system

Abstract

We provide the system. [Solution] A means for acquiring user gaze data using a gaze detection device, A means for analyzing the user's device usage posture and usage time using the aforementioned eye-tracking data, A means of sending notifications to users to encourage them to take a break based on the analysis results, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of 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] There is a need to prevent health problems that may be caused by long - term use of smart devices, especially symptoms such as visual fatigue, headaches due to straight neck, insomnia, dizziness, and nausea. Conventional alert systems regarding device use were not based on the specific usage status and posture of the user and were simple timer - type, making it difficult to give timely and appropriate break notifications according to individual conditions. Against such a background, it is an issue to provide a system that responds to the individual device usage status of users and promotes healthy device use.

Means for Solving the Problems

[0005] This invention provides a means for acquiring user eye-tracking data in real time using an eye-tracking device and analyzing the user's device usage posture and usage time. Furthermore, based on this analysis, the system measures the distance between the user and the device using the user's eye-tracking data and sends a notification prompting a break if the distance exceeds a predetermined threshold. In addition, by incorporating means for receiving user feedback and optimizing the notification timing based on that information, the system provides a system that can indicate a more appropriate break time for each individual user.

[0006] A "gaze detection device" is a device that detects the user's gaze and pupil position and analyzes their movements.

[0007] "Eye-gaze data" is a collection of information related to a user's gaze, such as the position of their eyes, the angle of their gaze, and the movement of their pupils.

[0008] "Device usage posture" refers to the position and angle of the user's body and head when using a device.

[0009] "Analysis" is the process of evaluating user behavior and status based on specific data, and deriving relevant results and trends.

[0010] "Distance" refers to the physical space between the user's eyes and the device's screen, and is measured using eye-tracking data.

[0011] A "threshold" is a reference value set for making a specific judgment or taking a particular action.

[0012] A "notification" is a message sent to a user via their device that contains information to prompt specific action.

[0013] "Feedback" refers to information and reactions received from users, which are used by the system to optimize its subsequent operations.

[0014] "Optimization" is the process of adjusting and improving systems and processes in order to maximize efficiency and effectiveness. [Brief explanation of the drawing]

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

Embodiments for Carrying Out the Invention

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

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

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

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

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

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

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

[0023] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0036] In an embodiment of the present invention, a program embedded in a smart device plays a major role. This program works in conjunction with an eye-tracking device to acquire the user's eye-tracking data and analyzes this data in real time to evaluate the user's device usage posture and time.

[0037] The program first activates the front camera on the device and continuously captures the user's gaze. Eye-tracking software installed on the device analyzes the position of the user's pupils and the movement of their eyes, recording this as gaze data. This gaze data includes information such as the user's gaze angle and distance from the screen.

[0038] This data is then sent to an AI algorithm within the device to analyze the user's eye movements and device usage time. The AI ​​algorithm analyzes patterns of changes in gaze angle and distance to determine whether the user's posture is inappropriate or whether the usage time may be affecting their health.

[0039] Based on this analysis, the server periodically receives data and generates notifications prompting users to take breaks at appropriate times. Notification criteria include situations where the distance between the user and device (based on eye-tracking data) exceeds a threshold, or when continuous use for a certain period of time is detected. Furthermore, feedback from users is received and managed as information to individually optimize break timing.

[0040] As a concrete example, consider a situation where a user is using a news reading app. Because this user is looking at the device at close range for an extended period, the device continuously collects eye-tracking data. As a result, the time spent at a distance of less than 20 cm exceeds a certain limit. In such cases, the server sends a notification to the user's screen via the device saying, "Please move away from the screen for a while. Take a break from your eyes."

[0041] This invention allows users to reduce potential health risks associated with their device use and develop healthier usage habits.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The device activates its front camera and captures the user's gaze. At this point, eye-tracking software runs to detect the user's pupil position and gaze angle in real time and collect gaze data.

[0045] Step 2:

[0046] The device sends the eye-tracking data it acquires to an AI algorithm. The AI ​​analyzes the data and evaluates the user's posture and device usage based on the user's eye movements and distance from the device.

[0047] Step 3:

[0048] The device sends analysis results to the server at regular intervals (e.g., every 10 minutes). This data includes the angle of gaze, the distance between the user and the device, and the continuous usage time.

[0049] Step 4:

[0050] The server determines whether a break is necessary based on the data it receives. The need for a break is determined if the angle or distance of the user's gaze exceeds a set threshold, or if continuous use is detected.

[0051] Step 5:

[0052] The server generates and sends a notification message to the device based on its own judgment. Specific alert messages include content encouraging users to use their devices in a healthy manner.

[0053] Step 6:

[0054] The device prompts the user to take a break by displaying notifications received from the server. The notifications are displayed visually on the screen and may include phrases such as "Let's rest our eyes."

[0055] Step 7:

[0056] When a user takes a break based on a notification, they input that action into their device. One possible format is for the user to press a button to indicate that they have started a break.

[0057] Step 8:

[0058] The server receives feedback from the user and adjusts the timing of the next notification. This optimizes notifications, allowing users to be prompted to take breaks at more effective times.

[0059] (Example 1)

[0060] 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."

[0061] Excessive use of data devices in modern society can negatively impact user health. In particular, prolonged use can lead to vision problems and other health issues if the user's gaze position or distance from the device is inappropriate. However, determining appropriate break times is difficult for users, and it's challenging for them to be mindful of their usage.

[0062] 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.

[0063] In this invention, the server includes means for collecting user eye-tracking information, means for analyzing the eye-tracking information to evaluate the user's posture and usage time when using the data device, and means for notifying the user to take a break based on the evaluation results. This encourages the user to take breaks at appropriate times, enabling health-conscious use of the device.

[0064] A "user" refers to an individual operating a data device, and the system analyzes their gaze and actions.

[0065] "Eye-gaze information" includes data about the position and movement of the user's eyes, their angle, and their distance from the device.

[0066] "Data equipment" refers to electronic devices used by users, specifically terminals used to acquire eye-tracking information.

[0067] "Evaluation" refers to the analytical process of determining user behavior and health impacts based on collected eye-tracking data.

[0068] "Notifications" refer to displaying messages to users recommending breaks based on analysis results.

[0069] A "generative AI model" is a type of artificial intelligence used to create notification content based on eye-tracking information and user feedback, and to optimize it for individual users.

[0070] "Feedback" refers to reference information collected from users' responses and opinions to notifications.

[0071] To implement the present invention, the terminal must first incorporate an in-camera and eye-tracking software. When the user begins using the data device, the terminal activates the in-camera and continuously captures gaze information regarding the position and movement of the user's pupils. This gaze information serves as basic data for understanding the user's posture and physical distance from the device.

[0072] The eye-tracking software within the device analyzes this captured gaze information. The analysis records numerical data such as gaze angle, movement patterns, and distance from the device, and uses this data to evaluate the user's usage. This helps determine how the user's data device usage might affect their health.

[0073] The server receives data sent from the terminal and uses a generative AI model to generate appropriate notifications for the user. These notifications are designed to encourage the user to take a break and are optimized based on each user's usage patterns. For example, if a user has been looking at the screen at close range for an extended period, a message such as "Take a step back from the screen. Rest your eyes." will be generated and displayed to the user by the terminal.

[0074] User feedback is used for further data collection and analysis to make notifications more effective. The server aggregates this feedback and incorporates it into the generating AI model, resulting in notification content and timing that are more tailored to the user's situation.

[0075] As a concrete example, a possible prompt for a generative AI model might be, "How can we optimize the timing of health-conscious breaks based on eye-tracking data?" This prompt allows the AI ​​to improve notifications and support users in using data devices in a healthier way.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The device activates the front camera.

[0079] When a user begins using a data device, the terminal automatically activates its front-facing camera. The input is data of the user's gaze, and based on this, it continuously captures information about the position of the eyes. The output is raw data including the position of the user's face and eyes.

[0080] Step 2:

[0081] The device analyzes eye-tracking information.

[0082] Eye-tracking software analyzes the user's pupil movements based on captured data. This analysis calculates the angle of gaze, movement patterns, and distance from the device. The input is raw data captured by the front camera, and the output is digitized gaze information.

[0083] Step 3:

[0084] The device sends data to the AI ​​algorithm.

[0085] The device transmits the analyzed eye-tracking information to an AI algorithm. The AI ​​uses this information to evaluate the user's device usage. The input is quantified eye-tracking information, and the output is the user's health status assessment result.

[0086] Step 4:

[0087] The server generates the notification.

[0088] Based on the AI-generated evaluation results, the server uses a generative AI model to generate notifications prompting users to take a break. The input is the user's health status evaluation result, and the output is a notification message such as "Take a break from the screen."

[0089] Step 5:

[0090] The server collects user feedback.

[0091] The server collects user feedback on notifications and incorporates it into the generating AI model. The input is user feedback data, and the output is updated notifications and timing adjustments that reflect the feedback.

[0092] (Application Example 1)

[0093] 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."

[0094] In today's digital society, it is common for people to use devices for extended periods, raising concerns about negative health effects such as poor posture and vision problems. Furthermore, as consumer robots become more widespread in homes, there is a growing demand for robots that can support users' healthy lifestyles. This invention aims to provide technology that mitigates these health risks and promotes healthier device use.

[0095] 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.

[0096] In this invention, the server includes means for acquiring user eye-tracking information using an eye-tracking module, means for analyzing the user's device operating posture and operating time using the eye-tracking information, means for transmitting information to the user prompting them to interrupt the device based on the analysis results, and means for transmitting information through voice or screen display. This allows users to be encouraged to use devices that are mindful of their health, and improves the quality of life support provided by consumer robots.

[0097] An "eye-tracking module" is a combination of hardware and software that detects the user's gaze and eye movements and acquires their location information as digital data.

[0098] "Eye-gaze information" refers to data about the user's eye movements, including information such as pupil position, gaze direction, and point of fixation.

[0099] "Device operation posture" refers to the position and angle of the user's body when using the device, and includes the degree of curvature of the spine and the tilt of the neck.

[0100] "Operation time" refers to the length of time a user continuously operates or uses the device.

[0101] "Analysis results" refer to digital data or judgment results obtained after analyzing eye-tracking information, the user's device operation posture, and operation time.

[0102] "Interruption information" refers to messages or notifications designed to alert users to temporarily stop using their devices.

[0103] "Means of information transmission" refer to methods and media used to convey information to users that encourage interruption, and include audio, text, and visual displays.

[0104] The system for realizing this invention is centered around an eye-tracking module installed in consumer robots and smart devices. The server incorporates a program to acquire eye-tracking information, analyze it, and evaluate the user's device operation posture and operation time. Specifically, the server analyzes the eye-tracking information obtained through the eye-tracking module using eye-tracking software (e.g., commonly known as "eye-tracking detection software"). This software records the user's eye movements and gaze direction as digital data and processes it in real time.

[0105] The analyzed eye-tracking data is further analyzed by an AI model (e.g., a generic term "generative AI model") to determine whether the user's device operation may have adverse health effects. Based on the analysis results, the server provides the user with information prompting them to stop using the device, either verbally or visually. A speech synthesis system and a display system are used to help the user decide whether or not to stop immediately.

[0106] Users can receive this information and temporarily suspend their device use. For example, a user watching a rental video for an extended period with their eyes fixed on the screen might receive a voice notification saying, "Let's take a short break," and a message encouraging them to take a break would appear on the screen along with a timer. In this way, appropriate support is provided to maintain the user's health.

[0107] An example of a prompt to the generating AI model could be, "How can I generate an interruption notification when a user is looking at their device for an extended period?" This prompt would allow the AI ​​model to generate optimal notifications based on the user's behavior patterns, thereby supporting healthy device use.

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The server receives gaze information acquired in real time from the eye-tracking module. This information includes the position of the user's pupils and the direction of their gaze. The input is gaze data, and the output becomes the basic data for eye tracking. The data is sent to eye-tracking software, which registers the movement of the gaze as digital data.

[0111] Step 2:

[0112] The server uses gaze data obtained from eye-tracking software to analyze the user's device operation posture and operation time. The input is processed gaze data, and the output is an evaluation metric for the user's device operation. An AI algorithm analyzes the direction and change patterns of gaze to detect usage habits that may affect health.

[0113] Step 3:

[0114] The server generates information to prompt the user to interrupt based on the analysis results. The input is the analyzed evaluation metrics, and the output is a notification message to encourage interruption. A generation AI model is used to create an appropriate interruption message and notify the user in the form of audio or screen display.

[0115] Step 4:

[0116] The terminal communicates interruption messages sent from the server to the user using a speech synthesis system or display. The input is a notification message, and the output is an audio or visual warning to the user. This prompts the user to immediately suspend the use of the device.

[0117] Step 5:

[0118] The user receives a notification and chooses whether to temporarily suspend or continue using the device. The user's choice or feedback is sent to the server and used to optimize future notification timing. The input is user feedback, and the output is data for improving the notification algorithm.

[0119] 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.

[0120] One embodiment of the present invention is a program embedded on a smart device that simultaneously monitors the user's gaze and emotional state to promote healthy device use. First, the device continuously captures the user's gaze using its front camera, and obtains the pupil position and gaze angle using a gaze detection device. In addition, the device is equipped with an emotion engine that analyzes facial expressions, voice tone, etc., to recognize the user's emotional state.

[0121] Based on eye-tracking and emotional data collected by the device, an AI algorithm comprehensively analyzes the user's device usage posture, duration, and emotional state. This analysis evaluates the user's stress level and concentration. For example, if the user's gaze is too fixed or stress is detected from their facial expressions, the system may determine that there are factors hindering healthy device use.

[0122] The analysis results are periodically sent to the server, which uses this data to determine notification requirements. Alerts are generated based on the user's emotional state, and messages such as "Your stress levels are high. Take a deep breath to relax" are displayed on the user's device.

[0123] As a concrete example, suppose a user is using a device for work for an extended period. In this case, the device's emotion engine recognizes a high level of stress from the user's facial expressions and detects that their gaze is fixed on the screen. The server then sends a notification to the user encouraging them to relax. As a result, the user is more likely to consider taking a break and temporarily interrupt their work to take actions that alleviate stress.

[0124] This invention enables a multifaceted improvement in device usage that considers not only eye-tracking data but also the user's emotional state, thereby realizing more personalized health promotion.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] The device activates its front camera and captures the user's gaze. This allows eye-tracking software to identify the position of the user's pupils and the angle of their gaze, and record this as gaze data.

[0128] Step 2:

[0129] The device simultaneously uses an emotion engine to assess the user's emotional state. This includes a process of analyzing the user's facial expressions and voice tone to collect emotional data.

[0130] Step 3:

[0131] The device sends eye-tracking and emotional data it collects to an AI algorithm. The AI ​​analyzes the user's posture, usage time, and emotional state to provide an overall evaluation.

[0132] Step 4:

[0133] The device sends the analysis results to the server. Based on this data, the server decides what kind of notification to send to the user. Specifically, the user's stress level and concentration level are used as evaluation criteria.

[0134] Step 5:

[0135] The server considers the user's state and generates appropriate notifications. For example, if the user is experiencing high stress levels, it will create a notification encouraging stress reduction.

[0136] Step 6:

[0137] The device displays notifications received from the server to the user. These notifications may include messages such as, "Take a break to relax," or "Take a deep breath and refresh yourself."

[0138] Step 7:

[0139] Users can choose an action based on the displayed notification. If a user takes a break, they input that action into their device, and that information is sent to the server.

[0140] Step 8:

[0141] The server receives feedback from users and optimizes the timing and content of future notifications. This provides users with more personalized device usage support.

[0142] (Example 2)

[0143] 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".

[0144] In modern times, users' reliance on information and communication devices is increasing, leading to a greater accumulation of eye strain and stress. Conventional systems only provide uniform rest instructions based on gaze and device usage time, making it difficult to effectively promote healthy usage that takes into account the user's emotional state. This invention aims to solve this problem by realizing a system that provides more personalized health support by simultaneously acquiring and analyzing the user's gaze and emotional state.

[0145] 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.

[0146] In this invention, the server includes means for acquiring user gaze information using a gaze detection device, means for acquiring the user's emotional state using an emotion recognition device, and means for analyzing the user's device usage using the gaze information and emotional state. This makes it possible to send personalized notifications to support the user's healthy device usage.

[0147] A "gaze detection device" is a device that can acquire a user's gaze information in real time and is used to identify the position of the pupil and the movement of the gaze.

[0148] An "emotion recognition device" is a device that analyzes facial expressions, voice tone, and other factors to acquire the user's emotional state, and can measure the user's psychological state.

[0149] "Means for analyzing device usage" refers to methods that use user eye-tracking information and emotional states to evaluate how users use information and communication devices and to determine their level of concentration, stress levels, etc.

[0150] "Means of sending notifications" refers to means of sending messages or alerts to information and communication devices based on analysis results to encourage users to rest or take other actions.

[0151] "Means for measuring distance" refers to means for evaluating the physical distance between the user and the device from eye-tracking information, enabling the maintenance of an appropriate usage environment.

[0152] "Means of receiving responses" refers to means of obtaining user feedback, which can then be used to optimize notifications and system operations.

[0153] This invention is a system that operates on an information and communication device and simultaneously monitors the user's gaze and emotional state to promote healthy device use. The user uses a terminal that incorporates a gaze detection device and an emotion recognition device. The gaze detection device uses a built-in camera to acquire the user's pupil position and gaze angle in real time. The emotion recognition device determines the user's emotional state by analyzing facial expressions and voice tone.

[0154] The device analyzes collected eye-tracking data and emotional states using an AI algorithm. This allows for a comprehensive evaluation of the user's posture, usage time, stress level, and concentration level. The server receives the analysis results and generates notifications tailored to the user's state. For example, if the user's gaze is fixed on the screen for an extended period and a high stress level is detected, the server will send a notification such as, "We recommend taking a short break."

[0155] For example, when a user is working at a desk for a long time, the device can monitor the user's gaze and emotions and send notifications to encourage relaxation as needed, allowing the user to continue working while being mindful of their health. Furthermore, by using a generative AI model, the content of the notifications can be personalized to suit the user's diverse states. An example of a prompt message would be, "Generate a notification message encouraging healthy device use based on the user's gaze data and emotional state."

[0156] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0157] Step 1:

[0158] The device acquires user gaze information using an eye-tracking device. Specifically, it uses the front camera to capture the position of the user's pupils and the angle of their gaze in real time. The input is camera video, and the output is gaze position data and angle data. This data is used to identify gaze movement and fixed positions.

[0159] Step 2:

[0160] The device uses an emotion recognition device to recognize the user's emotional state. Specifically, it analyzes the user's facial expressions and voice tone to determine emotions such as stress and relaxation. Inputs include voice waveform data and facial image data, and output is emotional state data. This data is used to identify the user's psychological state.

[0161] Step 3:

[0162] The device analyzes eye-tracking data and emotional states using an AI algorithm. Specifically, it plots the duration of fixed gaze and the degree of emotional change to quantify the user's concentration level and stress level. The inputs are eye-tracking data and emotional data, and the output is the analyzed user's device usage status. This result is used to comprehensively evaluate the user's usage.

[0163] Step 4:

[0164] The server generates a notification based on the analysis results and sends it to the device. Specifically, the AI ​​model generates an alert message when stress levels are high. The input is user usage data, and the output is a notification message. This sends alerts to encourage users to use their devices in a healthy way.

[0165] Step 5:

[0166] Users receive notifications on their devices and take action based on the alerts presented. Specifically, they check the notifications and take actions such as taking a break if necessary. The input is notifications sent from the server, and the output is changes in the user's behavior. This encourages users to practice healthy device usage.

[0167] (Application Example 2)

[0168] 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".

[0169] Conventional device usage monitoring systems primarily rely on simple analyses based solely on user eye-tracking data, lacking personalization that considers the user's emotional state or stress level. Therefore, they fail to effectively support healthy device use, making it difficult to reduce stress and fatigue.

[0170] 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.

[0171] In this invention, the server includes means for acquiring user gaze data using a gaze detection device, means for evaluating stress levels by combining the gaze data with emotional data from an facial recognition engine, and means for optimizing notification content according to the user's emotional state using a generative AI model. This makes it possible to analyze the user's gaze and emotional state from multiple angles and provide personalized health support.

[0172] A "gaze detection device" is a device that tracks a user's gaze and acquires data regarding the direction of their gaze and the position of their pupils.

[0173] "Eye-gaze data" refers to information about the user's gaze direction and point of focus, acquired by an eye-gaze detection device.

[0174] "Device usage posture" is a concept that refers to the physical posture and position of a user while using a device.

[0175] A "facial expression recognition engine" is a software algorithm that analyzes a user's facial expressions and evaluates their emotional state.

[0176] "Emotional data" refers to information indicating the user's emotional state, obtained through facial recognition engines and other means.

[0177] "Stress level" is an indicator that shows the current degree of mental burden or tension experienced by the user.

[0178] A "generative AI model" is an artificial intelligence computational model that generates information optimized for the user based on a set of data.

[0179] A "notification transmission method" refers to a mechanism or device used to send information or alerts to a user.

[0180] "Feedback" refers to responses and information provided by users, which is used as data to optimize the system.

[0181] This system is designed to promote healthy device use by users and is specifically built by combining an eye-tracking device, a facial recognition engine, and a generative AI model. The camera on the device functions as an eye-tracking device, acquiring the user's gaze data. In addition, the facial recognition engine built into the device analyzes the video captured by the camera to acquire the user's emotional data.

[0182] The server receives eye-tracking and emotional data transmitted by the device and integrates them to evaluate the user's stress level and concentration. An AI algorithm is used for this evaluation, comprehensively analyzing the user's posture, usage time, and emotional state during device use. Based on this analysis, the server utilizes a generative AI model to generate personalized notifications and send them to the device. These notifications might include messages such as, "Your stress levels are high. Take a deep breath to relax."

[0183] As a concrete example, when a user is watching television for an extended period, the device detects that the user's gaze is fixed and, based on their facial expression, indicates a high level of stress. The server then sends a voice reminder such as, "Let's take a short break." This process is supported by various software components, including gaze and emotion analysis using TENSORFLOW®, facial recognition using Google Cloud's Vision AI, and voice generation using Amazon Polly.

[0184] An example of a prompt message might be: "While the user is relaxing in the living room and watching TV, an advanced AI camera begins tracking their gaze. The robot notices that the user's expression is gradually showing signs of fatigue. How will it respond?" This allows for the provision of appropriate feedback as the user interacts with the device.

[0185] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0186] Step 1:

[0187] The device uses its built-in camera to capture the user's gaze in real time. The gaze data includes the position of the user's pupils and the direction of their gaze. This data is processed by a gaze detection algorithm to analyze the user's fixation points and changes in their gaze.

[0188] Step 2:

[0189] The device simultaneously analyzes the user's face using an expression recognition engine. Video data is input, and emotional data is extracted from the facial expressions. In this process, a machine learning model analyzes the input data and outputs emotional data that quantifies the user's emotional state.

[0190] Step 3:

[0191] The device sends eye-tracking and emotional data to a server. The server receives this data and uses an AI algorithm to analyze it comprehensively. Specifically, it calculates the degree of eye-tracking fixation and emotional changes to evaluate the user's stress level and concentration. As a result of the evaluation, a score indicating the user's current mental state is output.

[0192] Step 4:

[0193] The server generates notification content using a generative AI model. Using the evaluation results as input, it creates appropriate alert and reminder messages for the user. For example, if the stress level is high, it might generate a message such as "Take a short break," and the message is output in text or audio format.

[0194] Step 5:

[0195] The server sends the generated notification content to the device. The device then displays the received notification content to the user. In the case of voice output, it uses speech synthesis to communicate directly to the user. This process creates a feedback loop that supports the user's healthy device use.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] [Second Embodiment]

[0200] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0201] 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.

[0202] 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).

[0203] 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.

[0204] 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.

[0205] 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).

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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".

[0212] In an embodiment of the present invention, a program embedded in a smart device plays a major role. This program works in conjunction with an eye-tracking device to acquire the user's eye-tracking data and analyzes this data in real time to evaluate the user's device usage posture and time.

[0213] The program first activates the front camera on the device and continuously captures the user's gaze. Eye-tracking software installed on the device analyzes the position of the user's pupils and the movement of their eyes, recording this as gaze data. This gaze data includes information such as the user's gaze angle and distance from the screen.

[0214] This data is then sent to an AI algorithm within the device to analyze the user's eye movements and device usage time. The AI ​​algorithm analyzes patterns of changes in gaze angle and distance to determine whether the user's posture is inappropriate or whether the usage time may be affecting their health.

[0215] Based on this analysis, the server periodically receives data and generates notifications prompting users to take breaks at appropriate times. Notification criteria include situations where the distance between the user and device (based on eye-tracking data) exceeds a threshold, or when continuous use for a certain period of time is detected. Furthermore, feedback from users is received and managed as information to individually optimize break timing.

[0216] As a concrete example, consider a situation where a user is using a news reading app. Because this user is looking at the device at close range for an extended period, the device continuously collects eye-tracking data. As a result, the time spent at a distance of less than 20 cm exceeds a certain limit. In such cases, the server sends a notification to the user's screen via the device saying, "Please move away from the screen for a while. Take a break from your eyes."

[0217] This invention allows users to reduce potential health risks associated with their device use and develop healthier usage habits.

[0218] The following describes the processing flow.

[0219] Step 1:

[0220] The device activates its front camera and captures the user's gaze. At this point, eye-tracking software runs to detect the user's pupil position and gaze angle in real time and collect gaze data.

[0221] Step 2:

[0222] The device sends the eye-tracking data it acquires to an AI algorithm. The AI ​​analyzes the data and evaluates the user's posture and device usage based on the user's eye movements and distance from the device.

[0223] Step 3:

[0224] The device sends analysis results to the server at regular intervals (e.g., every 10 minutes). This data includes the angle of gaze, the distance between the user and the device, and the continuous usage time.

[0225] Step 4:

[0226] The server determines whether a break is necessary based on the data it receives. The need for a break is determined if the angle or distance of the user's gaze exceeds a set threshold, or if continuous use is detected.

[0227] Step 5:

[0228] The server generates and sends a notification message to the device based on its own judgment. Specific alert messages include content encouraging users to use their devices in a healthy manner.

[0229] Step 6:

[0230] The device prompts the user to take a break by displaying notifications received from the server. The notifications are displayed visually on the screen and may include phrases such as "Let's rest our eyes."

[0231] Step 7:

[0232] When a user takes a break based on a notification, they input that action into their device. One possible format is for the user to press a button to indicate that they have started a break.

[0233] Step 8:

[0234] The server receives feedback from the user and adjusts the timing of the next notification. This optimizes notifications, allowing users to be prompted to take breaks at more effective times.

[0235] (Example 1)

[0236] 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."

[0237] Excessive use of data devices in modern society can negatively impact user health. In particular, prolonged use can lead to vision problems and other health issues if the user's gaze position or distance from the device is inappropriate. However, determining appropriate break times is difficult for users, and it's challenging for them to be mindful of their usage.

[0238] 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.

[0239] In this invention, the server includes means for collecting user eye-tracking information, means for analyzing the eye-tracking information to evaluate the user's posture and usage time when using the data device, and means for notifying the user to take a break based on the evaluation results. This encourages the user to take breaks at appropriate times, enabling health-conscious use of the device.

[0240] A "user" refers to an individual operating a data device, and the system analyzes their gaze and actions.

[0241] "Eye-gaze information" includes data about the position and movement of the user's eyes, their angle, and their distance from the device.

[0242] "Data equipment" refers to electronic devices used by users, specifically terminals used to acquire eye-tracking information.

[0243] "Evaluation" refers to the analytical process of determining user behavior and health impacts based on collected eye-tracking data.

[0244] "Notifications" refer to displaying messages to users recommending breaks based on analysis results.

[0245] A "generative AI model" is a type of artificial intelligence used to create notification content based on eye-tracking information and user feedback, and to optimize it for individual users.

[0246] "Feedback" refers to reference information collected from users' responses and opinions to notifications.

[0247] To implement the present invention, the terminal must first incorporate an in-camera and eye-tracking software. When the user begins using the data device, the terminal activates the in-camera and continuously captures gaze information regarding the position and movement of the user's pupils. This gaze information serves as basic data for understanding the user's posture and physical distance from the device.

[0248] The eye-tracking software within the device analyzes this captured gaze information. The analysis records numerical data such as gaze angle, movement patterns, and distance from the device, and uses this data to evaluate the user's usage. This helps determine how the user's data device usage might affect their health.

[0249] The server receives data sent from the terminal and uses a generative AI model to generate appropriate notifications for the user. These notifications are designed to encourage the user to take a break and are optimized based on each user's usage patterns. For example, if a user has been looking at the screen at close range for an extended period, a message such as "Take a step back from the screen. Rest your eyes." will be generated and displayed to the user by the terminal.

[0250] User feedback is used for further data collection and analysis to make notifications more effective. The server aggregates this feedback and incorporates it into the generating AI model, resulting in notification content and timing that are more tailored to the user's situation.

[0251] As a concrete example, a possible prompt for a generative AI model might be, "How can we optimize the timing of health-conscious breaks based on eye-tracking data?" This prompt allows the AI ​​to improve notifications and support users in using data devices in a healthier way.

[0252] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0253] Step 1:

[0254] The device activates the front camera.

[0255] When a user begins using a data device, the terminal automatically activates its front-facing camera. The input is data of the user's gaze, and based on this, it continuously captures information about the position of the eyes. The output is raw data including the position of the user's face and eyes.

[0256] Step 2:

[0257] The device analyzes eye-tracking information.

[0258] Eye-tracking software analyzes the user's pupil movements based on captured data. This analysis calculates the angle of gaze, movement patterns, and distance from the device. The input is raw data captured by the front camera, and the output is digitized gaze information.

[0259] Step 3:

[0260] The device sends data to the AI ​​algorithm.

[0261] The device transmits the analyzed eye-tracking information to an AI algorithm. The AI ​​uses this information to evaluate the user's device usage. The input is quantified eye-tracking information, and the output is the user's health status assessment result.

[0262] Step 4:

[0263] The server generates the notification.

[0264] Based on the AI-generated evaluation results, the server uses a generative AI model to generate notifications prompting users to take a break. The input is the user's health status evaluation result, and the output is a notification message such as "Take a break from the screen."

[0265] Step 5:

[0266] The server collects user feedback.

[0267] The server collects user feedback on notifications and incorporates it into the generating AI model. The input is user feedback data, and the output is updated notifications and timing adjustments that reflect the feedback.

[0268] (Application Example 1)

[0269] 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."

[0270] In today's digital society, it is common for people to use devices for extended periods, raising concerns about negative health effects such as poor posture and vision problems. Furthermore, as consumer robots become more widespread in homes, there is a growing demand for robots that can support users' healthy lifestyles. This invention aims to provide technology that mitigates these health risks and promotes healthier device use.

[0271] 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.

[0272] In this invention, the server includes means for acquiring user eye-tracking information using an eye-tracking module, means for analyzing the user's device operating posture and operating time using the eye-tracking information, means for transmitting information to the user prompting them to interrupt the device based on the analysis results, and means for transmitting information through voice or screen display. This allows users to be encouraged to use devices that are mindful of their health, and improves the quality of life support provided by consumer robots.

[0273] An "eye-tracking module" is a combination of hardware and software that detects the user's gaze and eye movements and acquires their location information as digital data.

[0274] "Eye-gaze information" refers to data about the user's eye movements, including information such as pupil position, gaze direction, and point of fixation.

[0275] "Device operation posture" refers to the position and angle of the user's body when using the device, and includes the degree of curvature of the spine and the tilt of the neck.

[0276] "Operation time" refers to the length of time a user continuously operates or uses the device.

[0277] "Analysis results" refer to digital data or judgment results obtained after analyzing eye-tracking information, the user's device operation posture, and operation time.

[0278] "Interruption information" refers to messages or notifications designed to alert users to temporarily stop using their devices.

[0279] "Means of information transmission" refer to methods and media used to convey information to users that encourage interruption, and include audio, text, and visual displays.

[0280] The system for realizing this invention is mainly composed of a gaze tracking module installed in consumer robots and smart devices. The server incorporates a program for acquiring gaze information and analyzing it to evaluate the user's device operation posture and operation time. Specifically, the server analyzes the gaze information obtained through the gaze tracking module using eye tracking software (e.g., the general name "gaze detection software"). This software records the movement of the user's pupils and the direction of the gaze as digital data and processes it in real time.

[0281] The analyzed gaze information is further analyzed by an AI model (e.g., the general name "generative AI model") to determine whether the user's device operation may have an adverse impact on health. Based on the analysis result, the server provides information prompting the user to interrupt, either by voice or on the screen display. A voice synthesis system and a display system are used to assist in determining whether the user should interrupt immediately.

[0282] The user can receive this information and temporarily interrupt the use of the device. For example, for a user watching a rental video with a fixed gaze for a long time, a voice notification saying "Let's take a little break" is sent, and a message prompting a break is displayed on the screen together with a timer. In this way, appropriate support for maintaining the user's health is provided.

[0283] As an example of a prompt for the generative AI model, an instruction such as "Please teach me a method for generating an interruption notification when the user continues to look at the device for a long time" can be considered. With this prompt, the AI model can generate an optimal notification based on the user's behavior pattern and assist in healthy device usage.

[0284] The flow of specific processing in Application Example 1 will be described using FIG. 12.

[0285] Step 1:

[0286] The server receives the gaze information obtained in real time from the gaze tracking module. This information includes the position of the user's pupil and the direction of the gaze. The input is the gaze data, and the output is the basic data for eye tracking. The data is sent to the eye tracking software to register the movement of the gaze as digital data.

[0287] Step 2:

[0288] The server uses the gaze information obtained from the eye tracking software to analyze the user's device operation posture and operation time. The input is the processed gaze data, and the output is the evaluation index related to the user's device operation. The AI algorithm analyzes the direction and change pattern of the gaze to detect usage habits that may affect health.

[0289] Step 3:

[0290] Based on the analysis results, the server generates information to prompt the user to interrupt. The input is the analyzed evaluation index, and the output is the notification message to prompt interruption. The generated AI model is used to create an appropriate interruption message and notify it in the form of voice or screen display.

[0291] Step 4:

[0292] The terminal transmits the interruption message sent from the server to the user using a voice synthesis system or a display. The input is the notification message, and the output is an audio or visual warning to the user. Thus, the user is prompted to immediately suspend the use of the device.

[0293] Step 5:

[0294] The user receives a notification and chooses whether to temporarily suspend or continue using the device. The user's choice or feedback is sent to the server and used to optimize future notification timing. The input is user feedback, and the output is data for improving the notification algorithm.

[0295] 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.

[0296] One embodiment of the present invention is a program embedded on a smart device that simultaneously monitors the user's gaze and emotional state to promote healthy device use. First, the device continuously captures the user's gaze using its front camera, and obtains the pupil position and gaze angle using a gaze detection device. In addition, the device is equipped with an emotion engine that analyzes facial expressions, voice tone, etc., to recognize the user's emotional state.

[0297] Based on eye-tracking and emotional data collected by the device, an AI algorithm comprehensively analyzes the user's device usage posture, duration, and emotional state. This analysis evaluates the user's stress level and concentration. For example, if the user's gaze is too fixed or stress is detected from their facial expressions, the system may determine that there are factors hindering healthy device use.

[0298] The analysis results are periodically sent to the server, which uses this data to determine notification requirements. Alerts are generated based on the user's emotional state, and messages such as "Your stress levels are high. Take a deep breath to relax" are displayed on the user's device.

[0299] As a specific example, assume that a user is using a device for work over a long period of time. At this time, if the emotion engine of the terminal recognizes a high-stress state from the user's expression and detects that the line of sight is fixed on the screen, the server sends a notification prompting the user to relax. As a result, the user is likely to consider taking a break and take actions to temporarily interrupt the work and relieve stress.

[0300] According to the present invention, it becomes possible to improve the use of the device in a multi-faceted manner considering the user's emotional state, rather than simply making a judgment based on only line-of-sight data, and more personalized health promotion is realized.

[0301] The following describes the processing flow.

[0302] Step 1:

[0303] The terminal activates the in-camera and captures the user's line of sight. As a result, the eye-tracking software identifies the position of the user's pupil and the angle of the line of sight, and records them as line-of-sight data.

[0304] Step 2:

[0305] The terminal simultaneously uses the emotion engine to evaluate the user's emotional state. This includes a process of analyzing the user's facial expression and voice tone and collecting emotion data.

[0306] Step 3:

[0307] The terminal sends the line-of-sight data and emotion data collected to an AI algorithm. The AI analyzes the device usage posture, usage time, and the user's emotional state, and makes a comprehensive evaluation.

[0308] Step 4:

[0309] The device sends the analysis results to the server. Based on this data, the server decides what kind of notification to send to the user. Specifically, the user's stress level and concentration level are used as evaluation criteria.

[0310] Step 5:

[0311] The server considers the user's state and generates appropriate notifications. For example, if the user is experiencing high stress levels, it will create a notification encouraging stress reduction.

[0312] Step 6:

[0313] The device displays notifications received from the server to the user. These notifications may include messages such as, "Take a break to relax," or "Take a deep breath and refresh yourself."

[0314] Step 7:

[0315] Users can choose an action based on the displayed notification. If a user takes a break, they input that action into their device, and that information is sent to the server.

[0316] Step 8:

[0317] The server receives feedback from users and optimizes the timing and content of future notifications. This provides users with more personalized device usage support.

[0318] (Example 2)

[0319] 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".

[0320] In modern times, users' reliance on information and communication devices is increasing, leading to a greater accumulation of eye strain and stress. Conventional systems only provide uniform rest instructions based on gaze and device usage time, making it difficult to effectively promote healthy usage that takes into account the user's emotional state. This invention aims to solve this problem by realizing a system that provides more personalized health support by simultaneously acquiring and analyzing the user's gaze and emotional state.

[0321] 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.

[0322] In this invention, the server includes means for acquiring user gaze information using a gaze detection device, means for acquiring the user's emotional state using an emotion recognition device, and means for analyzing the user's device usage using the gaze information and emotional state. This makes it possible to send personalized notifications to support the user's healthy device usage.

[0323] A "gaze detection device" is a device that can acquire a user's gaze information in real time and is used to identify the position of the pupil and the movement of the gaze.

[0324] An "emotion recognition device" is a device that analyzes facial expressions, voice tone, and other factors to acquire the user's emotional state, and can measure the user's psychological state.

[0325] "Means for analyzing device usage" refers to methods that use user eye-tracking information and emotional states to evaluate how users use information and communication devices and to determine their level of concentration, stress levels, etc.

[0326] "Means of sending notifications" refers to means of sending messages or alerts to information and communication devices based on analysis results to encourage users to rest or take other actions.

[0327] "Means for measuring distance" refers to means for evaluating the physical distance between the user and the device from eye-tracking information, enabling the maintenance of an appropriate usage environment.

[0328] "Means of receiving responses" refers to means of obtaining user feedback, which can then be used to optimize notifications and system operations.

[0329] This invention is a system that operates on an information and communication device and simultaneously monitors the user's gaze and emotional state to promote healthy device use. The user uses a terminal that incorporates a gaze detection device and an emotion recognition device. The gaze detection device uses a built-in camera to acquire the user's pupil position and gaze angle in real time. The emotion recognition device determines the user's emotional state by analyzing facial expressions and voice tone.

[0330] The device analyzes collected eye-tracking data and emotional states using an AI algorithm. This allows for a comprehensive evaluation of the user's posture, usage time, stress level, and concentration level. The server receives the analysis results and generates notifications tailored to the user's state. For example, if the user's gaze is fixed on the screen for an extended period and a high stress level is detected, the server will send a notification such as, "We recommend taking a short break."

[0331] For example, when a user is working at a desk for a long time, the device can monitor the user's gaze and emotions and send notifications to encourage relaxation as needed, allowing the user to continue working while being mindful of their health. Furthermore, by using a generative AI model, the content of the notifications can be personalized to suit the user's diverse states. An example of a prompt message would be, "Generate a notification message encouraging healthy device use based on the user's gaze data and emotional state."

[0332] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0333] Step 1:

[0334] The device acquires user gaze information using an eye-tracking device. Specifically, it uses the front camera to capture the position of the user's pupils and the angle of their gaze in real time. The input is camera video, and the output is gaze position data and angle data. This data is used to identify gaze movement and fixed positions.

[0335] Step 2:

[0336] The device uses an emotion recognition device to recognize the user's emotional state. Specifically, it analyzes the user's facial expressions and voice tone to determine emotions such as stress and relaxation. Inputs include voice waveform data and facial image data, and output is emotional state data. This data is used to identify the user's psychological state.

[0337] Step 3:

[0338] The device analyzes eye-tracking data and emotional states using an AI algorithm. Specifically, it plots the duration of fixed gaze and the degree of emotional change to quantify the user's concentration level and stress level. The inputs are eye-tracking data and emotional data, and the output is the analyzed user's device usage status. This result is used to comprehensively evaluate the user's usage.

[0339] Step 4:

[0340] The server generates a notification based on the analysis results and sends it to the device. Specifically, the AI ​​model generates an alert message when stress levels are high. The input is user usage data, and the output is a notification message. This sends alerts to encourage users to use their devices in a healthy way.

[0341] Step 5:

[0342] Users receive notifications on their devices and take action based on the alerts presented. Specifically, they check the notifications and take actions such as taking a break if necessary. The input is notifications sent from the server, and the output is changes in the user's behavior. This encourages users to practice healthy device usage.

[0343] (Application Example 2)

[0344] 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 as the "terminal".

[0345] Conventional device usage monitoring systems primarily rely on simple analyses based solely on user eye-tracking data, lacking personalization that considers the user's emotional state or stress level. Therefore, they fail to effectively support healthy device use, making it difficult to reduce stress and fatigue.

[0346] 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.

[0347] In this invention, the server includes means for acquiring user gaze data using a gaze detection device, means for evaluating stress levels by combining the gaze data with emotional data from an facial recognition engine, and means for optimizing notification content according to the user's emotional state using a generative AI model. This makes it possible to analyze the user's gaze and emotional state from multiple angles and provide personalized health support.

[0348] A "gaze detection device" is a device that tracks a user's gaze and acquires data regarding the direction of their gaze and the position of their pupils.

[0349] "Eye-gaze data" refers to information about the user's gaze direction and point of focus, acquired by an eye-gaze detection device.

[0350] "Device usage posture" is a concept that refers to the physical posture and position of a user while using a device.

[0351] A "facial expression recognition engine" is a software algorithm that analyzes a user's facial expressions and evaluates their emotional state.

[0352] "Emotional data" refers to information indicating the user's emotional state, obtained through facial recognition engines and other means.

[0353] "Stress level" is an indicator that shows the current degree of mental burden or tension experienced by the user.

[0354] A "generative AI model" is an artificial intelligence computational model that generates information optimized for the user based on a set of data.

[0355] A "notification transmission method" refers to a mechanism or device used to send information or alerts to a user.

[0356] "Feedback" refers to responses and information provided by users, which is used as data to optimize the system.

[0357] This system is designed to promote healthy device use by users and is specifically built by combining an eye-tracking device, a facial recognition engine, and a generative AI model. The camera on the device functions as an eye-tracking device, acquiring the user's gaze data. In addition, the facial recognition engine built into the device analyzes the video captured by the camera to acquire the user's emotional data.

[0358] The server receives eye-tracking and emotional data transmitted by the device and integrates them to evaluate the user's stress level and concentration. An AI algorithm is used for this evaluation, comprehensively analyzing the user's posture, usage time, and emotional state during device use. Based on this analysis, the server utilizes a generative AI model to generate personalized notifications and send them to the device. These notifications might include messages such as, "Your stress levels are high. Take a deep breath to relax."

[0359] As a concrete example, when a user is watching television for an extended period, the device detects that the user's gaze is fixed and, based on their facial expression, indicates a high level of stress. The server then sends a voice reminder such as, "Let's take a short break." This process is supported by various software components, including gaze and emotion analysis using TensorFlow, facial recognition using Google Cloud's Vision AI, and voice generation using Amazon Polly.

[0360] An example of a prompt message might be: "While the user is relaxing in the living room and watching TV, an advanced AI camera begins tracking their gaze. The robot notices that the user's expression is gradually showing signs of fatigue. How will it respond?" This allows for the provision of appropriate feedback as the user interacts with the device.

[0361] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0362] Step 1:

[0363] The device uses its built-in camera to capture the user's gaze in real time. The gaze data includes the position of the user's pupils and the direction of their gaze. This data is processed by a gaze detection algorithm to analyze the user's fixation points and changes in their gaze.

[0364] Step 2:

[0365] The device simultaneously analyzes the user's face using an expression recognition engine. Video data is input, and emotional data is extracted from the facial expressions. In this process, a machine learning model analyzes the input data and outputs emotional data that quantifies the user's emotional state.

[0366] Step 3:

[0367] The device sends eye-tracking and emotional data to a server. The server receives this data and uses an AI algorithm to analyze it comprehensively. Specifically, it calculates the degree of eye-tracking fixation and emotional changes to evaluate the user's stress level and concentration. As a result of the evaluation, a score indicating the user's current mental state is output.

[0368] Step 4:

[0369] The server generates notification content using a generative AI model. Using the evaluation results as input, it creates appropriate alert and reminder messages for the user. For example, if the stress level is high, it might generate a message such as "Take a short break," and the message is output in text or audio format.

[0370] Step 5:

[0371] The server sends the generated notification content to the device. The device then displays the received notification content to the user. In the case of voice output, it uses speech synthesis to communicate directly to the user. This process creates a feedback loop that supports the user's healthy device use.

[0372] 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.

[0373] 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.

[0374] 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.

[0375] [Third Embodiment]

[0376] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0377] 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.

[0378] 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).

[0379] 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.

[0380] 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.

[0381] 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).

[0382] 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.

[0383] 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.

[0384] 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.

[0385] 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.

[0386] 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.

[0387] 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".

[0388] In an embodiment of the present invention, a program embedded in a smart device plays a major role. This program works in conjunction with an eye-tracking device to acquire the user's eye-tracking data and analyzes this data in real time to evaluate the user's device usage posture and time.

[0389] The program first activates the front camera on the device and continuously captures the user's gaze. Eye-tracking software installed on the device analyzes the position of the user's pupils and the movement of their eyes, recording this as gaze data. This gaze data includes information such as the user's gaze angle and distance from the screen.

[0390] This data is then sent to an AI algorithm within the device to analyze the user's eye movements and device usage time. The AI ​​algorithm analyzes patterns of changes in gaze angle and distance to determine whether the user's posture is inappropriate or whether the usage time may be affecting their health.

[0391] Based on this analysis, the server periodically receives data and generates notifications prompting users to take breaks at appropriate times. Notification criteria include situations where the distance between the user and device (based on eye-tracking data) exceeds a threshold, or when continuous use for a certain period of time is detected. Furthermore, feedback from users is received and managed as information to individually optimize break timing.

[0392] As a concrete example, consider a situation where a user is using a news reading app. Because this user is looking at the device at close range for an extended period, the device continuously collects eye-tracking data. As a result, the time spent at a distance of less than 20 cm exceeds a certain limit. In such cases, the server sends a notification to the user's screen via the device saying, "Please move away from the screen for a while. Take a break from your eyes."

[0393] This invention allows users to reduce potential health risks associated with their device use and develop healthier usage habits.

[0394] The following describes the processing flow.

[0395] Step 1:

[0396] The device activates its front camera and captures the user's gaze. At this point, eye-tracking software runs to detect the user's pupil position and gaze angle in real time and collect gaze data.

[0397] Step 2:

[0398] The device sends the eye-tracking data it acquires to an AI algorithm. The AI ​​analyzes the data and evaluates the user's posture and device usage based on the user's eye movements and distance from the device.

[0399] Step 3:

[0400] The device sends analysis results to the server at regular intervals (e.g., every 10 minutes). This data includes the angle of gaze, the distance between the user and the device, and the continuous usage time.

[0401] Step 4:

[0402] The server determines whether a break is necessary based on the data it receives. The need for a break is determined if the angle or distance of the user's gaze exceeds a set threshold, or if continuous use is detected.

[0403] Step 5:

[0404] The server generates and sends a notification message to the device based on its own judgment. Specific alert messages include content encouraging users to use their devices in a healthy manner.

[0405] Step 6:

[0406] The device prompts the user to take a break by displaying notifications received from the server. The notifications are displayed visually on the screen and may include phrases such as "Let's rest our eyes."

[0407] Step 7:

[0408] When a user takes a break based on a notification, they input that action into their device. One possible format is for the user to press a button to indicate that they have started a break.

[0409] Step 8:

[0410] The server receives feedback from the user and adjusts the timing of the next notification. This optimizes notifications, allowing users to be prompted to take breaks at more effective times.

[0411] (Example 1)

[0412] 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."

[0413] Excessive use of data devices in modern society can negatively impact user health. In particular, prolonged use can lead to vision problems and other health issues if the user's gaze position or distance from the device is inappropriate. However, determining appropriate break times is difficult for users, and it's challenging for them to be mindful of their usage.

[0414] 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.

[0415] In this invention, the server includes means for collecting user eye-tracking information, means for analyzing the eye-tracking information to evaluate the user's posture and usage time when using the data device, and means for notifying the user to take a break based on the evaluation results. This encourages the user to take breaks at appropriate times, enabling health-conscious use of the device.

[0416] A "user" refers to an individual operating a data device, and the system analyzes their gaze and actions.

[0417] "Eye-gaze information" includes data about the position and movement of the user's eyes, their angle, and their distance from the device.

[0418] "Data equipment" refers to electronic devices used by users, specifically terminals used to acquire eye-tracking information.

[0419] "Evaluation" refers to the analytical process of determining user behavior and health impacts based on collected eye-tracking data.

[0420] "Notifications" refer to displaying messages to users recommending breaks based on analysis results.

[0421] A "generative AI model" is a type of artificial intelligence used to create notification content based on eye-tracking information and user feedback, and to optimize it for individual users.

[0422] "Feedback" refers to reference information collected from users' responses and opinions to notifications.

[0423] To implement the present invention, the terminal must first incorporate an in-camera and eye-tracking software. When the user begins using the data device, the terminal activates the in-camera and continuously captures gaze information regarding the position and movement of the user's pupils. This gaze information serves as basic data for understanding the user's posture and physical distance from the device.

[0424] The eye-tracking software within the device analyzes this captured gaze information. The analysis records numerical data such as gaze angle, movement patterns, and distance from the device, and uses this data to evaluate the user's usage. This helps determine how the user's data device usage might affect their health.

[0425] The server receives data sent from the terminal and uses a generative AI model to generate appropriate notifications for the user. These notifications are designed to encourage the user to take a break and are optimized based on each user's usage patterns. For example, if a user has been looking at the screen at close range for an extended period, a message such as "Take a step back from the screen. Rest your eyes." will be generated and displayed to the user by the terminal.

[0426] User feedback is used for further data collection and analysis to make notifications more effective. The server aggregates this feedback and incorporates it into the generating AI model, resulting in notification content and timing that are more tailored to the user's situation.

[0427] As a concrete example, a possible prompt for a generative AI model might be, "How can we optimize the timing of health-conscious breaks based on eye-tracking data?" This prompt allows the AI ​​to improve notifications and support users in using data devices in a healthier way.

[0428] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0429] Step 1:

[0430] The device activates the front camera.

[0431] When a user begins using a data device, the terminal automatically activates its front-facing camera. The input is data of the user's gaze, and based on this, it continuously captures information about the position of the eyes. The output is raw data including the position of the user's face and eyes.

[0432] Step 2:

[0433] The device analyzes eye-tracking information.

[0434] Eye-tracking software analyzes the user's pupil movements based on captured data. This analysis calculates the angle of gaze, movement patterns, and distance from the device. The input is raw data captured by the front camera, and the output is digitized gaze information.

[0435] Step 3:

[0436] The device sends data to the AI ​​algorithm.

[0437] The device transmits the analyzed eye-tracking information to an AI algorithm. The AI ​​uses this information to evaluate the user's device usage. The input is quantified eye-tracking information, and the output is the user's health status assessment result.

[0438] Step 4:

[0439] The server generates the notification.

[0440] Based on the AI-generated evaluation results, the server uses a generative AI model to generate notifications prompting users to take a break. The input is the user's health status evaluation result, and the output is a notification message such as "Take a break from the screen."

[0441] Step 5:

[0442] The server collects user feedback.

[0443] The server collects user feedback on notifications and incorporates it into the generating AI model. The input is user feedback data, and the output is updated notifications and timing adjustments that reflect the feedback.

[0444] (Application Example 1)

[0445] 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."

[0446] In today's digital society, it is common for people to use devices for extended periods, raising concerns about negative health effects such as poor posture and vision problems. Furthermore, as consumer robots become more widespread in homes, there is a growing demand for robots that can support users' healthy lifestyles. This invention aims to provide technology that mitigates these health risks and promotes healthier device use.

[0447] 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.

[0448] In this invention, the server includes means for acquiring user eye-tracking information using an eye-tracking module, means for analyzing the user's device operating posture and operating time using the eye-tracking information, means for transmitting information to the user prompting them to interrupt the device based on the analysis results, and means for transmitting information through voice or screen display. This allows users to be encouraged to use devices that are mindful of their health, and improves the quality of life support provided by consumer robots.

[0449] An "eye-tracking module" is a combination of hardware and software that detects the user's gaze and eye movements and acquires their location information as digital data.

[0450] "Eye-gaze information" refers to data about the user's eye movements, including information such as pupil position, gaze direction, and point of fixation.

[0451] "Device operation posture" refers to the position and angle of the user's body when using the device, and includes the degree of curvature of the spine and the tilt of the neck.

[0452] "Operation time" refers to the length of time a user continuously operates or uses the device.

[0453] "Analysis results" refer to digital data or judgment results obtained after analyzing eye-tracking information, the user's device operation posture, and operation time.

[0454] "Interruption information" refers to messages or notifications designed to alert users to temporarily stop using their devices.

[0455] "Means of information transmission" refer to methods and media used to convey information to users that encourage interruption, and include audio, text, and visual displays.

[0456] The system for realizing this invention is centered around an eye-tracking module installed in consumer robots and smart devices. The server incorporates a program to acquire eye-tracking information, analyze it, and evaluate the user's device operation posture and operation time. Specifically, the server analyzes the eye-tracking information obtained through the eye-tracking module using eye-tracking software (e.g., commonly known as "eye-tracking detection software"). This software records the user's eye movements and gaze direction as digital data and processes it in real time.

[0457] The analyzed eye-tracking data is further analyzed by an AI model (e.g., a generic term "generative AI model") to determine whether the user's device operation may have adverse health effects. Based on the analysis results, the server provides the user with information prompting them to stop using the device, either verbally or visually. A speech synthesis system and a display system are used to help the user decide whether or not to stop immediately.

[0458] Users can receive this information and temporarily suspend their device use. For example, a user watching a rental video for an extended period with their eyes fixed on the screen might receive a voice notification saying, "Let's take a short break," and a message encouraging them to take a break would appear on the screen along with a timer. In this way, appropriate support is provided to maintain the user's health.

[0459] An example of a prompt to the generating AI model could be, "How can I generate an interruption notification when a user is looking at their device for an extended period?" This prompt would allow the AI ​​model to generate optimal notifications based on the user's behavior patterns, thereby supporting healthy device use.

[0460] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0461] Step 1:

[0462] The server receives gaze information acquired in real time from the eye-tracking module. This information includes the position of the user's pupils and the direction of their gaze. The input is gaze data, and the output becomes the basic data for eye tracking. The data is sent to eye-tracking software, which registers the movement of the gaze as digital data.

[0463] Step 2:

[0464] The server uses gaze data obtained from eye-tracking software to analyze the user's device operation posture and operation time. The input is processed gaze data, and the output is an evaluation metric for the user's device operation. An AI algorithm analyzes the direction and change patterns of gaze to detect usage habits that may affect health.

[0465] Step 3:

[0466] The server generates information to prompt the user to interrupt based on the analysis results. The input is the analyzed evaluation metrics, and the output is a notification message to encourage interruption. A generation AI model is used to create an appropriate interruption message and notify the user in the form of audio or screen display.

[0467] Step 4:

[0468] The terminal communicates interruption messages sent from the server to the user using a speech synthesis system or display. The input is a notification message, and the output is an audio or visual warning to the user. This prompts the user to immediately suspend the use of the device.

[0469] Step 5:

[0470] The user receives a notification and chooses whether to temporarily suspend or continue using the device. The user's choice or feedback is sent to the server and used to optimize future notification timing. The input is user feedback, and the output is data for improving the notification algorithm.

[0471] 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.

[0472] One embodiment of the present invention is a program embedded on a smart device that simultaneously monitors the user's gaze and emotional state to promote healthy device use. First, the device continuously captures the user's gaze using its front camera, and obtains the pupil position and gaze angle using a gaze detection device. In addition, the device is equipped with an emotion engine that analyzes facial expressions, voice tone, etc., to recognize the user's emotional state.

[0473] Based on eye-tracking and emotional data collected by the device, an AI algorithm comprehensively analyzes the user's device usage posture, duration, and emotional state. This analysis evaluates the user's stress level and concentration. For example, if the user's gaze is too fixed or stress is detected from their facial expressions, the system may determine that there are factors hindering healthy device use.

[0474] The analysis results are periodically sent to the server, which uses this data to determine notification requirements. Alerts are generated based on the user's emotional state, and messages such as "Your stress levels are high. Take a deep breath to relax" are displayed on the user's device.

[0475] As a concrete example, suppose a user is using a device for work for an extended period. In this case, the device's emotion engine recognizes a high level of stress from the user's facial expressions and detects that their gaze is fixed on the screen. The server then sends a notification to the user encouraging them to relax. As a result, the user is more likely to consider taking a break and temporarily interrupt their work to take actions that alleviate stress.

[0476] This invention enables a multifaceted improvement in device usage that considers not only eye-tracking data but also the user's emotional state, thereby realizing more personalized health promotion.

[0477] The following describes the processing flow.

[0478] Step 1:

[0479] The device activates its front camera and captures the user's gaze. This allows eye-tracking software to identify the position of the user's pupils and the angle of their gaze, and record this as gaze data.

[0480] Step 2:

[0481] The device simultaneously uses an emotion engine to assess the user's emotional state. This includes a process of analyzing the user's facial expressions and voice tone to collect emotional data.

[0482] Step 3:

[0483] The device sends eye-tracking and emotional data it collects to an AI algorithm. The AI ​​analyzes the user's posture, usage time, and emotional state to provide an overall evaluation.

[0484] Step 4:

[0485] The device sends the analysis results to the server. Based on this data, the server decides what kind of notification to send to the user. Specifically, the user's stress level and concentration level are used as evaluation criteria.

[0486] Step 5:

[0487] The server considers the user's state and generates appropriate notifications. For example, if the user is experiencing high stress levels, it will create a notification encouraging stress reduction.

[0488] Step 6:

[0489] The device displays notifications received from the server to the user. These notifications may include messages such as, "Take a break to relax," or "Take a deep breath and refresh yourself."

[0490] Step 7:

[0491] Users can choose an action based on the displayed notification. If a user takes a break, they input that action into their device, and that information is sent to the server.

[0492] Step 8:

[0493] The server receives feedback from users and optimizes the timing and content of future notifications. This provides users with more personalized device usage support.

[0494] (Example 2)

[0495] 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."

[0496] In modern times, users' reliance on information and communication devices is increasing, leading to a greater accumulation of eye strain and stress. Conventional systems only provide uniform rest instructions based on gaze and device usage time, making it difficult to effectively promote healthy usage that takes into account the user's emotional state. This invention aims to solve this problem by realizing a system that provides more personalized health support by simultaneously acquiring and analyzing the user's gaze and emotional state.

[0497] 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.

[0498] In this invention, the server includes means for acquiring user gaze information using a gaze detection device, means for acquiring the user's emotional state using an emotion recognition device, and means for analyzing the user's device usage using the gaze information and emotional state. This makes it possible to send personalized notifications to support the user's healthy device usage.

[0499] A "gaze detection device" is a device that can acquire a user's gaze information in real time and is used to identify the position of the pupil and the movement of the gaze.

[0500] An "emotion recognition device" is a device that analyzes facial expressions, voice tone, and other factors to acquire the user's emotional state, and can measure the user's psychological state.

[0501] "Means for analyzing device usage" refers to methods that use user eye-tracking information and emotional states to evaluate how users use information and communication devices and to determine their level of concentration, stress levels, etc.

[0502] "Means of sending notifications" refers to means of sending messages or alerts to information and communication devices based on analysis results to encourage users to rest or take other actions.

[0503] "Means for measuring distance" refers to means for evaluating the physical distance between the user and the device from eye-tracking information, enabling the maintenance of an appropriate usage environment.

[0504] "Means of receiving responses" refers to means of obtaining user feedback, which can then be used to optimize notifications and system operations.

[0505] This invention is a system that operates on an information and communication device and simultaneously monitors the user's gaze and emotional state to promote healthy device use. The user uses a terminal that incorporates a gaze detection device and an emotion recognition device. The gaze detection device uses a built-in camera to acquire the user's pupil position and gaze angle in real time. The emotion recognition device determines the user's emotional state by analyzing facial expressions and voice tone.

[0506] The device analyzes collected eye-tracking data and emotional states using an AI algorithm. This allows for a comprehensive evaluation of the user's posture, usage time, stress level, and concentration level. The server receives the analysis results and generates notifications tailored to the user's state. For example, if the user's gaze is fixed on the screen for an extended period and a high stress level is detected, the server will send a notification such as, "We recommend taking a short break."

[0507] For example, when a user is working at a desk for a long time, the device can monitor the user's gaze and emotions and send notifications to encourage relaxation as needed, allowing the user to continue working while being mindful of their health. Furthermore, by using a generative AI model, the content of the notifications can be personalized to suit the user's diverse states. An example of a prompt message would be, "Generate a notification message encouraging healthy device use based on the user's gaze data and emotional state."

[0508] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0509] Step 1:

[0510] The device acquires user gaze information using an eye-tracking device. Specifically, it uses the front camera to capture the position of the user's pupils and the angle of their gaze in real time. The input is camera video, and the output is gaze position data and angle data. This data is used to identify gaze movement and fixed positions.

[0511] Step 2:

[0512] The device uses an emotion recognition device to recognize the user's emotional state. Specifically, it analyzes the user's facial expressions and voice tone to determine emotions such as stress and relaxation. Inputs include voice waveform data and facial image data, and output is emotional state data. This data is used to identify the user's psychological state.

[0513] Step 3:

[0514] The device analyzes eye-tracking data and emotional states using an AI algorithm. Specifically, it plots the duration of fixed gaze and the degree of emotional change to quantify the user's concentration level and stress level. The inputs are eye-tracking data and emotional data, and the output is the analyzed user's device usage status. This result is used to comprehensively evaluate the user's usage.

[0515] Step 4:

[0516] The server generates a notification based on the analysis results and sends it to the device. Specifically, the AI ​​model generates an alert message when stress levels are high. The input is user usage data, and the output is a notification message. This sends alerts to encourage users to use their devices in a healthy way.

[0517] Step 5:

[0518] Users receive notifications on their devices and take action based on the alerts presented. Specifically, they check the notifications and take actions such as taking a break if necessary. The input is notifications sent from the server, and the output is changes in the user's behavior. This encourages users to practice healthy device usage.

[0519] (Application Example 2)

[0520] 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."

[0521] Conventional device usage monitoring systems primarily rely on simple analyses based solely on user eye-tracking data, lacking personalization that considers the user's emotional state or stress level. Therefore, they fail to effectively support healthy device use, making it difficult to reduce stress and fatigue.

[0522] 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.

[0523] In this invention, the server includes means for acquiring user gaze data using a gaze detection device, means for evaluating stress levels by combining the gaze data with emotional data from an facial recognition engine, and means for optimizing notification content according to the user's emotional state using a generative AI model. This makes it possible to analyze the user's gaze and emotional state from multiple angles and provide personalized health support.

[0524] A "gaze detection device" is a device that tracks a user's gaze and acquires data regarding the direction of their gaze and the position of their pupils.

[0525] "Eye-gaze data" refers to information about the user's gaze direction and point of focus, acquired by an eye-gaze detection device.

[0526] "Device usage posture" is a concept that refers to the physical posture and position of a user while using a device.

[0527] A "facial expression recognition engine" is a software algorithm that analyzes a user's facial expressions and evaluates their emotional state.

[0528] "Emotional data" refers to information indicating the user's emotional state, obtained through facial recognition engines and other means.

[0529] "Stress level" is an indicator that shows the current degree of mental burden or tension experienced by the user.

[0530] A "generative AI model" is an artificial intelligence computational model that generates information optimized for the user based on a set of data.

[0531] A "notification transmission method" refers to a mechanism or device used to send information or alerts to a user.

[0532] "Feedback" refers to responses and information provided by users, which is used as data to optimize the system.

[0533] This system is designed to promote healthy device use by users and is specifically built by combining an eye-tracking device, a facial recognition engine, and a generative AI model. The camera on the device functions as an eye-tracking device, acquiring the user's gaze data. In addition, the facial recognition engine built into the device analyzes the video captured by the camera to acquire the user's emotional data.

[0534] The server receives eye-tracking and emotional data transmitted by the device and integrates them to evaluate the user's stress level and concentration. An AI algorithm is used for this evaluation, comprehensively analyzing the user's posture, usage time, and emotional state during device use. Based on this analysis, the server utilizes a generative AI model to generate personalized notifications and send them to the device. These notifications might include messages such as, "Your stress levels are high. Take a deep breath to relax."

[0535] As a concrete example, when a user is watching television for an extended period, the device detects that the user's gaze is fixed and, based on their facial expression, indicates a high level of stress. The server then sends a voice reminder such as, "Let's take a short break." This process is supported by various software components, including gaze and emotion analysis using TensorFlow, facial recognition using Google Cloud's Vision AI, and voice generation using Amazon Polly.

[0536] An example of a prompt message might be: "While the user is relaxing in the living room and watching TV, an advanced AI camera begins tracking their gaze. The robot notices that the user's expression is gradually showing signs of fatigue. How will it respond?" This allows for the provision of appropriate feedback as the user interacts with the device.

[0537] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0538] Step 1:

[0539] The device uses its built-in camera to capture the user's gaze in real time. The gaze data includes the position of the user's pupils and the direction of their gaze. This data is processed by a gaze detection algorithm to analyze the user's fixation points and changes in their gaze.

[0540] Step 2:

[0541] The device simultaneously analyzes the user's face using an expression recognition engine. Video data is input, and emotional data is extracted from the facial expressions. In this process, a machine learning model analyzes the input data and outputs emotional data that quantifies the user's emotional state.

[0542] Step 3:

[0543] The device sends eye-tracking and emotional data to a server. The server receives this data and uses an AI algorithm to analyze it comprehensively. Specifically, it calculates the degree of eye-tracking fixation and emotional changes to evaluate the user's stress level and concentration. As a result of the evaluation, a score indicating the user's current mental state is output.

[0544] Step 4:

[0545] The server generates notification content using a generative AI model. Using the evaluation results as input, it creates appropriate alert and reminder messages for the user. For example, if the stress level is high, it might generate a message such as "Take a short break," and the message is output in text or audio format.

[0546] Step 5:

[0547] The server sends the generated notification content to the device. The device then displays the received notification content to the user. In the case of voice output, it uses speech synthesis to communicate directly to the user. This process creates a feedback loop that supports the user's healthy device use.

[0548] 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.

[0549] 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.

[0550] 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.

[0551] [Fourth Embodiment]

[0552] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0553] 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.

[0554] 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).

[0555] 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.

[0556] 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.

[0557] 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).

[0558] 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.

[0559] 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.

[0560] 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.

[0561] 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.

[0562] 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.

[0563] 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.

[0564] 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".

[0565] In an embodiment of the present invention, a program embedded in a smart device plays a major role. This program works in conjunction with an eye-tracking device to acquire the user's eye-tracking data and analyzes this data in real time to evaluate the user's device usage posture and time.

[0566] The program first activates the front camera on the device and continuously captures the user's gaze. Eye-tracking software installed on the device analyzes the position of the user's pupils and the movement of their eyes, recording this as gaze data. This gaze data includes information such as the user's gaze angle and distance from the screen.

[0567] This data is then sent to an AI algorithm within the device to analyze the user's eye movements and device usage time. The AI ​​algorithm analyzes patterns of changes in gaze angle and distance to determine whether the user's posture is inappropriate or whether the usage time may be affecting their health.

[0568] Based on this analysis, the server periodically receives data and generates notifications prompting users to take breaks at appropriate times. Notification criteria include situations where the distance between the user and device (based on eye-tracking data) exceeds a threshold, or when continuous use for a certain period of time is detected. Furthermore, feedback from users is received and managed as information to individually optimize break timing.

[0569] As a concrete example, consider a situation where a user is using a news reading app. Because this user is looking at the device at close range for an extended period, the device continuously collects eye-tracking data. As a result, the time spent at a distance of less than 20 cm exceeds a certain limit. In such cases, the server sends a notification to the user's screen via the device saying, "Please move away from the screen for a while. Take a break from your eyes."

[0570] This invention allows users to reduce potential health risks associated with their device use and develop healthier usage habits.

[0571] The following describes the processing flow.

[0572] Step 1:

[0573] The device activates its front camera and captures the user's gaze. At this point, eye-tracking software runs to detect the user's pupil position and gaze angle in real time and collect gaze data.

[0574] Step 2:

[0575] The device sends the eye-tracking data it acquires to an AI algorithm. The AI ​​analyzes the data and evaluates the user's posture and device usage based on the user's eye movements and distance from the device.

[0576] Step 3:

[0577] The device sends analysis results to the server at regular intervals (e.g., every 10 minutes). This data includes the angle of gaze, the distance between the user and the device, and the continuous usage time.

[0578] Step 4:

[0579] The server determines whether a break is necessary based on the data it receives. The need for a break is determined if the angle or distance of the user's gaze exceeds a set threshold, or if continuous use is detected.

[0580] Step 5:

[0581] The server generates and sends a notification message to the device based on its own judgment. Specific alert messages include content encouraging users to use their devices in a healthy manner.

[0582] Step 6:

[0583] The device prompts the user to take a break by displaying notifications received from the server. The notifications are displayed visually on the screen and may include phrases such as "Let's rest our eyes."

[0584] Step 7:

[0585] When a user takes a break based on a notification, they input that action into their device. One possible format is for the user to press a button to indicate that they have started a break.

[0586] Step 8:

[0587] The server receives feedback from the user and adjusts the timing of the next notification. This optimizes notifications, allowing users to be prompted to take breaks at more effective times.

[0588] (Example 1)

[0589] 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".

[0590] Excessive use of data devices in modern society can negatively impact user health. In particular, prolonged use can lead to vision problems and other health issues if the user's gaze position or distance from the device is inappropriate. However, determining appropriate break times is difficult for users, and it's challenging for them to be mindful of their usage.

[0591] 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.

[0592] In this invention, the server includes means for collecting user eye-tracking information, means for analyzing the eye-tracking information to evaluate the user's posture and usage time when using the data device, and means for notifying the user to take a break based on the evaluation results. This encourages the user to take breaks at appropriate times, enabling health-conscious use of the device.

[0593] A "user" refers to an individual operating a data device, and the system analyzes their gaze and actions.

[0594] "Eye-gaze information" includes data about the position and movement of the user's eyes, their angle, and their distance from the device.

[0595] "Data equipment" refers to electronic devices used by users, specifically terminals used to acquire eye-tracking information.

[0596] "Evaluation" refers to the analytical process of determining user behavior and health impacts based on collected eye-tracking data.

[0597] "Notifications" refer to displaying messages to users recommending breaks based on analysis results.

[0598] A "generative AI model" is a type of artificial intelligence used to create notification content based on eye-tracking information and user feedback, and to optimize it for individual users.

[0599] "Feedback" refers to reference information collected from users' responses and opinions to notifications.

[0600] To implement the present invention, the terminal must first incorporate an in-camera and eye-tracking software. When the user begins using the data device, the terminal activates the in-camera and continuously captures gaze information regarding the position and movement of the user's pupils. This gaze information serves as basic data for understanding the user's posture and physical distance from the device.

[0601] The eye-tracking software within the device analyzes this captured gaze information. The analysis records numerical data such as gaze angle, movement patterns, and distance from the device, and uses this data to evaluate the user's usage. This helps determine how the user's data device usage might affect their health.

[0602] The server receives data sent from the terminal and uses a generative AI model to generate appropriate notifications for the user. These notifications are designed to encourage the user to take a break and are optimized based on each user's usage patterns. For example, if a user has been looking at the screen at close range for an extended period, a message such as "Take a step back from the screen. Rest your eyes." will be generated and displayed to the user by the terminal.

[0603] User feedback is used for further data collection and analysis to make notifications more effective. The server aggregates this feedback and incorporates it into the generating AI model, resulting in notification content and timing that are more tailored to the user's situation.

[0604] As a concrete example, a possible prompt for a generative AI model might be, "How can we optimize the timing of health-conscious breaks based on eye-tracking data?" This prompt allows the AI ​​to improve notifications and support users in using data devices in a healthier way.

[0605] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0606] Step 1:

[0607] The device activates the front camera.

[0608] When a user begins using a data device, the terminal automatically activates its front-facing camera. The input is data of the user's gaze, and based on this, it continuously captures information about the position of the eyes. The output is raw data including the position of the user's face and eyes.

[0609] Step 2:

[0610] The device analyzes eye-tracking information.

[0611] Eye-tracking software analyzes the user's pupil movements based on captured data. This analysis calculates the angle of gaze, movement patterns, and distance from the device. The input is raw data captured by the front camera, and the output is digitized gaze information.

[0612] Step 3:

[0613] The device sends data to the AI ​​algorithm.

[0614] The device transmits the analyzed eye-tracking information to an AI algorithm. The AI ​​uses this information to evaluate the user's device usage. The input is quantified eye-tracking information, and the output is the user's health status assessment result.

[0615] Step 4:

[0616] The server generates the notification.

[0617] Based on the AI-generated evaluation results, the server uses a generative AI model to generate notifications prompting users to take a break. The input is the user's health status evaluation result, and the output is a notification message such as "Take a break from the screen."

[0618] Step 5:

[0619] The server collects user feedback.

[0620] The server collects user feedback on notifications and incorporates it into the generating AI model. The input is user feedback data, and the output is updated notifications and timing adjustments that reflect the feedback.

[0621] (Application Example 1)

[0622] 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".

[0623] In today's digital society, it is common for people to use devices for extended periods, raising concerns about negative health effects such as poor posture and vision problems. Furthermore, as consumer robots become more widespread in homes, there is a growing demand for robots that can support users' healthy lifestyles. This invention aims to provide technology that mitigates these health risks and promotes healthier device use.

[0624] 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.

[0625] In this invention, the server includes means for acquiring user eye-tracking information using an eye-tracking module, means for analyzing the user's device operating posture and operating time using the eye-tracking information, means for transmitting information to the user prompting them to interrupt the device based on the analysis results, and means for transmitting information through voice or screen display. This allows users to be encouraged to use devices that are mindful of their health, and improves the quality of life support provided by consumer robots.

[0626] An "eye-tracking module" is a combination of hardware and software that detects the user's gaze and eye movements and acquires their location information as digital data.

[0627] "Eye-gaze information" refers to data about the user's eye movements, including information such as pupil position, gaze direction, and point of fixation.

[0628] "Device operation posture" refers to the position and angle of the user's body when using the device, and includes the degree of curvature of the spine and the tilt of the neck.

[0629] "Operation time" refers to the length of time a user continuously operates or uses the device.

[0630] "Analysis results" refer to digital data or judgment results obtained after analyzing eye-tracking information, the user's device operation posture, and operation time.

[0631] "Interruption information" refers to messages or notifications designed to alert users to temporarily stop using their devices.

[0632] "Means of information transmission" refer to methods and media used to convey information to users that encourage interruption, and include audio, text, and visual displays.

[0633] The system for realizing this invention is centered around an eye-tracking module installed in consumer robots and smart devices. The server incorporates a program to acquire eye-tracking information, analyze it, and evaluate the user's device operation posture and operation time. Specifically, the server analyzes the eye-tracking information obtained through the eye-tracking module using eye-tracking software (e.g., commonly known as "eye-tracking detection software"). This software records the user's eye movements and gaze direction as digital data and processes it in real time.

[0634] The analyzed eye-tracking data is further analyzed by an AI model (e.g., a generic term "generative AI model") to determine whether the user's device operation may have adverse health effects. Based on the analysis results, the server provides the user with information prompting them to stop using the device, either verbally or visually. A speech synthesis system and a display system are used to help the user decide whether or not to stop immediately.

[0635] Users can receive this information and temporarily suspend their device use. For example, a user watching a rental video for an extended period with their eyes fixed on the screen might receive a voice notification saying, "Let's take a short break," and a message encouraging them to take a break would appear on the screen along with a timer. In this way, appropriate support is provided to maintain the user's health.

[0636] An example of a prompt to the generating AI model could be, "How can I generate an interruption notification when a user is looking at their device for an extended period?" This prompt would allow the AI ​​model to generate optimal notifications based on the user's behavior patterns, thereby supporting healthy device use.

[0637] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0638] Step 1:

[0639] The server receives gaze information acquired in real time from the eye-tracking module. This information includes the position of the user's pupils and the direction of their gaze. The input is gaze data, and the output becomes the basic data for eye tracking. The data is sent to eye-tracking software, which registers the movement of the gaze as digital data.

[0640] Step 2:

[0641] The server uses gaze data obtained from eye-tracking software to analyze the user's device operation posture and operation time. The input is processed gaze data, and the output is an evaluation metric for the user's device operation. An AI algorithm analyzes the direction and change patterns of gaze to detect usage habits that may affect health.

[0642] Step 3:

[0643] The server generates information to prompt the user to interrupt based on the analysis results. The input is the analyzed evaluation metrics, and the output is a notification message to encourage interruption. A generation AI model is used to create an appropriate interruption message and notify the user in the form of audio or screen display.

[0644] Step 4:

[0645] The terminal communicates interruption messages sent from the server to the user using a speech synthesis system or display. The input is a notification message, and the output is an audio or visual warning to the user. This prompts the user to immediately suspend the use of the device.

[0646] Step 5:

[0647] The user receives a notification and chooses whether to temporarily suspend or continue using the device. The user's choice or feedback is sent to the server and used to optimize future notification timing. The input is user feedback, and the output is data for improving the notification algorithm.

[0648] 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.

[0649] One embodiment of the present invention is a program embedded on a smart device that simultaneously monitors the user's gaze and emotional state to promote healthy device use. First, the device continuously captures the user's gaze using its front camera, and obtains the pupil position and gaze angle using a gaze detection device. In addition, the device is equipped with an emotion engine that analyzes facial expressions, voice tone, etc., to recognize the user's emotional state.

[0650] Based on eye-tracking and emotional data collected by the device, an AI algorithm comprehensively analyzes the user's device usage posture, duration, and emotional state. This analysis evaluates the user's stress level and concentration. For example, if the user's gaze is too fixed or stress is detected from their facial expressions, the system may determine that there are factors hindering healthy device use.

[0651] The analysis results are periodically sent to the server, which uses this data to determine notification requirements. Alerts are generated based on the user's emotional state, and messages such as "Your stress levels are high. Take a deep breath to relax" are displayed on the user's device.

[0652] As a concrete example, suppose a user is using a device for work for an extended period. In this case, the device's emotion engine recognizes a high level of stress from the user's facial expressions and detects that their gaze is fixed on the screen. The server then sends a notification to the user encouraging them to relax. As a result, the user is more likely to consider taking a break and temporarily interrupt their work to take actions that alleviate stress.

[0653] This invention enables a multifaceted improvement in device usage that considers not only eye-tracking data but also the user's emotional state, thereby realizing more personalized health promotion.

[0654] The following describes the processing flow.

[0655] Step 1:

[0656] The device activates its front camera and captures the user's gaze. This allows eye-tracking software to identify the position of the user's pupils and the angle of their gaze, and record this as gaze data.

[0657] Step 2:

[0658] The device simultaneously uses an emotion engine to assess the user's emotional state. This includes a process of analyzing the user's facial expressions and voice tone to collect emotional data.

[0659] Step 3:

[0660] The device sends eye-tracking and emotional data it collects to an AI algorithm. The AI ​​analyzes the user's posture, usage time, and emotional state to provide an overall evaluation.

[0661] Step 4:

[0662] The device sends the analysis results to the server. Based on this data, the server decides what kind of notification to send to the user. Specifically, the user's stress level and concentration level are used as evaluation criteria.

[0663] Step 5:

[0664] The server considers the user's state and generates appropriate notifications. For example, if the user is experiencing high stress levels, it will create a notification encouraging stress reduction.

[0665] Step 6:

[0666] The device displays notifications received from the server to the user. These notifications may include messages such as, "Take a break to relax," or "Take a deep breath and refresh yourself."

[0667] Step 7:

[0668] Users can choose an action based on the displayed notification. If a user takes a break, they input that action into their device, and that information is sent to the server.

[0669] Step 8:

[0670] The server receives feedback from users and optimizes the timing and content of future notifications. This provides users with more personalized device usage support.

[0671] (Example 2)

[0672] 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".

[0673] In modern times, users' reliance on information and communication devices is increasing, leading to a greater accumulation of eye strain and stress. Conventional systems only provide uniform rest instructions based on gaze and device usage time, making it difficult to effectively promote healthy usage that takes into account the user's emotional state. This invention aims to solve this problem by realizing a system that provides more personalized health support by simultaneously acquiring and analyzing the user's gaze and emotional state.

[0674] 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.

[0675] In this invention, the server includes means for acquiring user gaze information using a gaze detection device, means for acquiring the user's emotional state using an emotion recognition device, and means for analyzing the user's device usage using the gaze information and emotional state. This makes it possible to send personalized notifications to support the user's healthy device usage.

[0676] A "gaze detection device" is a device that can acquire a user's gaze information in real time and is used to identify the position of the pupil and the movement of the gaze.

[0677] An "emotion recognition device" is a device that analyzes facial expressions, voice tone, and other factors to acquire the user's emotional state, and can measure the user's psychological state.

[0678] "Means for analyzing device usage" refers to methods that use user eye-tracking information and emotional states to evaluate how users use information and communication devices and to determine their level of concentration, stress levels, etc.

[0679] "Means of sending notifications" refers to means of sending messages or alerts to information and communication devices based on analysis results to encourage users to rest or take other actions.

[0680] "Means for measuring distance" refers to means for evaluating the physical distance between the user and the device from eye-tracking information, enabling the maintenance of an appropriate usage environment.

[0681] "Means of receiving responses" refers to means of obtaining user feedback, which can then be used to optimize notifications and system operations.

[0682] This invention is a system that operates on an information and communication device and simultaneously monitors the user's gaze and emotional state to promote healthy device use. The user uses a terminal that incorporates a gaze detection device and an emotion recognition device. The gaze detection device uses a built-in camera to acquire the user's pupil position and gaze angle in real time. The emotion recognition device determines the user's emotional state by analyzing facial expressions and voice tone.

[0683] The device analyzes collected eye-tracking data and emotional states using an AI algorithm. This allows for a comprehensive evaluation of the user's posture, usage time, stress level, and concentration level. The server receives the analysis results and generates notifications tailored to the user's state. For example, if the user's gaze is fixed on the screen for an extended period and a high stress level is detected, the server will send a notification such as, "We recommend taking a short break."

[0684] For example, when a user is working at a desk for a long time, the device can monitor the user's gaze and emotions and send notifications to encourage relaxation as needed, allowing the user to continue working while being mindful of their health. Furthermore, by using a generative AI model, the content of the notifications can be personalized to suit the user's diverse states. An example of a prompt message would be, "Generate a notification message encouraging healthy device use based on the user's gaze data and emotional state."

[0685] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0686] Step 1:

[0687] The device acquires user gaze information using an eye-tracking device. Specifically, it uses the front camera to capture the position of the user's pupils and the angle of their gaze in real time. The input is camera video, and the output is gaze position data and angle data. This data is used to identify gaze movement and fixed positions.

[0688] Step 2:

[0689] The device uses an emotion recognition device to recognize the user's emotional state. Specifically, it analyzes the user's facial expressions and voice tone to determine emotions such as stress and relaxation. Inputs include voice waveform data and facial image data, and output is emotional state data. This data is used to identify the user's psychological state.

[0690] Step 3:

[0691] The device analyzes eye-tracking data and emotional states using an AI algorithm. Specifically, it plots the duration of fixed gaze and the degree of emotional change to quantify the user's concentration level and stress level. The inputs are eye-tracking data and emotional data, and the output is the analyzed user's device usage status. This result is used to comprehensively evaluate the user's usage.

[0692] Step 4:

[0693] The server generates a notification based on the analysis results and sends it to the device. Specifically, the AI ​​model generates an alert message when stress levels are high. The input is user usage data, and the output is a notification message. This sends alerts to encourage users to use their devices in a healthy way.

[0694] Step 5:

[0695] Users receive notifications on their devices and take action based on the alerts presented. Specifically, they check the notifications and take actions such as taking a break if necessary. The input is notifications sent from the server, and the output is changes in the user's behavior. This encourages users to practice healthy device usage.

[0696] (Application Example 2)

[0697] 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".

[0698] Conventional device usage monitoring systems primarily rely on simple analyses based solely on user eye-tracking data, lacking personalization that considers the user's emotional state or stress level. Therefore, they fail to effectively support healthy device use, making it difficult to reduce stress and fatigue.

[0699] 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.

[0700] In this invention, the server includes means for acquiring user gaze data using a gaze detection device, means for evaluating stress levels by combining the gaze data with emotional data from an facial recognition engine, and means for optimizing notification content according to the user's emotional state using a generative AI model. This makes it possible to analyze the user's gaze and emotional state from multiple angles and provide personalized health support.

[0701] A "gaze detection device" is a device that tracks a user's gaze and acquires data regarding the direction of their gaze and the position of their pupils.

[0702] "Eye-gaze data" refers to information about the user's gaze direction and point of focus, acquired by an eye-gaze detection device.

[0703] "Device usage posture" is a concept that refers to the physical posture and position of a user while using a device.

[0704] A "facial expression recognition engine" is a software algorithm that analyzes a user's facial expressions and evaluates their emotional state.

[0705] "Emotional data" refers to information indicating the user's emotional state, obtained through facial recognition engines and other means.

[0706] "Stress level" is an indicator that shows the current degree of mental burden or tension experienced by the user.

[0707] A "generative AI model" is an artificial intelligence computational model that generates information optimized for the user based on a set of data.

[0708] A "notification transmission method" refers to a mechanism or device used to send information or alerts to a user.

[0709] "Feedback" refers to responses and information provided by users, which is used as data to optimize the system.

[0710] This system is designed to promote healthy device use by users and is specifically built by combining an eye-tracking device, a facial recognition engine, and a generative AI model. The camera on the device functions as an eye-tracking device, acquiring the user's gaze data. In addition, the facial recognition engine built into the device analyzes the video captured by the camera to acquire the user's emotional data.

[0711] The server receives eye-tracking and emotional data transmitted by the device and integrates them to evaluate the user's stress level and concentration. An AI algorithm is used for this evaluation, comprehensively analyzing the user's posture, usage time, and emotional state during device use. Based on this analysis, the server utilizes a generative AI model to generate personalized notifications and send them to the device. These notifications might include messages such as, "Your stress levels are high. Take a deep breath to relax."

[0712] As a concrete example, when a user is watching television for an extended period, the device detects that the user's gaze is fixed and, based on their facial expression, indicates a high level of stress. The server then sends a voice reminder such as, "Let's take a short break." This process is supported by various software components, including gaze and emotion analysis using TensorFlow, facial recognition using Google Cloud's Vision AI, and voice generation using Amazon Polly.

[0713] An example of a prompt message might be: "While the user is relaxing in the living room and watching TV, an advanced AI camera begins tracking their gaze. The robot notices that the user's expression is gradually showing signs of fatigue. How will it respond?" This allows for the provision of appropriate feedback as the user interacts with the device.

[0714] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0715] Step 1:

[0716] The device uses its built-in camera to capture the user's gaze in real time. The gaze data includes the position of the user's pupils and the direction of their gaze. This data is processed by a gaze detection algorithm to analyze the user's fixation points and changes in their gaze.

[0717] Step 2:

[0718] The device simultaneously analyzes the user's face using an expression recognition engine. Video data is input, and emotional data is extracted from the facial expressions. In this process, a machine learning model analyzes the input data and outputs emotional data that quantifies the user's emotional state.

[0719] Step 3:

[0720] The device sends eye-tracking and emotional data to a server. The server receives this data and uses an AI algorithm to analyze it comprehensively. Specifically, it calculates the degree of eye-tracking fixation and emotional changes to evaluate the user's stress level and concentration. As a result of the evaluation, a score indicating the user's current mental state is output.

[0721] Step 4:

[0722] The server generates notification content using a generative AI model. Using the evaluation results as input, it creates appropriate alert and reminder messages for the user. For example, if the stress level is high, it might generate a message such as "Take a short break," and the message is output in text or audio format.

[0723] Step 5:

[0724] The server sends the generated notification content to the device. The device then displays the received notification content to the user. In the case of voice output, it uses speech synthesis to communicate directly to the user. This process creates a feedback loop that supports the user's healthy device use.

[0725] 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.

[0726] 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.

[0727] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0728] 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.

[0729] 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.

[0730] 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.

[0731] 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.

[0732] 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.

[0733] 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."

[0734] 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.

[0735] 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.

[0736] 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.

[0737] 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.

[0738] 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.

[0739] 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.

[0740] 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.

[0741] 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.

[0742] 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.

[0743] 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.

[0744] 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.

[0745] 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.

[0746] The following is further disclosed regarding the embodiments described above.

[0747] (Claim 1)

[0748] A means for acquiring user gaze data using a gaze detection device,

[0749] A means for analyzing the user's device usage posture and usage time using the aforementioned eye-tracking data,

[0750] A means of sending notifications to users to encourage them to take a break based on the analysis results,

[0751] A system that includes this.

[0752] (Claim 2)

[0753] A means for measuring the distance between the user and the device based on the user's eye-tracking data,

[0754] The system according to claim 1, further comprising means for issuing a notification prompting a break when the distance exceeds a predetermined threshold.

[0755] (Claim 3)

[0756] The notification sending means further includes means for receiving user feedback,

[0757] The system according to claim 1, further comprising means for optimizing the timing of notifications based on the aforementioned feedback.

[0758] "Example 1"

[0759] (Claim 1)

[0760] Means for collecting user eye-tracking information,

[0761] A means for analyzing the aforementioned gaze information to evaluate the user's posture and usage time of data devices,

[0762] A means for notifying the user to take a break based on the evaluation results,

[0763] A means for generating notification content using a generative AI model,

[0764] A system that includes this.

[0765] (Claim 2)

[0766] A means for calculating the distance between the user and the data device based on the user's gaze information,

[0767] The system according to claim 1, further comprising means for providing a notification recommending a break when the distance exceeds a set threshold.

[0768] (Claim 3)

[0769] The means for making the aforementioned notification further includes means for receiving feedback from the user,

[0770] The system according to claim 1, further comprising means for improving the timing of notifications based on the aforementioned feedback.

[0771] "Application Example 1"

[0772] (Claim 1)

[0773] A means of acquiring user eye-tracking information using an eye-tracking module,

[0774] A means for analyzing the user's device operating posture and operating time using the aforementioned eye-tracking information,

[0775] A means of sending information to the user prompting them to interrupt based on the analysis results,

[0776] Means of transmitting information through sound or screen display,

[0777] Information processing device including

[0778] (Claim 2)

[0779] A means for measuring the distance between the user and the device based on the user's eye-tracking information,

[0780] The information processing apparatus according to claim 1, further comprising means for transmitting information prompting interruption when the interval exceeds a set upper limit.

[0781] (Claim 3)

[0782] The aforementioned information transmission means further includes means for receiving user opinions,

[0783] The information processing apparatus according to claim 1, having means for improving the timing of information transmission based on the aforementioned opinion.

[0784] "Example 2 of combining an emotion engine"

[0785] (Claim 1)

[0786] A means for acquiring user gaze information using a gaze detection device,

[0787] A means for acquiring a user's emotional state using an emotion recognition device,

[0788] A means for analyzing the user's device usage using the aforementioned eye-tracking information and emotional state,

[0789] A means of sending notifications to users recommending rest based on the analysis results,

[0790] A system that includes this.

[0791] (Claim 2)

[0792] The system according to claim 1, comprising means for measuring the distance between the user and the device based on the gaze information, and issuing a notification recommending rest when the distance exceeds a predetermined threshold.

[0793] (Claim 3)

[0794] The system according to claim 1, wherein the notification sending means further includes means for receiving a user response and means for improving the timing of the notification based on the response.

[0795] "Application example 2 when combining with an emotional engine"

[0796] (Claim 1)

[0797] A means for acquiring user gaze data using a gaze detection device,

[0798] A means for analyzing the user's device usage posture and usage time using the aforementioned eye-tracking data,

[0799] A means for evaluating stress levels by combining the aforementioned gaze data with emotional data from a facial recognition engine,

[0800] A means of sending notifications to users to encourage them to take a break based on the analysis results,

[0801] A method for optimizing notification content according to the user's emotional state using a generative AI model,

[0802] A system that includes this.

[0803] (Claim 2)

[0804] A means for measuring the distance between the user and the device based on the user's eye-tracking data,

[0805] The system according to claim 1, further comprising means for issuing a notification prompting a break when the distance exceeds a predetermined threshold.

[0806] (Claim 3)

[0807] The notification sending means further includes means for receiving user feedback,

[0808] The system according to claim 1, further comprising means for optimizing the timing of notifications based on the aforementioned feedback. [Explanation of symbols]

[0809] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for acquiring user gaze data using a gaze detection device, A means for analyzing the user's device usage posture and usage time using the aforementioned eye-tracking data, A means of sending notifications to users to encourage them to take a break based on the analysis results, A system that includes this.

2. A means for measuring the distance between the user and the device based on the user's eye-tracking data, The system according to claim 1, further comprising means for issuing a notification prompting a break when the distance exceeds a predetermined threshold.

3. The notification sending means further includes means for receiving user feedback, The system according to claim 1, further comprising means for optimizing the timing of notifications based on the aforementioned feedback.

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