system

Abstract

The system according to this embodiment aims to support parents in childcare by suggesting the optimal action based on the behavior of infants and toddlers. [Solution] The system according to the embodiment comprises a monitoring unit, a suggestion unit, a support unit, and a toilet support unit. The monitoring unit monitors the behavior of the infant. The suggestion unit suggests the optimal action based on the behavior of the infant monitored by the monitoring unit. The support unit supports the infant's sleep rhythm based on the action suggested by the suggestion unit. The toilet support unit supports the infant's toilet habits based on the action suggested by the suggestion unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it only monitors the behavior of infants and toddlers and lacks the function of proposing specific actions that parents should take next, and does not provide sufficient support for parents who are troubled by the progress of child-rearing.

[0005] The system according to the embodiment aims to propose an optimal action based on the behavior of infants and toddlers and support parents in child-rearing.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a monitoring unit, a suggestion unit, a support unit, and a toilet support unit. The monitoring unit monitors the behavior of the infant. The suggestion unit suggests the optimal action based on the behavior of the infant monitored by the monitoring unit. The support unit supports the infant's sleep rhythm based on the action suggested by the suggestion unit. The toilet support unit supports the infant's toilet habits based on the action suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to this embodiment can support parents in childcare by suggesting the optimal action based on the behavior of infants and toddlers. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. 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. Also, the database 24 and the communication I / F 26 are 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).

[0019] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The autonomous learning type childcare support AI agent system according to an embodiment of the present invention is a product (app) for parents who are exhausted from juggling many tasks such as work, childcare, and housework, and who suffer from sleep deprivation and difficulties in raising their children. This system can provide end-to-end support for monitoring, sleep training, and toilet training. Specifically, it learns the behavior of infants and toddlers and the parent's childcare style, and can propose the optimal action in a timely manner based on the baby's sleep rhythm and toilet habits. Unlike existing baby monitors and childcare apps, this system links data from monitoring, sleep training, and toilet training, and has functions to propose specific actions that parents should take next in real time (for example, put the baby to sleep at ● o'clock tonight, guide the baby to the toilet at ● o'clock, etc.), and also has functions to immediately answer questions about childcare and provide specific advice based on expert knowledge and past success stories. For example, as a monitoring function, an old smartphone can be used as a baby monitor. Using image recognition AI that utilizes computer vision, it monitors the movements of infants in real time and notifies parents with a push notification if a dangerous situation is detected. For example, when a baby learns to stand and is about to fall from the railing, it will send a notification such as "Watch out! Stop!" and suggest specific solutions (such as adjusting the railing height). Next, as a sleep training function, it analyzes the baby's cries and movements using voice processing AI and optimizes the notification content according to the situation. For example, it supports the baby's sleep by generating and playing music or white noise that helps them fall asleep. It also automatically records and analyzes daily sleep data and predicts and suggests the optimal sleep cycle. For example, it provides specific advice such as "Make sure the environment is dark at 8 PM." Furthermore, as a potty training function, parents can easily record the intervals between their child's daily toilet visits, and the data analysis AI predicts the optimal timing for toilet breaks. For example, it provides specific suggestions such as "Take your child to the toilet at ● o'clock." It also provides feedback according to the progress of potty training and specific countermeasures for when failures occur. Finally, it uses natural language processing to provide instant answers to questions about childcare and generates specific advice based on expert knowledge and past success stories.This allows parents to resolve their questions and anxieties about childcare and approach parenting with peace of mind. Thus, the present invention aims to reduce the burden of childcare and bring peace of mind and smiles to parents by providing an autonomous learning type childcare support AI agent that provides end-to-end support for monitoring, sleep training, and toilet training. In this way, the autonomous learning type childcare support AI agent system can reduce the burden of childcare on parents and improve the quality of childcare.

[0029] The autonomous learning type childcare support AI agent system according to this embodiment comprises a monitoring unit, a suggestion unit, a support unit, and a toilet support unit. The monitoring unit monitors the behavior of infants. The monitoring unit can use cameras and sensors, for example, to monitor the movements of infants in real time. The monitoring unit can also record the behavior of infants and collect data to detect abnormal behavior. For example, the monitoring unit can detect movements such as when an infant stands up or falls. The monitoring unit can also store the infant's behavior data in the cloud and make it accessible from multiple devices. The suggestion unit suggests the optimal action based on the behavior of the infant monitored by the monitoring unit. The suggestion unit analyzes the infant's cries and movements using voice processing AI, for example, and optimizes the notification content according to the situation. The suggestion unit can also refer to the infant's behavior data to make more accurate suggestions. The suggestion unit can also estimate the parent's emotions and adjust the way the suggestion content is expressed based on the estimated parent's emotions. The support unit supports the infant's sleep rhythm based on the actions suggested by the suggestion unit. The support unit, for example, automatically adjusts the sleep environment of infants. The support unit can also record infant sleep data over the long term and suggest optimal sleep patterns. The support unit can also estimate parental emotions and adjust sleep support methods based on the estimated parental emotions. The toilet support unit supports infant toilet habits based on actions suggested by the suggestion unit. The toilet support unit, for example, records infant toilet habit data over the long term and suggests optimal toilet timing. The toilet support unit can also analyze the infant's toilet success rate and provide specific advice to improve the success rate. The toilet support unit can also estimate parental emotions and adjust toilet support methods based on the estimated parental emotions. As a result, the autonomous learning type childcare support AI agent system according to the embodiment can support sleep rhythms and toilet habits by monitoring the behavior of infants and suggesting optimal actions.

[0030] The monitoring unit monitors the behavior of infants and toddlers. For example, the monitoring unit can use cameras and sensors to monitor the movements of infants and toddlers in real time. Specifically, the monitoring unit uses high-resolution cameras installed on the ceiling and walls of the room to monitor the movements of infants and toddlers over a wide area. This allows for accurate tracking of the infant's movements no matter where they are in the room. Pressure sensors and motion sensors installed on the floor and cribs can also be used to detect subtle movements and changes in the infant's weight. These sensors can detect movements such as standing up or falling in real time and immediately notify of abnormal behavior. Furthermore, the monitoring unit can store the infant's behavior data in the cloud and make it accessible from multiple devices. This allows parents and caregivers to check on the infant's situation anytime, anywhere via smartphones and tablets. The monitoring unit analyzes the collected data using AI to learn the infant's behavior patterns. This improves the accuracy of detecting abnormal behavior and enables appropriate monitoring according to the infant's growth and development. For example, if an infant frequently falls during a specific time period, the cause can be identified and appropriate measures can be taken. This allows the monitoring unit to ensure the safety of infants and reduce the burden on parents and caregivers.

[0031] The suggestion department proposes the optimal action based on the behavior of infants monitored by the monitoring department. For example, the suggestion department analyzes the infant's cries and movements using voice processing AI and optimizes the notification content according to the situation. Specifically, the suggestion department analyzes the volume, frequency, and duration of the infant's cries to identify the cause of the crying. For example, it identifies causes such as hunger, diaper discomfort, or sleepiness and proposes appropriate responses to the parents. It also analyzes the infant's movements and facial expressions to estimate the infant's emotional state. This allows it to identify the cause of anxiety or stress if the infant is feeling it and propose appropriate responses. The suggestion department can also make more accurate suggestions by referring to the infant's behavior data. For example, based on past data, if an infant tends to perform a specific action at a particular time of day, it can predict that action and propose countermeasures in advance. The suggestion department can also estimate the parents' emotions and adjust the way the suggestions are expressed based on the estimated emotions of the parents. For example, if the parents are tired, it will make simple and easy-to-implement suggestions, and if the parents are relaxed, it will make suggestions that include detailed explanations. This will allow the proposal department to reduce the burden on parents and provide more efficient care for infants and toddlers.

[0032] The support unit supports the sleep rhythm of infants and toddlers based on actions proposed by the suggestion unit. For example, the support unit automatically adjusts the sleep environment for infants and toddlers. Specifically, the support unit automatically adjusts the room lighting, temperature, and humidity to create a comfortable environment for sleep. For example, it gradually dims the lights and maintains the temperature within an appropriate range during the time when infants and toddlers are likely to fall asleep. It also supports infants and toddlers falling asleep by playing white noise or lullabies. The support unit can also record infants and toddlers' sleep data over the long term and suggest optimal sleep patterns. For example, it records the infant's sleep duration, wake-up time, and number of awakenings during the night, and based on this data, it suggests an optimal sleep schedule for the infant. Furthermore, the support unit can estimate the parent's emotions and adjust the sleep support methods based on the estimated parent's emotions. For example, if the parent is tired, it suggests easy-to-implement sleep support methods, and if the parent is relaxed, it suggests support methods with detailed explanations. In this way, the support unit can effectively support the infant's sleep rhythm and reduce the burden on parents.

[0033] The Toilet Support Department supports infants' toilet habits based on actions proposed by the Proposal Department. For example, the Toilet Support Department records infants' toilet habit data over the long term and proposes the optimal timing for toilet use. Specifically, the Toilet Support Department records the timing of urination and defecation of infants and proposes the optimal time for infants to go to the toilet based on this data. The Toilet Support Department can also analyze the success rate of infants' toilet use and provide specific advice to improve the success rate. For example, if an infant is reluctant to go to the toilet, the department identifies the cause and proposes appropriate countermeasures. Furthermore, the Toilet Support Department can estimate the parents' emotions and adjust the toilet support method based on the estimated emotions of the parents. For example, if the parents are stressed, the department proposes easy-to-implement toilet support methods, and if the parents are relaxed, it proposes support methods that include detailed explanations. In this way, the Toilet Support Department can effectively support infants' toilet habits and reduce the burden on parents.

[0034] The monitoring unit can monitor the movements of infants and toddlers in real time using image recognition AI that utilizes computer vision. For example, the monitoring unit uses a camera to capture the movements of infants and toddlers, and the image recognition AI analyzes the footage. The monitoring unit can detect movements such as infants and toddlers standing up or falling. If the monitoring unit detects abnormal movements, it can notify the parents. For example, if an infant or toddler is about to fall over a fence, the monitoring unit will send a notification such as "Watch out! Stop!". In this way, the movements of infants and toddlers can be monitored in real time using image recognition AI. Computer vision technology can be used, for example, image recognition algorithms using deep learning models or high-resolution cameras. The image recognition AI can be improved in accuracy by, for example, using a large amount of data on infants and toddlers' movements as training data. Some or all of the above processing in the monitoring unit may be performed using AI, or not using AI. For example, the monitoring unit can input video data acquired by the camera into a generating AI and have the generating AI perform the analysis of infants and toddlers' movements.

[0035] The proposed system can analyze the cries and movements of infants using voice processing AI and optimize notification content according to the situation. For example, the proposed system can record the infant's cries with a microphone and analyze the voice processing AI. The proposed system can analyze the crying patterns of the infant and estimate the cause of the crying. The proposed system can also detect the infant's movements with sensors and analyze the movement patterns. Based on the crying and movement patterns, the proposed system optimizes the content of notifications sent to parents. For example, if the infant is crying, the proposed system sends a notification such as, "Your baby is crying. Please check their diaper." In this way, by using voice processing AI, the system can analyze the crying and movements of infants and optimize notification content. The voice processing AI can, for example, use a speech recognition algorithm to extract characteristics of crying and classify crying patterns. Optimization of notification content is achieved, for example, by adjusting the timing and details of the notification content. Some or all of the above processing in the proposed system may be performed using AI, or not using AI. For example, the proposal unit can input data on infant crying sounds into a generating AI and have the AI ​​perform an analysis of the crying sounds.

[0036] The monitoring unit can learn the behavioral patterns of infants and toddlers over the long term and detect abnormal behavior early. For example, the monitoring unit records the behavioral data of infants and toddlers over a long period of time and learns their behavioral patterns. The monitoring unit can detect abnormal behavior if an infant or toddler moves in a way that is different from normal. The monitoring unit can issue an early warning if abnormal behavior is repeated during a specific time period. The monitoring unit can analyze the behavioral patterns of infants and toddlers and detect signs of abnormal behavior in advance. As a result, by learning the behavioral patterns of infants and toddlers, abnormal behavior can be detected early. Behavioral patterns include, for example, daily behavior and behavior during specific events. Abnormal behavior includes, for example, behavior that is different from normal or dangerous behavior. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input the behavioral data of infants and toddlers into a generating AI and have the generating AI perform abnormal behavior detection.

[0037] The monitoring unit can monitor the health status of infants and toddlers (body temperature, heart rate, etc.) in real time during monitoring and notify if there is an abnormality. For example, the monitoring unit can measure the infant's body temperature with a sensor and detect an abnormal rise in body temperature. The monitoring unit can monitor the infant's heart rate and detect abnormal fluctuations in heart rate. The monitoring unit constantly monitors the health status of infants and toddlers and immediately notifies if an abnormality is detected. For example, if the infant's body temperature rises sharply, it will notify the parents. If the infant's heart rate is abnormally high, it will issue a warning. In this way, by monitoring the health status of infants and toddlers in real time, any abnormalities can be notified immediately. Health status includes, for example, body temperature, heart rate, respiratory rate, etc. Abnormalities include, for example, deviations from the normal range or conditions of high urgency. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input the infant's health data into a generating AI and have the generating AI perform abnormality detection.

[0038] The monitoring unit can monitor the environment surrounding infants and toddlers (temperature, humidity, noise level, etc.) during monitoring and propose an optimal environment. For example, the monitoring unit can measure the room temperature with a sensor and propose air conditioning or heating to maintain an appropriate temperature range. The monitoring unit can monitor the room humidity and propose the use of a humidifier or dehumidifier to maintain an appropriate humidity level. The monitoring unit can also measure the room noise level and propose the removal of noise sources to maintain a quiet environment. For example, if the room temperature is too high, it will propose air conditioning. If the room humidity is too low, it will propose the use of a humidifier. If the room noise level is high, it will propose a quiet environment. In this way, by monitoring the environment surrounding infants and toddlers, an optimal environment can be proposed. The surrounding environment includes, for example, temperature, humidity, and noise level. The optimal environment includes, for example, an appropriate temperature range and humidity level. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input environmental data into a generating AI and have the generating AI execute a proposal for an optimal environment.

[0039] The monitoring unit can save infant behavior data to the cloud during monitoring and make it accessible from multiple devices. For example, the monitoring unit can upload infant behavior data to the cloud and make it accessible from smartphones, tablets, and PCs. The monitoring unit can update the data stored in the cloud in real time, ensuring that the latest data is always available. By making the data accessible from multiple devices, the monitoring unit allows parents to check on their infant's behavior no matter where they are. For example, infant behavior data can be saved to the cloud and made accessible from a smartphone. Infant behavior data can be saved to the cloud and made accessible from a tablet. Infant behavior data can be saved to the cloud and made accessible from a PC. This makes the infant behavior data accessible from multiple devices by saving it to the cloud. Behavioral data includes, for example, daily activity records and data from specific events. The cloud includes, for example, the selection of cloud services and data security. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input behavior data into a generating AI and have the generating AI perform the saving to the cloud.

[0040] The suggestion unit can make more accurate suggestions by referring to the infant's past behavioral data. For example, the suggestion unit can refer to the infant's past sleep data to suggest the optimal sleep duration. The suggestion unit can refer to the infant's past toilet data to suggest the optimal toilet timing. The suggestion unit analyzes the infant's past behavioral data to suggest the optimal action. For example, it can refer to the infant's past sleep data to suggest the optimal sleep duration. It can refer to the infant's past toilet data to suggest the optimal toilet timing. It can refer to the infant's past behavioral data to suggest the optimal action. This allows for more accurate suggestions by referring to the infant's past behavioral data. Past behavioral data includes, for example, daily activity records and data from specific events. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input past behavioral data into a generating AI and have the generating AI perform analysis to improve the accuracy of the suggestions.

[0041] The suggestion unit can provide customized suggestions tailored to the individual developmental stage of the infant. For example, the suggestion unit can suggest the optimal sleep duration according to the infant's developmental stage. The suggestion unit can suggest the optimal toilet timing according to the infant's developmental stage. The suggestion unit can suggest the optimal actions according to the infant's developmental stage. For example, it can suggest the optimal sleep duration according to the infant's developmental stage. For example, it can suggest the optimal toilet timing according to the infant's developmental stage. For example, it can suggest the optimal actions according to the infant's developmental stage. This allows for more appropriate support to be provided by making suggestions tailored to the infant's developmental stage. Developmental stages include, for example, age-specific developmental standards and individual growth records. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input developmental stage data into a generating AI and have the generating AI execute customized suggestions.

[0042] The proposal unit can learn the parents' parenting style and past responses when making a proposal, and select the optimal proposal method. For example, the proposal unit selects the optimal proposal method according to the parents' parenting style. The proposal unit can learn the parents' past responses and select the optimal proposal method. The proposal unit selects the optimal proposal method by comprehensively considering the parents' parenting style and past responses. For example, it selects the optimal proposal method according to the parents' parenting style. It learns the parents' past responses and selects the optimal proposal method. It selects the optimal proposal method by comprehensively considering the parents' parenting style and past responses. In this way, the optimal proposal method can be selected by learning the parents' parenting style and past responses. Parenting style includes, for example, the parents' parenting policies and past parenting experiences. Past responses include, for example, the parents' responses to proposals and the results of their parenting actions. Some or all of the above processing in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal department can input childcare style data and past response data into a generating AI, and have the AI ​​select the optimal proposal method.

[0043] The proposal unit can make suggestions based on the overall family's childcare policy by referring to the behavioral data of the infant's siblings. The proposal unit can make optimal suggestions by referring to the behavioral data of the infant's siblings. The proposal unit can make optimal suggestions based on the overall family's childcare policy. The proposal unit makes optimal suggestions by comprehensively considering the behavioral data of the infant's siblings and the overall family's childcare policy. For example, it can make optimal suggestions by referring to the behavioral data of the infant's siblings. It makes optimal suggestions based on the overall family's childcare policy. It makes optimal suggestions by comprehensively considering the behavioral data of the infant's siblings and the overall family's childcare policy. This allows the proposal to be based on the overall family's childcare policy by referring to the behavioral data of the infant's siblings. The behavioral data of siblings includes, for example, daily behavioral records and data from specific events. The overall family's childcare policy includes, for example, the results of family meetings and the parents' childcare policy. Some or all of the above processing in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal function can input data on sibling behavior and overall family parenting policies into the generating AI, allowing the AI ​​to execute optimal suggestions.

[0044] The support unit can automatically adjust the infant's sleep environment (lighting, music, etc.) during sleep support. For example, the support unit can automatically adjust the lighting according to the infant's sleep environment. The support unit can automatically adjust the music according to the infant's sleep environment. The support unit can also automatically adjust the temperature according to the infant's sleep environment. For example, it can automatically adjust the lighting according to the infant's sleep environment. It can automatically adjust the music according to the infant's sleep environment. It can automatically adjust the temperature according to the infant's sleep environment. This allows the support unit to provide an optimal sleep environment by automatically adjusting the infant's sleep environment. The sleep environment includes, for example, lighting, music, and temperature. Some or all of the above processing in the support unit may be performed using, for example, AI, or without AI. For example, the support unit can input sleep environment data into a generating AI and have the generating AI perform the environment adjustments.

[0045] The support unit can record infant and toddler sleep data over the long term during sleep support and propose an optimal sleep pattern. For example, the support unit can record infant and toddler sleep data over the long term and propose an optimal sleep pattern. The support unit can analyze infant and toddler sleep data and propose an optimal sleep duration. The support unit proposes an optimal sleep environment based on infant and toddler sleep data. This allows for the proposal of an optimal sleep pattern by recording infant and toddler sleep data over the long term. Sleep data includes, for example, sleep duration, sleep quality, and wake-up time. An optimal sleep pattern includes, for example, sleep cycles and sleep duration. Some or all of the above-described processes in the support unit may be performed using, for example, AI, or without AI. For example, the support unit can input sleep data into a generating AI and have the generating AI propose an optimal sleep pattern.

[0046] The support unit can monitor the health status of infants and toddlers (body temperature, heart rate, etc.) during sleep support and provide an optimal sleep environment. For example, the support unit can measure the infant's body temperature with a sensor and adjust the air conditioning or heating to maintain an appropriate temperature range. The support unit can monitor the infant's heart rate and adjust the environment to maintain an appropriate heart rate. The support unit comprehensively monitors the health status of infants and toddlers and provides an optimal sleep environment. For example, it can monitor the infant's body temperature and provide an optimal sleep environment. It can monitor the infant's heart rate and provide an optimal sleep environment. It comprehensively monitors the health status of infants and toddlers and provides an optimal sleep environment. In this way, an optimal sleep environment can be provided by monitoring the health status of infants and toddlers. Health status includes, for example, body temperature, heart rate, respiratory rate, etc. An optimal sleep environment includes, for example, an appropriate temperature range and humidity level. Some or all of the above processing in the support unit may be performed using, for example, AI, or not using AI. For example, the support department can input health data into a generating AI and have the AI ​​provide the optimal sleep environment.

[0047] The support unit can save infant sleep data to the cloud during sleep support and make it accessible from multiple devices. For example, the support unit can upload infant sleep data to the cloud and make it accessible from smartphones, tablets, and PCs. The support unit can update the data stored in the cloud in real time, ensuring that the latest data is always available. By making it accessible from multiple devices, the support unit allows parents to check their infant sleep data no matter where they are. For example, infant sleep data can be saved to the cloud and made accessible from a smartphone. Infant sleep data can be saved to the cloud and made accessible from a tablet. Infant sleep data can be saved to the cloud and made accessible from a PC. This makes infant sleep data accessible from multiple devices by saving it to the cloud. Sleep data includes, for example, sleep duration, sleep quality, and wake-up time. The cloud includes, for example, the selection of cloud services and data security. Some or all of the above processing in the support unit may be performed using, for example, AI, or not using AI. For example, the support department can input sleep data into a generating AI and have the AI ​​perform the task of saving it to the cloud.

[0048] The toilet support unit can record infant and toddler toilet habit data over the long term during toilet support and suggest the optimal toilet timing. For example, the toilet support unit can record infant and toddler toilet habit data over the long term and suggest the optimal toilet timing. The toilet support unit can analyze infant and toddler toilet habit data and suggest the optimal toilet guidance time. The toilet support unit can suggest the optimal toilet training method based on infant and toddler toilet habit data. For example, it can record infant and toddler toilet habit data over the long term and suggest the optimal toilet timing. It can analyze infant and toddler toilet habit data and suggest the optimal toilet guidance time. It can suggest the optimal toilet training method based on infant and toddler toilet habit data. In this way, by recording infant and toddler toilet habit data over the long term, the optimal toilet timing can be suggested. Toilet habit data includes, for example, the frequency and timing of urination and defecation. Optimal toilet timing includes, for example, the interval and timing of urination and defecation. Some or all of the above processing in the toilet support unit may be performed using, for example, AI, or not using AI. For example, the toilet support unit can input toilet habit data into a generating AI and have the AI ​​suggest the optimal timing for using the toilet.

[0049] The toilet support unit can analyze the success rate of infants and toddlers using the toilet during toilet support sessions and provide specific advice to improve the success rate. For example, the toilet support unit can analyze the success rate of infants and toddlers and provide specific advice to improve the success rate. Based on the success rate, the toilet support unit can propose the optimal toilet training method. The toilet support unit can record the success rate of infants and toddlers over the long term and provide feedback to improve the success rate. For example, it can analyze the success rate of infants and toddlers and provide specific advice to improve the success rate. Based on the success rate, it can propose the optimal toilet training method. It can record the success rate of infants and toddlers over the long term and provide feedback to improve the success rate. This allows for the provision of specific advice to improve the success rate by analyzing the success rate of infants and toddlers. The toilet success rate includes, for example, criteria for success and methods for calculating the success rate. Specific advice includes, for example, methods for toilet training and hints for improving the success rate. Some or all of the above processes in the toilet support unit may be performed using AI, or not. For example, the toilet support unit can input toilet success rate data into a generating AI and have the AI ​​issue advice to improve the success rate.

[0050] The toilet support unit can monitor the infant's health status (body temperature, frequency of urination and defecation, etc.) during toilet support and provide the optimal toilet timing. For example, the toilet support unit can measure the infant's body temperature with a sensor and suggest an appropriate toilet timing. The toilet support unit can monitor the infant's frequency of urination and defecation and suggest the optimal toilet timing. The toilet support unit comprehensively monitors the infant's health status and provides the optimal toilet timing. For example, it can monitor the infant's body temperature and provide the optimal toilet timing. It can monitor the infant's frequency of urination and defecation and provide the optimal toilet timing. It comprehensively monitors the infant's health status and provides the optimal toilet timing. In this way, by monitoring the infant's health status, the optimal toilet timing can be provided. Health status includes, for example, body temperature and frequency of urination and defecation. Optimal toilet timing includes, for example, the interval and timing of urination and defecation. Some or all of the above processing in the toilet support unit may be performed using, for example, AI, or not using AI. For example, the toilet support unit can input health data into a generating AI and have the AI ​​provide the optimal timing for using the toilet.

[0051] The toilet support unit can save infant and toddler toilet habit data to the cloud during toilet support, making it accessible from multiple devices. For example, the toilet support unit can upload infant and toddler toilet habit data to the cloud, making it accessible from smartphones, tablets, and PCs. The toilet support unit can update the data stored in the cloud in real time, ensuring that the latest data is always available. By making the data accessible from multiple devices, the toilet support unit allows parents to check their infant and toddler's toilet habit data no matter where they are. For example, infant and toddler toilet habit data can be saved to the cloud and made accessible from a smartphone. Infant and toddler toilet habit data can be saved to the cloud and made accessible from a tablet. Infant and toddler toilet habit data can be saved to the cloud and made accessible from a PC. This makes infant and toddler toilet habit data accessible from multiple devices by saving it to the cloud. Toilet habit data includes, for example, the frequency and timing of urination and defecation. The cloud includes, for example, the selection of cloud services and data security. Some or all of the above processing in the toilet support unit may be performed using, for example, AI, or not using AI. For example, the toilet support unit can input toilet habit data into a generating AI and have the AI ​​save it to the cloud.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The autonomous learning-type childcare support AI agent system can further monitor parents' eating habits and nutritional status and offer healthy meal suggestions. For example, it can record parents' meals and suggest balanced meal menus if the nutritional balance is off. If parents are busy, it can suggest easy-to-make, healthy recipes. If parents are stressed, it can suggest recipes that include ingredients effective in reducing stress. In this way, it can offer healthy meal suggestions by monitoring parents' eating habits and nutritional status. Monitoring of eating habits and nutritional status is achieved, for example, by recording meal contents and analyzing nutrients. Healthy meal suggestions can be provided, for example, through advice from a nutritionist or by using health apps. By offering healthy meal suggestions tailored to parents' eating habits and nutritional status, it can support parents' health and improve the quality of childcare.

[0054] The autonomous learning-type childcare support AI agent system can further monitor parents' exercise habits and suggest appropriate exercises. For example, it can record the amount of exercise parents do and suggest simple exercises if they are not getting enough exercise. If parents are busy, it can suggest short, effective exercises. If parents are stressed, it can suggest relaxing yoga or stretches. In this way, by monitoring parents' exercise habits, it can suggest appropriate exercises. Monitoring exercise habits can be achieved, for example, by using a pedometer or fitness app. Appropriate exercise suggestions can be obtained, for example, by using advice from a fitness instructor or an exercise app. By providing appropriate exercise suggestions tailored to parents' exercise habits, it can support parents' health and improve the quality of childcare.

[0055] The autonomous learning-type childcare support AI agent system can further provide educational content to support parents in improving their childcare skills. For example, it can introduce online courses and workshops on childcare. If parents feel insecure about a particular childcare skill, it can provide educational content specifically tailored to that skill. If parents are busy, it can provide videos on childcare skills that can be learned in a short amount of time. In this way, by supporting parents in improving their childcare skills, the quality of childcare can be improved. Support for improving childcare skills can be achieved, for example, by utilizing advice from childcare experts or online courses. By providing educational content that supports parents in improving their childcare skills, the burden of childcare on parents can be reduced and the quality of childcare can be improved.

[0056] The autonomous learning-type childcare support AI agent system can further provide a news feed to support parents in gathering information about childcare. For example, it can provide a news feed summarizing the latest childcare information and expert advice. If parents are interested in a particular childcare topic, it can provide articles and videos related to that topic. If parents are busy, it can provide childcare information that can be read in a short time. In this way, by supporting parents in gathering information about childcare, the quality of childcare can be improved. Support for gathering information about childcare can be achieved, for example, by using advice from childcare experts and childcare information websites. By providing a news feed that supports parents in gathering information about childcare, the burden of childcare on parents can be reduced and the quality of childcare can be improved.

[0057] The autonomous learning-type childcare support AI agent system can also provide a Q&A function to alleviate parents' questions and anxieties about childcare. For example, when a question about childcare is entered, it provides answers based on expert advice and past success stories. If a parent is interested in a specific childcare topic, it will provide Q&A related to that topic. If a parent is busy, it will provide a Q&A function that provides answers in a short time. In this way, by alleviating parents' questions and anxieties about childcare, the quality of childcare can be improved. The childcare Q&A function is realized, for example, by utilizing advice from childcare experts and childcare information websites. By providing a Q&A function that alleviates parents' questions and anxieties about childcare, the burden of childcare on parents can be reduced and the quality of childcare can be improved.

[0058] The autonomous learning-type childcare support AI agent system can further provide functions to support parents in setting childcare goals. For example, it can help parents set childcare goals and monitor their progress. If a parent wants to improve a specific childcare skill, it can help them set a goal related to that skill and suggest steps to achieve it. If a parent is busy, it can help them set short-term goals and suggest steps that are easy to achieve. In this way, by supporting parents in setting childcare goals, the quality of childcare can be improved. Support for setting childcare goals can be achieved, for example, through advice from childcare experts or by using goal-setting apps. By providing functions that support parents in setting childcare goals, the burden of childcare on parents can be reduced and the quality of childcare can be improved.

[0059] The autonomous learning-type childcare support AI agent system can further provide functions to provide feedback on parents' childcare. For example, it can record parents' childcare actions and provide feedback on those actions. If parents want to improve a specific childcare skill, it can record actions related to that skill and provide feedback. If parents are busy, it can provide a function to get feedback in a short time. In this way, the quality of childcare can be improved by providing feedback on parents' childcare. The provision of childcare feedback can be achieved, for example, by using advice from childcare experts or childcare information websites. By providing functions to provide feedback on parents' childcare, the burden of childcare on parents can be reduced and the quality of childcare can be improved.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The monitoring unit monitors the infant's behavior. The monitoring unit can use cameras and sensors to monitor the infant's movements in real time, for example. The monitoring unit can also record the infant's behavior and collect data to detect abnormal behavior. For example, the monitoring unit can detect when the infant stands up or falls. The monitoring unit can also store the infant's behavior data in the cloud and make it accessible from multiple devices. Step 2: The suggestion unit proposes the optimal action based on the infant's behavior monitored by the monitoring unit. For example, the suggestion unit analyzes the infant's crying and movements using voice processing AI and optimizes the notification content according to the situation. The suggestion unit can also refer to the infant's behavior data to make more accurate suggestions. The suggestion unit can also estimate the parent's emotions and adjust the way the suggestion is expressed based on the estimated parent's emotions. Step 3: The support unit supports the infant's sleep rhythm based on the actions suggested by the suggestion unit. For example, the support unit automatically adjusts the infant's sleep environment. The support unit can also record the infant's sleep data over the long term and suggest the optimal sleep pattern. The support unit can also estimate the parents' emotions and adjust the sleep support method based on the estimated parents' emotions. Step 4: The Toilet Support Department supports the infant's toilet habits based on the actions proposed by the Proposal Department. For example, the Toilet Support Department records the infant's toilet habits data over the long term and suggests the optimal timing for toilet breaks. The Toilet Support Department can also analyze the infant's toilet success rate and provide specific advice to improve it. The Toilet Support Department can also estimate the parents' emotions and adjust the toilet support methods based on the estimated emotions.

[0062] (Example of form 2) The autonomous learning type childcare support AI agent system according to an embodiment of the present invention is a product (app) for parents who are exhausted from juggling many tasks such as work, childcare, and housework, and who suffer from sleep deprivation and difficulties in raising their children. This system can provide end-to-end support for monitoring, sleep training, and toilet training. Specifically, it learns the behavior of infants and toddlers and the parent's childcare style, and can propose the optimal action in a timely manner based on the baby's sleep rhythm and toilet habits. Unlike existing baby monitors and childcare apps, this system links data from monitoring, sleep training, and toilet training, and has functions to propose specific actions that parents should take next in real time (for example, put the baby to sleep at ● o'clock tonight, guide the baby to the toilet at ● o'clock, etc.), and also has functions to immediately answer questions about childcare and provide specific advice based on expert knowledge and past success stories. For example, as a monitoring function, an old smartphone can be used as a baby monitor. Using image recognition AI that utilizes computer vision, it monitors the movements of infants in real time and notifies parents with a push notification if a dangerous situation is detected. For example, when a baby learns to stand and is about to fall from the railing, it will send a notification such as "Watch out! Stop!" and suggest specific solutions (such as adjusting the railing height). Next, as a sleep training function, it analyzes the baby's cries and movements using voice processing AI and optimizes the notification content according to the situation. For example, it supports the baby's sleep by generating and playing music or white noise that helps them fall asleep. It also automatically records and analyzes daily sleep data and predicts and suggests the optimal sleep cycle. For example, it provides specific advice such as "Make sure the environment is dark at 8 PM." Furthermore, as a potty training function, parents can easily record the intervals between their child's daily toilet visits, and the data analysis AI predicts the optimal timing for toilet breaks. For example, it provides specific suggestions such as "Take your child to the toilet at ● o'clock." It also provides feedback according to the progress of potty training and specific countermeasures for when failures occur. Finally, it uses natural language processing to provide instant answers to questions about childcare and generates specific advice based on expert knowledge and past success stories.This allows parents to resolve their questions and anxieties about childcare and approach parenting with peace of mind. Thus, the present invention aims to reduce the burden of childcare and bring peace of mind and smiles to parents by providing an autonomous learning type childcare support AI agent that provides end-to-end support for monitoring, sleep training, and toilet training. In this way, the autonomous learning type childcare support AI agent system can reduce the burden of childcare on parents and improve the quality of childcare.

[0063] The autonomous learning type childcare support AI agent system according to this embodiment comprises a monitoring unit, a suggestion unit, a support unit, and a toilet support unit. The monitoring unit monitors the behavior of infants. The monitoring unit can use cameras and sensors, for example, to monitor the movements of infants in real time. The monitoring unit can also record the behavior of infants and collect data to detect abnormal behavior. For example, the monitoring unit can detect movements such as when an infant stands up or falls. The monitoring unit can also store the infant's behavior data in the cloud and make it accessible from multiple devices. The suggestion unit suggests the optimal action based on the behavior of the infant monitored by the monitoring unit. The suggestion unit analyzes the infant's cries and movements using voice processing AI, for example, and optimizes the notification content according to the situation. The suggestion unit can also refer to the infant's behavior data to make more accurate suggestions. The suggestion unit can also estimate the parent's emotions and adjust the way the suggestion content is expressed based on the estimated parent's emotions. The support unit supports the infant's sleep rhythm based on the actions suggested by the suggestion unit. The support unit, for example, automatically adjusts the sleep environment of infants. The support unit can also record infant sleep data over the long term and suggest optimal sleep patterns. The support unit can also estimate parental emotions and adjust sleep support methods based on the estimated parental emotions. The toilet support unit supports infant toilet habits based on actions suggested by the suggestion unit. The toilet support unit, for example, records infant toilet habit data over the long term and suggests optimal toilet timing. The toilet support unit can also analyze the infant's toilet success rate and provide specific advice to improve the success rate. The toilet support unit can also estimate parental emotions and adjust toilet support methods based on the estimated parental emotions. As a result, the autonomous learning type childcare support AI agent system according to the embodiment can support sleep rhythms and toilet habits by monitoring the behavior of infants and suggesting optimal actions.

[0064] The monitoring unit monitors the behavior of infants and toddlers. For example, the monitoring unit can use cameras and sensors to monitor the movements of infants and toddlers in real time. Specifically, the monitoring unit uses high-resolution cameras installed on the ceiling and walls of the room to monitor the movements of infants and toddlers over a wide area. This allows for accurate tracking of the infant's movements no matter where they are in the room. Pressure sensors and motion sensors installed on the floor and cribs can also be used to detect subtle movements and changes in the infant's weight. These sensors can detect movements such as standing up or falling in real time and immediately notify of abnormal behavior. Furthermore, the monitoring unit can store the infant's behavior data in the cloud and make it accessible from multiple devices. This allows parents and caregivers to check on the infant's situation anytime, anywhere via smartphones and tablets. The monitoring unit analyzes the collected data using AI to learn the infant's behavior patterns. This improves the accuracy of detecting abnormal behavior and enables appropriate monitoring according to the infant's growth and development. For example, if an infant frequently falls during a specific time period, the cause can be identified and appropriate measures can be taken. This allows the monitoring unit to ensure the safety of infants and reduce the burden on parents and caregivers.

[0065] The suggestion department proposes the optimal action based on the behavior of infants monitored by the monitoring department. For example, the suggestion department analyzes the infant's cries and movements using voice processing AI and optimizes the notification content according to the situation. Specifically, the suggestion department analyzes the volume, frequency, and duration of the infant's cries to identify the cause of the crying. For example, it identifies causes such as hunger, diaper discomfort, or sleepiness and proposes appropriate responses to the parents. It also analyzes the infant's movements and facial expressions to estimate the infant's emotional state. This allows it to identify the cause of anxiety or stress if the infant is feeling it and propose appropriate responses. The suggestion department can also make more accurate suggestions by referring to the infant's behavior data. For example, based on past data, if an infant tends to perform a specific action at a particular time of day, it can predict that action and propose countermeasures in advance. The suggestion department can also estimate the parents' emotions and adjust the way the suggestions are expressed based on the estimated emotions of the parents. For example, if the parents are tired, it will make simple and easy-to-implement suggestions, and if the parents are relaxed, it will make suggestions that include detailed explanations. This will allow the proposal department to reduce the burden on parents and provide more efficient care for infants and toddlers.

[0066] The support unit supports the sleep rhythm of infants and toddlers based on actions proposed by the suggestion unit. For example, the support unit automatically adjusts the sleep environment for infants and toddlers. Specifically, the support unit automatically adjusts the room lighting, temperature, and humidity to create a comfortable environment for sleep. For example, it gradually dims the lights and maintains the temperature within an appropriate range during the time when infants and toddlers are likely to fall asleep. It also supports infants and toddlers falling asleep by playing white noise or lullabies. The support unit can also record infants and toddlers' sleep data over the long term and suggest optimal sleep patterns. For example, it records the infant's sleep duration, wake-up time, and number of awakenings during the night, and based on this data, it suggests an optimal sleep schedule for the infant. Furthermore, the support unit can estimate the parent's emotions and adjust the sleep support methods based on the estimated parent's emotions. For example, if the parent is tired, it suggests easy-to-implement sleep support methods, and if the parent is relaxed, it suggests support methods with detailed explanations. In this way, the support unit can effectively support the infant's sleep rhythm and reduce the burden on parents.

[0067] The Toilet Support Department supports infants' toilet habits based on actions proposed by the Proposal Department. For example, the Toilet Support Department records infants' toilet habit data over the long term and proposes the optimal timing for toilet use. Specifically, the Toilet Support Department records the timing of urination and defecation of infants and proposes the optimal time for infants to go to the toilet based on this data. The Toilet Support Department can also analyze the success rate of infants' toilet use and provide specific advice to improve the success rate. For example, if an infant is reluctant to go to the toilet, the department identifies the cause and proposes appropriate countermeasures. Furthermore, the Toilet Support Department can estimate the parents' emotions and adjust the toilet support method based on the estimated emotions of the parents. For example, if the parents are stressed, the department proposes easy-to-implement toilet support methods, and if the parents are relaxed, it proposes support methods that include detailed explanations. In this way, the Toilet Support Department can effectively support infants' toilet habits and reduce the burden on parents.

[0068] The monitoring unit can monitor the movements of infants and toddlers in real time using image recognition AI that utilizes computer vision. For example, the monitoring unit uses a camera to capture the movements of infants and toddlers, and the image recognition AI analyzes the footage. The monitoring unit can detect movements such as infants and toddlers standing up or falling. If the monitoring unit detects abnormal movements, it can notify the parents. For example, if an infant or toddler is about to fall over a fence, the monitoring unit will send a notification such as "Watch out! Stop!". In this way, the movements of infants and toddlers can be monitored in real time using image recognition AI. Computer vision technology can be used, for example, image recognition algorithms using deep learning models or high-resolution cameras. The image recognition AI can be improved in accuracy by, for example, using a large amount of data on infants and toddlers' movements as training data. Some or all of the above processing in the monitoring unit may be performed using AI, or not using AI. For example, the monitoring unit can input video data acquired by the camera into a generating AI and have the generating AI perform the analysis of infants and toddlers' movements.

[0069] The proposed system can analyze the cries and movements of infants using voice processing AI and optimize notification content according to the situation. For example, the proposed system can record the infant's cries with a microphone and analyze the voice processing AI. The proposed system can analyze the crying patterns of the infant and estimate the cause of the crying. The proposed system can also detect the infant's movements with sensors and analyze the movement patterns. Based on the crying and movement patterns, the proposed system optimizes the content of notifications sent to parents. For example, if the infant is crying, the proposed system sends a notification such as, "Your baby is crying. Please check their diaper." In this way, by using voice processing AI, the system can analyze the crying and movements of infants and optimize notification content. The voice processing AI can, for example, use a speech recognition algorithm to extract characteristics of crying and classify crying patterns. Optimization of notification content is achieved, for example, by adjusting the timing and details of the notification content. Some or all of the above processing in the proposed system may be performed using AI, or not using AI. For example, the proposal unit can input data on infant crying sounds into a generating AI and have the AI ​​perform an analysis of the crying sounds.

[0070] The monitoring unit can estimate the parent's emotions and adjust the monitoring frequency based on the estimated emotions. For example, the monitoring unit can capture the parent's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The monitoring unit can analyze changes in the parent's facial expressions and estimate their stress or relaxation state. The monitoring unit adjusts the monitoring frequency according to the parent's emotions. For example, if the parent is stressed, the monitoring frequency is increased to provide a sense of security. If the parent is relaxed, the monitoring frequency is reduced to respect their privacy. If the parent is busy, the monitoring frequency is adjusted to notify only at important times. In this way, the parent's sense of security and privacy can be respected by adjusting the monitoring frequency according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input parental facial expression data into a generating AI and have the AI ​​perform emotion estimation.

[0071] The monitoring unit can learn the behavioral patterns of infants and toddlers over the long term and detect abnormal behavior early. For example, the monitoring unit records the behavioral data of infants and toddlers over a long period of time and learns their behavioral patterns. The monitoring unit can detect abnormal behavior if an infant or toddler moves in a way that is different from normal. The monitoring unit can issue an early warning if abnormal behavior is repeated during a specific time period. The monitoring unit can analyze the behavioral patterns of infants and toddlers and detect signs of abnormal behavior in advance. As a result, by learning the behavioral patterns of infants and toddlers, abnormal behavior can be detected early. Behavioral patterns include, for example, daily behavior and behavior during specific events. Abnormal behavior includes, for example, behavior that is different from normal or dangerous behavior. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input the behavioral data of infants and toddlers into a generating AI and have the generating AI perform abnormal behavior detection.

[0072] The monitoring unit can monitor the health status of infants and toddlers (body temperature, heart rate, etc.) in real time during monitoring and notify if there is an abnormality. For example, the monitoring unit can measure the infant's body temperature with a sensor and detect an abnormal rise in body temperature. The monitoring unit can monitor the infant's heart rate and detect abnormal fluctuations in heart rate. The monitoring unit constantly monitors the health status of infants and toddlers and immediately notifies if an abnormality is detected. For example, if the infant's body temperature rises sharply, it will notify the parents. If the infant's heart rate is abnormally high, it will issue a warning. In this way, by monitoring the health status of infants and toddlers in real time, any abnormalities can be notified immediately. Health status includes, for example, body temperature, heart rate, respiratory rate, etc. Abnormalities include, for example, deviations from the normal range or conditions of high urgency. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input the infant's health data into a generating AI and have the generating AI perform abnormality detection.

[0073] The monitoring unit can estimate the parent's emotions and adjust the notification method of the monitoring results based on the estimated parent's emotions. For example, the monitoring unit can capture the parent's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The monitoring unit can analyze changes in the parent's facial expressions and estimate their stress or relaxation state. The monitoring unit adjusts the notification method according to the parent's emotions. For example, if the parent is stressed, a concise notification is given. If the parent is relaxed, a detailed notification is given. If the parent is busy, only important notifications are given. In this way, by adjusting the notification method according to the parent's emotions, the optimal notification can be provided to the parent. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the parent's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0074] The monitoring unit can monitor the environment surrounding infants and toddlers (temperature, humidity, noise level, etc.) during monitoring and propose an optimal environment. For example, the monitoring unit can measure the room temperature with a sensor and propose air conditioning or heating to maintain an appropriate temperature range. The monitoring unit can monitor the room humidity and propose the use of a humidifier or dehumidifier to maintain an appropriate humidity level. The monitoring unit can also measure the room noise level and propose the removal of noise sources to maintain a quiet environment. For example, if the room temperature is too high, it will propose air conditioning. If the room humidity is too low, it will propose the use of a humidifier. If the room noise level is high, it will propose a quiet environment. In this way, by monitoring the environment surrounding infants and toddlers, an optimal environment can be proposed. The surrounding environment includes, for example, temperature, humidity, and noise level. The optimal environment includes, for example, an appropriate temperature range and humidity level. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input environmental data into a generating AI and have the generating AI execute a proposal for an optimal environment.

[0075] The monitoring unit can save infant behavior data to the cloud during monitoring and make it accessible from multiple devices. For example, the monitoring unit can upload infant behavior data to the cloud and make it accessible from smartphones, tablets, and PCs. The monitoring unit can update the data stored in the cloud in real time, ensuring that the latest data is always available. By making the data accessible from multiple devices, the monitoring unit allows parents to check on their infant's behavior no matter where they are. For example, infant behavior data can be saved to the cloud and made accessible from a smartphone. Infant behavior data can be saved to the cloud and made accessible from a tablet. Infant behavior data can be saved to the cloud and made accessible from a PC. This makes the infant behavior data accessible from multiple devices by saving it to the cloud. Behavioral data includes, for example, daily activity records and data from specific events. The cloud includes, for example, the selection of cloud services and data security. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input behavior data into a generating AI and have the generating AI perform the saving to the cloud.

[0076] The suggestion unit can estimate the parent's emotions and adjust the way the suggestion is presented based on the estimated emotions. For example, the suggestion unit can capture the parent's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The suggestion unit can analyze changes in the parent's facial expressions and estimate their stress or relaxation state. The suggestion unit adjusts the way the suggestion is presented according to the parent's emotions. For example, if the parent is stressed, it will make concise and easy-to-understand suggestions. If the parent is relaxed, it will make detailed suggestions. If the parent is busy, it will only make important suggestions. In this way, by adjusting the way the suggestion is presented according to the parent's emotions, the optimal suggestion can be made for the parent. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the parent's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0077] The suggestion unit can make more accurate suggestions by referring to the infant's past behavioral data. For example, the suggestion unit can refer to the infant's past sleep data to suggest the optimal sleep duration. The suggestion unit can refer to the infant's past toilet data to suggest the optimal toilet timing. The suggestion unit analyzes the infant's past behavioral data to suggest the optimal action. For example, it can refer to the infant's past sleep data to suggest the optimal sleep duration. It can refer to the infant's past toilet data to suggest the optimal toilet timing. It can refer to the infant's past behavioral data to suggest the optimal action. This allows for more accurate suggestions by referring to the infant's past behavioral data. Past behavioral data includes, for example, daily activity records and data from specific events. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input past behavioral data into a generating AI and have the generating AI perform analysis to improve the accuracy of the suggestions.

[0078] The suggestion unit can provide customized suggestions tailored to the individual developmental stage of the infant. For example, the suggestion unit can suggest the optimal sleep duration according to the infant's developmental stage. The suggestion unit can suggest the optimal toilet timing according to the infant's developmental stage. The suggestion unit can suggest the optimal actions according to the infant's developmental stage. For example, it can suggest the optimal sleep duration according to the infant's developmental stage. For example, it can suggest the optimal toilet timing according to the infant's developmental stage. For example, it can suggest the optimal actions according to the infant's developmental stage. This allows for more appropriate support to be provided by making suggestions tailored to the infant's developmental stage. Developmental stages include, for example, age-specific developmental standards and individual growth records. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input developmental stage data into a generating AI and have the generating AI execute customized suggestions.

[0079] The suggestion unit can estimate the parent's emotions and adjust the timing of suggestions based on the estimated emotions. For example, the suggestion unit can capture the parent's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The suggestion unit can analyze changes in the parent's facial expressions and estimate their stress or relaxation state. The suggestion unit adjusts the timing of suggestions according to the parent's emotions. For example, if the parent is stressed, the suggestion timing is delayed. If the parent is relaxed, the suggestion timing is advanced. If the parent is busy, suggestions are made only at important times. In this way, by adjusting the timing of suggestions according to the parent's emotions, suggestions can be made at the optimal time for the parent. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI. For example, the proposal unit can input parental facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0080] The proposal unit can learn the parents' parenting style and past responses when making a proposal, and select the optimal proposal method. For example, the proposal unit selects the optimal proposal method according to the parents' parenting style. The proposal unit can learn the parents' past responses and select the optimal proposal method. The proposal unit selects the optimal proposal method by comprehensively considering the parents' parenting style and past responses. For example, it selects the optimal proposal method according to the parents' parenting style. It learns the parents' past responses and selects the optimal proposal method. It selects the optimal proposal method by comprehensively considering the parents' parenting style and past responses. In this way, the optimal proposal method can be selected by learning the parents' parenting style and past responses. Parenting style includes, for example, the parents' parenting policies and past parenting experiences. Past responses include, for example, the parents' responses to proposals and the results of their parenting actions. Some or all of the above processing in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal department can input childcare style data and past response data into a generating AI, and have the AI ​​select the optimal proposal method.

[0081] The proposal unit can make suggestions based on the overall family's childcare policy by referring to the behavioral data of the infant's siblings. The proposal unit can make optimal suggestions by referring to the behavioral data of the infant's siblings. The proposal unit can make optimal suggestions based on the overall family's childcare policy. The proposal unit makes optimal suggestions by comprehensively considering the behavioral data of the infant's siblings and the overall family's childcare policy. For example, it can make optimal suggestions by referring to the behavioral data of the infant's siblings. It makes optimal suggestions based on the overall family's childcare policy. It makes optimal suggestions by comprehensively considering the behavioral data of the infant's siblings and the overall family's childcare policy. This allows the proposal to be based on the overall family's childcare policy by referring to the behavioral data of the infant's siblings. The behavioral data of siblings includes, for example, daily behavioral records and data from specific events. The overall family's childcare policy includes, for example, the results of family meetings and the parents' childcare policy. Some or all of the above processing in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal function can input data on sibling behavior and overall family parenting policies into the generating AI, allowing the AI ​​to execute optimal suggestions.

[0082] The support unit can estimate the parent's emotions and adjust the sleep support method based on the estimated emotions. For example, the support unit can capture the parent's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The support unit can analyze changes in the parent's facial expressions and estimate their stress and relaxation levels. The support unit adjusts the sleep support method according to the parent's emotions. For example, if the parent is stressed, it provides concise and easy-to-understand sleep support. If the parent is relaxed, it provides detailed sleep support. If the parent is busy, it provides only essential sleep support. In this way, by adjusting the sleep support method according to the parent's emotions, the optimal support can be provided for the parent. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the parent's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0083] The support unit can automatically adjust the infant's sleep environment (lighting, music, etc.) during sleep support. For example, the support unit can automatically adjust the lighting according to the infant's sleep environment. The support unit can automatically adjust the music according to the infant's sleep environment. The support unit can also automatically adjust the temperature according to the infant's sleep environment. For example, it can automatically adjust the lighting according to the infant's sleep environment. It can automatically adjust the music according to the infant's sleep environment. It can automatically adjust the temperature according to the infant's sleep environment. This allows the support unit to provide an optimal sleep environment by automatically adjusting the infant's sleep environment. The sleep environment includes, for example, lighting, music, and temperature. Some or all of the above processing in the support unit may be performed using, for example, AI, or without AI. For example, the support unit can input sleep environment data into a generating AI and have the generating AI perform the environment adjustments.

[0084] The support unit can record infant and toddler sleep data over the long term during sleep support and propose an optimal sleep pattern. For example, the support unit can record infant and toddler sleep data over the long term and propose an optimal sleep pattern. The support unit can analyze infant and toddler sleep data and propose an optimal sleep duration. The support unit proposes an optimal sleep environment based on infant and toddler sleep data. This allows for the proposal of an optimal sleep pattern by recording infant and toddler sleep data over the long term. Sleep data includes, for example, sleep duration, sleep quality, and wake-up time. An optimal sleep pattern includes, for example, sleep cycles and sleep duration. Some or all of the above-described processes in the support unit may be performed using, for example, AI, or without AI. For example, the support unit can input sleep data into a generating AI and have the generating AI propose an optimal sleep pattern.

[0085] The support unit can estimate the parent's emotions and adjust the frequency of sleep support based on the estimated emotions. For example, the support unit can capture the parent's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The support unit can analyze changes in the parent's facial expressions and estimate their stress and relaxation levels. The support unit adjusts the frequency of sleep support according to the parent's emotions. For example, if the parent is stressed, the frequency of sleep support is increased. If the parent is relaxed, the frequency of sleep support is decreased. If the parent is busy, sleep support is provided only at important times. In this way, by adjusting the frequency of sleep support according to the parent's emotions, the optimal support can be provided to the parent. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input parental facial expression data into a generating AI and have the AI ​​perform emotion estimation.

[0086] The support unit can monitor the health status of infants and toddlers (body temperature, heart rate, etc.) during sleep support and provide an optimal sleep environment. For example, the support unit can measure the infant's body temperature with a sensor and adjust the air conditioning or heating to maintain an appropriate temperature range. The support unit can monitor the infant's heart rate and adjust the environment to maintain an appropriate heart rate. The support unit comprehensively monitors the health status of infants and toddlers and provides an optimal sleep environment. For example, it can monitor the infant's body temperature and provide an optimal sleep environment. It can monitor the infant's heart rate and provide an optimal sleep environment. It comprehensively monitors the health status of infants and toddlers and provides an optimal sleep environment. In this way, an optimal sleep environment can be provided by monitoring the health status of infants and toddlers. Health status includes, for example, body temperature, heart rate, respiratory rate, etc. An optimal sleep environment includes, for example, an appropriate temperature range and humidity level. Some or all of the above processing in the support unit may be performed using, for example, AI, or not using AI. For example, the support department can input health data into a generating AI and have the AI ​​provide the optimal sleep environment.

[0087] The support unit can save infant sleep data to the cloud during sleep support and make it accessible from multiple devices. For example, the support unit can upload infant sleep data to the cloud and make it accessible from smartphones, tablets, and PCs. The support unit can update the data stored in the cloud in real time, ensuring that the latest data is always available. By making it accessible from multiple devices, the support unit allows parents to check their infant sleep data no matter where they are. For example, infant sleep data can be saved to the cloud and made accessible from a smartphone. Infant sleep data can be saved to the cloud and made accessible from a tablet. Infant sleep data can be saved to the cloud and made accessible from a PC. This makes infant sleep data accessible from multiple devices by saving it to the cloud. Sleep data includes, for example, sleep duration, sleep quality, and wake-up time. The cloud includes, for example, the selection of cloud services and data security. Some or all of the above processing in the support unit may be performed using, for example, AI, or not using AI. For example, the support department can input sleep data into a generating AI and have the AI ​​perform the task of saving it to the cloud.

[0088] The toilet support unit can estimate the parent's emotions and adjust the toilet support method based on the estimated emotions. For example, the toilet support unit can capture the parent's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The toilet support unit can analyze changes in the parent's facial expression and estimate their state of stress or relaxation. The toilet support unit adjusts the toilet support method according to the parent's emotions. For example, if the parent is stressed, it provides concise and easy-to-understand toilet support. If the parent is relaxed, it provides detailed toilet support. If the parent is busy, it provides only essential toilet support. In this way, by adjusting the toilet support method according to the parent's emotions, the optimal support can be provided for the parent. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the toilet support unit may be performed using AI, for example, or without using AI. For example, the toilet support unit can input parental facial expression data into a generating AI and have the AI ​​perform emotion estimation.

[0089] The toilet support unit can record infant and toddler toilet habit data over the long term during toilet support and suggest the optimal toilet timing. For example, the toilet support unit can record infant and toddler toilet habit data over the long term and suggest the optimal toilet timing. The toilet support unit can analyze infant and toddler toilet habit data and suggest the optimal toilet guidance time. The toilet support unit can suggest the optimal toilet training method based on infant and toddler toilet habit data. For example, it can record infant and toddler toilet habit data over the long term and suggest the optimal toilet timing. It can analyze infant and toddler toilet habit data and suggest the optimal toilet guidance time. It can suggest the optimal toilet training method based on infant and toddler toilet habit data. In this way, by recording infant and toddler toilet habit data over the long term, the optimal toilet timing can be suggested. Toilet habit data includes, for example, the frequency and timing of urination and defecation. Optimal toilet timing includes, for example, the interval and timing of urination and defecation. Some or all of the above processing in the toilet support unit may be performed using, for example, AI, or not using AI. For example, the toilet support unit can input toilet habit data into a generating AI and have the AI ​​suggest the optimal timing for using the toilet.

[0090] The toilet support unit can analyze the success rate of infants and toddlers using the toilet during toilet support sessions and provide specific advice to improve the success rate. For example, the toilet support unit can analyze the success rate of infants and toddlers and provide specific advice to improve the success rate. Based on the success rate, the toilet support unit can propose the optimal toilet training method. The toilet support unit can record the success rate of infants and toddlers over the long term and provide feedback to improve the success rate. For example, it can analyze the success rate of infants and toddlers and provide specific advice to improve the success rate. Based on the success rate, it can propose the optimal toilet training method. It can record the success rate of infants and toddlers over the long term and provide feedback to improve the success rate. This allows for the provision of specific advice to improve the success rate by analyzing the success rate of infants and toddlers. The toilet success rate includes, for example, criteria for success and methods for calculating the success rate. Specific advice includes, for example, methods for toilet training and hints for improving the success rate. Some or all of the above processes in the toilet support unit may be performed using AI, or not. For example, the toilet support unit can input toilet success rate data into a generating AI and have the AI ​​issue advice to improve the success rate.

[0091] The toilet support unit can estimate the parent's emotions and adjust the frequency of toilet support based on the estimated emotions. For example, the toilet support unit can capture the parent's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The toilet support unit can analyze changes in the parent's facial expressions and estimate their stress or relaxation state. The toilet support unit adjusts the frequency of toilet support according to the parent's emotions. For example, if the parent is stressed, the frequency of toilet support will increase. If the parent is relaxed, the frequency of toilet support will decrease. If the parent is busy, toilet support will only be provided at important times. In this way, by adjusting the frequency of toilet support according to the parent's emotions, the optimal support for the parent can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the toilet support unit may be performed using AI, for example, or without using AI. For example, the toilet support unit can input parental facial expression data into a generating AI and have the AI ​​perform emotion estimation.

[0092] The toilet support unit can monitor the infant's health status (body temperature, frequency of urination and defecation, etc.) during toilet support and provide the optimal toilet timing. For example, the toilet support unit can measure the infant's body temperature with a sensor and suggest an appropriate toilet timing. The toilet support unit can monitor the infant's frequency of urination and defecation and suggest the optimal toilet timing. The toilet support unit comprehensively monitors the infant's health status and provides the optimal toilet timing. For example, it can monitor the infant's body temperature and provide the optimal toilet timing. It can monitor the infant's frequency of urination and defecation and provide the optimal toilet timing. It comprehensively monitors the infant's health status and provides the optimal toilet timing. In this way, by monitoring the infant's health status, the optimal toilet timing can be provided. Health status includes, for example, body temperature and frequency of urination and defecation. Optimal toilet timing includes, for example, the interval and timing of urination and defecation. Some or all of the above processing in the toilet support unit may be performed using, for example, AI, or not using AI. For example, the toilet support unit can input health data into a generating AI and have the AI ​​provide the optimal timing for using the toilet.

[0093] The toilet support unit can save infant and toddler toilet habit data to the cloud during toilet support, making it accessible from multiple devices. For example, the toilet support unit can upload infant and toddler toilet habit data to the cloud, making it accessible from smartphones, tablets, and PCs. The toilet support unit can update the data stored in the cloud in real time, ensuring that the latest data is always available. By making the data accessible from multiple devices, the toilet support unit allows parents to check their infant and toddler's toilet habit data no matter where they are. For example, infant and toddler toilet habit data can be saved to the cloud and made accessible from a smartphone. Infant and toddler toilet habit data can be saved to the cloud and made accessible from a tablet. Infant and toddler toilet habit data can be saved to the cloud and made accessible from a PC. This makes infant and toddler toilet habit data accessible from multiple devices by saving it to the cloud. Toilet habit data includes, for example, the frequency and timing of urination and defecation. The cloud includes, for example, the selection of cloud services and data security. Some or all of the above processing in the toilet support unit may be performed using, for example, AI, or not using AI. For example, the toilet support unit can input toilet habit data into a generating AI and have the AI ​​save it to the cloud.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The autonomous learning-type childcare support AI agent system can further monitor parents' stress levels and suggest relaxation techniques to reduce stress. For example, if a parent's stress level is high, it can suggest deep breathing or short meditation sessions. If the parent is relaxed, it can suggest light stretching or a refreshing walk. If the parent is busy, it can suggest quick and effective relaxation methods. By providing relaxation suggestions tailored to the parent's stress level, it can support the parent's mental health. Stress levels are monitored, for example, by analyzing heart rate and changes in facial expression. Relaxation suggestions can include, for example, music or guided meditation apps. By providing relaxation suggestions tailored to the parent's stress level, it can reduce the burden of childcare and improve the quality of childcare.

[0096] The autonomous learning-type childcare support AI agent system can further monitor parents' eating habits and nutritional status and offer healthy meal suggestions. For example, it can record parents' meals and suggest balanced meal menus if the nutritional balance is off. If parents are busy, it can suggest easy-to-make, healthy recipes. If parents are stressed, it can suggest recipes that include ingredients effective in reducing stress. In this way, it can offer healthy meal suggestions by monitoring parents' eating habits and nutritional status. Monitoring of eating habits and nutritional status is achieved, for example, by recording meal contents and analyzing nutrients. Healthy meal suggestions can be provided, for example, through advice from a nutritionist or by using health apps. By offering healthy meal suggestions tailored to parents' eating habits and nutritional status, it can support parents' health and improve the quality of childcare.

[0097] The autonomous learning-type childcare support AI agent system can further monitor parents' exercise habits and suggest appropriate exercises. For example, it can record the amount of exercise parents do and suggest simple exercises if they are not getting enough exercise. If parents are busy, it can suggest short, effective exercises. If parents are stressed, it can suggest relaxing yoga or stretches. In this way, by monitoring parents' exercise habits, it can suggest appropriate exercises. Monitoring exercise habits can be achieved, for example, by using a pedometer or fitness app. Appropriate exercise suggestions can be obtained, for example, by using advice from a fitness instructor or an exercise app. By providing appropriate exercise suggestions tailored to parents' exercise habits, it can support parents' health and improve the quality of childcare.

[0098] The autonomous learning-type AI agent system for childcare support can further monitor parents' sleep patterns and offer suggestions to improve sleep quality. For example, it can record parents' sleep duration and quality, and if they are sleep-deprived, it can suggest early bedtime and early rising habits. If parents are stressed, it can suggest relaxing music or aromatherapy. If parents are busy, it can suggest short, effective naps. In this way, by monitoring parents' sleep patterns, it can offer suggestions to improve sleep quality. Sleep monitoring can be achieved, for example, using sleep trackers or smartwatches. Suggestions for improving sleep quality can be provided, for example, through advice from sleep specialists or sleep apps. By offering suggestions tailored to parents' sleep patterns, it can support parents' health and improve the quality of childcare.

[0099] The autonomous learning-type parenting support AI agent system can further offer suggestions to support parents' social connections. For example, if a parent feels isolated, it can introduce them to local parenting groups or online communities. If a parent is stressed, it can suggest ways to encourage communication with friends and family. If a parent is relaxed, it can provide a space for exchanging information and seeking advice about parenting. By supporting parents' social connections, it can reduce the burden of parenting and improve their mental health. Support for social connections is achieved, for example, by providing information on local parenting groups and online communities. By offering suggestions to support parents' social connections, it can reduce the burden of parenting and improve the quality of parenting.

[0100] The autonomous learning-type childcare support AI agent system can further provide educational content to support parents in improving their childcare skills. For example, it can introduce online courses and workshops on childcare. If parents feel insecure about a particular childcare skill, it can provide educational content specifically tailored to that skill. If parents are busy, it can provide videos on childcare skills that can be learned in a short amount of time. In this way, by supporting parents in improving their childcare skills, the quality of childcare can be improved. Support for improving childcare skills can be achieved, for example, by utilizing advice from childcare experts or online courses. By providing educational content that supports parents in improving their childcare skills, the burden of childcare on parents can be reduced and the quality of childcare can be improved.

[0101] The autonomous learning-type childcare support AI agent system can further provide a news feed to support parents in gathering information about childcare. For example, it can provide a news feed summarizing the latest childcare information and expert advice. If parents are interested in a particular childcare topic, it can provide articles and videos related to that topic. If parents are busy, it can provide childcare information that can be read in a short time. In this way, by supporting parents in gathering information about childcare, the quality of childcare can be improved. Support for gathering information about childcare can be achieved, for example, by using advice from childcare experts and childcare information websites. By providing a news feed that supports parents in gathering information about childcare, the burden of childcare on parents can be reduced and the quality of childcare can be improved.

[0102] The autonomous learning-type childcare support AI agent system can also provide a Q&A function to alleviate parents' questions and anxieties about childcare. For example, when a question about childcare is entered, it provides answers based on expert advice and past success stories. If a parent is interested in a specific childcare topic, it will provide Q&A related to that topic. If a parent is busy, it will provide a Q&A function that provides answers in a short time. In this way, by alleviating parents' questions and anxieties about childcare, the quality of childcare can be improved. The childcare Q&A function is realized, for example, by utilizing advice from childcare experts and childcare information websites. By providing a Q&A function that alleviates parents' questions and anxieties about childcare, the burden of childcare on parents can be reduced and the quality of childcare can be improved.

[0103] The autonomous learning-type childcare support AI agent system can further provide functions to support parents in setting childcare goals. For example, it can help parents set childcare goals and monitor their progress. If a parent wants to improve a specific childcare skill, it can help them set a goal related to that skill and suggest steps to achieve it. If a parent is busy, it can help them set short-term goals and suggest steps that are easy to achieve. In this way, by supporting parents in setting childcare goals, the quality of childcare can be improved. Support for setting childcare goals can be achieved, for example, through advice from childcare experts or by using goal-setting apps. By providing functions that support parents in setting childcare goals, the burden of childcare on parents can be reduced and the quality of childcare can be improved.

[0104] The autonomous learning-type childcare support AI agent system can further provide functions to provide feedback on parents' childcare. For example, it can record parents' childcare actions and provide feedback on those actions. If parents want to improve a specific childcare skill, it can record actions related to that skill and provide feedback. If parents are busy, it can provide a function to get feedback in a short time. In this way, the quality of childcare can be improved by providing feedback on parents' childcare. The provision of childcare feedback can be achieved, for example, by using advice from childcare experts or childcare information websites. By providing functions to provide feedback on parents' childcare, the burden of childcare on parents can be reduced and the quality of childcare can be improved.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The monitoring unit monitors the infant's behavior. The monitoring unit can use cameras and sensors to monitor the infant's movements in real time, for example. The monitoring unit can also record the infant's behavior and collect data to detect abnormal behavior. For example, the monitoring unit can detect when the infant stands up or falls. The monitoring unit can also store the infant's behavior data in the cloud and make it accessible from multiple devices. Step 2: The suggestion unit proposes the optimal action based on the infant's behavior monitored by the monitoring unit. For example, the suggestion unit analyzes the infant's crying and movements using voice processing AI and optimizes the notification content according to the situation. The suggestion unit can also refer to the infant's behavior data to make more accurate suggestions. The suggestion unit can also estimate the parent's emotions and adjust the way the suggestion is expressed based on the estimated parent's emotions. Step 3: The support unit supports the infant's sleep rhythm based on the actions suggested by the suggestion unit. For example, the support unit automatically adjusts the infant's sleep environment. The support unit can also record the infant's sleep data over the long term and suggest the optimal sleep pattern. The support unit can also estimate the parents' emotions and adjust the sleep support method based on the estimated parents' emotions. Step 4: The Toilet Support Department supports the infant's toilet habits based on the actions proposed by the Proposal Department. For example, the Toilet Support Department records the infant's toilet habits data over the long term and suggests the optimal timing for toilet breaks. The Toilet Support Department can also analyze the infant's toilet success rate and provide specific advice to improve it. The Toilet Support Department can also estimate the parents' emotions and adjust the toilet support methods based on the estimated emotions.

[0107] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0108] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0109] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0110] Each of the multiple elements described above, including the monitoring unit, suggestion unit, support unit, and toilet support unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit monitors the infant's behavior in real time using the camera 42 and sensors of the smart device 14 and detects abnormal behavior using the specific processing unit 290 of the data processing unit 12. The suggestion unit analyzes the infant's behavior data using the specific processing unit 290 of the data processing unit 12 and proposes the optimal action. The support unit adjusts the infant's sleep environment using the control unit 46A of the smart device 14 and proposes the optimal sleep pattern using the specific processing unit 290 of the data processing unit 12. The toilet support unit analyzes the infant's toilet habit data using the specific processing unit 290 of the data processing unit 12 and proposes the optimal toilet timing. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0114] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0115] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0117] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0118] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0119] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0120] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0121] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0122] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0125] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the monitoring unit, suggestion unit, support unit, and toilet support unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit monitors the infant's behavior in real time using the camera 42 and sensors of the smart glasses 214 and detects abnormal behavior using the specific processing unit 290 of the data processing unit 12. The suggestion unit analyzes the infant's behavior data using the specific processing unit 290 of the data processing unit 12 and proposes the optimal action. The support unit adjusts the infant's sleep environment using the control unit 46A of the smart glasses 214 and proposes the optimal sleep pattern using the specific processing unit 290 of the data processing unit 12. The toilet support unit analyzes the infant's toilet habit data using the specific processing unit 290 of the data processing unit 12 and proposes the optimal toilet timing. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the monitoring unit, suggestion unit, support unit, and toilet support unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit monitors the infant's behavior in real time using the camera 42 and sensors of the headset terminal 314 and detects abnormal behavior using the specific processing unit 290 of the data processing unit 12. The suggestion unit analyzes the infant's behavior data using the specific processing unit 290 of the data processing unit 12 and proposes the optimal action. The support unit adjusts the infant's sleep environment using the control unit 46A of the headset terminal 314 and proposes the optimal sleep pattern using the specific processing unit 290 of the data processing unit 12. The toilet support unit analyzes the infant's toilet habit data using the specific processing unit 290 of the data processing unit 12 and proposes the optimal toilet timing. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0151] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the monitoring unit, suggestion unit, support unit, and toilet support unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit monitors the infant's behavior in real time using the camera 42 and sensors of the robot 414 and detects abnormal behavior using the specific processing unit 290 of the data processing unit 12. The suggestion unit analyzes the infant's behavior data using the specific processing unit 290 of the data processing unit 12 and proposes the optimal action. The support unit adjusts the infant's sleep environment using the control unit 46A of the robot 414 and proposes the optimal sleep pattern using the specific processing unit 290 of the data processing unit 12. The toilet support unit analyzes the infant's toilet habit data using the specific processing unit 290 of the data processing unit 12 and proposes the optimal toilet timing. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0160] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0161] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0162] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0163] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0164] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0165] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0167] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0168] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0169] 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.

[0170] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0171] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0172] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0173] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0174] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0175] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0176] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0177] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0178] (Note 1) A monitoring unit that monitors the behavior of infants and toddlers, Based on the behavior of infants monitored by the aforementioned monitoring unit, a proposal unit proposes the optimal action. Based on the actions proposed by the aforementioned proposal unit, a support unit is provided to support the sleep rhythm of infants and toddlers, Based on the actions proposed by the aforementioned proposal unit, the toilet support unit provides support for infants' toilet habits. A system characterized by the following features. (Note 2) The aforementioned monitoring unit, Using computer vision-based image recognition AI, the movements of infants and toddlers are monitored in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, The system uses voice processing AI to analyze the cries and movements of infants and toddlers, and optimizes notification content according to the situation. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned monitoring unit, It estimates the parent's emotions and adjusts the frequency of monitoring based on the estimated parent's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned monitoring unit, Learn the behavioral patterns of infants and toddlers over the long term and detect abnormal behavior early. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned monitoring unit, During monitoring, the health status of infants and toddlers is monitored in real time, and notifications are sent if any abnormalities are detected. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned monitoring unit, The system estimates the parent's emotions and adjusts the notification method for monitoring results based on the estimated parent's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned monitoring unit, During monitoring, we will monitor the environment surrounding infants and propose the optimal environment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned monitoring unit, During monitoring, infant behavior data is saved to the cloud and made accessible from multiple devices. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned proposal section is, The system estimates the parent's emotions and adjusts the way the proposal is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned proposal section is, When making suggestions, we refer to past behavioral data of infants and toddlers to make more accurate suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, When making a proposal, we will provide a customized proposal tailored to the individual developmental stage of the infant or toddler. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, It estimates the parent's emotions and adjusts the timing of suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making a proposal, the system learns about the parents' parenting style and past responses to select the most appropriate proposal method. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making a proposal, refer to behavioral data of infants and toddlers' siblings and make suggestions based on the overall childcare policy for the family. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned support unit is It estimates the parent's emotions and adjusts the sleep support method based on the estimated parent's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned support unit is When providing sleep support, it automatically adjusts the sleep environment for infants and toddlers. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned support unit is During sleep support, we record infant and toddler sleep data over the long term and propose optimal sleep patterns. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned support unit is It estimates the parent's emotions and adjusts the frequency of sleep support based on the estimated parent's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned support unit is During sleep support, we monitor the health of infants and toddlers and provide an optimal sleep environment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned support unit is During sleep support, infant sleep data is saved to the cloud and made accessible from multiple devices. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned toilet support section is Estimate the parent's emotions and adjust the toilet support method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned toilet support section is During toilet support sessions, we record infant and toddler toilet habits over the long term and suggest the optimal timing for toilet breaks. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned toilet support section is During toilet training support, we analyze the success rate of infants and toddlers using the toilet and provide specific advice to improve that success rate. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned toilet support section is The system estimates the parent's emotions and adjusts the frequency of toilet training support based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned toilet support section is During toilet training, monitor the infant's health and provide the optimal toilet time. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned toilet support section is During toilet training support, infant and toddler toilet habit data is saved to the cloud and made accessible from multiple devices. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A monitoring unit that monitors the behavior of infants and toddlers, Based on the behavior of infants monitored by the aforementioned monitoring unit, a proposal unit proposes the optimal action. Based on the actions proposed by the aforementioned proposal unit, a support unit is provided to support the sleep rhythm of infants and toddlers, Based on the actions proposed by the aforementioned proposal unit, the toilet support unit provides support for infants' toilet habits. A system characterized by the following features.

2. The aforementioned monitoring unit, Using computer vision-based image recognition AI, the movements of infants and toddlers are monitored in real time. The system according to feature 1.

3. The aforementioned proposal section is, The system uses voice processing AI to analyze the cries and movements of infants and toddlers, and optimizes notification content according to the situation. The system according to feature 1.

4. The aforementioned monitoring unit, It estimates the parent's emotions and adjusts the frequency of monitoring based on the estimated parent's emotions. The system according to feature 1.

5. The aforementioned monitoring unit, Learn the behavioral patterns of infants and toddlers over the long term and detect abnormal behavior early. The system according to feature 1.

6. The aforementioned monitoring unit, During monitoring, the health status of infants and toddlers is monitored in real time, and notifications are sent if any abnormalities are detected. The system according to feature 1.

7. The aforementioned monitoring unit, The system estimates the parent's emotions and adjusts the notification method for monitoring results based on the estimated parent's emotions. The system according to feature 1.

8. The aforementioned monitoring unit, During monitoring, we will monitor the environment surrounding infants and propose the optimal environment. The system according to feature 1.

9. The aforementioned monitoring unit, During monitoring, infant behavior data is saved to the cloud and made accessible from multiple devices. The system according to feature 1.

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