System

Abstract

A system is provided.SOLUTION: A system, comprising: means for monitoring a face, a motion, and a sound of a baby in real time; means for sending the monitored information to a cloud-end server; means for analyzing the received information in the cloud-end server to generate a AI model for determining a need and a danger of the baby; and means for notifying a parent's end of an analysis result.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Parents often face the challenge of quickly understanding their baby's needs and condition. This increases the burden of childcare, especially when parents are unable to quickly understand the specific reasons for their baby's crying or discomfort. Furthermore, parents may miss dangerous situations when their baby is in danger. These issues increase parents' physical and mental stress and cause anxiety about childcare. [Means for solving the problem]

[0005] To solve this problem, the present invention provides the following system. First, a device means is provided for monitoring the baby's face, movements, and voice in real time, thereby constantly monitoring the baby's condition. Next, a communication means is provided for transmitting the monitoring data to a cloud server, supporting data accumulation and real-time analysis. The cloud server is provided with a generative AI model means for analyzing the received data and determining the baby's needs and dangers, thereby quickly identifying the baby's condition and requests. Finally, a notification means is provided for notifying the parent's device of the analysis results, allowing the parent to immediately take any necessary action. This allows parents to understand the baby's condition and requests in real time and quickly take appropriate action, thereby reducing the burden of childcare and ensuring safety.

[0006] "Device Means" refers to equipment used to monitor the baby's face, movements, and voice in real time.

[0007] The "communication means" refers to a function or device for transmitting monitoring data acquired by the device means to the cloud server.

[0008] The "cloud server" refers to a remote server that receives and analyzes data sent from device means.

[0009] "Generative AI model means" refers to algorithms and machine learning models that analyze received data and determine the baby's needs and risks.

[0010] The "notification means" refers to a function or device for transmitting the analysis results generated by the cloud server to the parent's terminal.

[0011] "Parent's device" refers to a device (e.g., smartphone, tablet, etc.) that displays received notifications and allows parents to keep track of their baby's condition.

[0012] "Analysis results" refers to the output information of the data analyzed by the generating AI model means, and includes the results of assessment of the baby's needs and risks.

[0013] "Danger" refers to a situation in which the baby is in physical or psychological danger, such as being lying face down or having something fall over.

[0014] "Real-time" refers to data acquisition and analysis occurring immediately, without delay.

[0015] "Needs" refer to a baby's current wants and needs, such as hunger or the need for a diaper change. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a 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.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0030] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] Overall system overview

[0038] This system monitors a baby's face, movements, and voice in real time, analyzes the data on a cloud server, and notifies the parents of the results. This system allows parents to instantly understand their baby's needs and dangerous situations, reducing the burden of childcare.

[0039] How does a baby monitor work?

[0040] A baby monitor is a device that captures a baby's face, movements, and voice in real time. This device is equipped with a camera and microphone for continuous monitoring. It also has a communication means to send the monitoring data to a cloud server.

[0041] The baby monitor works in the following steps:

[0042] 1. The device captures your baby's monitoring data.

[0043] 2. The acquired data is sent to the cloud server.

[0044] Data analysis on a cloud server

[0045] The cloud server receives monitoring data sent from the baby monitor and has a generative AI model means for analyzing it. The generative AI model on the cloud server analyzes the baby's facial expressions, movements, and voice data to determine the baby's needs and dangers.

[0046] The cloud server works in the following steps:

[0047] 1. The server receives monitoring data from the baby monitor.

[0048] 2. Analyze the data using a generative AI model to determine the baby's needs and risks.

[0049] 3. Send the analysis results to the parent's device for notification.

[0050] Notification display on parent's device

[0051] The parent's device receives the analysis results from the cloud server and has a notification function that displays them. This notification function allows the parent to immediately understand the baby's condition. If the analysis results indicate an emergency, an alarm can also be sent.

[0052] The parent device operates in the following steps:

[0053] 1. The device receives the analysis results from the cloud server.

[0054] 2. Display the received analysis results on the app's UI.

[0055] 3. If necessary, an alarm will be sent to notify parents of an emergency.

[0056] Specific examples

[0057] For example, if a baby is crying because it is hungry, the system will:

[0058] 1. Baby monitor: Detects the baby's crying and displeased face and sends the data to a cloud server.

[0059] 2. Cloud server: Analyzes the received data and determines whether the baby is hungry. As a result, it generates a message saying "The baby is hungry" and sends it to the parent's device.

[0060] 3. Parent device: The received analysis result "Baby is hungry" is displayed on the UI and an alarm is sounded if necessary.

[0061] In this way, parents can immediately understand the condition of their baby and take appropriate measures, reducing the burden of childcare and supporting the provision of prompt and appropriate care.

[0062] The processing flow will be explained below.

[0063] Program processing steps

[0064] How does a baby monitor work?

[0065] Step 1:

[0066] Baby monitors use cameras and microphones to monitor your baby's face, movements, and voice in real time, collecting data continuously.

[0067] Step 2:

[0068] The monitoring data is temporarily stored on the device and prepared for transmission to a cloud server in batches at regular intervals, where the data is properly formatted and security checked.

[0069] Step 3:

[0070] Data is sent to a cloud server using secure HTTP or WebSocket protocols to ensure minimal latency.

[0071] Data analysis on a cloud server

[0072] Step 4:

[0073] The server receives the data sent from the baby monitor, which is first temporarily stored in a database and then passed to the analytics engine.

[0074] Step 5:

[0075] A generative AI model in the cloud server analyzes the data, specifically voice analysis, facial expression recognition, and movement analysis, and then combines the results.

[0076] Step 6:

[0077] The analytics engine determines the baby's needs (hunger, sleepiness, discomfort, etc.) and potential dangers (face down, overturned objects, etc.) not only based on real-time data but also by comparing it with past data.

[0078] Step 7:

[0079] Analysis results are generated and prepared for transmission to the parent's device, including the baby's status, recommended actions, and any necessary alerts.

[0080] Notification display on parent's device

[0081] Step 8:

[0082] The device receives the analysis results from the cloud server. The device retrieves the data through an internet connection and stores it locally.

[0083] Step 9:

[0084] The received analysis results are displayed on the application's user interface (UI), which displays the results in a format that is intuitively understandable to the user, allowing them to grasp the situation at a glance.

[0085] Step 10:

[0086] If necessary, alarm notifications will be sent to alert parents to emergencies. The alarms can be sent in multiple forms, including sound, vibration, and pop-up notifications, so parents can get their immediate attention.

[0087] Specific examples

[0088] For example, if your baby is crying because he is hungry:

[0089] Step 1:

[0090] Baby monitors keep an eye on your baby's cries and irritability.

[0091] Step 2:

[0092] The monitoring data is temporarily stored and ready to be sent to the cloud server.

[0093] Step 3:

[0094] The data is sent to a cloud server.

[0095] Step 4:

[0096] The server receives the monitoring data and stores it in a database.

[0097] Step 5:

[0098] A generative AI model analyzes the monitoring data, detecting signs of hunger from crying patterns and facial expressions.

[0099] Step 6:

[0100] The analytical engine determines that the baby is hungry.

[0101] Step 7:

[0102] The analysis result "Baby is hungry" is generated and ready to be sent to the parent's device.

[0103] Step 8:

[0104] The terminal receives the analysis results.

[0105] Step 9:

[0106] The analysis results are displayed on the app's UI as "Baby is hungry."

[0107] Step 10:

[0108] An alarm will sound to alert the parents.

[0109] Example 1

[0110] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0111] In modern childcare, parents are required to constantly monitor their baby's needs and condition and respond promptly. However, accurately understanding a baby's condition is extremely difficult, especially at night or when the parent is in a remote location. In addition, the burden of childcare can cause significant stress for parents. Furthermore, if a baby is in a dangerous situation, parents are required to immediately recognize and respond, but current methods make this difficult. The present invention aims to provide a system that allows parents to quickly understand and respond to their baby's needs and dangerous situations.

[0112] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0113] In this invention, the server includes a sensor means for monitoring the baby's face, movements, and voice in real time, a communication means for transmitting the monitored data to a cloud computing platform, and a machine learning model means for analyzing the received data in the cloud computing platform and determining the baby's needs and dangers. This makes it possible to grasp the baby's condition in real time and send warning notifications to the parent's device as needed. This allows the parent to quickly check the baby's needs and dangers and take appropriate measures, reducing the burden of childcare.

[0114] The "sensor means" is a device for monitoring the baby's face, movements, and voice in real time and acquiring data.

[0115] The "communication means" is a means for transmitting the monitoring data acquired by the sensor means to the cloud computing infrastructure.

[0116] A "cloud computing platform" is a remote server that receives and analyzes monitoring data.

[0117] "Machine learning model means" is a general term for algorithms and programs that analyze monitoring data on a cloud computing platform and determine the baby's needs and risks.

[0118] The "notification means" is a means for notifying the parent's terminal of the results of the analysis performed by the cloud computing platform.

[0119] The "parent's device" is an electronic device that receives notifications from the cloud computing platform and displays the analysis results.

[0120] The present invention provides a system that monitors a baby's face, movements, and voice in real time, analyzes the data using a cloud computing platform, and notifies the parent or guardian of the results of the analysis. Specific embodiments will be described below.

[0121] Baby monitor

[0122] Baby monitors are equipped with sensors for monitoring the baby's face, movements, and voice. These sensors include, for example, a high-resolution camera and a highly sensitive microphone, allowing for highly accurate capture of the baby's facial expressions, movements, and voice data such as crying.

[0123] communication means

[0124] Baby monitors are equipped with a communication means to transmit the acquired monitoring data to a cloud computing platform using wireless communication technologies such as Wi-Fi or 4G / 5G, which allows data to be transmitted in real time.

[0125] Cloud Computing Platform

[0126] The server receives monitoring data sent from the baby monitor using a cloud computing platform. The received data is analyzed using a generative AI model. Specific analysis uses machine learning frameworks such as TensorFlow and PyTorch. This allows for highly accurate facial expression recognition and voice analysis.

[0127] Analysis means

[0128] The generative AI model, installed on a cloud computing platform, analyzes the received facial expression, movement, and voice data to determine the baby's needs and dangers. For example, if the baby is crying, the frequency distribution of the crying is analyzed to identify the cause. Facial expression recognition technology is used to determine whether the baby is smiling.

[0129] Notification means

[0130] The server then sends a notification to the parent's device based on the analysis results. The notification is sent using a cloud messaging service such as Firebase Cloud Messaging. For example, a text message such as "Your baby is hungry" is generated and sent to the parent's smartphone or tablet.

[0131] Parent's device

[0132] The parent's device receives the notification message and displays it in a dedicated application. The application is developed in Swift or Java, for example, and has a built-in push notification function. At the same time as displaying the analysis results, an alarm sound or vibration alert is issued depending on the urgency of the situation.

[0133] Examples and prompts

[0134] For example, if your baby is crying because he is hungry:

[0135] 1. A baby monitor detects a baby's crying and unhappy facial expressions and sends the data to a cloud computing platform.

[0136] 2. The cloud computing platform analyzes the data and determines whether the baby is hungry. As a result, a message saying "The baby is hungry" is generated and sent to the parent's device.

[0137] 3. The parent's device will display the received message on the app's UI and may sound an alarm.

[0138] Example prompt sentence:

[0139] "Detect when your baby is hungry and crying and send a real-time notification to the parent's device."

[0140] This allows parents to immediately understand the baby's condition and take prompt and appropriate action. The present invention reduces the burden of childcare as a whole and supports the provision of appropriate care for babies.

[0141] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0142] Step 1:

[0143] Baby monitors monitor a baby's face, movements, and voice in real time and collect data. A camera captures the baby's facial expressions, and a microphone records audio data such as crying and laughter. The collected data consists of video and audio data, which are output as streams in JPEG or MP4 format video files and WAV format audio files.

[0144] Step 2:

[0145] The monitoring data acquired by the baby monitor is sent to a cloud computing platform. The data is sent via wireless communication (Wi-Fi or 4G / 5G). For example, the data is sent in encrypted form using the HTTPS protocol. This transmission process sends the input data (video and audio files) to the cloud computing platform, which then becomes the input for the next step.

[0146] Step 3:

[0147] The server receives the monitoring data sent from the baby monitor. The cloud computing platform (server) temporarily stores the received video and audio files in preparation for the next analysis step. In this step, data reception is the first processing step, and it is confirmed that the received data has been saved correctly.

[0148] Step 4:

[0149] The server analyzes the received data using the generative AI model. On the cloud computing platform, TensorFlow is used to run the facial expression recognition model, and PyTorch is used to run the audio analysis model. The input data consists of video and audio files, and each is analyzed. Specifically, the facial expression recognition model analyzes the video data and detects changes in the baby's facial expression. The audio analysis model also analyzes the audio data, analyzing the frequency distribution and amplitude of the crying. From this analysis, a judgment result such as "the baby is crying because he is hungry" is output.

[0150] Step 5:

[0151] The server notifies the parent's device of the analysis results. The analysis results obtained by the generative AI model are converted into a text message. For example, a message such as "The baby is hungry" is generated, and a service such as Firebase Cloud Messaging is used to send it to the parent's device. The output of this step is a text message, which is then sent to the parent's device.

[0152] Step 6:

[0153] The device receives the analysis results from the cloud server. The parent's device (smartphone, tablet, etc.) receives the text message sent from the cloud server. A dedicated application runs in the background and captures the received message. Through this process, the device application obtains input data (analysis result message) for the next display step.

[0154] Step 7:

[0155] The device displays the received analysis results and sounds an alarm if necessary. The dedicated application displays the received analysis results on the UI. For example, a message saying "Baby is hungry" is displayed on the app screen. At the same time, an alarm sound or vibration is generated depending on the urgency, immediately informing the parent of the situation. The output of this step is a visual display and an audible notification.

[0156] Step 8:

[0157] The user (guardian) checks the analysis results and takes appropriate action. The user checks the analysis results on their smartphone screen, and if it determines that the baby is hungry, they take action such as preparing milk, or if the baby is crying, they pick up the baby and comfort him or her. This allows the user to respond to the baby's needs immediately.

[0158] (Application example 1)

[0159] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0160] While there are systems that monitor a baby's safety and needs in real time, there are few systems that comprehensively monitor the safety of the entire home and immediately detect and alert to any abnormalities. Furthermore, technology for integrated management of the baby's condition and abnormalities in the home to ensure safety is still in its infancy. Given this background, there is a need for a system that can centrally monitor the safety of not only the baby but the entire home and respond immediately.

[0161] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0162] In this invention, the server includes a device means for monitoring the baby's face, movements, and voice in real time, a communication means for transmitting the monitored data to a cloud server, a generating AI model means for analyzing the received data in the cloud server and determining the baby's needs and dangers, a notification means for notifying the parent's terminal of the analysis results, a means for monitoring the safety of the entire household to detect suspicious movements and sounds, and an alarm means for immediately issuing a warning notification when a dangerous situation is detected. This makes it possible to comprehensively monitor not only the baby's condition but also the overall safety of the household, and immediately notify the parent's terminal when an abnormality is detected, and even automatically contact the police or fire department if necessary.

[0163] "Device means" refers to a device for monitoring the baby's face, movements, and voice in real time.

[0164] "Communication means" refers to the technology or equipment used to transmit monitored data to the cloud server.

[0165] The "generative AI model means" is an artificial intelligence model that is used to analyze received data on a cloud server and determine the baby's needs and risks.

[0166] "Notification means" refers to a system or method for transmitting the analysis results from the cloud server to the parent's device.

[0167] "Home safety monitoring measures" are devices and technologies used to monitor suspicious activity and sounds within the home in real time and detect abnormalities.

[0168] An "alarm means" is a system or device that immediately issues a warning to the parent's terminal and to any necessary emergency services when a dangerous situation is detected.

[0169] This invention is a system for comprehensively monitoring the safety of babies and the entire household. The system includes a device means, a communication means, a generating AI model means, a notification means, a monitoring means for suspicious movements and sounds, and an alarm means.

[0170] Device Means

[0171] The device means is a device equipped with a camera and a microphone for monitoring the baby's face, movements, and voice in real time. This device constantly acquires data on the baby's behavior, facial expressions, and voice, and uses this data for analysis.

[0172] communication means

[0173] The communication means is a technology for transmitting acquired monitoring data to a cloud server. Common communication technologies such as wireless LAN and mobile networks are used for communication. This allows data acquired by the device means to be transmitted to the cloud server in near real time.

[0174] Generative AI model means

[0175] The cloud server is equipped with a generative AI model that analyzes the received data. The generative AI model uses the monitoring data to determine the baby's condition (e.g., hunger, discomfort, fatigue, etc.) and also detects suspicious movements and abnormal sounds throughout the household. Machine learning algorithms and large datasets are used to train the model.

[0176] Notification means

[0177] The results of the analysis performed by the cloud server are then sent to the parent's device. Notification methods are used to quickly send the analysis results to common devices such as smartphones and PCs. Notifications can be sent in a variety of forms, including pop-up alerts, audio alerts, and message notifications.

[0178] Monitoring for suspicious activity and sounds

[0179] To ensure the safety of the entire home, a means of monitoring suspicious movements and sounds has been added. This means that multiple cameras and microphones installed in each room monitor the movements of the entire house and detect any abnormalities.

[0180] Alarm means

[0181] The alarm system is a system that immediately issues a warning notification when a dangerous situation is detected. This system issues an alert to the parent's device and automatically contacts the police or fire department if necessary. Specifically, it uses Text-to-Speech (TTS) technology to generate and play audio alerts. This processing is performed using gTTS and mpg321.

[0182] Specific examples

[0183] For example, if a baby starts crying, the system detects the crying and facial expression in real time and sends them to a cloud server. The cloud server then uses a generative AI model to determine if the baby is hungry and sends a message to the parent's device saying, "The baby is hungry." Furthermore, if a suspicious person breaks into the home while the parents are away, the camera and microphone detect the abnormality and immediately send a warning notification to the parents via the cloud server, while automatically contacting the police if necessary.

[0184] Example prompts for generative AI models

[0185] "A security system that analyzes data obtained from cameras and microphones installed in a room, detects suspicious movements and sounds, and notifies you."

[0186] In this way, the present invention makes it possible to monitor the safety of the baby and the entire household in a unified manner and to respond quickly when necessary.

[0187] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0188] Step 1:

[0189] The device captures your baby's face, movements, and voice in real time.

[0190] Input: Video data from the camera and audio data from the microphone.

[0191] Data processing: Video data is captured frame by frame, and audio data is sampled periodically. The video data is passed through a facial recognition algorithm to extract the baby's facial features, and the audio data is appropriately filtered to remove noise.

[0192] Output: Enhanced face, movement, and voice data.

[0193] Step 2:

[0194] The acquired data is sent to a cloud server.

[0195] Input: Face, movement, and voice data processed in step 1.

[0196] Data processing: converting data into the appropriate format, encrypting it and sending it.

[0197] Output: Monitoring data sent to the cloud server.

[0198] Step 3:

[0199] The cloud server receives the monitoring data and analyzes it using a generative AI model.

[0200] Input: Monitoring data sent in step 2.

[0201] Data processing: Transforming the data into an input format suitable for the model.

[0202] Data computation: Generative AI models analyze facial expressions, movements, and voice to determine the baby's needs and abnormalities in the home.

[0203] Output: Analysis result (e.g. "Baby is hungry" or "Suspicious movement detected").

[0204] Step 4:

[0205] The parent's device is notified based on the analysis results.

[0206] Input: Analysis results obtained in step 3.

[0207] Data processing: Converting the analysis results into a user-friendly format, for example, preparing them as text or voice messages.

[0208] Data calculation: Determines the content of the notification and sends it in the appropriate format.

[0209] Output: A notification displayed on the parent's device (e.g., a pop-up notification or an alarm sound on a smartphone).

[0210] Step 5:

[0211] If necessary, the police and fire department will be automatically contacted.

[0212] Input: Analysis results of the emergency situation notified in step 4.

[0213] Data processing: Formatting information necessary for emergency calls (e.g., location information, details of the abnormality, etc.).

[0214] Data Calculation: Contact the appropriate emergency services through the emergency communication system.

[0215] Output: Automatic notification to emergency services (e.g. police to report suspicious person, fire to fire).

[0216] Through these steps, the entire system works together to monitor the safety of your baby and the entire household in real time.

[0217] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0218] Overall system overview

[0219] This system monitors a baby's face, movements, and voice, as well as the user's emotions, in real time, analyzes the data on a cloud server, and notifies the parent of the results. This system not only enables parents to instantly grasp their baby's needs and dangerous situations, but also uses an emotion engine to receive notifications based on the parent's emotional state, further reducing the burden of childcare.

[0220] How does a baby monitor work?

[0221] A baby monitor is a device that captures a baby's face, movements, and voice in real time. This device is equipped with a camera and microphone for continuous monitoring. It also has a communication means to send the monitoring data to a cloud server.

[0222] The baby monitor works in the following steps:

[0223] 1. The device captures your baby's monitoring data.

[0224] 2. The acquired data is sent to the cloud server.

[0225] Data analysis on a cloud server

[0226] The cloud server receives monitoring data sent from the baby monitor and has a generative AI model means for analyzing it. The generative AI model on the cloud server analyzes the baby's facial expressions, movements, and voice data to determine the baby's needs and dangers. It also analyzes the user's emotional data using an emotion engine and integrates this data with the analysis results of the baby.

[0227] The cloud server works in the following steps:

[0228] 1. The server receives monitoring data from the baby monitor.

[0229] 2. Analyze the data using a generative AI model to determine the baby's needs and risks.

[0230] 3. The emotion engine analyzes the user's emotion data and determines the user's emotional state.

[0231] 4. Integrate the baby's analysis results with the user's emotional state to adjust the notification content.

[0232] 5. Send the analysis results to the parent's device for notification.

[0233] Notification display on parent's device

[0234] The parent's device receives the analysis results from the cloud server and has a notification means to display them. This notification means allows the parent to immediately understand the baby's condition. If the analysis results indicate an emergency, an alarm can be sent. Furthermore, the priority and content of the notification are adjusted according to the parent's emotional state as recognized by the emotion engine.

[0235] The parent device operates in the following steps:

[0236] 1. The device receives the analysis results from the cloud server.

[0237] 2. Display the received analysis results on the application's user interface (UI).

[0238] 3. If necessary, an alarm will be sent to notify parents of an emergency.

[0239] 4. Display advice and suggestions based on the results of emotion analysis by the emotion engine.

[0240] Specific examples

[0241] For example, if a baby is hungry and crying and the user is tired, the system will act as follows:

[0242] 1. Baby monitor: Detects the baby's crying and displeased face and sends the data to a cloud server.

[0243] 2. Cloud server: Analyzes the received data and determines whether the baby is hungry. As a result, it generates a message saying "The baby is hungry." The emotion engine then determines the user's level of fatigue. Based on this, it sends a notification indicating urgency to the parent's device.

[0244] 3. Parent's device: The received analysis result "Baby is hungry" is displayed on the UI, and if the parent is fatigued, a notification with higher priority than usual is sent. Appropriate advice and suggestions are also provided.

[0245] In this way, parents can quickly take appropriate measures based on their baby's condition and their own emotional state, reducing the burden of childcare and providing support to ensure the safety and health of both parents and their baby.

[0246] The processing flow will be explained below.

[0247] Program processing steps

[0248] How does a baby monitor work?

[0249] Step 1:

[0250] Baby monitors use cameras and microphones to monitor your baby's face, movements, and voice in real time, collecting data continuously.

[0251] Step 2:

[0252] Collected monitoring data is temporarily stored on the device, where it is properly formatted and prepared for later transmission to a cloud server.

[0253] Step 3:

[0254] The device sends the monitoring data to the cloud server using secure HTTP or WebSocket protocols to ensure minimal latency.

[0255] Data analysis on a cloud server

[0256] Step 4:

[0257] The server receives the data sent from the baby monitor and temporarily stores it in a database.

[0258] Step 5:

[0259] A generative AI model analyzes surveillance data, analyzing voice, facial expression, and motion separately, and then combines the results.

[0260] Step 6:

[0261] The server uses an analytical engine to determine the baby's needs (hunger, discomfort, fatigue, etc.) and potential dangers (lying face down, objects falling, etc.).

[0262] Step 7:

[0263] The server receives the user's emotional data, and the emotion engine analyzes the user's emotional state, including the user's voice, facial expressions, and operation logs.

[0264] Step 8:

[0265] The system integrates analysis of the baby's needs and dangers with the user's emotional state to tailor notifications, for example, increasing the priority of notifications if the user is very tired.

[0266] Step 9:

[0267] The server sends the consolidated analysis results to the parent's device, which includes the baby's condition, recommended measures, and necessary alert information.

[0268] Notification display on parent's device

[0269] Step 10:

[0270] The device receives the analysis results sent from the cloud server, and the received data is loaded into the application, starting the process.

[0271] Step 11:

[0272] The received analysis results are displayed in the app's user interface (UI), which displays the results in an intuitive and easy-to-understand format, allowing users to understand the situation at a glance.

[0273] Step 12:

[0274] If necessary, an alarm notification is issued to notify the user of an emergency. Notification methods include sound, vibration, and pop-up notification.

[0275] Step 13:

[0276] The server provides appropriate advice and suggestions based on the user's emotional state, for example, advising the user to take a rest if they are tired.

[0277] Specific examples

[0278] For example, if your baby is hungry and crying and you are very tired:

[0279] Step 1:

[0280] Baby monitors keep an eye on your baby's cries and irritability.

[0281] Step 2:

[0282] The monitoring data is temporarily stored and ready to be sent to a cloud server later.

[0283] Step 3:

[0284] The device sends the monitoring data to a cloud server.

[0285] Step 4:

[0286] The server receives the monitoring data and stores it in a database.

[0287] Step 5:

[0288] A generative AI model analyzes the monitoring data, detecting signs of hunger from crying patterns and facial expressions.

[0289] Step 6:

[0290] The server determines that the baby is hungry.

[0291] Step 7:

[0292] A server receives the user's emotion data, and an emotion engine determines the user's fatigue.

[0293] Step 8:

[0294] The server combines the baby's hunger determination result with the user's fatigue state and increases the priority of the notification.

[0295] Step 9:

[0296] The server then sends the combined analysis results to the parent's device, which includes a message saying "Your baby is hungry" along with advice to get some rest.

[0297] Step 10:

[0298] The terminal receives the analysis results.

[0299] Step 11:

[0300] The analysis results are displayed on the app's UI as "Baby is hungry."

[0301] Step 12:

[0302] An alarm will sound and say "Baby is hungry" to alert the parent, and in this case, a vibration will also be added since the notification is set to high priority.

[0303] Step 13:

[0304] Based on the user's emotional state, "advice to take a rest" is displayed.

[0305] Example 2

[0306] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0307] Conventional baby monitoring systems only monitor a baby's face, movements, and voice, and only have the ability to identify the baby's needs or dangerous situations, which limits the ability of parents to respond appropriately to their baby's situation.In addition, because notifications are given without taking into account the parent's emotional state, there is the problem that they cannot provide appropriate support to parents who are tired or stressed.

[0308] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0309] In this invention, the server includes device means for monitoring the baby's face, movements, and voice in real time, communication means for transmitting the monitored data to a cloud server, generation AI model means for analyzing the received data in the cloud server and determining the baby's needs and dangers, emotion engine means for analyzing the user's emotion data and integrating it with the analysis results of the baby, and notification means for notifying the parent's device of the analysis results. This makes it possible to provide optimal notifications based on the baby's condition and the parent's emotional state, reducing the burden of childcare and ensuring the safety and health of the parent and baby.

[0310] "Device means" refers to a device that includes hardware and software for monitoring the baby's face, movements, and voice in real time.

[0311] The "communication means" is a network communication function for transmitting monitoring data acquired by the device means to the cloud server.

[0312] The "generative AI model means" refers to artificial intelligence algorithms and software that run on a cloud server and analyze the received data to determine the baby's needs and risks.

[0313] The "emotion engine means" is software and algorithms for analyzing the user's emotion data and integrating it with the baby's analysis results.

[0314] The "notification means" refers to a communication function and software for transmitting the analysis results from the cloud server to the parent's terminal and making the notification.

[0315] The "analysis results" are the results of the judgments of the baby's needs and dangerous situations, and the user's emotional state, obtained by the generative AI model means and the emotion engine means.

[0316] "Parent Device" refers to a mobile device or computer used by a parent to receive and display the analysis results.

[0317] This system monitors a baby's face, movements, and voice, as well as the user's emotions, in real time, analyzes the data on a cloud server, and notifies the parent of the results of the analysis. This not only enables parents to immediately grasp their baby's needs and dangerous situations, but also allows them to receive notifications based on their emotional state using an emotion engine, further reducing the burden of childcare.

[0318] Hardware and Software Configuration

[0319] Baby monitor

[0320] A baby monitor is a device that captures a baby's face, movements, and voice in real time. It is equipped with a high-resolution camera and a highly sensitive microphone, which allows it to capture facial expressions in detail and pick up even the nuances of a baby's voice.

[0321] Specifically, the baby monitor works as follows.

[0322] The camera detects the baby's face and movements.

[0323] The microphone picks up the baby's voice.

[0324] It is equipped with a Wi-Fi module to transmit this data to a cloud server in real time.

[0325] Cloud Server

[0326] The cloud server receives the data sent from the baby monitor and analyzes it using generative AI models, which include facial recognition and voice recognition algorithms.

[0327] The server operates as follows:

[0328] Receive data from the baby monitor.

[0329] A generative AI model is used to analyze the data and determine the baby's needs and risks.

[0330] An emotion engine is used to analyze the user's emotion data and determine the user's emotional state.

[0331] The baby's analysis results are integrated with the user's emotional state to tailor the notification content.

[0332] The adjusted notification content is sent to the parent's device.

[0333] Parent's device

[0334] An application is installed on the parent's device to receive and display notifications from the server. This application has a user interface (UI) that displays notifications in real time and has the ability to issue alerts as needed. It also has the ability to display appropriate advice and suggestions based on the analysis results of the emotion engine.

[0335] The terminal operates as follows:

[0336] Receives notification content sent from the server.

[0337] The received analysis results are displayed on the UI.

[0338] In the event of an emergency, an alert will be issued to notify parents of the emergency.

[0339] Display advice and suggestions based on the results of the sentiment engine.

[0340] Specific examples

[0341] For example, if a baby is hungry and crying, or if the user feels tired, the system will operate as follows:

[0342] 1. Baby monitor: The camera captures the baby's crying face, the microphone records the crying sound, and sends the data to a cloud server.

[0343] 2. Cloud server: The generative AI model analyzes the received data and determines that the baby is hungry. It generates a message saying "Baby is hungry," and the emotion engine determines the user's fatigue level. Based on this, it sends a notification indicating urgency to the parent's device.

[0344] 3. Parent's device: The received analysis result "Baby is hungry" is displayed on the UI. If the parent is fatigued, a notification with higher priority than usual will be sent, and appropriate advice and suggestions will be provided.

[0345] Prompt Sentence Examples

[0346] An example prompt for a generative AI model might be:

[0347] "Analyze your baby's cries and facial expressions to determine if he or she is hungry."

[0348] "Analyze the user's current emotional state with an emotion engine and adjust the priority of notifications based on that."

[0349] This system allows parents to comprehensively understand their baby's condition and their own emotional state, enabling them to quickly take the most appropriate measures, thereby reducing the burden of childcare and helping to ensure the safety and health of both parents and their babies.

[0350] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0351] Step 1:

[0352] Baby monitor data acquisition

[0353] A baby monitor captures the baby's face, movements, and voice in real time. The camera detects the baby's face and movements, and the microphone picks up the baby's voice. This action produces image and audio data. The inputs are the baby's face, movements, and voice, and the output is a camera image and an audio file.

[0354] Specific behavior:

[0355] The camera captures the baby's crying face.

[0356] The microphone records the baby's cry.

[0357] Step 2:

[0358] Sending data to the cloud server

[0359] The baby monitor sends the acquired data to a cloud server. The baby monitor's communication module uploads the camera images and audio files to the cloud server via Wi-Fi. The input is the camera images and audio files, and the output is the data uploaded to the cloud server.

[0360] Specific behavior:

[0361] The baby monitor transmits camera images and audio data to a cloud server in real time.

[0362] Step 3:

[0363] Data analysis on the server

[0364] The server receives data sent from the baby monitor and analyzes it using a generative AI model. It analyzes the baby's facial expressions, movements, and voice data to determine the baby's condition (e.g., hungry, sleepy, dangerous, etc.). The input is the monitoring data uploaded to the cloud server, and the output is the analysis results.

[0365] Specific behavior:

[0366] The server uses a facial recognition algorithm to analyze the baby's crying face.

[0367] A voice recognition algorithm is used to analyze the characteristics of the cry and determine whether the baby is hungry.

[0368] Step 4:

[0369] User sentiment analysis

[0370] The server analyzes the user's emotional data using an emotion engine. Based on past smartphone operation data and vital data (e.g., heart rate), the server determines the user's emotional state (e.g., fatigue, stress, relaxation). The input is the emotional data, and the output is the emotion analysis results.

[0371] Specific behavior:

[0372] The server checks the user's heart rate data and determines that the user is in a "fatigue state" using an emotion engine.

[0373] Step 5:

[0374] Consolidating and aligning notifications

[0375] The server integrates the baby's analysis results with the user's emotional state and adjusts the notification content. Based on the output of the emotion engine, it generates notification text and determines the priority of the notification. The input is the baby's analysis results and the emotion analysis results, and the output is the adjusted notification content.

[0376] Specific behavior:

[0377] Generate a notification message saying, "The baby is crying because he is hungry. He seems tired, so please take action as soon as possible."

[0378] Step 6:

[0379] Notifications on parent's device

[0380] The server sends the adjusted notification content to the parent device. The server's communication module pushes the notification content to the parent device. The input is the adjusted notification content, and the output is the notification sent to the parent device.

[0381] Specific behavior:

[0382] The server pushes the notification to the parent's smartphone.

[0383] Step 7:

[0384] Notifications and alerts on parent's device

[0385] The parent device receives notifications from the server and displays the content. The app displays the notifications on the UI and issues emergency alerts if necessary. It also displays appropriate advice and suggestions based on the analysis results of the emotion engine. The input is the notification content from the server, and the output is the notification content displayed on the UI.

[0386] Specific behavior:

[0387] A message appears on the parent's smartphone saying "Baby is hungry."

[0388] The app displays advice such as, "Parents are tired, so we suggest an easy way to make milk."

[0389] (Application example 2)

[0390] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0391] There is a need for a system that can monitor the stress and emotional state of workers in factories, as well as the safety of the work environment, in real time and take appropriate measures. Conventional systems have difficulty accurately grasping fluctuations in workers' health and emotions, and lack the functionality to immediately detect dangers in the work environment, making it difficult to ensure worker safety and efficiency. Furthermore, measures based on workers' stress and emotional state are sometimes delayed, which can have a negative impact on work efficiency and safety.

[0392] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes device means for monitoring the faces, movements, and voices of the baby and worker in real time, communication means for transmitting the monitored data to a cloud server, generation AI model means for analyzing the received data in the cloud server and determining the needs and dangers of the baby and worker, and notification means for notifying the parent and manager terminals of the analysis results. This makes it possible to grasp the worker's stress and emotional state, as well as the state of the work environment, in real time and take appropriate measures promptly.

[0393] "Device means" refers to equipment or devices for capturing and monitoring the face, movements, and voice of workers in real time.

[0394] "Communication means" refers to the network technology and communication protocol used to transmit the monitoring data acquired by the device means to the cloud server.

[0395] "Cloud server" refers to a remote server that stores and processes data over the Internet.

[0396] "Generative AI model means" refers to an artificial intelligence algorithm that runs on a cloud server and analyzes the received monitoring data to determine the needs and risks of workers and babies.

[0397] "Notification means" refers to the functions and systems that notify parents and administrators of the results of analysis performed on the cloud server via their devices.

[0398] "Stress" refers to the mental or physical tension that a worker feels due to the work environment or work content.

[0399] "Emotional state" refers to the psychological state that indicates the worker's current feelings and sensations.

[0400] "Work environment" refers to the place where workers actually perform their work and its physical conditions (e.g., noise, temperature, humidity, etc.).

[0401] "Manager" refers to a person who is responsible for supervising workers in a factory or work site and managing the safety of the work environment and work efficiency.

[0402] This invention is a system that monitors the stress and emotional state of workers in a factory, as well as the safety of the working environment, in real time, and takes appropriate measures promptly. Specifically, it consists of the following parts.

[0403] Hardware Configuration

[0404] 1. Device means:

[0405] Camera: Captures the worker's facial expressions and movements in real time.

[0406] Microphone: Captures the worker's voice in real time.

[0407] Wearable devices: Monitor workers' biometric data such as heart rate and body temperature.

[0408] Environmental sensors: Collect data on the working environment, such as temperature, humidity, and noise levels.

[0409] 2. Means of communication:

[0410] Network technologies such as Wi-Fi and LTE are used to transmit data acquired by the device means to a cloud server.

[0411] Software Configuration

[0412] 1. Cloud Server:

[0413] The cloud server receives the data transmitted from the device means and analyzes it.

[0414] The generative AI model used (e.g. TensorFlow) analyzes the incoming data in real time to determine the worker's stress and emotional state.

[0415] Based on the analysis results, the safety of the working environment will also be evaluated.

[0416] 2. Generative AI model means:

[0417] Facial expression analysis, voice analysis, and biometric data analysis are performed to determine the worker's stress and emotional state.

[0418] The generative AI models used include TensorFlow and Google Cloud AI.

[0419] Based on the analysis, appropriate measures and advice are generated.

[0420] 3. Means of notification:

[0421] The results analyzed on the cloud server are notified to parents or administrators via smartphones or robot terminals.

[0422] The notification includes advice to encourage workers to take breaks and suggestions for improving the work environment.

[0423] Specific examples

[0424] 1. If worker A is stressed:

[0425] Camera and microphone: Worker A has a stern facial expression and an irritated voice.

[0426] Analysis result: It was determined that worker A's stress level was high.

[0427] Notification: A message is sent to the manager's smartphone saying, "Worker A's stress level is high. Please encourage him to take a break."

[0428] 2. If the working environment has high noise levels:

[0429] Environmental sensor: Noise levels exceed acceptable limits.

[0430] Analysis result: The working environment was determined to be dangerous.

[0431] Notification: Robotic terminals in factories warn workers, "Noise levels are high. Please use earplugs."

[0432] Prompt Sentence Examples

[0433] Facial Expression Analysis: "Analyze the facial expressions of the workers in this image to determine their stress levels."

[0434] Voice Analysis: "Infer the emotional state of the worker in this audio file from their tone of voice."

[0435] Environmental Data Analysis: "Analyze this environmental sensor data to assess safety."

[0436] In this way, we ensure the safety and health of workers in the factory and provide support to improve work efficiency.

[0437] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0438] Step 1:

[0439] The user installs a baby monitor, and the device captures the worker's face, movements, voice, and work environment in real time. The input is monitoring data (facial expressions, voice, biometric data, and environmental sensor data). The output is the acquisition of these monitoring data.

[0440] Step 2:

[0441] The monitoring data acquired by the device means is sent to the cloud server using the communication means. The input is the monitoring data acquired in step 1, and the data is sent. The output is the monitoring data sent to the cloud server.

[0442] Step 3:

[0443] The cloud server receives the monitoring data and uses a generative AI model to analyze it. The input is the transmitted monitoring data, and the cloud server performs data analysis. Specifically, it performs facial expression analysis, vocal emotion analysis, biometric data analysis, and environmental data analysis. The output is the analysis results (the worker's stress level, emotional state, and safety assessment of the work environment).

[0444] Step 4:

[0445] Based on the analysis results, the cloud server notifies the parent or administrator's device. The input is the analysis result from step 3, which generates a notification message. The output is the notification message sent to the parent or administrator's device.

[0446] Step 5:

[0447] The parent or administrator's device receives the notification and displays it on the UI. The input is the notification message sent from the cloud server. The output is the notification message displayed on the device screen (e.g., "Worker A's stress level is high. Please encourage him to take a break.").

[0448] Step 6:

[0449] If necessary, an alarm notification will be sent to notify parents or administrators of an emergency. The input is a notification message that includes the urgency. The output is an alarm (audio, vibration, screen flash, etc.) to the parent or administrator.

[0450] Step 7:

[0451] Parents or administrators can take appropriate measures based on the notification. The inputs are notification messages and alarms. The outputs are instructions and corrective actions for workers (e.g., taking a break, using earplugs).

[0452] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0453] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0454] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0455] [Second embodiment]

[0456] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0457] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0458] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0459] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0460] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0461] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0462] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0463] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0464] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0465] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0466] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0467] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0468] Overall system overview

[0469] This system monitors a baby's face, movements, and voice in real time, analyzes the data on a cloud server, and notifies the parents of the results. This system allows parents to instantly understand their baby's needs and dangerous situations, reducing the burden of childcare.

[0470] How does a baby monitor work?

[0471] A baby monitor is a device that captures a baby's face, movements, and voice in real time. This device is equipped with a camera and microphone for continuous monitoring. It also has a communication means to send the monitoring data to a cloud server.

[0472] The baby monitor works in the following steps:

[0473] 1. The device captures your baby's monitoring data.

[0474] 2. The acquired data is sent to the cloud server.

[0475] Data analysis on a cloud server

[0476] The cloud server receives monitoring data sent from the baby monitor and has a generative AI model means for analyzing it. The generative AI model on the cloud server analyzes the baby's facial expressions, movements, and voice data to determine the baby's needs and dangers.

[0477] The cloud server works in the following steps:

[0478] 1. The server receives monitoring data from the baby monitor.

[0479] 2. Analyze the data using a generative AI model to determine the baby's needs and risks.

[0480] 3. Send the analysis results to the parent's device for notification.

[0481] Notification display on parent's device

[0482] The parent's device receives the analysis results from the cloud server and has a notification function that displays them. This notification function allows the parent to immediately understand the baby's condition. If the analysis results indicate an emergency, an alarm can also be sent.

[0483] The parent device operates in the following steps:

[0484] 1. The device receives the analysis results from the cloud server.

[0485] 2. Display the received analysis results on the app's UI.

[0486] 3. If necessary, an alarm will be sent to notify parents of an emergency.

[0487] Specific examples

[0488] For example, if a baby is crying because it is hungry, the system will:

[0489] 1. Baby monitor: Detects the baby's crying and displeased face and sends the data to a cloud server.

[0490] 2. Cloud server: Analyzes the received data and determines whether the baby is hungry. As a result, it generates a message saying "The baby is hungry" and sends it to the parent's device.

[0491] 3. Parent device: The received analysis result "Baby is hungry" is displayed on the UI and an alarm is sounded if necessary.

[0492] In this way, parents can immediately understand the condition of their baby and take appropriate measures, reducing the burden of childcare and supporting the provision of prompt and appropriate care.

[0493] The processing flow will be explained below.

[0494] Program processing steps

[0495] How does a baby monitor work?

[0496] Step 1:

[0497] Baby monitors use cameras and microphones to monitor your baby's face, movements, and voice in real time, collecting data continuously.

[0498] Step 2:

[0499] The monitoring data is temporarily stored on the device and prepared for transmission to a cloud server in batches at regular intervals, where the data is properly formatted and security checked.

[0500] Step 3:

[0501] Data is sent to a cloud server using secure HTTP or WebSocket protocols to ensure minimal latency.

[0502] Data analysis on a cloud server

[0503] Step 4:

[0504] The server receives the data sent from the baby monitor, which is first temporarily stored in a database and then passed to the analytics engine.

[0505] Step 5:

[0506] A generative AI model in the cloud server analyzes the data, specifically voice analysis, facial expression recognition, and movement analysis, and then combines the results.

[0507] Step 6:

[0508] The analytics engine determines the baby's needs (hunger, sleepiness, discomfort, etc.) and potential dangers (face down, overturned objects, etc.) not only based on real-time data but also by comparing it with past data.

[0509] Step 7:

[0510] Analysis results are generated and prepared for transmission to the parent's device, including the baby's status, recommended actions, and any necessary alerts.

[0511] Notification display on parent's device

[0512] Step 8:

[0513] The device receives the analysis results from the cloud server. The device retrieves the data through an internet connection and stores it locally.

[0514] Step 9:

[0515] The received analysis results are displayed on the application's user interface (UI), which displays the results in a format that is intuitively understandable to the user, allowing them to grasp the situation at a glance.

[0516] Step 10:

[0517] If necessary, alarm notifications will be sent to alert parents to emergencies. The alarms can be sent in multiple forms, including sound, vibration, and pop-up notifications, so parents can get their immediate attention.

[0518] Specific examples

[0519] For example, if your baby is crying because he is hungry:

[0520] Step 1:

[0521] Baby monitors keep an eye on your baby's cries and irritability.

[0522] Step 2:

[0523] The monitoring data is temporarily stored and ready to be sent to the cloud server.

[0524] Step 3:

[0525] The data is sent to a cloud server.

[0526] Step 4:

[0527] The server receives the monitoring data and stores it in a database.

[0528] Step 5:

[0529] A generative AI model analyzes the monitoring data, detecting signs of hunger from crying patterns and facial expressions.

[0530] Step 6:

[0531] The analytical engine determines that the baby is hungry.

[0532] Step 7:

[0533] The analysis result "Baby is hungry" is generated and ready to be sent to the parent's device.

[0534] Step 8:

[0535] The terminal receives the analysis results.

[0536] Step 9:

[0537] The analysis results are displayed on the app's UI as "Baby is hungry."

[0538] Step 10:

[0539] An alarm will sound to alert the parents.

[0540] Example 1

[0541] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0542] In modern childcare, parents are required to constantly monitor their baby's needs and condition and respond promptly. However, accurately understanding a baby's condition is extremely difficult, especially at night or when the parent is in a remote location. In addition, the burden of childcare can cause significant stress for parents. Furthermore, if a baby is in a dangerous situation, parents are required to immediately recognize and respond, but current methods make this difficult. The present invention aims to provide a system that allows parents to quickly understand and respond to their baby's needs and dangerous situations.

[0543] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0544] In this invention, the server includes a sensor means for monitoring the baby's face, movements, and voice in real time, a communication means for transmitting the monitored data to a cloud computing platform, and a machine learning model means for analyzing the received data in the cloud computing platform and determining the baby's needs and dangers. This makes it possible to grasp the baby's condition in real time and send warning notifications to the parent's device as needed. This allows the parent to quickly check the baby's needs and dangers and take appropriate measures, reducing the burden of childcare.

[0545] The "sensor means" is a device for monitoring the baby's face, movements, and voice in real time and acquiring data.

[0546] The "communication means" is a means for transmitting the monitoring data acquired by the sensor means to the cloud computing infrastructure.

[0547] A "cloud computing platform" is a remote server that receives and analyzes monitoring data.

[0548] "Machine learning model means" is a general term for algorithms and programs that analyze monitoring data on a cloud computing platform and determine the baby's needs and risks.

[0549] The "notification means" is a means for notifying the parent's terminal of the results of the analysis performed by the cloud computing platform.

[0550] The "parent's device" is an electronic device that receives notifications from the cloud computing platform and displays the analysis results.

[0551] The present invention provides a system that monitors a baby's face, movements, and voice in real time, analyzes the data using a cloud computing platform, and notifies the parent or guardian of the results of the analysis. Specific embodiments will be described below.

[0552] Baby monitor

[0553] Baby monitors are equipped with sensors for monitoring the baby's face, movements, and voice. These sensors include, for example, a high-resolution camera and a highly sensitive microphone, allowing for highly accurate capture of the baby's facial expressions, movements, and voice data such as crying.

[0554] communication means

[0555] Baby monitors are equipped with a communication means to transmit the acquired monitoring data to a cloud computing platform using wireless communication technologies such as Wi-Fi or 4G / 5G, which allows data to be transmitted in real time.

[0556] Cloud Computing Platform

[0557] The server receives monitoring data sent from the baby monitor using a cloud computing platform. The received data is analyzed using a generative AI model. Specific analysis uses machine learning frameworks such as TensorFlow and PyTorch. This allows for highly accurate facial expression recognition and voice analysis.

[0558] Analysis means

[0559] The generative AI model, installed on a cloud computing platform, analyzes the received facial expression, movement, and voice data to determine the baby's needs and dangers. For example, if the baby is crying, the frequency distribution of the crying is analyzed to identify the cause. Facial expression recognition technology is used to determine whether the baby is smiling.

[0560] Notification means

[0561] The server then sends a notification to the parent's device based on the analysis results. The notification is sent using a cloud messaging service such as Firebase Cloud Messaging. For example, a text message such as "Your baby is hungry" is generated and sent to the parent's smartphone or tablet.

[0562] Parent's device

[0563] The parent's device receives the notification message and displays it in a dedicated application. The application is developed in Swift or Java, for example, and has a built-in push notification function. At the same time as displaying the analysis results, an alarm sound or vibration alert is issued depending on the urgency of the situation.

[0564] Examples and prompts

[0565] For example, if your baby is crying because he is hungry:

[0566] 1. A baby monitor detects a baby's crying and unhappy facial expressions and sends the data to a cloud computing platform.

[0567] 2. The cloud computing platform analyzes the data and determines whether the baby is hungry. As a result, a message saying "The baby is hungry" is generated and sent to the parent's device.

[0568] 3. The parent's device will display the received message on the app's UI and may sound an alarm.

[0569] Example prompt sentence:

[0570] "Detect when your baby is hungry and crying and send a real-time notification to the parent's device."

[0571] This allows parents to immediately understand the baby's condition and take prompt and appropriate action. The present invention reduces the burden of childcare as a whole and supports the provision of appropriate care for babies.

[0572] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0573] Step 1:

[0574] Baby monitors monitor a baby's face, movements, and voice in real time and collect data. A camera captures the baby's facial expressions, and a microphone records audio data such as crying and laughter. The collected data consists of video and audio data, which are output as streams in JPEG or MP4 format video files and WAV format audio files.

[0575] Step 2:

[0576] The monitoring data acquired by the baby monitor is sent to a cloud computing platform. The data is sent via wireless communication (Wi-Fi or 4G / 5G). For example, the data is sent in encrypted form using the HTTPS protocol. This transmission process sends the input data (video and audio files) to the cloud computing platform, which then becomes the input for the next step.

[0577] Step 3:

[0578] The server receives the monitoring data sent from the baby monitor. The cloud computing platform (server) temporarily stores the received video and audio files in preparation for the next analysis step. In this step, data reception is the first processing step, and it is confirmed that the received data has been saved correctly.

[0579] Step 4:

[0580] The server analyzes the received data using the generative AI model. On the cloud computing platform, TensorFlow is used to run the facial expression recognition model, and PyTorch is used to run the audio analysis model. The input data consists of video and audio files, and each is analyzed. Specifically, the facial expression recognition model analyzes the video data and detects changes in the baby's facial expression. The audio analysis model also analyzes the audio data, analyzing the frequency distribution and amplitude of the crying. From this analysis, a judgment result such as "the baby is crying because he is hungry" is output.

[0581] Step 5:

[0582] The server notifies the parent's device of the analysis results. The analysis results obtained by the generative AI model are converted into a text message. For example, a message such as "The baby is hungry" is generated, and a service such as Firebase Cloud Messaging is used to send it to the parent's device. The output of this step is a text message, which is then sent to the parent's device.

[0583] Step 6:

[0584] The device receives the analysis results from the cloud server. The parent's device (smartphone, tablet, etc.) receives the text message sent from the cloud server. A dedicated application runs in the background and captures the received message. Through this process, the device application obtains input data (analysis result message) for the next display step.

[0585] Step 7:

[0586] The device displays the received analysis results and sounds an alarm if necessary. The dedicated application displays the received analysis results on the UI. For example, a message saying "Baby is hungry" is displayed on the app screen. At the same time, an alarm sound or vibration is generated depending on the urgency, immediately informing the parent of the situation. The output of this step is a visual display and an audible notification.

[0587] Step 8:

[0588] The user (guardian) checks the analysis results and takes appropriate action. The user checks the analysis results on their smartphone screen, and if it determines that the baby is hungry, they take action such as preparing milk, or if the baby is crying, they pick up the baby and comfort him or her. This allows the user to respond to the baby's needs immediately.

[0589] (Application example 1)

[0590] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0591] While there are systems that monitor a baby's safety and needs in real time, there are few systems that comprehensively monitor the safety of the entire home and immediately detect and alert to any abnormalities. Furthermore, technology for integrated management of the baby's condition and abnormalities in the home to ensure safety is still in its infancy. Given this background, there is a need for a system that can centrally monitor the safety of not only the baby but the entire home and respond immediately.

[0592] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0593] In this invention, the server includes a device means for monitoring the baby's face, movements, and voice in real time, a communication means for transmitting the monitored data to a cloud server, a generating AI model means for analyzing the received data in the cloud server and determining the baby's needs and dangers, a notification means for notifying the parent's terminal of the analysis results, a means for monitoring the safety of the entire household to detect suspicious movements and sounds, and an alarm means for immediately issuing a warning notification when a dangerous situation is detected. This makes it possible to comprehensively monitor not only the baby's condition but also the overall safety of the household, and immediately notify the parent's terminal when an abnormality is detected, and even automatically contact the police or fire department if necessary.

[0594] "Device means" refers to a device for monitoring the baby's face, movements, and voice in real time.

[0595] "Communication means" refers to the technology or equipment used to transmit monitored data to the cloud server.

[0596] The "generative AI model means" is an artificial intelligence model that is used to analyze received data on a cloud server and determine the baby's needs and risks.

[0597] "Notification means" refers to a system or method for transmitting the analysis results from the cloud server to the parent's device.

[0598] "Home safety monitoring measures" are devices and technologies used to monitor suspicious activity and sounds within the home in real time and detect abnormalities.

[0599] An "alarm means" is a system or device that immediately issues a warning to the parent's terminal and to any necessary emergency services when a dangerous situation is detected.

[0600] This invention is a system for comprehensively monitoring the safety of babies and the entire household. The system includes a device means, a communication means, a generating AI model means, a notification means, a monitoring means for suspicious movements and sounds, and an alarm means.

[0601] Device Means

[0602] The device means is a device equipped with a camera and a microphone for monitoring the baby's face, movements, and voice in real time. This device constantly acquires data on the baby's behavior, facial expressions, and voice, and uses this data for analysis.

[0603] communication means

[0604] The communication means is a technology for transmitting acquired monitoring data to a cloud server. Common communication technologies such as wireless LAN and mobile networks are used for communication. This allows data acquired by the device means to be transmitted to the cloud server in near real time.

[0605] Generative AI model means

[0606] The cloud server is equipped with a generative AI model that analyzes the received data. The generative AI model uses the monitoring data to determine the baby's condition (e.g., hunger, discomfort, fatigue, etc.) and also detects suspicious movements and abnormal sounds throughout the household. Machine learning algorithms and large datasets are used to train the model.

[0607] Notification means

[0608] The results of the analysis performed by the cloud server are then sent to the parent's device. Notification methods are used to quickly send the analysis results to common devices such as smartphones and PCs. Notifications can be sent in a variety of forms, including pop-up alerts, audio alerts, and message notifications.

[0609] Monitoring for suspicious activity and sounds

[0610] To ensure the safety of the entire home, a means of monitoring suspicious movements and sounds has been added. This means that multiple cameras and microphones installed in each room monitor the movements of the entire house and detect any abnormalities.

[0611] Alarm means

[0612] The alarm system is a system that immediately issues a warning notification when a dangerous situation is detected. This system issues an alert to the parent's device and automatically contacts the police or fire department if necessary. Specifically, it uses Text-to-Speech (TTS) technology to generate and play audio alerts. This processing is performed using gTTS and mpg321.

[0613] Specific examples

[0614] For example, if a baby starts crying, the system detects the crying and facial expression in real time and sends them to a cloud server. The cloud server then uses a generative AI model to determine if the baby is hungry and sends a message to the parent's device saying, "The baby is hungry." Furthermore, if a suspicious person breaks into the home while the parents are away, the camera and microphone detect the abnormality and immediately send a warning notification to the parents via the cloud server, while automatically contacting the police if necessary.

[0615] Example prompts for generative AI models

[0616] "A security system that analyzes data obtained from cameras and microphones installed in a room, detects suspicious movements and sounds, and notifies you."

[0617] In this way, the present invention makes it possible to monitor the safety of the baby and the entire household in a unified manner and to respond quickly when necessary.

[0618] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0619] Step 1:

[0620] The device captures your baby's face, movements, and voice in real time.

[0621] Input: Video data from the camera and audio data from the microphone.

[0622] Data processing: Video data is captured frame by frame, and audio data is sampled periodically. The video data is passed through a facial recognition algorithm to extract the baby's facial features, and the audio data is appropriately filtered to remove noise.

[0623] Output: Enhanced face, movement, and voice data.

[0624] Step 2:

[0625] The acquired data is sent to a cloud server.

[0626] Input: Face, movement, and voice data processed in step 1.

[0627] Data processing: converting data into the appropriate format, encrypting it and sending it.

[0628] Output: Monitoring data sent to the cloud server.

[0629] Step 3:

[0630] The cloud server receives the monitoring data and analyzes it using a generative AI model.

[0631] Input: Monitoring data sent in step 2.

[0632] Data processing: Transforming the data into an input format suitable for the model.

[0633] Data computation: Generative AI models analyze facial expressions, movements, and voice to determine the baby's needs and abnormalities in the home.

[0634] Output: Analysis result (e.g. "Baby is hungry" or "Suspicious movement detected").

[0635] Step 4:

[0636] The parent's device is notified based on the analysis results.

[0637] Input: Analysis results obtained in step 3.

[0638] Data processing: Converting the analysis results into a user-friendly format, for example, preparing them as text or voice messages.

[0639] Data calculation: Determines the content of the notification and sends it in the appropriate format.

[0640] Output: A notification displayed on the parent's device (e.g., a pop-up notification or an alarm sound on a smartphone).

[0641] Step 5:

[0642] If necessary, the police and fire department will be automatically contacted.

[0643] Input: Analysis results of the emergency situation notified in step 4.

[0644] Data processing: Formatting information necessary for emergency calls (e.g., location information, details of the abnormality, etc.).

[0645] Data Calculation: Contact the appropriate emergency services through the emergency communication system.

[0646] Output: Automatic notification to emergency services (e.g. police to report suspicious person, fire to fire).

[0647] Through these steps, the entire system works together to monitor the safety of your baby and the entire household in real time.

[0648] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0649] Overall system overview

[0650] This system monitors a baby's face, movements, and voice, as well as the user's emotions, in real time, analyzes the data on a cloud server, and notifies the parent of the results. This system not only enables parents to instantly grasp their baby's needs and dangerous situations, but also uses an emotion engine to receive notifications based on the parent's emotional state, further reducing the burden of childcare.

[0651] How does a baby monitor work?

[0652] A baby monitor is a device that captures a baby's face, movements, and voice in real time. This device is equipped with a camera and microphone for continuous monitoring. It also has a communication means to send the monitoring data to a cloud server.

[0653] The baby monitor works in the following steps:

[0654] 1. The device captures your baby's monitoring data.

[0655] 2. The acquired data is sent to the cloud server.

[0656] Data analysis on a cloud server

[0657] The cloud server receives monitoring data sent from the baby monitor and has a generative AI model means for analyzing it. The generative AI model on the cloud server analyzes the baby's facial expressions, movements, and voice data to determine the baby's needs and dangers. It also analyzes the user's emotional data using an emotion engine and integrates this data with the analysis results of the baby.

[0658] The cloud server works in the following steps:

[0659] 1. The server receives monitoring data from the baby monitor.

[0660] 2. Analyze the data using a generative AI model to determine the baby's needs and risks.

[0661] 3. The emotion engine analyzes the user's emotion data and determines the user's emotional state.

[0662] 4. Integrate the baby's analysis results with the user's emotional state to adjust the notification content.

[0663] 5. Send the analysis results to the parent's device for notification.

[0664] Notification display on parent's device

[0665] The parent's device receives the analysis results from the cloud server and has a notification means to display them. This notification means allows the parent to immediately understand the baby's condition. If the analysis results indicate an emergency, an alarm can be sent. Furthermore, the priority and content of the notification are adjusted according to the parent's emotional state as recognized by the emotion engine.

[0666] The parent device operates in the following steps:

[0667] 1. The device receives the analysis results from the cloud server.

[0668] 2. Display the received analysis results on the application's user interface (UI).

[0669] 3. If necessary, an alarm will be sent to notify parents of an emergency.

[0670] 4. Display advice and suggestions based on the results of emotion analysis by the emotion engine.

[0671] Specific examples

[0672] For example, if a baby is hungry and crying and the user is tired, the system will act as follows:

[0673] 1. Baby monitor: Detects the baby's crying and displeased face and sends the data to a cloud server.

[0674] 2. Cloud server: Analyzes the received data and determines whether the baby is hungry. As a result, it generates a message saying "The baby is hungry." The emotion engine then determines the user's level of fatigue. Based on this, it sends a notification indicating urgency to the parent's device.

[0675] 3. Parent's device: The received analysis result "Baby is hungry" is displayed on the UI, and if the parent is fatigued, a notification with higher priority than usual is sent. Appropriate advice and suggestions are also provided.

[0676] In this way, parents can quickly take appropriate measures based on their baby's condition and their own emotional state, reducing the burden of childcare and providing support to ensure the safety and health of both parents and their baby.

[0677] The processing flow will be explained below.

[0678] Program processing steps

[0679] How does a baby monitor work?

[0680] Step 1:

[0681] Baby monitors use cameras and microphones to monitor your baby's face, movements, and voice in real time, collecting data continuously.

[0682] Step 2:

[0683] Collected monitoring data is temporarily stored on the device, where it is properly formatted and prepared for later transmission to a cloud server.

[0684] Step 3:

[0685] The device sends the monitoring data to the cloud server using secure HTTP or WebSocket protocols to ensure minimal latency.

[0686] Data analysis on a cloud server

[0687] Step 4:

[0688] The server receives the data sent from the baby monitor and temporarily stores it in a database.

[0689] Step 5:

[0690] A generative AI model analyzes surveillance data, analyzing voice, facial expression, and motion separately, and then combines the results.

[0691] Step 6:

[0692] The server uses an analytical engine to determine the baby's needs (hunger, discomfort, fatigue, etc.) and potential dangers (lying face down, objects falling, etc.).

[0693] Step 7:

[0694] The server receives the user's emotional data, and the emotion engine analyzes the user's emotional state, including the user's voice, facial expressions, and operation logs.

[0695] Step 8:

[0696] The system integrates analysis of the baby's needs and dangers with the user's emotional state to tailor notifications, for example, increasing the priority of notifications if the user is very tired.

[0697] Step 9:

[0698] The server sends the consolidated analysis results to the parent's device, which includes the baby's condition, recommended measures, and necessary alert information.

[0699] Notification display on parent's device

[0700] Step 10:

[0701] The device receives the analysis results sent from the cloud server, and the received data is loaded into the application, starting the process.

[0702] Step 11:

[0703] The received analysis results are displayed in the app's user interface (UI), which displays the results in an intuitive and easy-to-understand format, allowing users to understand the situation at a glance.

[0704] Step 12:

[0705] If necessary, an alarm notification is issued to notify the user of an emergency. Notification methods include sound, vibration, and pop-up notification.

[0706] Step 13:

[0707] The server provides appropriate advice and suggestions based on the user's emotional state, for example, advising the user to take a rest if they are tired.

[0708] Specific examples

[0709] For example, if your baby is hungry and crying and you are very tired:

[0710] Step 1:

[0711] Baby monitors keep an eye on your baby's cries and irritability.

[0712] Step 2:

[0713] The monitoring data is temporarily stored and ready to be sent to a cloud server later.

[0714] Step 3:

[0715] The device sends the monitoring data to a cloud server.

[0716] Step 4:

[0717] The server receives the monitoring data and stores it in a database.

[0718] Step 5:

[0719] A generative AI model analyzes the monitoring data, detecting signs of hunger from crying patterns and facial expressions.

[0720] Step 6:

[0721] The server determines that the baby is hungry.

[0722] Step 7:

[0723] A server receives the user's emotion data, and an emotion engine determines the user's fatigue.

[0724] Step 8:

[0725] The server combines the baby's hunger determination result with the user's fatigue state and increases the priority of the notification.

[0726] Step 9:

[0727] The server then sends the combined analysis results to the parent's device, which includes a message saying "Your baby is hungry" along with advice to get some rest.

[0728] Step 10:

[0729] The terminal receives the analysis results.

[0730] Step 11:

[0731] The analysis results are displayed on the app's UI as "Baby is hungry."

[0732] Step 12:

[0733] An alarm will sound and say "Baby is hungry" to alert the parent, and in this case, a vibration will also be added since the notification is set to high priority.

[0734] Step 13:

[0735] Based on the user's emotional state, "advice to take a rest" is displayed.

[0736] Example 2

[0737] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0738] Conventional baby monitoring systems only monitor a baby's face, movements, and voice, and only have the ability to identify the baby's needs or dangerous situations, which limits the ability of parents to respond appropriately to their baby's situation.In addition, because notifications are given without taking into account the parent's emotional state, there is the problem that they cannot provide appropriate support to parents who are tired or stressed.

[0739] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0740] In this invention, the server includes device means for monitoring the baby's face, movements, and voice in real time, communication means for transmitting the monitored data to a cloud server, generation AI model means for analyzing the received data in the cloud server and determining the baby's needs and dangers, emotion engine means for analyzing the user's emotion data and integrating it with the analysis results of the baby, and notification means for notifying the parent's device of the analysis results. This makes it possible to provide optimal notifications based on the baby's condition and the parent's emotional state, reducing the burden of childcare and ensuring the safety and health of the parent and baby.

[0741] "Device means" refers to a device that includes hardware and software for monitoring the baby's face, movements, and voice in real time.

[0742] The "communication means" is a network communication function for transmitting monitoring data acquired by the device means to the cloud server.

[0743] The "generative AI model means" refers to artificial intelligence algorithms and software that run on a cloud server and analyze the received data to determine the baby's needs and risks.

[0744] The "emotion engine means" is software and algorithms for analyzing the user's emotion data and integrating it with the baby's analysis results.

[0745] The "notification means" refers to a communication function and software for transmitting the analysis results from the cloud server to the parent's terminal and making the notification.

[0746] The "analysis results" are the results of the judgments of the baby's needs and dangerous situations, and the user's emotional state, obtained by the generative AI model means and the emotion engine means.

[0747] "Parent Device" refers to a mobile device or computer used by a parent to receive and display the analysis results.

[0748] This system monitors a baby's face, movements, and voice, as well as the user's emotions, in real time, analyzes the data on a cloud server, and notifies the parent of the results of the analysis. This not only enables parents to immediately grasp their baby's needs and dangerous situations, but also allows them to receive notifications based on their emotional state using an emotion engine, further reducing the burden of childcare.

[0749] Hardware and Software Configuration

[0750] Baby monitor

[0751] A baby monitor is a device that captures a baby's face, movements, and voice in real time. It is equipped with a high-resolution camera and a highly sensitive microphone, which allows it to capture facial expressions in detail and pick up even the nuances of a baby's voice.

[0752] Specifically, the baby monitor works as follows.

[0753] The camera detects the baby's face and movements.

[0754] The microphone picks up the baby's voice.

[0755] It is equipped with a Wi-Fi module to transmit this data to a cloud server in real time.

[0756] Cloud Server

[0757] The cloud server receives the data sent from the baby monitor and analyzes it using generative AI models, which include facial recognition and voice recognition algorithms.

[0758] The server operates as follows:

[0759] Receive data from the baby monitor.

[0760] A generative AI model is used to analyze the data and determine the baby's needs and risks.

[0761] An emotion engine is used to analyze the user's emotion data and determine the user's emotional state.

[0762] The baby's analysis results are integrated with the user's emotional state to tailor the notification content.

[0763] The adjusted notification content is sent to the parent's device.

[0764] Parent's device

[0765] An application is installed on the parent's device to receive and display notifications from the server. This application has a user interface (UI) that displays notifications in real time and has the ability to issue alerts as needed. It also has the ability to display appropriate advice and suggestions based on the analysis results of the emotion engine.

[0766] The terminal operates as follows:

[0767] Receives notification content sent from the server.

[0768] The received analysis results are displayed on the UI.

[0769] In the event of an emergency, an alert will be issued to notify parents of the emergency.

[0770] Display advice and suggestions based on the results of the sentiment engine.

[0771] Specific examples

[0772] For example, if a baby is hungry and crying, or if the user feels tired, the system will operate as follows:

[0773] 1. Baby monitor: The camera captures the baby's crying face, the microphone records the crying sound, and sends the data to a cloud server.

[0774] 2. Cloud server: The generative AI model analyzes the received data and determines that the baby is hungry. It generates a message saying "Baby is hungry," and the emotion engine determines the user's fatigue level. Based on this, it sends a notification indicating urgency to the parent's device.

[0775] 3. Parent's device: The received analysis result "Baby is hungry" is displayed on the UI. If the parent is fatigued, a notification with higher priority than usual will be sent, and appropriate advice and suggestions will be provided.

[0776] Prompt Sentence Examples

[0777] An example prompt for a generative AI model might be:

[0778] "Analyze your baby's cries and facial expressions to determine if he or she is hungry."

[0779] "Analyze the user's current emotional state with an emotion engine and adjust the priority of notifications based on that."

[0780] This system allows parents to comprehensively understand their baby's condition and their own emotional state, enabling them to quickly take the most appropriate measures, thereby reducing the burden of childcare and helping to ensure the safety and health of both parents and their babies.

[0781] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0782] Step 1:

[0783] Baby monitor data acquisition

[0784] A baby monitor captures the baby's face, movements, and voice in real time. The camera detects the baby's face and movements, and the microphone picks up the baby's voice. This action produces image and audio data. The inputs are the baby's face, movements, and voice, and the output is a camera image and an audio file.

[0785] Specific behavior:

[0786] The camera captures the baby's crying face.

[0787] The microphone records the baby's cry.

[0788] Step 2:

[0789] Sending data to the cloud server

[0790] The baby monitor sends the acquired data to a cloud server. The baby monitor's communication module uploads the camera images and audio files to the cloud server via Wi-Fi. The input is the camera images and audio files, and the output is the data uploaded to the cloud server.

[0791] Specific behavior:

[0792] The baby monitor transmits camera images and audio data to a cloud server in real time.

[0793] Step 3:

[0794] Data analysis on the server

[0795] The server receives data sent from the baby monitor and analyzes it using a generative AI model. It analyzes the baby's facial expressions, movements, and voice data to determine the baby's condition (e.g., hungry, sleepy, dangerous, etc.). The input is the monitoring data uploaded to the cloud server, and the output is the analysis results.

[0796] Specific behavior:

[0797] The server uses a facial recognition algorithm to analyze the baby's crying face.

[0798] A voice recognition algorithm is used to analyze the characteristics of the cry and determine whether the baby is hungry.

[0799] Step 4:

[0800] User sentiment analysis

[0801] The server analyzes the user's emotional data using an emotion engine. Based on past smartphone operation data and vital data (e.g., heart rate), the server determines the user's emotional state (e.g., fatigue, stress, relaxation). The input is the emotional data, and the output is the emotion analysis results.

[0802] Specific behavior:

[0803] The server checks the user's heart rate data and determines that the user is in a "fatigue state" using an emotion engine.

[0804] Step 5:

[0805] Consolidating and aligning notifications

[0806] The server integrates the baby's analysis results with the user's emotional state and adjusts the notification content. Based on the output of the emotion engine, it generates notification text and determines the priority of the notification. The input is the baby's analysis results and the emotion analysis results, and the output is the adjusted notification content.

[0807] Specific behavior:

[0808] Generate a notification message saying, "The baby is crying because he is hungry. He seems tired, so please take action as soon as possible."

[0809] Step 6:

[0810] Notifications on parent's device

[0811] The server sends the adjusted notification content to the parent device. The server's communication module pushes the notification content to the parent device. The input is the adjusted notification content, and the output is the notification sent to the parent device.

[0812] Specific behavior:

[0813] The server pushes the notification to the parent's smartphone.

[0814] Step 7:

[0815] Notifications and alerts on parent's device

[0816] The parent device receives notifications from the server and displays the content. The app displays the notifications on the UI and issues emergency alerts if necessary. It also displays appropriate advice and suggestions based on the analysis results of the emotion engine. The input is the notification content from the server, and the output is the notification content displayed on the UI.

[0817] Specific behavior:

[0818] A message appears on the parent's smartphone saying "Baby is hungry."

[0819] The app displays advice such as, "Parents are tired, so we suggest an easy way to make milk."

[0820] (Application example 2)

[0821] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0822] There is a need for a system that can monitor the stress and emotional state of workers in factories, as well as the safety of the work environment, in real time and take appropriate measures. Conventional systems have difficulty accurately grasping fluctuations in workers' health and emotions, and lack the functionality to immediately detect dangers in the work environment, making it difficult to ensure worker safety and efficiency. Furthermore, measures based on workers' stress and emotional state are sometimes delayed, which can have a negative impact on work efficiency and safety.

[0823] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes device means for monitoring the faces, movements, and voices of the baby and worker in real time, communication means for transmitting the monitored data to a cloud server, generation AI model means for analyzing the received data in the cloud server and determining the needs and dangers of the baby and worker, and notification means for notifying the parent and manager terminals of the analysis results. This makes it possible to grasp the worker's stress and emotional state, as well as the state of the work environment, in real time and take appropriate measures promptly.

[0824] "Device means" refers to equipment or devices for capturing and monitoring the face, movements, and voice of workers in real time.

[0825] "Communication means" refers to the network technology and communication protocol used to transmit the monitoring data acquired by the device means to the cloud server.

[0826] "Cloud server" refers to a remote server that stores and processes data over the Internet.

[0827] "Generative AI model means" refers to an artificial intelligence algorithm that runs on a cloud server and analyzes the received monitoring data to determine the needs and risks of workers and babies.

[0828] "Notification means" refers to the functions and systems that notify parents and administrators of the results of analysis performed on the cloud server via their devices.

[0829] "Stress" refers to the mental or physical tension that a worker feels due to the work environment or work content.

[0830] "Emotional state" refers to the psychological state that indicates the worker's current feelings and sensations.

[0831] "Work environment" refers to the place where workers actually perform their work and its physical conditions (e.g., noise, temperature, humidity, etc.).

[0832] "Manager" refers to a person who is responsible for supervising workers in a factory or work site and managing the safety of the work environment and work efficiency.

[0833] This invention is a system that monitors the stress and emotional state of workers in a factory, as well as the safety of the working environment, in real time, and takes appropriate measures promptly. Specifically, it consists of the following parts.

[0834] Hardware Configuration

[0835] 1. Device means:

[0836] Camera: Captures the worker's facial expressions and movements in real time.

[0837] Microphone: Captures the worker's voice in real time.

[0838] Wearable devices: Monitor workers' biometric data such as heart rate and body temperature.

[0839] Environmental sensors: Collect data on the working environment, such as temperature, humidity, and noise levels.

[0840] 2. Means of communication:

[0841] Network technologies such as Wi-Fi and LTE are used to transmit data acquired by the device means to a cloud server.

[0842] Software Configuration

[0843] 1. Cloud Server:

[0844] The cloud server receives the data transmitted from the device means and analyzes it.

[0845] The generative AI model used (e.g. TensorFlow) analyzes the incoming data in real time to determine the worker's stress and emotional state.

[0846] Based on the analysis results, the safety of the working environment will also be evaluated.

[0847] 2. Generative AI model means:

[0848] Facial expression analysis, voice analysis, and biometric data analysis are performed to determine the worker's stress and emotional state.

[0849] The generative AI models used include TensorFlow and Google Cloud AI.

[0850] Based on the analysis, appropriate measures and advice are generated.

[0851] 3. Means of notification:

[0852] The results analyzed on the cloud server are notified to parents or administrators via smartphones or robot terminals.

[0853] The notification includes advice to encourage workers to take breaks and suggestions for improving the work environment.

[0854] Specific examples

[0855] 1. If worker A is stressed:

[0856] Camera and microphone: Worker A has a stern facial expression and an irritated voice.

[0857] Analysis result: It was determined that worker A's stress level was high.

[0858] Notification: A message is sent to the manager's smartphone saying, "Worker A's stress level is high. Please encourage him to take a break."

[0859] 2. If the working environment has high noise levels:

[0860] Environmental sensor: Noise levels exceed acceptable limits.

[0861] Analysis result: The working environment was determined to be dangerous.

[0862] Notification: Robotic terminals in factories warn workers, "Noise levels are high. Please use earplugs."

[0863] Prompt Sentence Examples

[0864] Facial Expression Analysis: "Analyze the facial expressions of the workers in this image to determine their stress levels."

[0865] Voice Analysis: "Infer the emotional state of the worker in this audio file from their tone of voice."

[0866] Environmental Data Analysis: "Analyze this environmental sensor data to assess safety."

[0867] In this way, we ensure the safety and health of workers in the factory and provide support to improve work efficiency.

[0868] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0869] Step 1:

[0870] The user installs a baby monitor, and the device captures the worker's face, movements, voice, and work environment in real time. The input is monitoring data (facial expressions, voice, biometric data, and environmental sensor data). The output is the acquisition of these monitoring data.

[0871] Step 2:

[0872] The monitoring data acquired by the device means is sent to the cloud server using the communication means. The input is the monitoring data acquired in step 1, and the data is sent. The output is the monitoring data sent to the cloud server.

[0873] Step 3:

[0874] The cloud server receives the monitoring data and uses a generative AI model to analyze it. The input is the transmitted monitoring data, and the cloud server performs data analysis. Specifically, it performs facial expression analysis, vocal emotion analysis, biometric data analysis, and environmental data analysis. The output is the analysis results (the worker's stress level, emotional state, and safety assessment of the work environment).

[0875] Step 4:

[0876] Based on the analysis results, the cloud server notifies the parent or administrator's device. The input is the analysis result from step 3, which generates a notification message. The output is the notification message sent to the parent or administrator's device.

[0877] Step 5:

[0878] The parent or administrator's device receives the notification and displays it on the UI. The input is the notification message sent from the cloud server. The output is the notification message displayed on the device screen (e.g., "Worker A's stress level is high. Please encourage him to take a break.").

[0879] Step 6:

[0880] If necessary, an alarm notification will be sent to notify parents or administrators of an emergency. The input is a notification message that includes the urgency. The output is an alarm (audio, vibration, screen flash, etc.) to the parent or administrator.

[0881] Step 7:

[0882] Parents or administrators can take appropriate measures based on the notification. The inputs are notification messages and alarms. The outputs are instructions and corrective actions for workers (e.g., taking a break, using earplugs).

[0883] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0884] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0885] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0886] [Third embodiment]

[0887] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0888] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0889] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0890] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0891] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0892] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0893] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0894] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0895] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0896] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0897] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0898] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0899] Overall system overview

[0900] This system monitors a baby's face, movements, and voice in real time, analyzes the data on a cloud server, and notifies the parents of the results. This system allows parents to instantly understand their baby's needs and dangerous situations, reducing the burden of childcare.

[0901] How does a baby monitor work?

[0902] A baby monitor is a device that captures a baby's face, movements, and voice in real time. This device is equipped with a camera and microphone for continuous monitoring. It also has a communication means to send the monitoring data to a cloud server.

[0903] The baby monitor works in the following steps:

[0904] 1. The device captures your baby's monitoring data.

[0905] 2. The acquired data is sent to the cloud server.

[0906] Data analysis on a cloud server

[0907] The cloud server receives monitoring data sent from the baby monitor and has a generative AI model means for analyzing it. The generative AI model on the cloud server analyzes the baby's facial expressions, movements, and voice data to determine the baby's needs and dangers.

[0908] The cloud server works in the following steps:

[0909] 1. The server receives monitoring data from the baby monitor.

[0910] 2. Analyze the data using a generative AI model to determine the baby's needs and risks.

[0911] 3. Send the analysis results to the parent's device for notification.

[0912] Notification display on parent's device

[0913] The parent's device receives the analysis results from the cloud server and has a notification function that displays them. This notification function allows the parent to immediately understand the baby's condition. If the analysis results indicate an emergency, an alarm can also be sent.

[0914] The parent device operates in the following steps:

[0915] 1. The device receives the analysis results from the cloud server.

[0916] 2. Display the received analysis results on the app's UI.

[0917] 3. If necessary, an alarm will be sent to notify parents of an emergency.

[0918] Specific examples

[0919] For example, if a baby is crying because it is hungry, the system will:

[0920] 1. Baby monitor: Detects the baby's crying and displeased face and sends the data to a cloud server.

[0921] 2. Cloud server: Analyzes the received data and determines whether the baby is hungry. As a result, it generates a message saying "The baby is hungry" and sends it to the parent's device.

[0922] 3. Parent device: The received analysis result "Baby is hungry" is displayed on the UI and an alarm is sounded if necessary.

[0923] In this way, parents can immediately understand the condition of their baby and take appropriate measures, reducing the burden of childcare and supporting the provision of prompt and appropriate care.

[0924] The processing flow will be explained below.

[0925] Program processing steps

[0926] How does a baby monitor work?

[0927] Step 1:

[0928] Baby monitors use cameras and microphones to monitor your baby's face, movements, and voice in real time, collecting data continuously.

[0929] Step 2:

[0930] The monitoring data is temporarily stored on the device and prepared for transmission to a cloud server in batches at regular intervals, where the data is properly formatted and security checked.

[0931] Step 3:

[0932] Data is sent to a cloud server using secure HTTP or WebSocket protocols to ensure minimal latency.

[0933] Data analysis on a cloud server

[0934] Step 4:

[0935] The server receives the data sent from the baby monitor, which is first temporarily stored in a database and then passed to the analytics engine.

[0936] Step 5:

[0937] A generative AI model in the cloud server analyzes the data, specifically voice analysis, facial expression recognition, and movement analysis, and then combines the results.

[0938] Step 6:

[0939] The analytics engine determines the baby's needs (hunger, sleepiness, discomfort, etc.) and potential dangers (face down, overturned objects, etc.) not only based on real-time data but also by comparing it with past data.

[0940] Step 7:

[0941] Analysis results are generated and prepared for transmission to the parent's device, including the baby's status, recommended actions, and any necessary alerts.

[0942] Notification display on parent's device

[0943] Step 8:

[0944] The device receives the analysis results from the cloud server. The device retrieves the data through an internet connection and stores it locally.

[0945] Step 9:

[0946] The received analysis results are displayed on the application's user interface (UI), which displays the results in a format that is intuitively understandable to the user, allowing them to grasp the situation at a glance.

[0947] Step 10:

[0948] If necessary, alarm notifications will be sent to alert parents to emergencies. The alarms can be sent in multiple forms, including sound, vibration, and pop-up notifications, so parents can get their immediate attention.

[0949] Specific examples

[0950] For example, if your baby is crying because he is hungry:

[0951] Step 1:

[0952] Baby monitors keep an eye on your baby's cries and irritability.

[0953] Step 2:

[0954] The monitoring data is temporarily stored and ready to be sent to the cloud server.

[0955] Step 3:

[0956] The data is sent to a cloud server.

[0957] Step 4:

[0958] The server receives the monitoring data and stores it in a database.

[0959] Step 5:

[0960] A generative AI model analyzes the monitoring data, detecting signs of hunger from crying patterns and facial expressions.

[0961] Step 6:

[0962] The analytical engine determines that the baby is hungry.

[0963] Step 7:

[0964] The analysis result "Baby is hungry" is generated and ready to be sent to the parent's device.

[0965] Step 8:

[0966] The terminal receives the analysis results.

[0967] Step 9:

[0968] The analysis results are displayed on the app's UI as "Baby is hungry."

[0969] Step 10:

[0970] An alarm will sound to alert the parents.

[0971] Example 1

[0972] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0973] In modern childcare, parents are required to constantly monitor their baby's needs and condition and respond promptly. However, accurately understanding a baby's condition is extremely difficult, especially at night or when the parent is in a remote location. In addition, the burden of childcare can cause significant stress for parents. Furthermore, if a baby is in a dangerous situation, parents are required to immediately recognize and respond, but current methods make this difficult. The present invention aims to provide a system that allows parents to quickly understand and respond to their baby's needs and dangerous situations.

[0974] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0975] In this invention, the server includes a sensor means for monitoring the baby's face, movements, and voice in real time, a communication means for transmitting the monitored data to a cloud computing platform, and a machine learning model means for analyzing the received data in the cloud computing platform and determining the baby's needs and dangers. This makes it possible to grasp the baby's condition in real time and send warning notifications to the parent's device as needed. This allows the parent to quickly check the baby's needs and dangers and take appropriate measures, reducing the burden of childcare.

[0976] The "sensor means" is a device for monitoring the baby's face, movements, and voice in real time and acquiring data.

[0977] The "communication means" is a means for transmitting the monitoring data acquired by the sensor means to the cloud computing infrastructure.

[0978] A "cloud computing platform" is a remote server that receives and analyzes monitoring data.

[0979] "Machine learning model means" is a general term for algorithms and programs that analyze monitoring data on a cloud computing platform and determine the baby's needs and risks.

[0980] The "notification means" is a means for notifying the parent's terminal of the results of the analysis performed by the cloud computing platform.

[0981] The "parent's device" is an electronic device that receives notifications from the cloud computing platform and displays the analysis results.

[0982] The present invention provides a system that monitors a baby's face, movements, and voice in real time, analyzes the data using a cloud computing platform, and notifies the parent or guardian of the results of the analysis. Specific embodiments will be described below.

[0983] Baby monitor

[0984] Baby monitors are equipped with sensors for monitoring the baby's face, movements, and voice. These sensors include, for example, a high-resolution camera and a highly sensitive microphone, allowing for highly accurate capture of the baby's facial expressions, movements, and voice data such as crying.

[0985] communication means

[0986] Baby monitors are equipped with a communication means to transmit the acquired monitoring data to a cloud computing platform using wireless communication technologies such as Wi-Fi or 4G / 5G, which allows data to be transmitted in real time.

[0987] Cloud Computing Platform

[0988] The server receives monitoring data sent from the baby monitor using a cloud computing platform. The received data is analyzed using a generative AI model. Specific analysis uses machine learning frameworks such as TensorFlow and PyTorch. This allows for highly accurate facial expression recognition and voice analysis.

[0989] Analysis means

[0990] The generative AI model, installed on a cloud computing platform, analyzes the received facial expression, movement, and voice data to determine the baby's needs and dangers. For example, if the baby is crying, the frequency distribution of the crying is analyzed to identify the cause. Facial expression recognition technology is used to determine whether the baby is smiling.

[0991] Notification means

[0992] The server then sends a notification to the parent's device based on the analysis results. The notification is sent using a cloud messaging service such as Firebase Cloud Messaging. For example, a text message such as "Your baby is hungry" is generated and sent to the parent's smartphone or tablet.

[0993] Parent's device

[0994] The parent's device receives the notification message and displays it in a dedicated application. The application is developed in Swift or Java, for example, and has a built-in push notification function. At the same time as displaying the analysis results, an alarm sound or vibration alert is issued depending on the urgency of the situation.

[0995] Examples and prompts

[0996] For example, if your baby is crying because he is hungry:

[0997] 1. A baby monitor detects a baby's crying and unhappy facial expressions and sends the data to a cloud computing platform.

[0998] 2. The cloud computing platform analyzes the data and determines whether the baby is hungry. As a result, a message saying "The baby is hungry" is generated and sent to the parent's device.

[0999] 3. The parent's device will display the received message on the app's UI and may sound an alarm.

[1000] Example prompt sentence:

[1001] "Detect when your baby is hungry and crying and send a real-time notification to the parent's device."

[1002] This allows parents to immediately understand the baby's condition and take prompt and appropriate action. The present invention reduces the burden of childcare as a whole and supports the provision of appropriate care for babies.

[1003] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1004] Step 1:

[1005] Baby monitors monitor a baby's face, movements, and voice in real time and collect data. A camera captures the baby's facial expressions, and a microphone records audio data such as crying and laughter. The collected data consists of video and audio data, which are output as streams in JPEG or MP4 format video files and WAV format audio files.

[1006] Step 2:

[1007] The monitoring data acquired by the baby monitor is sent to a cloud computing platform. The data is sent via wireless communication (Wi-Fi or 4G / 5G). For example, the data is sent in encrypted form using the HTTPS protocol. This transmission process sends the input data (video and audio files) to the cloud computing platform, which then becomes the input for the next step.

[1008] Step 3:

[1009] The server receives the monitoring data sent from the baby monitor. The cloud computing platform (server) temporarily stores the received video and audio files in preparation for the next analysis step. In this step, data reception is the first processing step, and it is confirmed that the received data has been saved correctly.

[1010] Step 4:

[1011] The server analyzes the received data using the generative AI model. On the cloud computing platform, TensorFlow is used to run the facial expression recognition model, and PyTorch is used to run the audio analysis model. The input data consists of video and audio files, and each is analyzed. Specifically, the facial expression recognition model analyzes the video data and detects changes in the baby's facial expression. The audio analysis model also analyzes the audio data, analyzing the frequency distribution and amplitude of the crying. From this analysis, a judgment result such as "the baby is crying because he is hungry" is output.

[1012] Step 5:

[1013] The server notifies the parent's device of the analysis results. The analysis results obtained by the generative AI model are converted into a text message. For example, a message such as "The baby is hungry" is generated, and a service such as Firebase Cloud Messaging is used to send it to the parent's device. The output of this step is a text message, which is then sent to the parent's device.

[1014] Step 6:

[1015] The device receives the analysis results from the cloud server. The parent's device (smartphone, tablet, etc.) receives the text message sent from the cloud server. A dedicated application runs in the background and captures the received message. Through this process, the device application obtains input data (analysis result message) for the next display step.

[1016] Step 7:

[1017] The device displays the received analysis results and sounds an alarm if necessary. The dedicated application displays the received analysis results on the UI. For example, a message saying "Baby is hungry" is displayed on the app screen. At the same time, an alarm sound or vibration is generated depending on the urgency, immediately informing the parent of the situation. The output of this step is a visual display and an audible notification.

[1018] Step 8:

[1019] The user (guardian) checks the analysis results and takes appropriate action. The user checks the analysis results on their smartphone screen, and if it determines that the baby is hungry, they take action such as preparing milk, or if the baby is crying, they pick up the baby and comfort him or her. This allows the user to respond to the baby's needs immediately.

[1020] (Application example 1)

[1021] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1022] While there are systems that monitor a baby's safety and needs in real time, there are few systems that comprehensively monitor the safety of the entire home and immediately detect and alert to any abnormalities. Furthermore, technology for integrated management of the baby's condition and abnormalities in the home to ensure safety is still in its infancy. Given this background, there is a need for a system that can centrally monitor the safety of not only the baby but the entire home and respond immediately.

[1023] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1024] In this invention, the server includes a device means for monitoring the baby's face, movements, and voice in real time, a communication means for transmitting the monitored data to a cloud server, a generating AI model means for analyzing the received data in the cloud server and determining the baby's needs and dangers, a notification means for notifying the parent's terminal of the analysis results, a means for monitoring the safety of the entire household to detect suspicious movements and sounds, and an alarm means for immediately issuing a warning notification when a dangerous situation is detected. This makes it possible to comprehensively monitor not only the baby's condition but also the overall safety of the household, and immediately notify the parent's terminal when an abnormality is detected, and even automatically contact the police or fire department if necessary.

[1025] "Device means" refers to a device for monitoring the baby's face, movements, and voice in real time.

[1026] "Communication means" refers to the technology or equipment used to transmit monitored data to the cloud server.

[1027] The "generative AI model means" is an artificial intelligence model that is used to analyze received data on a cloud server and determine the baby's needs and risks.

[1028] "Notification means" refers to a system or method for transmitting the analysis results from the cloud server to the parent's device.

[1029] "Home safety monitoring measures" are devices and technologies used to monitor suspicious activity and sounds within the home in real time and detect abnormalities.

[1030] An "alarm means" is a system or device that immediately issues a warning to the parent's terminal and to any necessary emergency services when a dangerous situation is detected.

[1031] This invention is a system for comprehensively monitoring the safety of babies and the entire household. The system includes a device means, a communication means, a generating AI model means, a notification means, a monitoring means for suspicious movements and sounds, and an alarm means.

[1032] Device Means

[1033] The device means is a device equipped with a camera and a microphone for monitoring the baby's face, movements, and voice in real time. This device constantly acquires data on the baby's behavior, facial expressions, and voice, and uses this data for analysis.

[1034] communication means

[1035] The communication means is a technology for transmitting acquired monitoring data to a cloud server. Common communication technologies such as wireless LAN and mobile networks are used for communication. This allows data acquired by the device means to be transmitted to the cloud server in near real time.

[1036] Generative AI model means

[1037] The cloud server is equipped with a generative AI model that analyzes the received data. The generative AI model uses the monitoring data to determine the baby's condition (e.g., hunger, discomfort, fatigue, etc.) and also detects suspicious movements and abnormal sounds throughout the household. Machine learning algorithms and large datasets are used to train the model.

[1038] Notification means

[1039] The results of the analysis performed by the cloud server are then sent to the parent's device. Notification methods are used to quickly send the analysis results to common devices such as smartphones and PCs. Notifications can be sent in a variety of forms, including pop-up alerts, audio alerts, and message notifications.

[1040] Monitoring for suspicious activity and sounds

[1041] To ensure the safety of the entire home, a means of monitoring suspicious movements and sounds has been added. This means that multiple cameras and microphones installed in each room monitor the movements of the entire house and detect any abnormalities.

[1042] Alarm means

[1043] The alarm system is a system that immediately issues a warning notification when a dangerous situation is detected. This system issues an alert to the parent's device and automatically contacts the police or fire department if necessary. Specifically, it uses Text-to-Speech (TTS) technology to generate and play audio alerts. This processing is performed using gTTS and mpg321.

[1044] Specific examples

[1045] For example, if a baby starts crying, the system detects the crying and facial expression in real time and sends them to a cloud server. The cloud server then uses a generative AI model to determine if the baby is hungry and sends a message to the parent's device saying, "The baby is hungry." Furthermore, if a suspicious person breaks into the home while the parents are away, the camera and microphone detect the abnormality and immediately send a warning notification to the parents via the cloud server, while automatically contacting the police if necessary.

[1046] Example prompts for generative AI models

[1047] "A security system that analyzes data obtained from cameras and microphones installed in a room, detects suspicious movements and sounds, and notifies you."

[1048] In this way, the present invention makes it possible to monitor the safety of the baby and the entire household in a unified manner and to respond quickly when necessary.

[1049] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1050] Step 1:

[1051] The device captures your baby's face, movements, and voice in real time.

[1052] Input: Video data from the camera and audio data from the microphone.

[1053] Data processing: Video data is captured frame by frame, and audio data is sampled periodically. The video data is passed through a facial recognition algorithm to extract the baby's facial features, and the audio data is appropriately filtered to remove noise.

[1054] Output: Enhanced face, movement, and voice data.

[1055] Step 2:

[1056] The acquired data is sent to a cloud server.

[1057] Input: Face, movement, and voice data processed in step 1.

[1058] Data processing: converting data into the appropriate format, encrypting it and sending it.

[1059] Output: Monitoring data sent to the cloud server.

[1060] Step 3:

[1061] The cloud server receives the monitoring data and analyzes it using a generative AI model.

[1062] Input: Monitoring data sent in step 2.

[1063] Data processing: Transforming the data into an input format suitable for the model.

[1064] Data computation: Generative AI models analyze facial expressions, movements, and voice to determine the baby's needs and abnormalities in the home.

[1065] Output: Analysis result (e.g. "Baby is hungry" or "Suspicious movement detected").

[1066] Step 4:

[1067] The parent's device is notified based on the analysis results.

[1068] Input: Analysis results obtained in step 3.

[1069] Data processing: Converting the analysis results into a user-friendly format, for example, preparing them as text or voice messages.

[1070] Data calculation: Determines the content of the notification and sends it in the appropriate format.

[1071] Output: A notification displayed on the parent's device (e.g., a pop-up notification or an alarm sound on a smartphone).

[1072] Step 5:

[1073] If necessary, the police and fire department will be automatically contacted.

[1074] Input: Analysis results of the emergency situation notified in step 4.

[1075] Data processing: Formatting information necessary for emergency calls (e.g., location information, details of the abnormality, etc.).

[1076] Data Calculation: Contact the appropriate emergency services through the emergency communication system.

[1077] Output: Automatic notification to emergency services (e.g. police to report suspicious person, fire to fire).

[1078] Through these steps, the entire system works together to monitor the safety of your baby and the entire household in real time.

[1079] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1080] Overall system overview

[1081] This system monitors a baby's face, movements, and voice, as well as the user's emotions, in real time, analyzes the data on a cloud server, and notifies the parent of the results. This system not only enables parents to instantly grasp their baby's needs and dangerous situations, but also uses an emotion engine to receive notifications based on the parent's emotional state, further reducing the burden of childcare.

[1082] How does a baby monitor work?

[1083] A baby monitor is a device that captures a baby's face, movements, and voice in real time. This device is equipped with a camera and microphone for continuous monitoring. It also has a communication means to send the monitoring data to a cloud server.

[1084] The baby monitor works in the following steps:

[1085] 1. The device captures your baby's monitoring data.

[1086] 2. The acquired data is sent to the cloud server.

[1087] Data analysis on a cloud server

[1088] The cloud server receives monitoring data sent from the baby monitor and has a generative AI model means for analyzing it. The generative AI model on the cloud server analyzes the baby's facial expressions, movements, and voice data to determine the baby's needs and dangers. It also analyzes the user's emotional data using an emotion engine and integrates this data with the analysis results of the baby.

[1089] The cloud server works in the following steps:

[1090] 1. The server receives monitoring data from the baby monitor.

[1091] 2. Analyze the data using a generative AI model to determine the baby's needs and risks.

[1092] 3. The emotion engine analyzes the user's emotion data and determines the user's emotional state.

[1093] 4. Integrate the baby's analysis results with the user's emotional state to adjust the notification content.

[1094] 5. Send the analysis results to the parent's device for notification.

[1095] Notification display on parent's device

[1096] The parent's device receives the analysis results from the cloud server and has a notification means to display them. This notification means allows the parent to immediately understand the baby's condition. If the analysis results indicate an emergency, an alarm can be sent. Furthermore, the priority and content of the notification are adjusted according to the parent's emotional state as recognized by the emotion engine.

[1097] The parent device operates in the following steps:

[1098] 1. The device receives the analysis results from the cloud server.

[1099] 2. Display the received analysis results on the application's user interface (UI).

[1100] 3. If necessary, an alarm will be sent to notify parents of an emergency.

[1101] 4. Display advice and suggestions based on the results of emotion analysis by the emotion engine.

[1102] Specific examples

[1103] For example, if a baby is hungry and crying and the user is tired, the system will act as follows:

[1104] 1. Baby monitor: Detects the baby's crying and displeased face and sends the data to a cloud server.

[1105] 2. Cloud server: Analyzes the received data and determines whether the baby is hungry. As a result, it generates a message saying "The baby is hungry." The emotion engine then determines the user's level of fatigue. Based on this, it sends a notification indicating urgency to the parent's device.

[1106] 3. Parent's device: The received analysis result "Baby is hungry" is displayed on the UI, and if the parent is fatigued, a notification with higher priority than usual is sent. Appropriate advice and suggestions are also provided.

[1107] In this way, parents can quickly take appropriate measures based on their baby's condition and their own emotional state, reducing the burden of childcare and providing support to ensure the safety and health of both parents and their baby.

[1108] The processing flow will be explained below.

[1109] Program processing steps

[1110] How does a baby monitor work?

[1111] Step 1:

[1112] Baby monitors use cameras and microphones to monitor your baby's face, movements, and voice in real time, collecting data continuously.

[1113] Step 2:

[1114] Collected monitoring data is temporarily stored on the device, where it is properly formatted and prepared for later transmission to a cloud server.

[1115] Step 3:

[1116] The device sends the monitoring data to the cloud server using secure HTTP or WebSocket protocols to ensure minimal latency.

[1117] Data analysis on a cloud server

[1118] Step 4:

[1119] The server receives the data sent from the baby monitor and temporarily stores it in a database.

[1120] Step 5:

[1121] A generative AI model analyzes surveillance data, analyzing voice, facial expression, and motion separately, and then combines the results.

[1122] Step 6:

[1123] The server uses an analytical engine to determine the baby's needs (hunger, discomfort, fatigue, etc.) and potential dangers (lying face down, objects falling, etc.).

[1124] Step 7:

[1125] The server receives the user's emotional data, and the emotion engine analyzes the user's emotional state, including the user's voice, facial expressions, and operation logs.

[1126] Step 8:

[1127] The system integrates analysis of the baby's needs and dangers with the user's emotional state to tailor notifications, for example, increasing the priority of notifications if the user is very tired.

[1128] Step 9:

[1129] The server sends the consolidated analysis results to the parent's device, which includes the baby's condition, recommended measures, and necessary alert information.

[1130] Notification display on parent's device

[1131] Step 10:

[1132] The device receives the analysis results sent from the cloud server, and the received data is loaded into the application, starting the process.

[1133] Step 11:

[1134] The received analysis results are displayed in the app's user interface (UI), which displays the results in an intuitive and easy-to-understand format, allowing users to understand the situation at a glance.

[1135] Step 12:

[1136] If necessary, an alarm notification is issued to notify the user of an emergency. Notification methods include sound, vibration, and pop-up notification.

[1137] Step 13:

[1138] The server provides appropriate advice and suggestions based on the user's emotional state, for example, advising the user to take a rest if they are tired.

[1139] Specific examples

[1140] For example, if your baby is hungry and crying and you are very tired:

[1141] Step 1:

[1142] Baby monitors keep an eye on your baby's cries and irritability.

[1143] Step 2:

[1144] The monitoring data is temporarily stored and ready to be sent to a cloud server later.

[1145] Step 3:

[1146] The device sends the monitoring data to a cloud server.

[1147] Step 4:

[1148] The server receives the monitoring data and stores it in a database.

[1149] Step 5:

[1150] A generative AI model analyzes the monitoring data, detecting signs of hunger from crying patterns and facial expressions.

[1151] Step 6:

[1152] The server determines that the baby is hungry.

[1153] Step 7:

[1154] A server receives the user's emotion data, and an emotion engine determines the user's fatigue.

[1155] Step 8:

[1156] The server combines the baby's hunger determination result with the user's fatigue state and increases the priority of the notification.

[1157] Step 9:

[1158] The server then sends the combined analysis results to the parent's device, which includes a message saying "Your baby is hungry" along with advice to get some rest.

[1159] Step 10:

[1160] The terminal receives the analysis results.

[1161] Step 11:

[1162] The analysis results are displayed on the app's UI as "Baby is hungry."

[1163] Step 12:

[1164] An alarm will sound and say "Baby is hungry" to alert the parent, and in this case, a vibration will also be added since the notification is set to high priority.

[1165] Step 13:

[1166] Based on the user's emotional state, "advice to take a rest" is displayed.

[1167] Example 2

[1168] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1169] Conventional baby monitoring systems only monitor a baby's face, movements, and voice, and only have the ability to identify the baby's needs or dangerous situations, which limits the ability of parents to respond appropriately to their baby's situation.In addition, because notifications are given without taking into account the parent's emotional state, there is the problem that they cannot provide appropriate support to parents who are tired or stressed.

[1170] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1171] In this invention, the server includes device means for monitoring the baby's face, movements, and voice in real time, communication means for transmitting the monitored data to a cloud server, generation AI model means for analyzing the received data in the cloud server and determining the baby's needs and dangers, emotion engine means for analyzing the user's emotion data and integrating it with the analysis results of the baby, and notification means for notifying the parent's device of the analysis results. This makes it possible to provide optimal notifications based on the baby's condition and the parent's emotional state, reducing the burden of childcare and ensuring the safety and health of the parent and baby.

[1172] "Device means" refers to a device that includes hardware and software for monitoring the baby's face, movements, and voice in real time.

[1173] The "communication means" is a network communication function for transmitting monitoring data acquired by the device means to the cloud server.

[1174] The "generative AI model means" refers to artificial intelligence algorithms and software that run on a cloud server and analyze the received data to determine the baby's needs and risks.

[1175] The "emotion engine means" is software and algorithms for analyzing the user's emotion data and integrating it with the baby's analysis results.

[1176] The "notification means" refers to a communication function and software for transmitting the analysis results from the cloud server to the parent's terminal and making the notification.

[1177] The "analysis results" are the results of the judgments of the baby's needs and dangerous situations, and the user's emotional state, obtained by the generative AI model means and the emotion engine means.

[1178] "Parent Device" refers to a mobile device or computer used by a parent to receive and display the analysis results.

[1179] This system monitors a baby's face, movements, and voice, as well as the user's emotions, in real time, analyzes the data on a cloud server, and notifies the parent of the results of the analysis. This not only enables parents to immediately grasp their baby's needs and dangerous situations, but also allows them to receive notifications based on their emotional state using an emotion engine, further reducing the burden of childcare.

[1180] Hardware and Software Configuration

[1181] Baby monitor

[1182] A baby monitor is a device that captures a baby's face, movements, and voice in real time. It is equipped with a high-resolution camera and a highly sensitive microphone, which allows it to capture facial expressions in detail and pick up even the nuances of a baby's voice.

[1183] Specifically, the baby monitor works as follows.

[1184] The camera detects the baby's face and movements.

[1185] The microphone picks up the baby's voice.

[1186] It is equipped with a Wi-Fi module to transmit this data to a cloud server in real time.

[1187] Cloud Server

[1188] The cloud server receives the data sent from the baby monitor and analyzes it using generative AI models, which include facial recognition and voice recognition algorithms.

[1189] The server operates as follows:

[1190] Receive data from the baby monitor.

[1191] A generative AI model is used to analyze the data and determine the baby's needs and risks.

[1192] An emotion engine is used to analyze the user's emotion data and determine the user's emotional state.

[1193] The baby's analysis results are integrated with the user's emotional state to tailor the notification content.

[1194] The adjusted notification content is sent to the parent's device.

[1195] Parent's device

[1196] An application is installed on the parent's device to receive and display notifications from the server. This application has a user interface (UI) that displays notifications in real time and has the ability to issue alerts as needed. It also has the ability to display appropriate advice and suggestions based on the analysis results of the emotion engine.

[1197] The terminal operates as follows:

[1198] Receives notification content sent from the server.

[1199] The received analysis results are displayed on the UI.

[1200] In the event of an emergency, an alert will be issued to notify parents of the emergency.

[1201] Display advice and suggestions based on the results of the sentiment engine.

[1202] Specific examples

[1203] For example, if a baby is hungry and crying, or if the user feels tired, the system will operate as follows:

[1204] 1. Baby monitor: The camera captures the baby's crying face, the microphone records the crying sound, and sends the data to a cloud server.

[1205] 2. Cloud server: The generative AI model analyzes the received data and determines that the baby is hungry. It generates a message saying "Baby is hungry," and the emotion engine determines the user's fatigue level. Based on this, it sends a notification indicating urgency to the parent's device.

[1206] 3. Parent's device: The received analysis result "Baby is hungry" is displayed on the UI. If the parent is fatigued, a notification with higher priority than usual will be sent, and appropriate advice and suggestions will be provided.

[1207] Prompt Sentence Examples

[1208] An example prompt for a generative AI model might be:

[1209] "Analyze your baby's cries and facial expressions to determine if he or she is hungry."

[1210] "Analyze the user's current emotional state with an emotion engine and adjust the priority of notifications based on that."

[1211] This system allows parents to comprehensively understand their baby's condition and their own emotional state, enabling them to quickly take the most appropriate measures, thereby reducing the burden of childcare and helping to ensure the safety and health of both parents and their babies.

[1212] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1213] Step 1:

[1214] Baby monitor data acquisition

[1215] A baby monitor captures the baby's face, movements, and voice in real time. The camera detects the baby's face and movements, and the microphone picks up the baby's voice. This action produces image and audio data. The inputs are the baby's face, movements, and voice, and the output is a camera image and an audio file.

[1216] Specific behavior:

[1217] The camera captures the baby's crying face.

[1218] The microphone records the baby's cry.

[1219] Step 2:

[1220] Sending data to the cloud server

[1221] The baby monitor sends the acquired data to a cloud server. The baby monitor's communication module uploads the camera images and audio files to the cloud server via Wi-Fi. The input is the camera images and audio files, and the output is the data uploaded to the cloud server.

[1222] Specific behavior:

[1223] The baby monitor transmits camera images and audio data to a cloud server in real time.

[1224] Step 3:

[1225] Data analysis on the server

[1226] The server receives data sent from the baby monitor and analyzes it using a generative AI model. It analyzes the baby's facial expressions, movements, and voice data to determine the baby's condition (e.g., hungry, sleepy, dangerous, etc.). The input is the monitoring data uploaded to the cloud server, and the output is the analysis results.

[1227] Specific behavior:

[1228] The server uses a facial recognition algorithm to analyze the baby's crying face.

[1229] A voice recognition algorithm is used to analyze the characteristics of the cry and determine whether the baby is hungry.

[1230] Step 4:

[1231] User sentiment analysis

[1232] The server analyzes the user's emotional data using an emotion engine. Based on past smartphone operation data and vital data (e.g., heart rate), the server determines the user's emotional state (e.g., fatigue, stress, relaxation). The input is the emotional data, and the output is the emotion analysis results.

[1233] Specific behavior:

[1234] The server checks the user's heart rate data and determines that the user is in a "fatigue state" using an emotion engine.

[1235] Step 5:

[1236] Consolidating and aligning notifications

[1237] The server integrates the baby's analysis results with the user's emotional state and adjusts the notification content. Based on the output of the emotion engine, it generates notification text and determines the priority of the notification. The input is the baby's analysis results and the emotion analysis results, and the output is the adjusted notification content.

[1238] Specific behavior:

[1239] Generate a notification message saying, "The baby is crying because he is hungry. He seems tired, so please take action as soon as possible."

[1240] Step 6:

[1241] Notifications on parent's device

[1242] The server sends the adjusted notification content to the parent device. The server's communication module pushes the notification content to the parent device. The input is the adjusted notification content, and the output is the notification sent to the parent device.

[1243] Specific behavior:

[1244] The server pushes the notification to the parent's smartphone.

[1245] Step 7:

[1246] Notifications and alerts on parent's device

[1247] The parent device receives notifications from the server and displays the content. The app displays the notifications on the UI and issues emergency alerts if necessary. It also displays appropriate advice and suggestions based on the analysis results of the emotion engine. The input is the notification content from the server, and the output is the notification content displayed on the UI.

[1248] Specific behavior:

[1249] A message appears on the parent's smartphone saying "Baby is hungry."

[1250] The app displays advice such as, "Parents are tired, so we suggest an easy way to make milk."

[1251] (Application example 2)

[1252] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1253] There is a need for a system that can monitor the stress and emotional state of workers in factories, as well as the safety of the work environment, in real time and take appropriate measures. Conventional systems have difficulty accurately grasping fluctuations in workers' health and emotions, and lack the functionality to immediately detect dangers in the work environment, making it difficult to ensure worker safety and efficiency. Furthermore, measures based on workers' stress and emotional state are sometimes delayed, which can have a negative impact on work efficiency and safety.

[1254] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes device means for monitoring the faces, movements, and voices of the baby and worker in real time, communication means for transmitting the monitored data to a cloud server, generation AI model means for analyzing the received data in the cloud server and determining the needs and dangers of the baby and worker, and notification means for notifying the parent and manager terminals of the analysis results. This makes it possible to grasp the worker's stress and emotional state, as well as the state of the work environment, in real time and take appropriate measures promptly.

[1255] "Device means" refers to equipment or devices for capturing and monitoring the face, movements, and voice of workers in real time.

[1256] "Communication means" refers to the network technology and communication protocol used to transmit the monitoring data acquired by the device means to the cloud server.

[1257] "Cloud server" refers to a remote server that stores and processes data over the Internet.

[1258] "Generative AI model means" refers to an artificial intelligence algorithm that runs on a cloud server and analyzes the received monitoring data to determine the needs and risks of workers and babies.

[1259] "Notification means" refers to the functions and systems that notify parents and administrators of the results of analysis performed on the cloud server via their devices.

[1260] "Stress" refers to the mental or physical tension that a worker feels due to the work environment or work content.

[1261] "Emotional state" refers to the psychological state that indicates the worker's current feelings and sensations.

[1262] "Work environment" refers to the place where workers actually perform their work and its physical conditions (e.g., noise, temperature, humidity, etc.).

[1263] "Manager" refers to a person who is responsible for supervising workers in a factory or work site and managing the safety of the work environment and work efficiency.

[1264] This invention is a system that monitors the stress and emotional state of workers in a factory, as well as the safety of the working environment, in real time, and takes appropriate measures promptly. Specifically, it consists of the following parts.

[1265] Hardware Configuration

[1266] 1. Device means:

[1267] Camera: Captures the worker's facial expressions and movements in real time.

[1268] Microphone: Captures the worker's voice in real time.

[1269] Wearable devices: Monitor workers' biometric data such as heart rate and body temperature.

[1270] Environmental sensors: Collect data on the working environment, such as temperature, humidity, and noise levels.

[1271] 2. Means of communication:

[1272] Network technologies such as Wi-Fi and LTE are used to transmit data acquired by the device means to a cloud server.

[1273] Software Configuration

[1274] 1. Cloud Server:

[1275] The cloud server receives the data transmitted from the device means and analyzes it.

[1276] The generative AI model used (e.g. TensorFlow) analyzes the incoming data in real time to determine the worker's stress and emotional state.

[1277] Based on the analysis results, the safety of the working environment will also be evaluated.

[1278] 2. Generative AI model means:

[1279] Facial expression analysis, voice analysis, and biometric data analysis are performed to determine the worker's stress and emotional state.

[1280] The generative AI models used include TensorFlow and Google Cloud AI.

[1281] Based on the analysis, appropriate measures and advice are generated.

[1282] 3. Means of notification:

[1283] The results analyzed on the cloud server are notified to parents or administrators via smartphones or robot terminals.

[1284] The notification includes advice to encourage workers to take breaks and suggestions for improving the work environment.

[1285] Specific examples

[1286] 1. If worker A is stressed:

[1287] Camera and microphone: Worker A has a stern facial expression and an irritated voice.

[1288] Analysis result: It was determined that worker A's stress level was high.

[1289] Notification: A message is sent to the manager's smartphone saying, "Worker A's stress level is high. Please encourage him to take a break."

[1290] 2. If the working environment has high noise levels:

[1291] Environmental sensor: Noise levels exceed acceptable limits.

[1292] Analysis result: The working environment was determined to be dangerous.

[1293] Notification: Robotic terminals in factories warn workers, "Noise levels are high. Please use earplugs."

[1294] Prompt Sentence Examples

[1295] Facial Expression Analysis: "Analyze the facial expressions of the workers in this image to determine their stress levels."

[1296] Voice Analysis: "Infer the emotional state of the worker in this audio file from their tone of voice."

[1297] Environmental Data Analysis: "Analyze this environmental sensor data to assess safety."

[1298] In this way, we ensure the safety and health of workers in the factory and provide support to improve work efficiency.

[1299] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1300] Step 1:

[1301] The user installs a baby monitor, and the device captures the worker's face, movements, voice, and work environment in real time. The input is monitoring data (facial expressions, voice, biometric data, and environmental sensor data). The output is the acquisition of these monitoring data.

[1302] Step 2:

[1303] The monitoring data acquired by the device means is sent to the cloud server using the communication means. The input is the monitoring data acquired in step 1, and the data is sent. The output is the monitoring data sent to the cloud server.

[1304] Step 3:

[1305] The cloud server receives the monitoring data and uses a generative AI model to analyze it. The input is the transmitted monitoring data, and the cloud server performs data analysis. Specifically, it performs facial expression analysis, vocal emotion analysis, biometric data analysis, and environmental data analysis. The output is the analysis results (the worker's stress level, emotional state, and safety assessment of the work environment).

[1306] Step 4:

[1307] Based on the analysis results, the cloud server notifies the parent or administrator's device. The input is the analysis result from step 3, which generates a notification message. The output is the notification message sent to the parent or administrator's device.

[1308] Step 5:

[1309] The parent or administrator's device receives the notification and displays it on the UI. The input is the notification message sent from the cloud server. The output is the notification message displayed on the device screen (e.g., "Worker A's stress level is high. Please encourage him to take a break.").

[1310] Step 6:

[1311] If necessary, an alarm notification will be sent to notify parents or administrators of an emergency. The input is a notification message that includes the urgency. The output is an alarm (audio, vibration, screen flash, etc.) to the parent or administrator.

[1312] Step 7:

[1313] Parents or administrators can take appropriate measures based on the notification. The inputs are notification messages and alarms. The outputs are instructions and corrective actions for workers (e.g., taking a break, using earplugs).

[1314] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1315] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1316] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1317] [Fourth embodiment]

[1318] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1319] 7, a 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.

[1320] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1321] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1322] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1323] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1324] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1325] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1326] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1327] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1328] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1329] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1330] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1331] Overall system overview

[1332] This system monitors a baby's face, movements, and voice in real time, analyzes the data on a cloud server, and notifies the parents of the results. This system allows parents to instantly understand their baby's needs and dangerous situations, reducing the burden of childcare.

[1333] How does a baby monitor work?

[1334] A baby monitor is a device that captures a baby's face, movements, and voice in real time. This device is equipped with a camera and microphone for continuous monitoring. It also has a communication means to send the monitoring data to a cloud server.

[1335] The baby monitor works in the following steps:

[1336] 1. The device captures your baby's monitoring data.

[1337] 2. The acquired data is sent to the cloud server.

[1338] Data analysis on a cloud server

[1339] The cloud server receives monitoring data sent from the baby monitor and has a generative AI model means for analyzing it. The generative AI model on the cloud server analyzes the baby's facial expressions, movements, and voice data to determine the baby's needs and dangers.

[1340] The cloud server works in the following steps:

[1341] 1. The server receives monitoring data from the baby monitor.

[1342] 2. Analyze the data using a generative AI model to determine the baby's needs and risks.

[1343] 3. Send the analysis results to the parent's device for notification.

[1344] Notification display on parent's device

[1345] The parent's device receives the analysis results from the cloud server and has a notification function that displays them. This notification function allows the parent to immediately understand the baby's condition. If the analysis results indicate an emergency, an alarm can also be sent.

[1346] The parent device operates in the following steps:

[1347] 1. The device receives the analysis results from the cloud server.

[1348] 2. Display the received analysis results on the app's UI.

[1349] 3. If necessary, an alarm will be sent to notify parents of an emergency.

[1350] Specific examples

[1351] For example, if a baby is crying because it is hungry, the system will:

[1352] 1. Baby monitor: Detects the baby's crying and displeased face and sends the data to a cloud server.

[1353] 2. Cloud server: Analyzes the received data and determines whether the baby is hungry. As a result, it generates a message saying "The baby is hungry" and sends it to the parent's device.

[1354] 3. Parent device: The received analysis result "Baby is hungry" is displayed on the UI and an alarm is sounded if necessary.

[1355] In this way, parents can immediately understand the condition of their baby and take appropriate measures, reducing the burden of childcare and supporting the provision of prompt and appropriate care.

[1356] The processing flow will be explained below.

[1357] Program processing steps

[1358] How does a baby monitor work?

[1359] Step 1:

[1360] Baby monitors use cameras and microphones to monitor your baby's face, movements, and voice in real time, collecting data continuously.

[1361] Step 2:

[1362] The monitoring data is temporarily stored on the device and prepared for transmission to a cloud server in batches at regular intervals, where the data is properly formatted and security checked.

[1363] Step 3:

[1364] Data is sent to a cloud server using secure HTTP or WebSocket protocols to ensure minimal latency.

[1365] Data analysis on a cloud server

[1366] Step 4:

[1367] The server receives the data sent from the baby monitor, which is first temporarily stored in a database and then passed to the analytics engine.

[1368] Step 5:

[1369] A generative AI model in the cloud server analyzes the data, specifically voice analysis, facial expression recognition, and movement analysis, and then combines the results.

[1370] Step 6:

[1371] The analytics engine determines the baby's needs (hunger, sleepiness, discomfort, etc.) and potential dangers (face down, overturned objects, etc.) not only based on real-time data but also by comparing it with past data.

[1372] Step 7:

[1373] Analysis results are generated and prepared for transmission to the parent's device, including the baby's status, recommended actions, and any necessary alerts.

[1374] Notification display on parent's device

[1375] Step 8:

[1376] The device receives the analysis results from the cloud server. The device retrieves the data through an internet connection and stores it locally.

[1377] Step 9:

[1378] The received analysis results are displayed on the application's user interface (UI), which displays the results in a format that is intuitively understandable to the user, allowing them to grasp the situation at a glance.

[1379] Step 10:

[1380] If necessary, alarm notifications will be sent to alert parents to emergencies. The alarms can be sent in multiple forms, including sound, vibration, and pop-up notifications, so parents can get their immediate attention.

[1381] Specific examples

[1382] For example, if your baby is crying because he is hungry:

[1383] Step 1:

[1384] Baby monitors keep an eye on your baby's cries and irritability.

[1385] Step 2:

[1386] The monitoring data is temporarily stored and ready to be sent to the cloud server.

[1387] Step 3:

[1388] The data is sent to a cloud server.

[1389] Step 4:

[1390] The server receives the monitoring data and stores it in a database.

[1391] Step 5:

[1392] A generative AI model analyzes the monitoring data, detecting signs of hunger from crying patterns and facial expressions.

[1393] Step 6:

[1394] The analytical engine determines that the baby is hungry.

[1395] Step 7:

[1396] The analysis result "Baby is hungry" is generated and ready to be sent to the parent's device.

[1397] Step 8:

[1398] The terminal receives the analysis results.

[1399] Step 9:

[1400] The analysis results are displayed on the app's UI as "Baby is hungry."

[1401] Step 10:

[1402] An alarm will sound to alert the parents.

[1403] Example 1

[1404] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1405] In modern childcare, parents are required to constantly monitor their baby's needs and condition and respond promptly. However, accurately understanding a baby's condition is extremely difficult, especially at night or when the parent is in a remote location. In addition, the burden of childcare can cause significant stress for parents. Furthermore, if a baby is in a dangerous situation, parents are required to immediately recognize and respond, but current methods make this difficult. The present invention aims to provide a system that allows parents to quickly understand and respond to their baby's needs and dangerous situations.

[1406] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1407] In this invention, the server includes a sensor means for monitoring the baby's face, movements, and voice in real time, a communication means for transmitting the monitored data to a cloud computing platform, and a machine learning model means for analyzing the received data in the cloud computing platform and determining the baby's needs and dangers. This makes it possible to grasp the baby's condition in real time and send warning notifications to the parent's device as needed. This allows the parent to quickly check the baby's needs and dangers and take appropriate measures, reducing the burden of childcare.

[1408] The "sensor means" is a device for monitoring the baby's face, movements, and voice in real time and acquiring data.

[1409] The "communication means" is a means for transmitting the monitoring data acquired by the sensor means to the cloud computing infrastructure.

[1410] A "cloud computing platform" is a remote server that receives and analyzes monitoring data.

[1411] "Machine learning model means" is a general term for algorithms and programs that analyze monitoring data on a cloud computing platform and determine the baby's needs and risks.

[1412] The "notification means" is a means for notifying the parent's terminal of the results of the analysis performed by the cloud computing platform.

[1413] The "parent's device" is an electronic device that receives notifications from the cloud computing platform and displays the analysis results.

[1414] The present invention provides a system that monitors a baby's face, movements, and voice in real time, analyzes the data using a cloud computing platform, and notifies the parent or guardian of the results of the analysis. Specific embodiments will be described below.

[1415] Baby monitor

[1416] Baby monitors are equipped with sensors for monitoring the baby's face, movements, and voice. These sensors include, for example, a high-resolution camera and a highly sensitive microphone, allowing for highly accurate capture of the baby's facial expressions, movements, and voice data such as crying.

[1417] communication means

[1418] Baby monitors are equipped with a communication means to transmit the acquired monitoring data to a cloud computing platform using wireless communication technologies such as Wi-Fi or 4G / 5G, which allows data to be transmitted in real time.

[1419] Cloud Computing Platform

[1420] The server receives monitoring data sent from the baby monitor using a cloud computing platform. The received data is analyzed using a generative AI model. Specific analysis uses machine learning frameworks such as TensorFlow and PyTorch. This allows for highly accurate facial expression recognition and voice analysis.

[1421] Analysis means

[1422] The generative AI model, installed on a cloud computing platform, analyzes the received facial expression, movement, and voice data to determine the baby's needs and dangers. For example, if the baby is crying, the frequency distribution of the crying is analyzed to identify the cause. Facial expression recognition technology is used to determine whether the baby is smiling.

[1423] Notification means

[1424] The server then sends a notification to the parent's device based on the analysis results. The notification is sent using a cloud messaging service such as Firebase Cloud Messaging. For example, a text message such as "Your baby is hungry" is generated and sent to the parent's smartphone or tablet.

[1425] Parent's device

[1426] The parent's device receives the notification message and displays it in a dedicated application. The application is developed in Swift or Java, for example, and has a built-in push notification function. At the same time as displaying the analysis results, an alarm sound or vibration alert is issued depending on the urgency of the situation.

[1427] Examples and prompts

[1428] For example, if your baby is crying because he is hungry:

[1429] 1. A baby monitor detects a baby's crying and unhappy facial expressions and sends the data to a cloud computing platform.

[1430] 2. The cloud computing platform analyzes the data and determines whether the baby is hungry. As a result, a message saying "The baby is hungry" is generated and sent to the parent's device.

[1431] 3. The parent's device will display the received message on the app's UI and may sound an alarm.

[1432] Example prompt sentence:

[1433] "Detect when your baby is hungry and crying and send a real-time notification to the parent's device."

[1434] This allows parents to immediately understand the baby's condition and take prompt and appropriate action. The present invention reduces the burden of childcare as a whole and supports the provision of appropriate care for babies.

[1435] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1436] Step 1:

[1437] Baby monitors monitor a baby's face, movements, and voice in real time and collect data. A camera captures the baby's facial expressions, and a microphone records audio data such as crying and laughter. The collected data consists of video and audio data, which are output as streams in JPEG or MP4 format video files and WAV format audio files.

[1438] Step 2:

[1439] The monitoring data acquired by the baby monitor is sent to a cloud computing platform. The data is sent via wireless communication (Wi-Fi or 4G / 5G). For example, the data is sent in encrypted form using the HTTPS protocol. This transmission process sends the input data (video and audio files) to the cloud computing platform, which then becomes the input for the next step.

[1440] Step 3:

[1441] The server receives the monitoring data sent from the baby monitor. The cloud computing platform (server) temporarily stores the received video and audio files in preparation for the next analysis step. In this step, data reception is the first processing step, and it is confirmed that the received data has been saved correctly.

[1442] Step 4:

[1443] The server analyzes the received data using the generative AI model. On the cloud computing platform, TensorFlow is used to run the facial expression recognition model, and PyTorch is used to run the audio analysis model. The input data consists of video and audio files, and each is analyzed. Specifically, the facial expression recognition model analyzes the video data and detects changes in the baby's facial expression. The audio analysis model also analyzes the audio data, analyzing the frequency distribution and amplitude of the crying. From this analysis, a judgment result such as "the baby is crying because he is hungry" is output.

[1444] Step 5:

[1445] The server notifies the parent's device of the analysis results. The analysis results obtained by the generative AI model are converted into a text message. For example, a message such as "The baby is hungry" is generated, and a service such as Firebase Cloud Messaging is used to send it to the parent's device. The output of this step is a text message, which is then sent to the parent's device.

[1446] Step 6:

[1447] The device receives the analysis results from the cloud server. The parent's device (smartphone, tablet, etc.) receives the text message sent from the cloud server. A dedicated application runs in the background and captures the received message. Through this process, the device application obtains input data (analysis result message) for the next display step.

[1448] Step 7:

[1449] The device displays the received analysis results and sounds an alarm if necessary. The dedicated application displays the received analysis results on the UI. For example, a message saying "Baby is hungry" is displayed on the app screen. At the same time, an alarm sound or vibration is generated depending on the urgency, immediately informing the parent of the situation. The output of this step is a visual display and an audible notification.

[1450] Step 8:

[1451] The user (guardian) checks the analysis results and takes appropriate action. The user checks the analysis results on their smartphone screen, and if it determines that the baby is hungry, they take action such as preparing milk, or if the baby is crying, they pick up the baby and comfort him or her. This allows the user to respond to the baby's needs immediately.

[1452] (Application example 1)

[1453] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1454] While there are systems that monitor a baby's safety and needs in real time, there are few systems that comprehensively monitor the safety of the entire home and immediately detect and alert to any abnormalities. Furthermore, technology for integrated management of the baby's condition and abnormalities in the home to ensure safety is still in its infancy. Given this background, there is a need for a system that can centrally monitor the safety of not only the baby but the entire home and respond immediately.

[1455] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1456] In this invention, the server includes a device means for monitoring the baby's face, movements, and voice in real time, a communication means for transmitting the monitored data to a cloud server, a generating AI model means for analyzing the received data in the cloud server and determining the baby's needs and dangers, a notification means for notifying the parent's terminal of the analysis results, a means for monitoring the safety of the entire household to detect suspicious movements and sounds, and an alarm means for immediately issuing a warning notification when a dangerous situation is detected. This makes it possible to comprehensively monitor not only the baby's condition but also the overall safety of the household, and immediately notify the parent's terminal when an abnormality is detected, and even automatically contact the police or fire department if necessary.

[1457] "Device means" refers to a device for monitoring the baby's face, movements, and voice in real time.

[1458] "Communication means" refers to the technology or equipment used to transmit monitored data to the cloud server.

[1459] The "generative AI model means" is an artificial intelligence model that is used to analyze received data on a cloud server and determine the baby's needs and risks.

[1460] "Notification means" refers to a system or method for transmitting the analysis results from the cloud server to the parent's device.

[1461] "Home safety monitoring measures" are devices and technologies used to monitor suspicious activity and sounds within the home in real time and detect abnormalities.

[1462] An "alarm means" is a system or device that immediately issues a warning to the parent's terminal and to any necessary emergency services when a dangerous situation is detected.

[1463] This invention is a system for comprehensively monitoring the safety of babies and the entire household. The system includes a device means, a communication means, a generating AI model means, a notification means, a monitoring means for suspicious movements and sounds, and an alarm means.

[1464] Device Means

[1465] The device means is a device equipped with a camera and a microphone for monitoring the baby's face, movements, and voice in real time. This device constantly acquires data on the baby's behavior, facial expressions, and voice, and uses this data for analysis.

[1466] communication means

[1467] The communication means is a technology for transmitting acquired monitoring data to a cloud server. Common communication technologies such as wireless LAN and mobile networks are used for communication. This allows data acquired by the device means to be transmitted to the cloud server in near real time.

[1468] Generative AI model means

[1469] The cloud server is equipped with a generative AI model that analyzes the received data. The generative AI model uses the monitoring data to determine the baby's condition (e.g., hunger, discomfort, fatigue, etc.) and also detects suspicious movements and abnormal sounds throughout the household. Machine learning algorithms and large datasets are used to train the model.

[1470] Notification means

[1471] The results of the analysis performed by the cloud server are then sent to the parent's device. Notification methods are used to quickly send the analysis results to common devices such as smartphones and PCs. Notifications can be sent in a variety of forms, including pop-up alerts, audio alerts, and message notifications.

[1472] Monitoring for suspicious activity and sounds

[1473] To ensure the safety of the entire home, a means of monitoring suspicious movements and sounds has been added. This means that multiple cameras and microphones installed in each room monitor the movements of the entire house and detect any abnormalities.

[1474] Alarm means

[1475] The alarm system is a system that immediately issues a warning notification when a dangerous situation is detected. This system issues an alert to the parent's device and automatically contacts the police or fire department if necessary. Specifically, it uses Text-to-Speech (TTS) technology to generate and play audio alerts. This processing is performed using gTTS and mpg321.

[1476] Specific examples

[1477] For example, if a baby starts crying, the system detects the crying and facial expression in real time and sends them to a cloud server. The cloud server then uses a generative AI model to determine if the baby is hungry and sends a message to the parent's device saying, "The baby is hungry." Furthermore, if a suspicious person breaks into the home while the parents are away, the camera and microphone detect the abnormality and immediately send a warning notification to the parents via the cloud server, while automatically contacting the police if necessary.

[1478] Example prompts for generative AI models

[1479] "A security system that analyzes data obtained from cameras and microphones installed in a room, detects suspicious movements and sounds, and notifies you."

[1480] In this way, the present invention makes it possible to monitor the safety of the baby and the entire household in a unified manner and to respond quickly when necessary.

[1481] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1482] Step 1:

[1483] The device captures your baby's face, movements, and voice in real time.

[1484] Input: Video data from the camera and audio data from the microphone.

[1485] Data processing: Video data is captured frame by frame, and audio data is sampled periodically. The video data is passed through a facial recognition algorithm to extract the baby's facial features, and the audio data is appropriately filtered to remove noise.

[1486] Output: Enhanced face, movement, and voice data.

[1487] Step 2:

[1488] The acquired data is sent to a cloud server.

[1489] Input: Face, movement, and voice data processed in step 1.

[1490] Data processing: converting data into the appropriate format, encrypting it and sending it.

[1491] Output: Monitoring data sent to the cloud server.

[1492] Step 3:

[1493] The cloud server receives the monitoring data and analyzes it using a generative AI model.

[1494] Input: Monitoring data sent in step 2.

[1495] Data processing: Transforming the data into an input format suitable for the model.

[1496] Data computation: Generative AI models analyze facial expressions, movements, and voice to determine the baby's needs and abnormalities in the home.

[1497] Output: Analysis result (e.g. "Baby is hungry" or "Suspicious movement detected").

[1498] Step 4:

[1499] The parent's device is notified based on the analysis results.

[1500] Input: Analysis results obtained in step 3.

[1501] Data processing: Converting the analysis results into a user-friendly format, for example, preparing them as text or voice messages.

[1502] Data calculation: Determines the content of the notification and sends it in the appropriate format.

[1503] Output: A notification displayed on the parent's device (e.g., a pop-up notification or an alarm sound on a smartphone).

[1504] Step 5:

[1505] If necessary, the police and fire department will be automatically contacted.

[1506] Input: Analysis results of the emergency situation notified in step 4.

[1507] Data processing: Formatting information necessary for emergency calls (e.g., location information, details of the abnormality, etc.).

[1508] Data Calculation: Contact the appropriate emergency services through the emergency communication system.

[1509] Output: Automatic notification to emergency services (e.g. police to report suspicious person, fire to fire).

[1510] Through these steps, the entire system works together to monitor the safety of your baby and the entire household in real time.

[1511] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1512] Overall system overview

[1513] This system monitors a baby's face, movements, and voice, as well as the user's emotions, in real time, analyzes the data on a cloud server, and notifies the parent of the results. This system not only enables parents to instantly grasp their baby's needs and dangerous situations, but also uses an emotion engine to receive notifications based on the parent's emotional state, further reducing the burden of childcare.

[1514] How does a baby monitor work?

[1515] A baby monitor is a device that captures a baby's face, movements, and voice in real time. This device is equipped with a camera and microphone for continuous monitoring. It also has a communication means to send the monitoring data to a cloud server.

[1516] The baby monitor works in the following steps:

[1517] 1. The device captures your baby's monitoring data.

[1518] 2. The acquired data is sent to the cloud server.

[1519] Data analysis on a cloud server

[1520] The cloud server receives monitoring data sent from the baby monitor and has a generative AI model means for analyzing it. The generative AI model on the cloud server analyzes the baby's facial expressions, movements, and voice data to determine the baby's needs and dangers. It also analyzes the user's emotional data using an emotion engine and integrates this data with the analysis results of the baby.

[1521] The cloud server works in the following steps:

[1522] 1. The server receives monitoring data from the baby monitor.

[1523] 2. Analyze the data using a generative AI model to determine the baby's needs and risks.

[1524] 3. The emotion engine analyzes the user's emotion data and determines the user's emotional state.

[1525] 4. Integrate the baby's analysis results with the user's emotional state to adjust the notification content.

[1526] 5. Send the analysis results to the parent's device for notification.

[1527] Notification display on parent's device

[1528] The parent's device receives the analysis results from the cloud server and has a notification means to display them. This notification means allows the parent to immediately understand the baby's condition. If the analysis results indicate an emergency, an alarm can be sent. Furthermore, the priority and content of the notification are adjusted according to the parent's emotional state as recognized by the emotion engine.

[1529] The parent device operates in the following steps:

[1530] 1. The device receives the analysis results from the cloud server.

[1531] 2. Display the received analysis results on the application's user interface (UI).

[1532] 3. If necessary, an alarm will be sent to notify parents of an emergency.

[1533] 4. Display advice and suggestions based on the results of emotion analysis by the emotion engine.

[1534] Specific examples

[1535] For example, if a baby is hungry and crying and the user is tired, the system will act as follows:

[1536] 1. Baby monitor: Detects the baby's crying and displeased face and sends the data to a cloud server.

[1537] 2. Cloud server: Analyzes the received data and determines whether the baby is hungry. As a result, it generates a message saying "The baby is hungry." The emotion engine then determines the user's level of fatigue. Based on this, it sends a notification indicating urgency to the parent's device.

[1538] 3. Parent's device: The received analysis result "Baby is hungry" is displayed on the UI, and if the parent is fatigued, a notification with higher priority than usual is sent. Appropriate advice and suggestions are also provided.

[1539] In this way, parents can quickly take appropriate measures based on their baby's condition and their own emotional state, reducing the burden of childcare and providing support to ensure the safety and health of both parents and their baby.

[1540] The processing flow will be explained below.

[1541] Program processing steps

[1542] How does a baby monitor work?

[1543] Step 1:

[1544] Baby monitors use cameras and microphones to monitor your baby's face, movements, and voice in real time, collecting data continuously.

[1545] Step 2:

[1546] Collected monitoring data is temporarily stored on the device, where it is properly formatted and prepared for later transmission to a cloud server.

[1547] Step 3:

[1548] The device sends the monitoring data to the cloud server using secure HTTP or WebSocket protocols to ensure minimal latency.

[1549] Data analysis on a cloud server

[1550] Step 4:

[1551] The server receives the data sent from the baby monitor and temporarily stores it in a database.

[1552] Step 5:

[1553] A generative AI model analyzes surveillance data, analyzing voice, facial expression, and motion separately, and then combines the results.

[1554] Step 6:

[1555] The server uses an analytical engine to determine the baby's needs (hunger, discomfort, fatigue, etc.) and potential dangers (lying face down, objects falling, etc.).

[1556] Step 7:

[1557] The server receives the user's emotional data, and the emotion engine analyzes the user's emotional state, including the user's voice, facial expressions, and operation logs.

[1558] Step 8:

[1559] The system integrates analysis of the baby's needs and dangers with the user's emotional state to tailor notifications, for example, increasing the priority of notifications if the user is very tired.

[1560] Step 9:

[1561] The server sends the consolidated analysis results to the parent's device, which includes the baby's condition, recommended measures, and necessary alert information.

[1562] Notification display on parent's device

[1563] Step 10:

[1564] The device receives the analysis results sent from the cloud server, and the received data is loaded into the application, starting the process.

[1565] Step 11:

[1566] The received analysis results are displayed in the app's user interface (UI), which displays the results in an intuitive and easy-to-understand format, allowing users to understand the situation at a glance.

[1567] Step 12:

[1568] If necessary, an alarm notification is issued to notify the user of an emergency. Notification methods include sound, vibration, and pop-up notification.

[1569] Step 13:

[1570] The server provides appropriate advice and suggestions based on the user's emotional state, for example, advising the user to take a rest if they are tired.

[1571] Specific examples

[1572] For example, if your baby is hungry and crying and you are very tired:

[1573] Step 1:

[1574] Baby monitors keep an eye on your baby's cries and irritability.

[1575] Step 2:

[1576] The monitoring data is temporarily stored and ready to be sent to a cloud server later.

[1577] Step 3:

[1578] The device sends the monitoring data to a cloud server.

[1579] Step 4:

[1580] The server receives the monitoring data and stores it in a database.

[1581] Step 5:

[1582] A generative AI model analyzes the monitoring data, detecting signs of hunger from crying patterns and facial expressions.

[1583] Step 6:

[1584] The server determines that the baby is hungry.

[1585] Step 7:

[1586] A server receives the user's emotion data, and an emotion engine determines the user's fatigue.

[1587] Step 8:

[1588] The server combines the baby's hunger determination result with the user's fatigue state and increases the priority of the notification.

[1589] Step 9:

[1590] The server then sends the combined analysis results to the parent's device, which includes a message saying "Your baby is hungry" along with advice to get some rest.

[1591] Step 10:

[1592] The terminal receives the analysis results.

[1593] Step 11:

[1594] The analysis results are displayed on the app's UI as "Baby is hungry."

[1595] Step 12:

[1596] An alarm will sound and say "Baby is hungry" to alert the parent, and in this case, a vibration will also be added since the notification is set to high priority.

[1597] Step 13:

[1598] Based on the user's emotional state, "advice to take a rest" is displayed.

[1599] Example 2

[1600] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1601] Conventional baby monitoring systems only monitor a baby's face, movements, and voice, and only have the ability to identify the baby's needs or dangerous situations, which limits the ability of parents to respond appropriately to their baby's situation.In addition, because notifications are given without taking into account the parent's emotional state, there is the problem that they cannot provide appropriate support to parents who are tired or stressed.

[1602] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1603] In this invention, the server includes device means for monitoring the baby's face, movements, and voice in real time, communication means for transmitting the monitored data to a cloud server, generation AI model means for analyzing the received data in the cloud server and determining the baby's needs and dangers, emotion engine means for analyzing the user's emotion data and integrating it with the analysis results of the baby, and notification means for notifying the parent's device of the analysis results. This makes it possible to provide optimal notifications based on the baby's condition and the parent's emotional state, reducing the burden of childcare and ensuring the safety and health of the parent and baby.

[1604] "Device means" refers to a device that includes hardware and software for monitoring the baby's face, movements, and voice in real time.

[1605] The "communication means" is a network communication function for transmitting monitoring data acquired by the device means to the cloud server.

[1606] The "generative AI model means" refers to artificial intelligence algorithms and software that run on a cloud server and analyze the received data to determine the baby's needs and risks.

[1607] The "emotion engine means" is software and algorithms for analyzing the user's emotion data and integrating it with the baby's analysis results.

[1608] The "notification means" refers to a communication function and software for transmitting the analysis results from the cloud server to the parent's terminal and making the notification.

[1609] The "analysis results" are the results of the judgments of the baby's needs and dangerous situations, and the user's emotional state, obtained by the generative AI model means and the emotion engine means.

[1610] "Parent Device" refers to a mobile device or computer used by a parent to receive and display the analysis results.

[1611] This system monitors a baby's face, movements, and voice, as well as the user's emotions, in real time, analyzes the data on a cloud server, and notifies the parent of the results of the analysis. This not only enables parents to immediately grasp their baby's needs and dangerous situations, but also allows them to receive notifications based on their emotional state using an emotion engine, further reducing the burden of childcare.

[1612] Hardware and Software Configuration

[1613] Baby monitor

[1614] A baby monitor is a device that captures a baby's face, movements, and voice in real time. It is equipped with a high-resolution camera and a highly sensitive microphone, which allows it to capture facial expressions in detail and pick up even the nuances of a baby's voice.

[1615] Specifically, the baby monitor works as follows.

[1616] The camera detects the baby's face and movements.

[1617] The microphone picks up the baby's voice.

[1618] It is equipped with a Wi-Fi module to transmit this data to a cloud server in real time.

[1619] Cloud Server

[1620] The cloud server receives the data sent from the baby monitor and analyzes it using generative AI models, which include facial recognition and voice recognition algorithms.

[1621] The server operates as follows:

[1622] Receive data from the baby monitor.

[1623] A generative AI model is used to analyze the data and determine the baby's needs and risks.

[1624] An emotion engine is used to analyze the user's emotion data and determine the user's emotional state.

[1625] The baby's analysis results are integrated with the user's emotional state to tailor the notification content.

[1626] The adjusted notification content is sent to the parent's device.

[1627] Parent's device

[1628] An application is installed on the parent's device to receive and display notifications from the server. This application has a user interface (UI) that displays notifications in real time and has the ability to issue alerts as needed. It also has the ability to display appropriate advice and suggestions based on the analysis results of the emotion engine.

[1629] The terminal operates as follows:

[1630] Receives notification content sent from the server.

[1631] The received analysis results are displayed on the UI.

[1632] In the event of an emergency, an alert will be issued to notify parents of the emergency.

[1633] Display advice and suggestions based on the results of the sentiment engine.

[1634] Specific examples

[1635] For example, if a baby is hungry and crying, or if the user feels tired, the system will operate as follows:

[1636] 1. Baby monitor: The camera captures the baby's crying face, the microphone records the crying sound, and sends the data to a cloud server.

[1637] 2. Cloud server: The generative AI model analyzes the received data and determines that the baby is hungry. It generates a message saying "Baby is hungry," and the emotion engine determines the user's fatigue level. Based on this, it sends a notification indicating urgency to the parent's device.

[1638] 3. Parent's device: The received analysis result "Baby is hungry" is displayed on the UI. If the parent is fatigued, a notification with higher priority than usual will be sent, and appropriate advice and suggestions will be provided.

[1639] Prompt Sentence Examples

[1640] An example prompt for a generative AI model might be:

[1641] "Analyze your baby's cries and facial expressions to determine if he or she is hungry."

[1642] "Analyze the user's current emotional state with an emotion engine and adjust the priority of notifications based on that."

[1643] This system allows parents to comprehensively understand their baby's condition and their own emotional state, enabling them to quickly take the most appropriate measures, thereby reducing the burden of childcare and helping to ensure the safety and health of both parents and their babies.

[1644] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1645] Step 1:

[1646] Baby monitor data acquisition

[1647] A baby monitor captures the baby's face, movements, and voice in real time. The camera detects the baby's face and movements, and the microphone picks up the baby's voice. This action produces image and audio data. The inputs are the baby's face, movements, and voice, and the output is a camera image and an audio file.

[1648] Specific behavior:

[1649] The camera captures the baby's crying face.

[1650] The microphone records the baby's cry.

[1651] Step 2:

[1652] Sending data to the cloud server

[1653] The baby monitor sends the acquired data to a cloud server. The baby monitor's communication module uploads the camera images and audio files to the cloud server via Wi-Fi. The input is the camera images and audio files, and the output is the data uploaded to the cloud server.

[1654] Specific behavior:

[1655] The baby monitor transmits camera images and audio data to a cloud server in real time.

[1656] Step 3:

[1657] Data analysis on the server

[1658] The server receives data sent from the baby monitor and analyzes it using a generative AI model. It analyzes the baby's facial expressions, movements, and voice data to determine the baby's condition (e.g., hungry, sleepy, dangerous, etc.). The input is the monitoring data uploaded to the cloud server, and the output is the analysis results.

[1659] Specific behavior:

[1660] The server uses a facial recognition algorithm to analyze the baby's crying face.

[1661] A voice recognition algorithm is used to analyze the characteristics of the cry and determine whether the baby is hungry.

[1662] Step 4:

[1663] User sentiment analysis

[1664] The server analyzes the user's emotional data using an emotion engine. Based on past smartphone operation data and vital data (e.g., heart rate), the server determines the user's emotional state (e.g., fatigue, stress, relaxation). The input is the emotional data, and the output is the emotion analysis results.

[1665] Specific behavior:

[1666] The server checks the user's heart rate data and determines that the user is in a "fatigue state" using an emotion engine.

[1667] Step 5:

[1668] Consolidating and aligning notifications

[1669] The server integrates the baby's analysis results with the user's emotional state and adjusts the notification content. Based on the output of the emotion engine, it generates notification text and determines the priority of the notification. The input is the baby's analysis results and the emotion analysis results, and the output is the adjusted notification content.

[1670] Specific behavior:

[1671] Generate a notification message saying, "The baby is crying because he is hungry. He seems tired, so please take action as soon as possible."

[1672] Step 6:

[1673] Notifications on parent's device

[1674] The server sends the adjusted notification content to the parent device. The server's communication module pushes the notification content to the parent device. The input is the adjusted notification content, and the output is the notification sent to the parent device.

[1675] Specific behavior:

[1676] The server pushes the notification to the parent's smartphone.

[1677] Step 7:

[1678] Notifications and alerts on parent's device

[1679] The parent device receives notifications from the server and displays the content. The app displays the notifications on the UI and issues emergency alerts if necessary. It also displays appropriate advice and suggestions based on the analysis results of the emotion engine. The input is the notification content from the server, and the output is the notification content displayed on the UI.

[1680] Specific behavior:

[1681] A message appears on the parent's smartphone saying "Baby is hungry."

[1682] The app displays advice such as, "Parents are tired, so we suggest an easy way to make milk."

[1683] (Application example 2)

[1684] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1685] There is a need for a system that can monitor the stress and emotional state of workers in factories, as well as the safety of the work environment, in real time and take appropriate measures. Conventional systems have difficulty accurately grasping fluctuations in workers' health and emotions, and lack the functionality to immediately detect dangers in the work environment, making it difficult to ensure worker safety and efficiency. Furthermore, measures based on workers' stress and emotional state are sometimes delayed, which can have a negative impact on work efficiency and safety.

[1686] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes device means for monitoring the faces, movements, and voices of the baby and worker in real time, communication means for transmitting the monitored data to a cloud server, generation AI model means for analyzing the received data in the cloud server and determining the needs and dangers of the baby and worker, and notification means for notifying the parent and manager terminals of the analysis results. This makes it possible to grasp the worker's stress and emotional state, as well as the state of the work environment, in real time and take appropriate measures promptly.

[1687] "Device means" refers to equipment or devices for capturing and monitoring the face, movements, and voice of workers in real time.

[1688] "Communication means" refers to the network technology and communication protocol used to transmit the monitoring data acquired by the device means to the cloud server.

[1689] "Cloud server" refers to a remote server that stores and processes data over the Internet.

[1690] "Generative AI model means" refers to an artificial intelligence algorithm that runs on a cloud server and analyzes the received monitoring data to determine the needs and risks of workers and babies.

[1691] "Notification means" refers to the functions and systems that notify parents and administrators of the results of analysis performed on the cloud server via their devices.

[1692] "Stress" refers to the mental or physical tension that a worker feels due to the work environment or work content.

[1693] "Emotional state" refers to the psychological state that indicates the worker's current feelings and sensations.

[1694] "Work environment" refers to the place where workers actually perform their work and its physical conditions (e.g., noise, temperature, humidity, etc.).

[1695] "Manager" refers to a person who is responsible for supervising workers in a factory or work site and managing the safety of the work environment and work efficiency.

[1696] This invention is a system that monitors the stress and emotional state of workers in a factory, as well as the safety of the working environment, in real time, and takes appropriate measures promptly. Specifically, it consists of the following parts.

[1697] Hardware Configuration

[1698] 1. Device means:

[1699] Camera: Captures the worker's facial expressions and movements in real time.

[1700] Microphone: Captures the worker's voice in real time.

[1701] Wearable devices: Monitor workers' biometric data such as heart rate and body temperature.

[1702] Environmental sensors: Collect data on the working environment, such as temperature, humidity, and noise levels.

[1703] 2. Means of communication:

[1704] Network technologies such as Wi-Fi and LTE are used to transmit data acquired by the device means to a cloud server.

[1705] Software Configuration

[1706] 1. Cloud Server:

[1707] The cloud server receives the data transmitted from the device means and analyzes it.

[1708] The generative AI model used (e.g. TensorFlow) analyzes the incoming data in real time to determine the worker's stress and emotional state.

[1709] Based on the analysis results, the safety of the working environment will also be evaluated.

[1710] 2. Generative AI model means:

[1711] Facial expression analysis, voice analysis, and biometric data analysis are performed to determine the worker's stress and emotional state.

[1712] The generative AI models used include TensorFlow and Google Cloud AI.

[1713] Based on the analysis, appropriate measures and advice are generated.

[1714] 3. Means of notification:

[1715] The results analyzed on the cloud server are notified to parents or administrators via smartphones or robot terminals.

[1716] The notification includes advice to encourage workers to take breaks and suggestions for improving the work environment.

[1717] Specific examples

[1718] 1. If worker A is stressed:

[1719] Camera and microphone: Worker A has a stern facial expression and an irritated voice.

[1720] Analysis result: It was determined that worker A's stress level was high.

[1721] Notification: A message is sent to the manager's smartphone saying, "Worker A's stress level is high. Please encourage him to take a break."

[1722] 2. If the working environment has high noise levels:

[1723] Environmental sensor: Noise levels exceed acceptable limits.

[1724] Analysis result: The working environment was determined to be dangerous.

[1725] Notification: Robotic terminals in factories warn workers, "Noise levels are high. Please use earplugs."

[1726] Prompt Sentence Examples

[1727] Facial Expression Analysis: "Analyze the facial expressions of the workers in this image to determine their stress levels."

[1728] Voice Analysis: "Infer the emotional state of the worker in this audio file from their tone of voice."

[1729] Environmental Data Analysis: "Analyze this environmental sensor data to assess safety."

[1730] In this way, we ensure the safety and health of workers in the factory and provide support to improve work efficiency.

[1731] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1732] Step 1:

[1733] The user installs a baby monitor, and the device captures the worker's face, movements, voice, and work environment in real time. The input is monitoring data (facial expressions, voice, biometric data, and environmental sensor data). The output is the acquisition of these monitoring data.

[1734] Step 2:

[1735] The monitoring data acquired by the device means is sent to the cloud server using the communication means. The input is the monitoring data acquired in step 1, and the data is sent. The output is the monitoring data sent to the cloud server.

[1736] Step 3:

[1737] The cloud server receives the monitoring data and uses a generative AI model to analyze it. The input is the transmitted monitoring data, and the cloud server performs data analysis. Specifically, it performs facial expression analysis, vocal emotion analysis, biometric data analysis, and environmental data analysis. The output is the analysis results (the worker's stress level, emotional state, and safety assessment of the work environment).

[1738] Step 4:

[1739] Based on the analysis results, the cloud server notifies the parent or administrator's device. The input is the analysis result from step 3, which generates a notification message. The output is the notification message sent to the parent or administrator's device.

[1740] Step 5:

[1741] The parent or administrator's device receives the notification and displays it on the UI. The input is the notification message sent from the cloud server. The output is the notification message displayed on the device screen (e.g., "Worker A's stress level is high. Please encourage him to take a break.").

[1742] Step 6:

[1743] If necessary, an alarm notification will be sent to notify parents or administrators of an emergency. The input is a notification message that includes the urgency. The output is an alarm (audio, vibration, screen flash, etc.) to the parent or administrator.

[1744] Step 7:

[1745] Parents or administrators can take appropriate measures based on the notification. The inputs are notification messages and alarms. The outputs are instructions and corrective actions for workers (e.g., taking a break, using earplugs).

[1746] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1747] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1748] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1749] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1750] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1751] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1752] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1753] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1754] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1755] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1756] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1757] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1758] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1759] 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.

[1760] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1761] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1762] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1763] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1764] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1765] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1766] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1767] The following is further disclosed regarding the above embodiment.

[1768] (Claim 1)

[1769] A device means for monitoring the baby's face, movements, and voice in real time;

[1770] a communication means for transmitting the monitored data to a cloud server;

[1771] A generating AI model means for analyzing the received data in a cloud server and determining the baby's needs and risks;

[1772] A notification means for notifying the parent's device of the analysis result;

[1773] A system including:

[1774] (Claim 2)

[1775] A means for periodically transmitting facial expression, movement, and voice data acquired from the baby monitor to a cloud server;

[1776] A cloud server analyzes the received data and determines whether the baby is hungry, uncomfortable, or tired.

[1777] and means for sending an alarm notification to a parent terminal based on the analysis result.

[1778] 10. The system of claim 1.

[1779] (Claim 3)

[1780] If the cloud server detects an unexpected event or danger as a result of the analysis, it includes a means for immediately sending a warning to the parent's device.

[1781] 10. The system of claim 1.

[1782] "Example 1"

[1783] (Claim 1)

[1784] a sensor means for monitoring the baby's face, movements, and voice in real time;

[1785] a communication means for transmitting the monitored data to a cloud computing platform;

[1786] A machine learning model means for analyzing the received data on a cloud computing platform and determining the baby's needs and risks;

[1787] A notification means for notifying the parent or guardian of the analysis results;

[1788] A system including:

[1789] (Claim 2)

[1790] means for periodically transmitting facial expression, movement, and voice data acquired from the sensor to a cloud computing platform;

[1791] A cloud computing platform analyzes the received data and determines whether the baby is hungry, uncomfortable, or tired.

[1792] A means for sending a warning to a parent's device based on the analysis results is included.

[1793] 10. The system of claim 1.

[1794] (Claim 3)

[1795] If the cloud computing platform detects an unexpected event or danger as a result of analysis, it will immediately send a warning to the parent's device.

[1796] 10. The system of claim 1.

[1797] "Application Example 1"

[1798] (Claim 1)

[1799] A device means for monitoring the baby's face, movements, and voice in real time;

[1800] a communication means for transmitting the monitored data to a cloud server;

[1801] A generating AI model means for analyzing the received data in a cloud server and determining the baby's needs and risks;

[1802] A notification means for notifying the parent's device of the analysis result;

[1803] A means of monitoring the safety of the entire home to detect suspicious movements and sounds;

[1804] an alarm means for immediately issuing a warning notice when a dangerous situation is detected;

[1805] A system including:

[1806] (Claim 2)

[1807] A means for periodically transmitting facial expression, movement, and voice data acquired from the baby monitor to a cloud server;

[1808] A cloud server analyzes the received data and determines whether the baby is hungry, uncomfortable, or tired.

[1809] A cloud server analyzes abnormal movements and sounds in the home and determines whether there is any danger.

[1810] and means for sending an alarm notification to a parent terminal based on the analysis result.

[1811] 10. The system of claim 1.

[1812] (Claim 3)

[1813] If the cloud server detects an unexpected event or danger as a result of the analysis, a means for immediately sending a warning to the parent's device;

[1814] This includes monitoring the safety of the entire home and automatically contacting the police or fire department if necessary based on the analysis results.

[1815] 10. The system of claim 1.

[1816] "Example 2: Combining Emotion Engines"

[1817] (Claim 1)

[1818] A device means for monitoring the baby's face, movements, and voice in real time;

[1819] a communication means for transmitting the monitored data to a cloud server;

[1820] A generating AI model means for analyzing the received data in a cloud server and determining the baby's needs and risks;

[1821] an emotion engine means for analyzing the user's emotion data and integrating the analysis result of the baby;

[1822] A notification means for notifying the parent's device of the analysis result;

[1823] A system including:

[1824] (Claim 2)

[1825] A means for periodically transmitting facial expression, movement, and voice data acquired from the baby monitor to a cloud server;

[1826] A cloud server analyzes the received data and determines whether the baby is hungry, uncomfortable, or tired.

[1827] A means of analyzing emotional data and adjusting notification content according to urgency;

[1828] and means for sending an alarm notification to a parent terminal based on the analysis result.

[1829] 10. The system of claim 1.

[1830] (Claim 3)

[1831] If the cloud server detects an unexpected event or danger as a result of the analysis, it includes a means for immediately sending a warning to the parent's device.

[1832] 10. The system of claim 1.

[1833] "Application example 2 when combining emotion engines"

[1834] (Claim 1)

[1835] A device means for monitoring the baby's face, movements, and voice in real time;

[1836] a communication means for transmitting the monitored data to a cloud server;

[1837] A generating AI model means for analyzing the received data in a cloud server and determining the baby's needs and risks;

[1838] A notification means for notifying the parent's device of the analysis result;

[1839] A means for monitoring the stress and emotional state of workers and the state of the working environment in real time, analyzing the data on a cloud server, and notifying the manager;

[1840] A system including:

[1841] (Claim 2)

[1842] A means for periodically transmitting facial expression, movement, and voice data acquired from the baby monitor to a cloud server;

[1843] A cloud server analyzes the received data and determines whether the baby is hungry, uncomfortable, or tired.

[1844] and means for sending an alarm notification to a parent terminal based on the analysis result.

[1845] 10. The system of claim 1.

[1846] (Claim 3)

[1847] If the cloud server detects an unexpected event or danger as a result of the analysis, it includes a means for immediately sending a warning to the parent's device.

[1848] 10. The system of claim 1. [Explanation of symbols]

[1849] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A device means for monitoring the baby's face, movements, and voice in real time; a communication means for transmitting the monitored data to a cloud server; A generating AI model means for analyzing the received data in a cloud server and determining the baby's needs and risks; A notification means for notifying the parent's device of the analysis result; A system including:

2. A means for periodically transmitting facial expression, movement, and voice data acquired from the baby monitor to a cloud server; A cloud server analyzes the received data and determines whether the baby is hungry, uncomfortable, or tired. and means for sending an alarm notification to a parent terminal based on the analysis result. The system of claim 1 .

3. If the cloud server detects an unexpected event or danger as a result of the analysis, it includes a means for immediately sending a warning to the parent's device. The system of claim 1 .

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