[0643] A "generative AI model" is an artificial intelligence model that selects or generates visual information in accordance with the user's emotional state.

[0644] "Visual information generation means" refers to a means of implementing a process that uses a generation AI model to generate visual information that is appropriate to the user's emotions.

[0645] "Display means" refers to means including displays and monitors for presenting generated visual information to the user.

[0646] This invention is a system that analyzes a user's emotions from multiple perspectives and provides appropriate visual information. This system utilizes a terminal that the user uses daily to acquire biometric information and voice data, thereby recognizing the user's emotions in real time. This invention is implemented using components such as a server, terminals, and a generative AI model.

[0647] The device collects biometric information, including heart rate and body temperature, as well as voice data and facial expression data. Hardware such as sensors, cameras, and microphones are used for this purpose. For example, if the device is a wearable device, sensors will directly touch the skin to measure heart rate, and microphones will listen to the user's speech and record it as voice data. In addition, cameras will capture the user's face and detect subtle changes in facial expressions.

[0648] The collected data is sent to a server where it is analyzed using sentiment analysis techniques. This sentiment analysis incorporates natural language processing on the collected audio data to analyze the tone and emotion of the voice. In addition, facial expression data is analyzed using image analysis technology to extract visual emotional cues. This allows the server to evaluate the user's emotional state in detail.

[0649] Based on the analysis results, the server uses a generative AI model to generate visual information that improves the user's emotions. For example, by inputting a prompt such as "Create a relaxing landscape that soothes the user's feelings," the generative AI model can generate a calming natural landscape painting.

[0650] The generated visual information is transmitted to the terminal and displayed on the screen. This display is optimized for the user's emotional state and is set to induce relaxation and positive emotions in the user. For example, a user experiencing stress might be shown a tranquil lakeside landscape. This invention functions as a groundbreaking system that supports mental health through technological advancements.

[0651] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0652] Step 1:

[0653] The user's device collects biometric and audio data. Sensors, microphones, and cameras are used as inputs to acquire heart rate, body temperature, audio data, and facial expression data. Data processing is performed in real time, collecting biometric information from sensors and converting audio data into digital data. A buffer of the collected data is formed as output. Specifically, the device records audio data and activates the camera to capture facial expressions.

[0654] Step 2:

[0655] The terminal sends the collected data to the server. Inputs include biometric information, voice data, and facial expression data stored in a buffer. Data transmission is secure using an encrypted, secure protocol. The server receives this data as output and prepares it for the next analysis step. Specifically, the terminal assembles data packets at regular time intervals and sends them to the server over the network.

[0656] Step 3:

[0657] The server analyzes incoming data and estimates emotional states. The server takes biometric information, voice data, and facial expression data as input. Data processing involves converting voice data to text using natural language processing, and analyzing the tone of the text using an emotion analysis model. The output is an emotional state, such as categories like joy, anger, or sadness. In its specific operation, the server extracts emotional nuances from the voice using a voice analysis algorithm and categorizes them.

[0658] Step 4:

[0659] The server uses a generative AI model to generate appropriate visual information. The input is the user's emotional state category, obtained through emotion analysis. Data processing involves inputting prompts into the generative AI model, which then generates visual data based on those prompts. The output is visual data appropriate to the user's emotion, such as relaxing soundscapes or landscape images. Specifically, the server sends a prompt to the generative AI model such as, "Generate an image that will relax the user."

[0660] Step 5:

[0661] The generated visual information is sent from the server to the terminal. The input is the visual data generated by the server. Data transmission is optimized for efficiency while reducing the amount of data using a compression algorithm. The output is the completion of the visual information on the terminal. Specifically, the terminal receives the data sent by the server and decodes it into a format that can be displayed on the screen.

[0662] Step 6:

[0663] The device displays received visual information on its screen. The input is the visual data received by the device. The visual information is displayed in a format optimized for the user's emotional state. The output is images that evoke relaxation and positive emotions. Specifically, the device presents the visual data on the display screen while adjusting the image quality.

[0664] (Application Example 2)

[0665] 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".

[0666] In physical stores, it is difficult to consistently provide an appropriate environment tailored to the emotional state of each customer. This problem stems from the limitations of traditional manual responses, which cannot immediately address the comfort needs of individual customers.

[0667] 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.

[0668] In this invention, the server includes input means for collecting biometric and environmental information, processing means for analyzing an individual's emotional state based on the collected information, and management means for controlling predetermined environmental elements based on the analysis results. This makes it possible to provide customers with a comfortable environment that responds to their emotions in real time and to create a purchasing experience that suits their individual needs.

[0669] "Biometric information" refers to data that indicates an individual's health status and physical responses, including heart rate, body temperature, and facial expressions.

[0670] "Environmental information" refers to data that indicates the physical conditions of a place where an individual is located, and includes temperature, sound level, illuminance, etc.

[0671] "Input means" refers to equipment or devices used to detect and collect biometric and environmental information.

[0672] "Processing means" refers to software or algorithms used to analyze collected information and identify an individual's emotional state.

[0673] "Management means" refers to equipment or systems that adjust environmental elements based on the analysis results obtained by processing means.

[0674] "Output means" refers to devices or mechanisms that present environmental elements tailored to an individual and provide emotional care.

[0675] "Emotional care" refers to environmental adjustments or support aimed at promoting relaxation and stress reduction, taking into account an individual's emotional state.

[0676] The system that realizes this application consists of a combination of multiple hardware and software components. The server is responsible for collecting the user's biometric and environmental information in real time and analyzing their emotional state. Sensors and cameras are used as input means in this process. Specifically, it uses a face recognition camera and voice capture device in conjunction with a Raspberry Pi, as well as various sensors to detect the environment. This allows for the continuous acquisition of the user's heart rate, voice tone, and indoor environmental conditions.

[0677] Next, the collected data is sent to a server. On the server side, the received data is analyzed in detail using analysis software based on Python and libraries such as DeepFace and TensorFlow. This analysis detects the emotional state of each user and determines the optimal environmental elements according to that state. The management system then generates recommendations based on the analysis results and controls the store's lighting and sound equipment to present the user with the optimal environment.

[0678] For example, if the server analyzes that a user's stress level is high, a system controlled by a Raspberry Pi could deploy soft music and lighting in the store to enhance relaxation. This would allow individual users to naturally experience comfort in the space, and improve overall customer satisfaction for the store.

[0679] As an example of a prompt, it is possible to instruct the AI ​​model using a format such as, "I want to create a Python program that performs real-time emotion analysis from facial images captured using DeepFace, and then integrates the control of the music and lighting systems in the store based on the acquired emotion data."

[0680] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0681] Step 1:

[0682] The terminal collects the user's biometric and environmental information. Input here includes biometric information acquired by a facial recognition camera and voice capture device, as well as temperature, volume, and illuminance information obtained by environmental sensors. This data is then processed and prepared for transmission to the server.

[0683] Step 2:

[0684] The server analyzes biometric and environmental information received from the terminal. The input consists of collected biometric and environmental data. The DeepFace library is used to analyze emotional data from facial images, and TensorFlow is used to analyze tone from audio data. As a data processing step, each piece of information is output as a numerical representation of the emotional state.

[0685] Step 3:

[0686] The server evaluates the user's emotional state based on the analysis results and determines appropriate environmental adjustments. The input here is a quantified emotional state, and data processing is performed to generate control information such as lighting brightness and music selection based on this evaluation. The output is a control command for adjusting the environment.

[0687] Step 4:

[0688] The terminal executes control commands received from the server, adjusting the lighting and sound equipment within the store. The input consists of control commands to adjust the environment, and based on these, the terminal operates the actual hardware to generate output that provides comfortable emotional care for the user.

[0689] 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.

[0690] 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.

[0691] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0692] 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.

[0693] 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.

[0694] 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.

[0695] 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.

[0696] 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.

[0697] 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."

[0698] 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.

[0699] 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.

[0700] 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.

[0701] 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.

[0702] 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.

[0703] 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.

[0704] 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.

[0705] 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.

[0706] 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.

[0707] 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.

[0708] 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.

[0709] 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.

[0710] The following is further disclosed regarding the embodiments described above.

[0711] (Claim 1)

[0712] A means of collecting biometric information,

[0713] An analytical means for analyzing the user's emotional state based on collected biometric information,

[0714] A means for generating visual information corresponding to the user's emotional state,

[0715] A display means for displaying the generated visual information,

[0716] A system that includes this.

[0717] (Claim 2)

[0718] The system according to claim 1, wherein the analysis means detects the user's stress level based on changes in biological information.

[0719] (Claim 3)

[0720] The system according to claim 1, wherein the generating means selects visual information that promotes relaxation to the user based on the analysis results.

[0721] "Example 1"

[0722] (Claim 1)

[0723] A means of collecting user biometric information,

[0724] An analysis device for analyzing the emotional state of users based on collected biometric information,

[0725] A generation device for generating visual data corresponding to the user's emotional state,

[0726] A display device for outputting the generated visual data to a display device,

[0727] A system that includes this.

[0728] (Claim 2)

[0729] The system according to claim 1, wherein the analysis device detects the user's stress level based on fluctuations in biological information.

[0730] (Claim 3)

[0731] The system according to claim 1, wherein the generating device selects or generates visual data that provides comfort to the user based on the analysis results.

[0732] "Application Example 1"

[0733] (Claim 1)

[0734] Means for collecting biometric indicators,

[0735] A means of analyzing the emotional state of users based on collected biometric indicators,

[0736] A means for generating visual information corresponding to the user's emotional state,

[0737] Means for displaying the generated visual information,

[0738] A means for dynamically changing the displayed content within a virtual environment based on the user's emotional state,

[0739] A system that includes this.

[0740] (Claim 2)

[0741] The system according to claim 1, wherein the analysis means calculates the user's stress level based on changes in collected biometric indicators.

[0742] (Claim 3)

[0743] The system according to claim 1, wherein the generating means selects visual information that encourages the user to rest based on the analysis results.

[0744] "Example 2 of combining an emotion engine"

[0745] (Claim 1)

[0746] A data collection means for collecting biometric information and voice data,

[0747] An emotion analysis means that comprehensively analyzes the user's emotional state based on collected biometric information and voice data,

[0748] A facial expression analysis method for obtaining visual emotional cues from the user,

[0749] The analysis means is a classification means that uses an emotion engine to classify emotional states,

[0750] A visual information generation means including a generative AI model that selects or generates visual information according to the user's emotional state,

[0751] A display means for displaying the generated visual information on a display,

[0752] A system that includes this.

[0753] (Claim 2)

[0754] The system according to claim 1, wherein the analysis means detects the user's stress level based on biological information and changes in facial expression.

[0755] (Claim 3)

[0756] The system according to claim 1, wherein the generation means inputs a prompt sentence to a generation AI model based on the analysis results and generates visual information that encourages the user to relax.

[0757] "Application example 2 when combining with an emotional engine"

[0758] (Claim 1)

[0759] An input means for collecting biometric and environmental information,

[0760] A processing means for analyzing an individual's emotional state based on collected biometric and environmental information,

[0761] A management means for controlling predetermined environmental elements based on analysis results,

[0762] An output means that provides emotional care through controlled environmental elements,

[0763] A system that includes this.

[0764] (Claim 2)

[0765] The system according to claim 1, wherein the processing means detects an individual's stress state based on changes in biological information and environmental information.

[0766] (Claim 3)

[0767] The system according to claim 1, wherein the management means selects environmental elements that promote relaxation for an individual based on the analysis results. [Explanation of Symbols]

[0768] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting biometric information, An analytical means for analyzing the user's emotional state based on collected biometric information, A means for generating visual information corresponding to the user's emotional state, A display means for displaying the generated visual information, A system that includes this.

2. The system according to claim 1, wherein the analysis means detects the user's stress level based on changes in biological information.

3. The system according to claim 1, wherein the generating means selects visual information that promotes relaxation to the user based on the analysis results.

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