system

Abstract

The system according to this embodiment aims to monitor the health of a baby while ensuring safety during transit. [Solution] The system according to the embodiment comprises a tracking unit, an avoidance unit, a monitoring unit, a notification unit, and a charging unit. The tracking unit tracks the parent. The avoidance unit avoids obstacles based on the information tracked by the tracking unit. The monitoring unit monitors the baby's body temperature and heart rate. The notification unit notifies of abnormalities based on the information monitored by the monitoring unit. The charging unit self-charges using the rotation of the wheels and sunlight.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, ensuring safety during movement and monitoring the health of a baby are not sufficiently performed, and there is room for improvement.

[0005] The system according to the embodiment aims to monitor the health of a baby while ensuring safety during movement.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a tracking unit, an avoidance unit, a monitoring unit, a notification unit, and a charging unit. The tracking unit tracks the parent. The avoidance unit avoids obstacles based on the information tracked by the tracking unit. The monitoring unit monitors the baby's body temperature and heart rate. The notification unit notifies of any abnormalities based on the information monitored by the monitoring unit. The charging unit self-charges using the rotation of the wheels and sunlight. [Effects of the Invention]

[0007] The system according to this embodiment can monitor the baby's health while ensuring safety during transit. [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 numbered 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 applicable to the communication I / F include wireless communication standards such as 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 receiving 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 receiving 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 Merry Cart AI Agent according to an embodiment of the present invention is a stroller that integrates AI technology. This Merry Cart AI Agent is designed to solve the problems of conventional strollers, such as safety, inconvenience of movement, and complexity of management. The Merry Cart AI Agent tracks the parent using a smart wristband with an automatic follow mode that tracks the parent, enabling hands-free movement. This allows the parent to use their hands freely, improving convenience while on the go. Furthermore, it is equipped with an obstacle avoidance system using an AI camera and sensors, which can detect and automatically avoid obstacles, ensuring safety while on the go. It also has a vital signs monitoring function that monitors the baby's body temperature and heart rate in real time, and notifies the parent if an abnormality is detected, allowing the baby's health to be constantly monitored. In addition, it is equipped with an energy harvesting function that uses the rotation of the wheels and sunlight, enabling self-charging and continuous use. These components provide a safe and comfortable childcare experience. For example, the Merry Cart AI Agent uses a smart wristband to track the parent. The smart wristband acquires the parent's location information in real time and transmits it to the stroller. This allows the stroller to track the parent's position and automatically move in accordance with the parent's movements. Additionally, an AI camera and sensor-based obstacle avoidance system uses a camera and sensors mounted on the front of the stroller to detect obstacles. When an obstacle is detected, the stroller automatically takes evasive action. For example, the stroller can change direction or adjust speed to avoid the obstacle. Furthermore, the vital signs monitoring function includes sensors to monitor the baby's body temperature and heart rate in real time. The sensors measure the baby's body temperature and heart rate and transmit the data to the parent's smartphone. If an abnormality is detected, a notification is sent to the parent, allowing them to always be aware of the baby's health. Finally, the energy harvesting function allows the stroller to self-charge using the rotation of the wheels and sunlight. Energy generated by the rotation of the wheels is recovered and stored in the battery.Furthermore, it can generate electricity using solar panels and charge its battery using sunlight. This allows the stroller to be used continuously. As a result, the Merry Cart AI agent can provide a safe and comfortable childcare experience.

[0029] The Merry Cart AI Agent according to this embodiment comprises a tracking unit, an avoidance unit, a monitoring unit, a notification unit, and a charging unit. The tracking unit tracks the parent. The tracking unit can track the parent's location using technologies such as GPS, RFID, and camera recognition. For example, the tracking unit can acquire the parent's location information in real time using GPS and transmit it to the stroller. The tracking unit can also identify the parent's location using RFID tags. Furthermore, the tracking unit can recognize and track the parent's face and posture using camera recognition technology. The avoidance unit avoids obstacles based on the information tracked by the tracking unit. For example, the avoidance unit can detect obstacles using an AI camera and sensors and automatically avoid them. For example, the avoidance unit can detect obstacles in front of the stroller using an AI camera and take avoidance action. Furthermore, the avoidance unit can also detect and avoid obstacles using ultrasonic sensors or infrared sensors. Furthermore, the avoidance unit can predict the location and movement of obstacles using an AI algorithm and calculate the optimal avoidance path. The monitoring unit monitors the baby's body temperature and heart rate. The monitoring unit can, for example, monitor the baby's body temperature and heart rate in real time using sensors and wearable devices. The monitoring unit is equipped with a temperature sensor to measure the baby's body temperature. The monitoring unit is also equipped with a heart rate sensor to measure the baby's heart rate. The monitoring unit can transmit data acquired from these sensors to the parent's smartphone in real time. The notification unit notifies of abnormalities based on the information monitored by the monitoring unit. The notification unit can inform the parent of an abnormality using, for example, smartphone notifications or alarms. For example, the notification unit sends a notification to the parent's smartphone if the baby's body temperature is abnormally high or the heart rate is abnormally low. The notification unit can also sound an alarm to inform the parent of an abnormality. Furthermore, the notification unit can apply different notification methods depending on the type of abnormality. The charging unit self-charges using the rotation of the wheels and sunlight. The charging unit can, for example, recover energy generated by the rotation of the wheels and store it in a battery. The charging unit can also generate electricity using solar panels and charge the battery using sunlight.By using these energy harvesting technologies in combination, the charging unit allows for continuous use of the stroller. This enables the Merry Cart AI agent according to the embodiment to provide a safe and comfortable childcare experience.

[0030] The tracking unit tracks the parent. The tracking unit can track the parent's location using technologies such as GPS, RFID, and camera recognition. Specifically, it uses GPS to acquire the parent's location information in real time and transmits it to the stroller. GPS receives signals from satellites to pinpoint the parent's exact location and transmits this information to the stroller, ensuring the parent's location is always known. It can also use RFID tags to identify the parent's location. RFID tags are attached to the parent's devices or clothing, and an RFID reader on the stroller receives the signal to determine the parent's location. Furthermore, camera recognition technology can be used to recognize and track the parent's face and posture. Camera recognition technology learns the parent's facial and body features and analyzes the video in real time to determine the parent's location. This allows the tracking unit to combine multiple technologies to accurately track the parent's location and ensure the stroller stays close to the parent at all times. Additionally, the tracking unit can detect the parent's speed and direction and adjust the stroller's movement accordingly. For example, if the parent suddenly changes direction or speeds up, the tracking unit immediately detects the change, allowing the stroller to smoothly follow. This allows the tracking unit to always maintain an optimal distance between the parent and the stroller, providing a safe and comfortable parenting experience.

[0031] The avoidance unit avoids obstacles based on information tracked by the tracking unit. For example, the avoidance unit can detect obstacles using an AI camera and sensors and automatically avoid them. Specifically, it uses an AI camera to detect obstacles in front of the stroller and takes avoidance action. The AI ​​camera uses image recognition technology to analyze the image in front and identify the type and location of the obstacle. It can also detect and avoid obstacles using ultrasonic sensors and infrared sensors. Ultrasonic sensors measure the distance to obstacles by emitting sound waves and receiving the reflection, while infrared sensors identify the location of obstacles by emitting infrared rays and receiving the reflection. Furthermore, the avoidance unit can use an AI algorithm to predict the location and movement of obstacles and calculate the optimal avoidance path. The AI ​​algorithm predicts the movement and location of obstacles based on past data and real-time information and calculates the optimal avoidance path. As a result, the avoidance unit can avoid obstacles quickly and accurately, ensuring the safe movement of the stroller. Moreover, even when multiple obstacles are present, the avoidance unit can calculate the optimal avoidance path and avoid them smoothly. For example, even in narrow passages or crowded areas, the avoidance unit analyzes the position and movement of obstacles in real time and calculates the optimal avoidance path. This allows the avoidance unit to ensure that the stroller moves safely and smoothly, and that the safety of both parent and baby is ensured.

[0032] The monitoring unit monitors the baby's body temperature and heart rate. For example, it can monitor the baby's body temperature and heart rate in real time using sensors and wearable devices. Specifically, it is equipped with a temperature sensor to measure the baby's body temperature. The temperature sensor measures the baby's body temperature by contacting the baby's skin and transmits the data to the parent's smartphone in real time. The monitoring unit is also equipped with a heart rate sensor to measure the baby's heart rate. The heart rate sensor detects the baby's heartbeat and transmits the data to the parent's smartphone in real time. This allows the monitoring unit to constantly monitor the baby's body temperature and heart rate and immediately notify the parent if an abnormality occurs. Furthermore, the monitoring unit can accumulate data on the baby's body temperature and heart rate to monitor their health over the long term. For example, it can analyze fluctuations in the baby's body temperature and heart rate based on past data to understand health trends. The monitoring unit can also use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. This allows the monitoring unit to monitor the baby's health in real time and respond quickly if an abnormality occurs.

[0033] The notification unit notifies parents of abnormalities based on information monitored by the monitoring unit. For example, the notification unit can notify parents of abnormalities using smartphone notifications or alarms. Specifically, it sends a notification to the parent's smartphone if the baby's body temperature is abnormally high or their heart rate is abnormally low. Smartphone notifications allow parents to be notified of abnormalities in real time, enabling them to respond quickly. The notification unit can also alert parents of abnormalities by sounding an alarm. The alarm uses sound and vibration to alert parents of the abnormality and draw their attention. Furthermore, the notification unit can apply different notification methods depending on the type of abnormality. For example, it can select a notification method appropriate to the type and urgency of the abnormality, such as using only smartphone notifications for minor abnormalities and combining them with alarms for serious abnormalities. This allows the notification unit to quickly provide parents with appropriate information and ensure the baby's safety. Additionally, the notification unit can collect parental feedback and continuously improve the accuracy and effectiveness of its notifications. For example, it can review notification methods and content based on feedback from parents who receive notifications, resulting in more effective notifications. Furthermore, the notification unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information not only through smartphone notifications but also through voice calls, SMS, and email. This allows the notification unit to quickly and reliably notify parents of any abnormalities and ensure the baby's safety.

[0034] The charging unit self-charges using the rotation of the wheels and sunlight. For example, it can recover energy generated by the rotation of the wheels and store it in a battery. Specifically, a generator attached to the wheel converts rotational energy into electrical energy, which is then stored in the battery. The charging unit can also generate electricity using a solar panel and charge the battery. The solar panel is mounted on top of the stroller, receiving sunlight to generate electrical energy, which is then stored in the battery. This allows the charging unit to efficiently recover energy using both wheel rotation and sunlight, enabling continuous use of the stroller. Furthermore, the charging unit can maximize energy efficiency by combining it with energy harvesting technology. For example, simultaneously utilizing wheel rotation energy and solar energy improves the battery charging speed. The charging unit also features an energy management system that constantly monitors the battery status and selects the optimal charging method. This extends battery life and allows for the continuous use of the stroller. Additionally, the charging unit can notify parents of energy usage in real time via their smartphone, assisting with energy management. This allows the charging unit to supply energy efficiently and continuously, maximizing the stroller's performance.

[0035] The tracking unit can track the parent using a smart wristband. For example, the tracking unit can acquire the parent's location information in real time using the smart wristband and transmit it to the stroller. The smart wristband has GPS functionality and can accurately track the parent's location. The smart wristband also has heart rate monitoring functionality and can monitor the parent's health. This allows for accurate tracking of the parent using the smart wristband. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the location information acquired from the smart wristband into a generating AI and have the generating AI calculate the optimal route for tracking the parent's location.

[0036] The obstacle avoidance unit can detect obstacles using an AI camera and sensors and automatically avoid them. For example, the avoidance unit can use the AI ​​camera to detect an obstacle in front of the stroller and take avoidance action. The AI ​​camera can detect obstacles using image recognition technology and determine their position and size. The avoidance unit can also detect and avoid obstacles using ultrasonic sensors and infrared sensors. Ultrasonic sensors can measure the distance to obstacles by emitting sound waves and detecting their reflection. Infrared sensors can determine the position of obstacles by emitting infrared rays and detecting their reflection. Furthermore, the avoidance unit can use an AI algorithm to predict the position and movement of obstacles and calculate the optimal avoidance path. The AI ​​algorithm can learn patterns of obstacle movement and predict future movements. As a result, by using the AI ​​camera and sensors, obstacles can be accurately detected and automatically avoided. Some or all of the above-described processes in the avoidance unit may be performed using AI, or not. For example, the avoidance unit can input data acquired from the AI ​​camera and sensors into a generating AI and have the generating AI calculate an obstacle avoidance path.

[0037] The monitoring unit can monitor the baby's body temperature and heart rate in real time. The monitoring unit monitors the baby's body temperature and heart rate in real time using, for example, sensors or wearable devices. The monitoring unit is equipped with, for example, a temperature sensor for measuring the baby's body temperature. The temperature sensor can accurately measure the baby's body temperature and transmit the data to the parent's smartphone in real time. The monitoring unit is also equipped with a heart rate sensor for measuring the baby's heart rate. The heart rate sensor can accurately measure the baby's heart rate and transmit the data to the parent's smartphone in real time. This allows for early detection of abnormalities by monitoring the baby's body temperature and heart rate in real time. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or without AI. For example, the monitoring unit can input data acquired from sensors into a generating AI and have the generating AI detect abnormalities in the baby's body temperature and heart rate.

[0038] The notification unit can notify parents if an abnormality is detected. The notification unit can inform parents of abnormalities using, for example, smartphone notifications or alarms. For example, the notification unit sends a notification to the parent's smartphone if the baby's body temperature is abnormally high or the heart rate is abnormally low. Smartphone notifications can inform parents of abnormalities in real time. The notification unit can also notify parents of abnormalities by sounding an alarm. Alarms can notify parents of abnormalities using sound or vibration. Furthermore, the notification unit can apply different notification methods depending on the type of abnormality. For example, it can send a voice notification if an abnormal body temperature is detected, and a vibration notification if an abnormal heart rate is detected. This allows parents to constantly monitor their baby's health by notifying them when an abnormality is detected. Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can input abnormality data acquired from sensors into a generating AI, and have the generating AI select the optimal notification method according to the type of abnormality.

[0039] The charging unit can self-charge using the rotation of the wheels and sunlight. For example, the charging unit can recover the energy generated by the rotation of the wheels and store it in a battery. The rotational energy of the wheels is converted into electricity using a dynamo or generator. The charging unit can also use sunlight to generate electricity with a solar panel and charge the battery. The solar panel converts sunlight into electricity using photoelectric conversion technology. This allows for sustained use by self-charging using the rotation of the wheels and sunlight. Some or all of the above processes in the charging unit may be performed using AI, for example, or without AI. For example, the charging unit can input data on the rotational energy of the wheels and solar power generation into a generating AI and have the generating AI select the optimal charging method.

[0040] The tracking unit can learn the parent's walking patterns and apply the optimal tracking algorithm. For example, the tracking unit can learn the parent's walking speed and rhythm and adjust the tracking algorithm based on that pattern. The parent's walking speed and rhythm are acquired in real time using sensors and cameras. The tracking unit can also learn the parent's walking habits and characteristics and apply a tracking method that suits them. For example, if the parent has a specific walking pattern, the tracking algorithm can be optimized based on that pattern. Furthermore, the tracking unit can record the parent's walking patterns over the long term and apply a tracking algorithm that takes into account changes due to season and time of day. In this way, by learning the parent's walking patterns, the optimal tracking algorithm can be applied. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the parent's walking data into a generating AI and have the generating AI select the optimal tracking algorithm.

[0041] The tracking unit can improve tracking accuracy by integrating the parent's location information with surrounding environmental information during tracking. For example, the tracking unit can acquire the parent's location information in real time and integrate it with surrounding obstacle and terrain information to improve tracking accuracy. The parent's location information is acquired using GPS or Wi-Fi positioning technology. The tracking unit can also analyze the movement of people in the surrounding area and select the optimal tracking route. For example, it can analyze the parent's location information and the movement of people in the surrounding area in real time and select the optimal tracking route to avoid collisions. Furthermore, the tracking unit can improve tracking accuracy by integrating the parent's location information with weather information. For example, it can acquire the parent's location information and weather information in real time and adjust the tracking route according to changes in weather. In this way, tracking accuracy can be improved by integrating the parent's location information with surrounding environmental information. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the parent's location information and surrounding environmental information into a generating AI and have the generating AI select the optimal method for improving tracking accuracy.

[0042] The tracking unit can monitor the parent's health status during tracking and stop tracking if an abnormality is detected. For example, the tracking unit can monitor the parent's heart rate and body temperature and stop tracking if an abnormality is detected. The parent's heart rate and body temperature are acquired in real time using sensors or wearable devices. The tracking unit can also stop tracking to ensure the parent's safety if an abnormality is observed in the parent's walking pattern. For example, it can analyze the parent's walking pattern in real time and stop tracking if an abnormality is detected. Furthermore, the tracking unit can analyze the parent's health status in real time and issue an alert if an abnormality is detected. This allows tracking to be stopped if an abnormality is detected by monitoring the parent's health status. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the parent's health data into a generating AI and have the generating AI select the optimal method for stopping tracking when an abnormality is detected.

[0043] The tracking unit can share location information with the parent's smartphone during tracking. For example, the tracking unit can share location information in real time by linking with the parent's smartphone. The parent's smartphone has GPS functionality and can accurately obtain the parent's location information. The tracking unit can also improve tracking accuracy by utilizing the GPS functionality of the parent's smartphone. For example, it can link with the parent's smartphone and save the location information to the cloud for later review. This allows for real-time sharing of location information by linking with the parent's smartphone. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the location information obtained from the parent's smartphone into a generating AI and have the generating AI select the optimal method for improving tracking accuracy.

[0044] The avoidance unit can apply different avoidance algorithms depending on the type of obstacle. For example, for fixed obstacles, the avoidance unit pre-calculates an avoidance path and avoids them. Fixed obstacles are, for example, immovable objects such as buildings and walls. The avoidance unit can also predict the movement of moving obstacles in real time and avoid them. Moving obstacles are, for example, moving objects such as other pedestrians and vehicles. Furthermore, for obstacles with height, the avoidance unit can calculate an avoidance path in three dimensions and avoid them. Obstacles with height are, for example, three-dimensional objects such as stairs and steps. By applying different avoidance algorithms depending on the type of obstacle, optimal avoidance becomes possible. Some or all of the above processing in the avoidance unit may be performed using, for example, AI, or without AI. For example, the avoidance unit can input the type of obstacle into a generating AI and have the generating AI select the optimal avoidance algorithm.

[0045] The avoidance unit can analyze surrounding environmental information in real time during avoidance and select the optimal avoidance path. For example, the avoidance unit can analyze the position and movement of surrounding obstacles in real time and select the optimal avoidance path. The position and movement of surrounding obstacles are acquired in real time using sensors and cameras. The avoidance unit can also analyze the movement of people in the surroundings and select an avoidance path to avoid collisions. For example, it can analyze the movement of people in the surroundings in real time and select the optimal avoidance path. Furthermore, the avoidance unit can analyze surrounding terrain information and select the optimal avoidance path. Surrounding terrain information is analyzed using, for example, map data or terrain data acquired from sensors. This allows the optimal avoidance path to be selected by analyzing surrounding environmental information in real time. Some or all of the above processing in the avoidance unit may be performed using, for example, AI, or without using AI. For example, the avoidance unit can input surrounding environmental information into a generating AI and have the generating AI select the optimal avoidance path.

[0046] The avoidance unit can predict the movement of obstacles during avoidance and take avoidance actions based on those predictions. For example, the avoidance unit can predict the movement of obstacles in real time and take the optimal avoidance action. The movement of obstacles is acquired in real time using sensors or cameras. The avoidance unit can also learn patterns of obstacle movement and take avoidance actions based on those predictions. For example, it can learn patterns of obstacle movement, predict future movements, and take avoidance actions. Furthermore, the avoidance unit can predict the movement of obstacles and plan avoidance actions in advance. This allows it to take the optimal avoidance action by predicting the movement of obstacles. Some or all of the above processing in the avoidance unit may be performed using AI, for example, or without AI. For example, the avoidance unit can input obstacle movement data into a generating AI and have the generating AI select the optimal avoidance action.

[0047] The avoidance unit can perform coordinated actions to avoid collisions with other moving objects during avoidance. For example, the avoidance unit can analyze the position and movement of other moving objects in real time and perform coordinated actions to avoid collisions. The position and movement of other moving objects are acquired in real time using sensors and cameras. The avoidance unit can also communicate with other moving objects and perform coordinated actions to avoid collisions. For example, it can communicate with other moving objects and perform optimal coordinated actions. Furthermore, the avoidance unit can predict the movement of other moving objects and perform coordinated actions to avoid collisions. This enables safe movement by performing coordinated actions to avoid collisions with other moving objects. Some or all of the above processing in the avoidance unit may be performed using AI, for example, or without AI. For example, the avoidance unit can input position and movement data of other moving objects into a generating AI and have the generating AI select the optimal coordinated action.

[0048] The monitoring unit can learn the fluctuation patterns of the baby's body temperature and heart rate, enabling early detection of abnormalities. For example, the monitoring unit can record the fluctuation patterns of the baby's body temperature and heart rate over the long term to detect abnormalities early. The fluctuation patterns of the baby's body temperature and heart rate are acquired in real time using sensors or wearable devices. The monitoring unit can also analyze the fluctuation patterns of the baby's body temperature and heart rate in real time to detect abnormalities early. For example, it can analyze the fluctuation patterns of the baby's body temperature and heart rate in real time to detect signs of abnormality in advance. By learning the fluctuation patterns of the baby's body temperature and heart rate, early detection of abnormalities becomes possible. 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 baby's body temperature and heart rate data into a generating AI and have the generating AI select the optimal method for early detection of abnormalities.

[0049] The monitoring unit can analyze the baby's sleep state during monitoring and select the optimal monitoring timing. For example, the monitoring unit can analyze the baby's sleep state in real time and select the optimal monitoring timing. The baby's sleep state is acquired in real time using an electroencephalogram (EEG) sensor and motion sensors. The monitoring unit can also learn the baby's sleep patterns and select the optimal monitoring timing. For example, the baby's sleep patterns can be recorded over a long period, and monitoring can be strengthened by identifying times when abnormalities are likely to occur. This allows the optimal monitoring timing to be selected by analyzing the baby's sleep state. 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 baby's sleep data into a generating AI and have the generating AI select the optimal method for selecting the optimal monitoring timing.

[0050] The monitoring unit can additionally monitor the baby's respiratory status during monitoring. For example, the monitoring unit can monitor the baby's respiratory status in real time and issue an alert if an abnormality is detected. The baby's respiratory status is acquired in real time using a respiratory sensor. The monitoring unit can also learn the baby's respiratory pattern and perform early detection of abnormalities. For example, it can record the baby's respiratory pattern over a long period and detect signs of abnormality in advance. Furthermore, the monitoring unit can monitor the baby's respiratory status and integrate it with body temperature and heart rate to evaluate the overall health status. This allows for an evaluation of the baby's overall health status by additionally monitoring the baby's respiratory status. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input the baby's respiratory data into a generating AI and have the generating AI select the optimal method for evaluating the overall health status.

[0051] The monitoring unit can detect the baby's movements during monitoring and issue an alert if abnormal movements are detected. For example, the monitoring unit can detect the baby's movements in real time and issue an alert if abnormal movements are detected. The baby's movements are acquired in real time using motion sensors and image analysis technology. The monitoring unit can also learn the baby's movement patterns and detect abnormal movements early. For example, it can record the baby's movement patterns over a long period and detect signs of abnormality in advance. Furthermore, the monitoring unit can monitor the baby's movements and notify the parents if abnormal movements are detected. This allows the unit to issue an alert if abnormal movements are detected by detecting the baby's movements. 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 baby's movement data into a generating AI and have the generating AI select the optimal method for detecting abnormal movements.

[0052] The notification unit can apply different notification methods depending on the type of abnormality when it issues a notification. For example, if an abnormal body temperature is detected, the notification unit will issue an audible notification. Abnormal body temperature is acquired in real time using a temperature sensor. The notification unit can also issue a vibration notification if an abnormal heart rate is detected. Abnormal heart rate is acquired in real time using a heart rate sensor. Furthermore, the notification unit can issue a visual notification if an abnormal respiratory state is detected. Abnormal respiratory state is acquired in real time using a respiratory sensor. This allows for optimal notifications for parents by applying different notification methods depending on the type of abnormality. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input abnormality data into a generating AI and have the generating AI select the optimal notification method according to the type of abnormality.

[0053] The notification unit can select the optimal notification timing by considering the parent's current activity status. For example, if the parent is driving, the notification unit will delay the notification to a safe time. The parent's driving status is acquired in real time using smartphone sensors and in-vehicle systems. The notification unit can also send notifications in silent mode if the parent is in a meeting. The parent's meeting status is acquired in conjunction with a calendar app or meeting system. Furthermore, the notification unit can send an immediate notification if the parent is relaxed. This allows the system to select the optimal notification timing by considering the parent's current activity status. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the parent's activity data into a generating AI and have the generating AI select the optimal notification timing.

[0054] The notification unit can send notifications in conjunction with the parent's smartwatch when a notification is received. For example, the notification unit can send a vibration notification to the parent's smartwatch. The smartwatch can connect to a smartphone using Bluetooth® or Wi-Fi. The notification unit can also display visual notifications on the parent's smartwatch. Visual notifications display text and icons on the smartwatch screen. Furthermore, the notification unit can display the notification content in detail in conjunction with the parent's smartwatch. This allows for quick and reliable notifications by coordinating with the parent's smartwatch. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input notification data to be sent to the smartwatch into a generating AI and have the generating AI select the optimal notification method.

[0055] The notification unit can, upon notification, coordinate with the parent smart home system to alert of an anomaly. For example, the notification unit sends an anomaly notification to the parent smart home system. The smart home system can be coordinated using IoT protocols and communication technologies. The notification unit can also coordinate with the parent smart home system to sound an alarm in the event of an anomaly. The alarm uses sound and vibration to alert of the anomaly. Furthermore, the notification unit can coordinate with the parent smart home system to flash lights in the event of an anomaly. This allows for rapid notification of an anomaly by coordinating with the parent smart home system. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input anomaly data to be sent to the smart home system into a generating AI and have the generating AI select the optimal notification method.

[0056] The charging unit can optimize the recovery of wheel rotational energy during charging. For example, the charging unit optimizes energy recovery efficiency according to the wheel rotation speed. The wheel rotation speed is acquired in real time using sensors. The charging unit can also learn the wheel rotation pattern and apply the optimal energy recovery method. For example, it can record the wheel rotation pattern over a long period and apply the optimal energy recovery method. Furthermore, the charging unit can analyze the wheel rotation energy in real time and recover it efficiently. This improves charging efficiency by efficiently recovering the wheel rotation energy. Some or all of the above processing in the charging unit may be performed using AI, for example, or without AI. For example, the charging unit can input wheel rotation data into a generating AI and have the generating AI select the optimal method for optimizing energy recovery efficiency.

[0057] The charging unit can improve charging efficiency by adjusting the angle of sunlight irradiation during charging. For example, the charging unit can analyze the angle of sunlight irradiation in real time and adjust it to the optimal angle. The angle of sunlight irradiation is acquired in real time using a sensor. The charging unit can also learn the angle of sunlight irradiation and apply an efficient charging method. For example, it can record the angle of sunlight irradiation over a long period and apply the optimal charging method. Furthermore, the charging unit can adjust the angle of sunlight irradiation to maximize charging efficiency. In this way, charging efficiency can be improved by adjusting the angle of sunlight irradiation. Some or all of the above processing in the charging unit may be performed using AI, for example, or without AI. For example, the charging unit can input sunlight irradiation data into a generating AI and have the generating AI select the optimal method for adjusting the irradiation angle.

[0058] The charging unit can improve its charging efficiency by using other energy harvesting technologies in combination during charging. For example, the charging unit can improve charging efficiency by using solar and wind power in combination. Solar energy is converted into electricity using solar panels, and wind power is converted into electricity using a wind turbine. The charging unit can also improve charging efficiency by using the rotational energy of the wheels and vibration energy in combination. The rotational energy of the wheels is converted into electricity using a dynamo or generator, and vibration energy is converted into electricity using a vibration generator. Furthermore, the charging unit can also improve charging efficiency by using solar energy and thermal energy in combination. Solar energy is converted into electricity using solar panels, and thermal energy is converted into electricity using a thermoelectric generator. This allows for improved charging efficiency by using other energy harvesting technologies in combination. Some or all of the above processes in the charging unit may be performed using AI, for example, or without AI. For example, the charging unit can input energy harvesting data into a generating AI and have the generating AI select the optimal energy recovery method.

[0059] The charging unit can monitor the charging status in conjunction with the charging station during charging. For example, the charging unit can monitor the charging status in real time in conjunction with the charging station. The charging station can be connected using IoT protocols and communication technologies. The charging unit can also analyze data from the charging station and apply the optimal charging method. For example, it can analyze data from the charging station in real time and apply the optimal charging method. Furthermore, the charging unit can also save the charging status to the cloud in conjunction with the charging station for later review. This allows for real-time monitoring of the charging status by connecting with the charging station. Some or all of the above processes in the charging unit may be performed using AI, for example, or without AI. For example, the charging unit can input data from the charging station into a generating AI and have the generating AI select the optimal charging method.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The Merry Cart AI agent can learn the parent's walking patterns and apply the optimal tracking algorithm. For example, it can learn the parent's walking speed and rhythm and adjust the tracking algorithm based on that pattern. The parent's walking speed and rhythm are acquired in real time using sensors and cameras. It can also learn the parent's walking habits and characteristics and apply a tracking method accordingly. For example, if the parent has a specific walking pattern, the tracking algorithm will be optimized based on that pattern. Furthermore, it can record the parent's walking patterns over the long term and apply a tracking algorithm that takes into account changes due to season and time of day. In this way, by learning the parent's walking patterns, it can apply the optimal tracking algorithm.

[0062] The MerryCart AI agent can monitor the parent's health and stop tracking if an abnormality is detected. For example, it can monitor the parent's heart rate and body temperature and stop tracking if an abnormality is detected. The parent's heart rate and body temperature are obtained in real time using sensors and wearable devices. It can also stop tracking to ensure the parent's safety if an abnormality is detected in the parent's walking pattern. For example, it can analyze the parent's walking pattern in real time and stop tracking if an abnormality is detected. Furthermore, it can analyze the parent's health status in real time and issue an alert if an abnormality is detected. This allows the agent to monitor the parent's health status and stop tracking if an abnormality is detected.

[0063] The MerryCart AI agent can share location information by linking with the parent's smartphone. For example, it can link with the parent's smartphone and share location information in real time. The parent's smartphone has GPS functionality, allowing it to accurately obtain the parent's location information. Furthermore, the tracking accuracy can be improved by utilizing the parent's smartphone's GPS functionality. For example, it can link with the parent's smartphone and save location information to the cloud for later review. This allows for real-time sharing of location information by linking with the parent's smartphone.

[0064] The MerryCart AI agent can work with the parent's smart home system to alert it to anomalies. For example, it can send an anomaly notification to the parent's smart home system. The smart home system can work with the MerryCart AI agent using IoT protocols and communication technologies. It can also work with the parent's smart home system to sound an alarm in case of an anomaly. The alarm will alert it to the anomaly using voice and vibration. Furthermore, it can work with the parent's smart home system to make the lights flash in case of an anomaly. This allows for rapid notification of anomalies by working with the parent's smart home system.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The tracking unit tracks the parent. The tracking unit can track the parent's location using technologies such as GPS, RFID, and camera recognition. For example, the tracking unit can use GPS to obtain the parent's location information in real time and transmit it to the stroller. The tracking unit can also identify the parent's location using RFID tags. Furthermore, the tracking unit can use camera recognition technology to recognize and track the parent's face and posture. Step 2: The avoidance unit avoids obstacles based on the information tracked by the tracking unit. The avoidance unit can, for example, use an AI camera and sensors to detect obstacles and automatically avoid them. For example, the avoidance unit can use an AI camera to detect an obstacle in front of the stroller and take avoidance action. The avoidance unit can also use ultrasonic sensors or infrared sensors to detect and avoid obstacles. Furthermore, the avoidance unit can use an AI algorithm to predict the position and movement of obstacles and calculate the optimal avoidance path. Step 3: The monitoring unit monitors the baby's body temperature and heart rate. The monitoring unit can monitor the baby's body temperature and heart rate in real time, for example, using sensors or wearable devices. For example, the monitoring unit is equipped with a temperature sensor to measure the baby's body temperature. The monitoring unit is also equipped with a heart rate sensor to measure the baby's heart rate. The monitoring unit can transmit the data acquired from these sensors to the parent's smartphone in real time. Step 4: The notification unit notifies of abnormalities based on the information monitored by the monitoring unit. The notification unit can, for example, notify parents of abnormalities using smartphone notifications or alarms. For example, the notification unit sends a notification to the parent's smartphone if the baby's body temperature is abnormally high or their heart rate is abnormally low. The notification unit can also sound an alarm to notify parents of abnormalities. Furthermore, the notification unit can apply different notification methods depending on the type of abnormality. Step 5: The charging unit self-charges using the rotation of the wheels and sunlight. For example, the charging unit can recover energy generated by the rotation of the wheels and store it in a battery. The charging unit can also generate electricity using solar panels and charge the battery using sunlight. By using these energy harvesting technologies in combination, the stroller can be used continuously.

[0067] (Example of form 2) The Merry Cart AI Agent according to an embodiment of the present invention is a stroller that integrates AI technology. This Merry Cart AI Agent is designed to solve the problems of conventional strollers, such as safety, inconvenience of movement, and complexity of management. The Merry Cart AI Agent tracks the parent using a smart wristband with an automatic follow mode that tracks the parent, enabling hands-free movement. This allows the parent to use their hands freely, improving convenience while on the go. Furthermore, it is equipped with an obstacle avoidance system using an AI camera and sensors, which can detect and automatically avoid obstacles, ensuring safety while on the go. It also has a vital signs monitoring function that monitors the baby's body temperature and heart rate in real time, and notifies the parent if an abnormality is detected, allowing the baby's health to be constantly monitored. In addition, it is equipped with an energy harvesting function that uses the rotation of the wheels and sunlight, enabling self-charging and continuous use. These components provide a safe and comfortable childcare experience. For example, the Merry Cart AI Agent uses a smart wristband to track the parent. The smart wristband acquires the parent's location information in real time and transmits it to the stroller. This allows the stroller to track the parent's position and automatically move in accordance with the parent's movements. Additionally, an AI camera and sensor-based obstacle avoidance system uses a camera and sensors mounted on the front of the stroller to detect obstacles. When an obstacle is detected, the stroller automatically takes evasive action. For example, the stroller can change direction or adjust speed to avoid the obstacle. Furthermore, the vital signs monitoring function includes sensors to monitor the baby's body temperature and heart rate in real time. The sensors measure the baby's body temperature and heart rate and transmit the data to the parent's smartphone. If an abnormality is detected, a notification is sent to the parent, allowing them to always be aware of the baby's health. Finally, the energy harvesting function allows the stroller to self-charge using the rotation of the wheels and sunlight. Energy generated by the rotation of the wheels is recovered and stored in the battery.Furthermore, it can generate electricity using solar panels and charge its battery using sunlight. This allows the stroller to be used continuously. As a result, the Merry Cart AI agent can provide a safe and comfortable childcare experience.

[0068] The Merry Cart AI Agent according to this embodiment comprises a tracking unit, an avoidance unit, a monitoring unit, a notification unit, and a charging unit. The tracking unit tracks the parent. The tracking unit can track the parent's location using technologies such as GPS, RFID, and camera recognition. For example, the tracking unit can acquire the parent's location information in real time using GPS and transmit it to the stroller. The tracking unit can also identify the parent's location using RFID tags. Furthermore, the tracking unit can recognize and track the parent's face and posture using camera recognition technology. The avoidance unit avoids obstacles based on the information tracked by the tracking unit. For example, the avoidance unit can detect obstacles using an AI camera and sensors and automatically avoid them. For example, the avoidance unit can detect obstacles in front of the stroller using an AI camera and take avoidance action. Furthermore, the avoidance unit can also detect and avoid obstacles using ultrasonic sensors or infrared sensors. Furthermore, the avoidance unit can predict the location and movement of obstacles using an AI algorithm and calculate the optimal avoidance path. The monitoring unit monitors the baby's body temperature and heart rate. The monitoring unit can, for example, monitor the baby's body temperature and heart rate in real time using sensors and wearable devices. The monitoring unit is equipped with a temperature sensor to measure the baby's body temperature. The monitoring unit is also equipped with a heart rate sensor to measure the baby's heart rate. The monitoring unit can transmit data acquired from these sensors to the parent's smartphone in real time. The notification unit notifies of abnormalities based on the information monitored by the monitoring unit. The notification unit can inform the parent of an abnormality using, for example, smartphone notifications or alarms. For example, the notification unit sends a notification to the parent's smartphone if the baby's body temperature is abnormally high or the heart rate is abnormally low. The notification unit can also sound an alarm to inform the parent of an abnormality. Furthermore, the notification unit can apply different notification methods depending on the type of abnormality. The charging unit self-charges using the rotation of the wheels and sunlight. The charging unit can, for example, recover energy generated by the rotation of the wheels and store it in a battery. The charging unit can also generate electricity using solar panels and charge the battery using sunlight.By using these energy harvesting technologies in combination, the charging unit allows for continuous use of the stroller. This enables the Merry Cart AI agent according to the embodiment to provide a safe and comfortable childcare experience.

[0069] The tracking unit tracks the parent. The tracking unit can track the parent's location using technologies such as GPS, RFID, and camera recognition. Specifically, it uses GPS to acquire the parent's location information in real time and transmits it to the stroller. GPS receives signals from satellites to pinpoint the parent's exact location and transmits this information to the stroller, ensuring the parent's location is always known. It can also use RFID tags to identify the parent's location. RFID tags are attached to the parent's devices or clothing, and an RFID reader on the stroller receives the signal to determine the parent's location. Furthermore, camera recognition technology can be used to recognize and track the parent's face and posture. Camera recognition technology learns the parent's facial and body features and analyzes the video in real time to determine the parent's location. This allows the tracking unit to combine multiple technologies to accurately track the parent's location and ensure the stroller stays close to the parent at all times. Additionally, the tracking unit can detect the parent's speed and direction and adjust the stroller's movement accordingly. For example, if the parent suddenly changes direction or speeds up, the tracking unit immediately detects the change, allowing the stroller to smoothly follow. This allows the tracking unit to always maintain an optimal distance between the parent and the stroller, providing a safe and comfortable parenting experience.

[0070] The avoidance unit avoids obstacles based on information tracked by the tracking unit. For example, the avoidance unit can detect obstacles using an AI camera and sensors and automatically avoid them. Specifically, it uses an AI camera to detect obstacles in front of the stroller and takes avoidance action. The AI ​​camera uses image recognition technology to analyze the image in front and identify the type and location of the obstacle. It can also detect and avoid obstacles using ultrasonic sensors and infrared sensors. Ultrasonic sensors measure the distance to obstacles by emitting sound waves and receiving the reflection, while infrared sensors identify the location of obstacles by emitting infrared rays and receiving the reflection. Furthermore, the avoidance unit can use an AI algorithm to predict the location and movement of obstacles and calculate the optimal avoidance path. The AI ​​algorithm predicts the movement and location of obstacles based on past data and real-time information and calculates the optimal avoidance path. As a result, the avoidance unit can avoid obstacles quickly and accurately, ensuring the safe movement of the stroller. Moreover, even when multiple obstacles are present, the avoidance unit can calculate the optimal avoidance path and avoid them smoothly. For example, even in narrow passages or crowded areas, the avoidance unit analyzes the position and movement of obstacles in real time and calculates the optimal avoidance path. This allows the avoidance unit to ensure that the stroller moves safely and smoothly, and that the safety of both parent and baby is ensured.

[0071] The monitoring unit monitors the baby's body temperature and heart rate. For example, it can monitor the baby's body temperature and heart rate in real time using sensors and wearable devices. Specifically, it is equipped with a temperature sensor to measure the baby's body temperature. The temperature sensor measures the baby's body temperature by contacting the baby's skin and transmits the data to the parent's smartphone in real time. The monitoring unit is also equipped with a heart rate sensor to measure the baby's heart rate. The heart rate sensor detects the baby's heartbeat and transmits the data to the parent's smartphone in real time. This allows the monitoring unit to constantly monitor the baby's body temperature and heart rate and immediately notify the parent if an abnormality occurs. Furthermore, the monitoring unit can accumulate data on the baby's body temperature and heart rate to monitor their health over the long term. For example, it can analyze fluctuations in the baby's body temperature and heart rate based on past data to understand health trends. The monitoring unit can also use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. This allows the monitoring unit to monitor the baby's health in real time and respond quickly if an abnormality occurs.

[0072] The notification unit notifies parents of abnormalities based on information monitored by the monitoring unit. For example, the notification unit can notify parents of abnormalities using smartphone notifications or alarms. Specifically, it sends a notification to the parent's smartphone if the baby's body temperature is abnormally high or their heart rate is abnormally low. Smartphone notifications allow parents to be notified of abnormalities in real time, enabling them to respond quickly. The notification unit can also alert parents of abnormalities by sounding an alarm. The alarm uses sound and vibration to alert parents of the abnormality and draw their attention. Furthermore, the notification unit can apply different notification methods depending on the type of abnormality. For example, it can select a notification method appropriate to the type and urgency of the abnormality, such as using only smartphone notifications for minor abnormalities and combining them with alarms for serious abnormalities. This allows the notification unit to quickly provide parents with appropriate information and ensure the baby's safety. Additionally, the notification unit can collect parental feedback and continuously improve the accuracy and effectiveness of its notifications. For example, it can review notification methods and content based on feedback from parents who receive notifications, resulting in more effective notifications. Furthermore, the notification unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information not only through smartphone notifications but also through voice calls, SMS, and email. This allows the notification unit to quickly and reliably notify parents of any abnormalities and ensure the baby's safety.

[0073] The charging unit self-charges using the rotation of the wheels and sunlight. For example, it can recover energy generated by the rotation of the wheels and store it in a battery. Specifically, a generator attached to the wheel converts rotational energy into electrical energy, which is then stored in the battery. The charging unit can also generate electricity using a solar panel and charge the battery. The solar panel is mounted on top of the stroller, receiving sunlight to generate electrical energy, which is then stored in the battery. This allows the charging unit to efficiently recover energy using both wheel rotation and sunlight, enabling continuous use of the stroller. Furthermore, the charging unit can maximize energy efficiency by combining it with energy harvesting technology. For example, simultaneously utilizing wheel rotation energy and solar energy improves the battery charging speed. The charging unit also features an energy management system that constantly monitors the battery status and selects the optimal charging method. This extends battery life and allows for the continuous use of the stroller. Additionally, the charging unit can notify parents of energy usage in real time via their smartphone, assisting with energy management. This allows the charging unit to supply energy efficiently and continuously, maximizing the stroller's performance.

[0074] The tracking unit can track the parent using a smart wristband. For example, the tracking unit can acquire the parent's location information in real time using the smart wristband and transmit it to the stroller. The smart wristband has GPS functionality and can accurately track the parent's location. The smart wristband also has heart rate monitoring functionality and can monitor the parent's health. This allows for accurate tracking of the parent using the smart wristband. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the location information acquired from the smart wristband into a generating AI and have the generating AI calculate the optimal route for tracking the parent's location.

[0075] The obstacle avoidance unit can detect obstacles using an AI camera and sensors and automatically avoid them. For example, the avoidance unit can use the AI ​​camera to detect an obstacle in front of the stroller and take avoidance action. The AI ​​camera can detect obstacles using image recognition technology and determine their position and size. The avoidance unit can also detect and avoid obstacles using ultrasonic sensors and infrared sensors. Ultrasonic sensors can measure the distance to obstacles by emitting sound waves and detecting their reflection. Infrared sensors can determine the position of obstacles by emitting infrared rays and detecting their reflection. Furthermore, the avoidance unit can use an AI algorithm to predict the position and movement of obstacles and calculate the optimal avoidance path. The AI ​​algorithm can learn patterns of obstacle movement and predict future movements. As a result, by using the AI ​​camera and sensors, obstacles can be accurately detected and automatically avoided. Some or all of the above-described processes in the avoidance unit may be performed using AI, or not. For example, the avoidance unit can input data acquired from the AI ​​camera and sensors into a generating AI and have the generating AI calculate an obstacle avoidance path.

[0076] The monitoring unit can monitor the baby's body temperature and heart rate in real time. The monitoring unit monitors the baby's body temperature and heart rate in real time using, for example, sensors or wearable devices. The monitoring unit is equipped with, for example, a temperature sensor for measuring the baby's body temperature. The temperature sensor can accurately measure the baby's body temperature and transmit the data to the parent's smartphone in real time. The monitoring unit is also equipped with a heart rate sensor for measuring the baby's heart rate. The heart rate sensor can accurately measure the baby's heart rate and transmit the data to the parent's smartphone in real time. This allows for early detection of abnormalities by monitoring the baby's body temperature and heart rate in real time. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or without AI. For example, the monitoring unit can input data acquired from sensors into a generating AI and have the generating AI detect abnormalities in the baby's body temperature and heart rate.

[0077] The notification unit can notify parents if an abnormality is detected. The notification unit can inform parents of abnormalities using, for example, smartphone notifications or alarms. For example, the notification unit sends a notification to the parent's smartphone if the baby's body temperature is abnormally high or the heart rate is abnormally low. Smartphone notifications can inform parents of abnormalities in real time. The notification unit can also notify parents of abnormalities by sounding an alarm. Alarms can notify parents of abnormalities using sound or vibration. Furthermore, the notification unit can apply different notification methods depending on the type of abnormality. For example, it can send a voice notification if an abnormal body temperature is detected, and a vibration notification if an abnormal heart rate is detected. This allows parents to constantly monitor their baby's health by notifying them when an abnormality is detected. Some or all of the above processing in the notification unit may be performed using, for example, AI, or not using AI. For example, the notification unit can input abnormality data acquired from sensors into a generating AI, and have the generating AI select the optimal notification method according to the type of abnormality.

[0078] The charging unit can self-charge using the rotation of the wheels and sunlight. For example, the charging unit can recover the energy generated by the rotation of the wheels and store it in a battery. The rotational energy of the wheels is converted into electricity using a dynamo or generator. The charging unit can also use sunlight to generate electricity with a solar panel and charge the battery. The solar panel converts sunlight into electricity using photoelectric conversion technology. This allows for sustained use by self-charging using the rotation of the wheels and sunlight. Some or all of the above processes in the charging unit may be performed using AI, for example, or without AI. For example, the charging unit can input data on the rotational energy of the wheels and solar power generation into a generating AI and have the generating AI select the optimal charging method.

[0079] The tracking unit can estimate the parent's emotions and adjust the tracking accuracy based on the estimated emotions. For example, the tracking unit can capture the parent's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, if the parent is stressed, the tracking accuracy is increased to respond quickly to the parent's movements. The tracking unit can also record the parent's voice and estimate their emotions using voice analysis technology. For example, if the parent is relaxed, the tracking accuracy is slightly reduced to allow for more natural movements. Furthermore, the tracking unit can collect the parent's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, if the parent is in a hurry, the tracking accuracy is maximized to respond to the parent's sudden movements. This allows for a quick response to the parent's movements by adjusting the tracking accuracy based on the parent's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input parental emotion data into a generating AI and have the generating AI select the optimal method for adjusting the accuracy of the tracking.

[0080] The tracking unit can learn the parent's walking patterns and apply the optimal tracking algorithm. For example, the tracking unit can learn the parent's walking speed and rhythm and adjust the tracking algorithm based on that pattern. The parent's walking speed and rhythm are acquired in real time using sensors and cameras. The tracking unit can also learn the parent's walking habits and characteristics and apply a tracking method that suits them. For example, if the parent has a specific walking pattern, the tracking algorithm can be optimized based on that pattern. Furthermore, the tracking unit can record the parent's walking patterns over the long term and apply a tracking algorithm that takes into account changes due to season and time of day. In this way, by learning the parent's walking patterns, the optimal tracking algorithm can be applied. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the parent's walking data into a generating AI and have the generating AI select the optimal tracking algorithm.

[0081] The tracking unit can improve tracking accuracy by integrating the parent's location information with surrounding environmental information during tracking. For example, the tracking unit can acquire the parent's location information in real time and integrate it with surrounding obstacle and terrain information to improve tracking accuracy. The parent's location information is acquired using GPS or Wi-Fi positioning technology. The tracking unit can also analyze the movement of people in the surrounding area and select the optimal tracking route. For example, it can analyze the parent's location information and the movement of people in the surrounding area in real time and select the optimal tracking route to avoid collisions. Furthermore, the tracking unit can improve tracking accuracy by integrating the parent's location information with weather information. For example, it can acquire the parent's location information and weather information in real time and adjust the tracking route according to changes in weather. In this way, tracking accuracy can be improved by integrating the parent's location information with surrounding environmental information. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the parent's location information and surrounding environmental information into a generating AI and have the generating AI select the optimal method for improving tracking accuracy.

[0082] The tracking unit can estimate the parent's emotions and adjust the tracking speed based on the estimated emotions. For example, the tracking unit can capture the parent's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, if the parent is in a hurry, it can increase the tracking speed to follow the parent's movements. The tracking unit can also record the parent's voice and estimate emotions using voice analysis technology. For example, if the parent is relaxed, it can slow down the tracking speed to allow for natural movements. Furthermore, the tracking unit can collect the parent's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, if the parent is stressed, it can adjust the tracking speed appropriately to respond quickly to the parent's movements. This allows for a quick response to the parent's movements by adjusting the tracking speed based on the parent's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input parent emotion data into a generating AI and have the generating AI select the optimal method for adjusting the tracking speed.

[0083] The tracking unit can monitor the parent's health status during tracking and stop tracking if an abnormality is detected. For example, the tracking unit can monitor the parent's heart rate and body temperature and stop tracking if an abnormality is detected. The parent's heart rate and body temperature are acquired in real time using sensors or wearable devices. The tracking unit can also stop tracking to ensure the parent's safety if an abnormality is observed in the parent's walking pattern. For example, it can analyze the parent's walking pattern in real time and stop tracking if an abnormality is detected. Furthermore, the tracking unit can analyze the parent's health status in real time and issue an alert if an abnormality is detected. This allows tracking to be stopped if an abnormality is detected by monitoring the parent's health status. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the parent's health data into a generating AI and have the generating AI select the optimal method for stopping tracking when an abnormality is detected.

[0084] The tracking unit can share location information with the parent's smartphone during tracking. For example, the tracking unit can share location information in real time by linking with the parent's smartphone. The parent's smartphone has GPS functionality and can accurately obtain the parent's location information. The tracking unit can also improve tracking accuracy by utilizing the GPS functionality of the parent's smartphone. For example, it can link with the parent's smartphone and save the location information to the cloud for later review. This allows for real-time sharing of location information by linking with the parent's smartphone. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the location information obtained from the parent's smartphone into a generating AI and have the generating AI select the optimal method for improving tracking accuracy.

[0085] The avoidance unit can estimate the parent's emotions and adjust the timing of avoidance based on the estimated emotions. For example, the avoidance unit can capture the parent's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, if the parent is tense, it will avoid obstacles earlier. The avoidance unit can also record the parent's voice and estimate emotions using voice analysis technology. For example, if the parent is relaxed, it will avoid obstacles at a natural timing. Furthermore, the avoidance unit can collect the parent's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, if the parent is in a hurry, it will avoid obstacles quickly. This allows for a rapid response to the parent's movements by adjusting the timing of avoidance based on the parent's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The 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 avoidance unit may be performed using AI, for example, or without AI. For example, the avoidance unit can input parental emotional data into a generating AI, allowing the AI ​​to select the optimal method for adjusting the timing of avoidance.

[0086] The avoidance unit can apply different avoidance algorithms depending on the type of obstacle. For example, for fixed obstacles, the avoidance unit pre-calculates an avoidance path and avoids them. Fixed obstacles are, for example, immovable objects such as buildings and walls. The avoidance unit can also predict the movement of moving obstacles in real time and avoid them. Moving obstacles are, for example, moving objects such as other pedestrians and vehicles. Furthermore, for obstacles with height, the avoidance unit can calculate an avoidance path in three dimensions and avoid them. Obstacles with height are, for example, three-dimensional objects such as stairs and steps. By applying different avoidance algorithms depending on the type of obstacle, optimal avoidance becomes possible. Some or all of the above processing in the avoidance unit may be performed using, for example, AI, or without AI. For example, the avoidance unit can input the type of obstacle into a generating AI and have the generating AI select the optimal avoidance algorithm.

[0087] The avoidance unit can analyze surrounding environmental information in real time during avoidance and select the optimal avoidance path. For example, the avoidance unit can analyze the position and movement of surrounding obstacles in real time and select the optimal avoidance path. The position and movement of surrounding obstacles are acquired in real time using sensors and cameras. The avoidance unit can also analyze the movement of people in the surroundings and select an avoidance path to avoid collisions. For example, it can analyze the movement of people in the surroundings in real time and select the optimal avoidance path. Furthermore, the avoidance unit can analyze surrounding terrain information and select the optimal avoidance path. Surrounding terrain information is analyzed using, for example, map data or terrain data acquired from sensors. This allows the optimal avoidance path to be selected by analyzing surrounding environmental information in real time. Some or all of the above processing in the avoidance unit may be performed using, for example, AI, or without using AI. For example, the avoidance unit can input surrounding environmental information into a generating AI and have the generating AI select the optimal avoidance path.

[0088] The avoidance unit can estimate the parent's emotions and determine the priority of avoidance based on the estimated parent's emotions. For example, the avoidance unit can capture the parent's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, if the parent is tense, the avoidance priority is set high. The avoidance unit can also record the parent's voice and estimate the emotion using voice analysis technology. For example, if the parent is relaxed, the avoidance priority is set low. Furthermore, the avoidance unit can collect the parent's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotion using an emotion estimation algorithm. For example, if the parent is in a hurry, the avoidance priority is set to the highest level. This allows for a quick response to the parent's movements by determining the avoidance priority based on the parent's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The 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 avoidance unit may be performed using AI, for example, or without AI. For example, the avoidance unit can input parental emotional data into a generating AI, allowing the AI ​​to select the optimal method for determining the priority of avoidance.

[0089] The avoidance unit can predict the movement of obstacles during avoidance and take avoidance actions based on those predictions. For example, the avoidance unit can predict the movement of obstacles in real time and take the optimal avoidance action. The movement of obstacles is acquired in real time using sensors or cameras. The avoidance unit can also learn patterns of obstacle movement and take avoidance actions based on those predictions. For example, it can learn patterns of obstacle movement, predict future movements, and take avoidance actions. Furthermore, the avoidance unit can predict the movement of obstacles and plan avoidance actions in advance. This allows it to take the optimal avoidance action by predicting the movement of obstacles. Some or all of the above processing in the avoidance unit may be performed using AI, for example, or without AI. For example, the avoidance unit can input obstacle movement data into a generating AI and have the generating AI select the optimal avoidance action.

[0090] The avoidance unit can perform coordinated actions to avoid collisions with other moving objects during avoidance. For example, the avoidance unit can analyze the position and movement of other moving objects in real time and perform coordinated actions to avoid collisions. The position and movement of other moving objects are acquired in real time using sensors and cameras. The avoidance unit can also communicate with other moving objects and perform coordinated actions to avoid collisions. For example, it can communicate with other moving objects and perform optimal coordinated actions. Furthermore, the avoidance unit can predict the movement of other moving objects and perform coordinated actions to avoid collisions. This enables safe movement by performing coordinated actions to avoid collisions with other moving objects. Some or all of the above processing in the avoidance unit may be performed using AI, for example, or without AI. For example, the avoidance unit can input position and movement data of other moving objects into a generating AI and have the generating AI select the optimal coordinated action.

[0091] The monitoring unit can estimate the baby's emotions and adjust the monitoring frequency based on the estimated emotions. For example, the monitoring unit can capture the baby's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, if the baby is feeling anxious, the monitoring frequency can be increased to detect the abnormality early. The monitoring unit can also record the baby's voice and estimate the emotions using voice analysis technology. For example, if the baby is relaxed, the monitoring frequency can be slightly reduced to maintain a natural state. Furthermore, the monitoring unit can collect the baby's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. For example, if the baby is excited, the monitoring frequency can be appropriately adjusted to quickly detect the abnormality. In this way, abnormalities can be detected early by adjusting the monitoring frequency based on the baby's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the baby's emotional data into a generating AI and have the generating AI select the optimal method for adjusting the monitoring frequency.

[0092] The monitoring unit can learn the fluctuation patterns of the baby's body temperature and heart rate, enabling early detection of abnormalities. For example, the monitoring unit can record the fluctuation patterns of the baby's body temperature and heart rate over the long term to detect abnormalities early. The fluctuation patterns of the baby's body temperature and heart rate are acquired in real time using sensors or wearable devices. The monitoring unit can also analyze the fluctuation patterns of the baby's body temperature and heart rate in real time to detect abnormalities early. For example, it can analyze the fluctuation patterns of the baby's body temperature and heart rate in real time to detect signs of abnormality in advance. By learning the fluctuation patterns of the baby's body temperature and heart rate, early detection of abnormalities becomes possible. 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 baby's body temperature and heart rate data into a generating AI and have the generating AI select the optimal method for early detection of abnormalities.

[0093] The monitoring unit can analyze the baby's sleep state during monitoring and select the optimal monitoring timing. For example, the monitoring unit can analyze the baby's sleep state in real time and select the optimal monitoring timing. The baby's sleep state is acquired in real time using an electroencephalogram (EEG) sensor and motion sensors. The monitoring unit can also learn the baby's sleep patterns and select the optimal monitoring timing. For example, the baby's sleep patterns can be recorded over a long period, and monitoring can be strengthened by identifying times when abnormalities are likely to occur. This allows the optimal monitoring timing to be selected by analyzing the baby's sleep state. 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 baby's sleep data into a generating AI and have the generating AI select the optimal method for selecting the optimal monitoring timing.

[0094] The monitoring unit can estimate the baby's emotions and determine monitoring priorities based on the estimated emotions. For example, the monitoring unit can capture the baby's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, if the baby is feeling anxious, the monitoring priority will be set higher. The monitoring unit can also record the baby's voice and estimate the emotions using voice analysis technology. For example, if the baby is relaxed, the monitoring priority will be set lower. Furthermore, the monitoring unit can collect the baby's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. For example, if the baby is excited, the monitoring priority will be adjusted appropriately. By determining monitoring priorities based on the baby's emotions, abnormalities can be detected early. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the baby's emotional data into a generating AI and have the generating AI select the optimal method for determining monitoring priorities.

[0095] The monitoring unit can additionally monitor the baby's respiratory status during monitoring. For example, the monitoring unit can monitor the baby's respiratory status in real time and issue an alert if an abnormality is detected. The baby's respiratory status is acquired in real time using a respiratory sensor. The monitoring unit can also learn the baby's respiratory pattern and perform early detection of abnormalities. For example, it can record the baby's respiratory pattern over a long period and detect signs of abnormality in advance. Furthermore, the monitoring unit can monitor the baby's respiratory status and integrate it with body temperature and heart rate to evaluate the overall health status. This allows for an evaluation of the baby's overall health status by additionally monitoring the baby's respiratory status. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input the baby's respiratory data into a generating AI and have the generating AI select the optimal method for evaluating the overall health status.

[0096] The monitoring unit can detect the baby's movements during monitoring and issue an alert if abnormal movements are detected. For example, the monitoring unit can detect the baby's movements in real time and issue an alert if abnormal movements are detected. The baby's movements are acquired in real time using motion sensors and image analysis technology. The monitoring unit can also learn the baby's movement patterns and detect abnormal movements early. For example, it can record the baby's movement patterns over a long period and detect signs of abnormality in advance. Furthermore, the monitoring unit can monitor the baby's movements and notify the parents if abnormal movements are detected. This allows the unit to issue an alert if abnormal movements are detected by detecting the baby's movements. 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 baby's movement data into a generating AI and have the generating AI select the optimal method for detecting abnormal movements.

[0097] The notification unit can estimate the parent's emotions and adjust the content of the notification based on the estimated emotions. For example, the notification unit can capture the parent's facial expression with a camera and estimate the emotions using an emotion estimation algorithm. For example, if the parent is nervous, it will provide a concise and clear notification. The notification unit can also record the parent's voice and estimate the emotions using voice analysis technology. For example, if the parent is relaxed, it will provide a detailed notification. Furthermore, the notification unit can collect the parent's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. For example, if the parent is in a hurry, it will provide a quick and to-the-point notification. This allows for optimal notifications for the parent by adjusting the content of the notification based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input parental emotion data into a generating AI, allowing the AI ​​to select the optimal method for adjusting the content of the notification.

[0098] The notification unit can apply different notification methods depending on the type of abnormality when it issues a notification. For example, if an abnormal body temperature is detected, the notification unit will issue an audible notification. Abnormal body temperature is acquired in real time using a temperature sensor. The notification unit can also issue a vibration notification if an abnormal heart rate is detected. Abnormal heart rate is acquired in real time using a heart rate sensor. Furthermore, the notification unit can issue a visual notification if an abnormal respiratory state is detected. Abnormal respiratory state is acquired in real time using a respiratory sensor. This allows for optimal notifications for parents by applying different notification methods depending on the type of abnormality. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input abnormality data into a generating AI and have the generating AI select the optimal notification method according to the type of abnormality.

[0099] The notification unit can select the optimal notification timing by considering the parent's current activity status. For example, if the parent is driving, the notification unit will delay the notification to a safe time. The parent's driving status is acquired in real time using smartphone sensors and in-vehicle systems. The notification unit can also send notifications in silent mode if the parent is in a meeting. The parent's meeting status is acquired in conjunction with a calendar app or meeting system. Furthermore, the notification unit can send an immediate notification if the parent is relaxed. This allows the system to select the optimal notification timing by considering the parent's current activity status. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the parent's activity data into a generating AI and have the generating AI select the optimal notification timing.

[0100] The notification unit can estimate the parent's emotions and determine the priority of notifications based on the estimated emotions. For example, the notification unit can capture the parent's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, if the parent is tense, important notifications will be prioritized. The notification unit can also record the parent's voice and estimate their emotions using voice analysis technology. For example, if the parent is relaxed, normal notifications will be sent. Furthermore, the notification unit can collect the parent's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, if the parent is in a hurry, urgent notifications will be prioritized. This allows important notifications to be prioritized by determining the priority of notifications based on the parent's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input parental emotion data into a generating AI, allowing the AI ​​to select the optimal method for determining notification priorities.

[0101] The notification unit can send notifications in conjunction with the parent's smartwatch when a notification is received. For example, the notification unit can send a vibration notification to the parent's smartwatch. The smartwatch can connect to a smartphone using Bluetooth or Wi-Fi. The notification unit can also display visual notifications on the parent's smartwatch. Visual notifications display text and icons on the smartwatch screen. Furthermore, the notification unit can display the notification content in detail in conjunction with the parent's smartwatch. This allows for quick and reliable notifications by coordinating with the parent's smartwatch. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input notification data to be sent to the smartwatch into a generating AI and have the generating AI select the optimal notification method.

[0102] The notification unit can, upon notification, coordinate with the parent smart home system to alert of an anomaly. For example, the notification unit sends an anomaly notification to the parent smart home system. The smart home system can be coordinated using IoT protocols and communication technologies. The notification unit can also coordinate with the parent smart home system to sound an alarm in the event of an anomaly. The alarm uses sound and vibration to alert of the anomaly. Furthermore, the notification unit can coordinate with the parent smart home system to flash lights in the event of an anomaly. This allows for rapid notification of an anomaly by coordinating with the parent smart home system. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input anomaly data to be sent to the smart home system into a generating AI and have the generating AI select the optimal notification method.

[0103] The charging unit can estimate the parent's emotions and adjust the charging timing based on the estimated emotions. For example, the charging unit can capture the parent's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, if the parent is tired, the charging timing can be accelerated to ensure energy is available. The charging unit can also record the parent's voice and estimate emotions using voice analysis technology. For example, if the parent is relaxed, the charging timing can be delayed for natural charging. Furthermore, the charging unit can collect the parent's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, if the parent is in a hurry, the charging timing can be optimized for rapid charging. In this way, by adjusting the charging timing based on the parent's emotions, charging can be performed at the optimal time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the charging unit may be performed using AI, for example, or without AI. For example, the charging unit can input parental emotion data into a generating AI and have the generating AI select the optimal method for adjusting the timing of charging.

[0104] The charging unit can optimize the recovery of wheel rotational energy during charging. For example, the charging unit optimizes energy recovery efficiency according to the wheel rotation speed. The wheel rotation speed is acquired in real time using sensors. The charging unit can also learn the wheel rotation pattern and apply the optimal energy recovery method. For example, it can record the wheel rotation pattern over a long period and apply the optimal energy recovery method. Furthermore, the charging unit can analyze the wheel rotation energy in real time and recover it efficiently. This improves charging efficiency by efficiently recovering the wheel rotation energy. Some or all of the above processing in the charging unit may be performed using AI, for example, or without AI. For example, the charging unit can input wheel rotation data into a generating AI and have the generating AI select the optimal method for optimizing energy recovery efficiency.

[0105] The charging unit can improve charging efficiency by adjusting the angle of sunlight irradiation during charging. For example, the charging unit can analyze the angle of sunlight irradiation in real time and adjust it to the optimal angle. The angle of sunlight irradiation is acquired in real time using a sensor. The charging unit can also learn the angle of sunlight irradiation and apply an efficient charging method. For example, it can record the angle of sunlight irradiation over a long period and apply the optimal charging method. Furthermore, the charging unit can adjust the angle of sunlight irradiation to maximize charging efficiency. In this way, charging efficiency can be improved by adjusting the angle of sunlight irradiation. Some or all of the above processing in the charging unit may be performed using AI, for example, or without AI. For example, the charging unit can input sunlight irradiation data into a generating AI and have the generating AI select the optimal method for adjusting the irradiation angle.

[0106] The charging unit can estimate the parent's emotions and determine charging priorities based on the estimated emotions. For example, the charging unit can capture the parent's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, if the parent is tired, the charging priority is set high. The charging unit can also record the parent's voice and estimate their emotions using voice analysis technology. For example, if the parent is relaxed, the charging priority is set low. Furthermore, the charging unit can collect the parent's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, if the parent is in a hurry, the charging priority is set to the highest level. This allows charging to be performed at the optimal time by determining charging priorities based on the parent's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 charging unit may be performed using AI, for example, or without AI. For example, the charging unit can input parental emotional data into a generating AI, allowing the AI ​​to select the optimal method for determining charging priorities.

[0107] The charging unit can improve its charging efficiency by using other energy harvesting technologies in combination during charging. For example, the charging unit can improve charging efficiency by using solar and wind power in combination. Solar energy is converted into electricity using solar panels, and wind power is converted into electricity using a wind turbine. The charging unit can also improve charging efficiency by using the rotational energy of the wheels and vibration energy in combination. The rotational energy of the wheels is converted into electricity using a dynamo or generator, and vibration energy is converted into electricity using a vibration generator. Furthermore, the charging unit can also improve charging efficiency by using solar energy and thermal energy in combination. Solar energy is converted into electricity using solar panels, and thermal energy is converted into electricity using a thermoelectric generator. This allows for improved charging efficiency by using other energy harvesting technologies in combination. Some or all of the above processes in the charging unit may be performed using AI, for example, or without AI. For example, the charging unit can input energy harvesting data into a generating AI and have the generating AI select the optimal energy recovery method.

[0108] The charging unit can monitor the charging status in conjunction with the charging station during charging. For example, the charging unit can monitor the charging status in real time in conjunction with the charging station. The charging station can be connected using IoT protocols and communication technologies. The charging unit can also analyze data from the charging station and apply the optimal charging method. For example, it can analyze data from the charging station in real time and apply the optimal charging method. Furthermore, the charging unit can also save the charging status to the cloud in conjunction with the charging station for later review. This allows for real-time monitoring of the charging status by connecting with the charging station. Some or all of the above processes in the charging unit may be performed using AI, for example, or without AI. For example, the charging unit can input data from the charging station into a generating AI and have the generating AI select the optimal charging method.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The Merry Cart AI agent can learn the parent's walking patterns and apply the optimal tracking algorithm. For example, it can learn the parent's walking speed and rhythm and adjust the tracking algorithm based on that pattern. The parent's walking speed and rhythm are acquired in real time using sensors and cameras. It can also learn the parent's walking habits and characteristics and apply a tracking method accordingly. For example, if the parent has a specific walking pattern, the tracking algorithm will be optimized based on that pattern. Furthermore, it can record the parent's walking patterns over the long term and apply a tracking algorithm that takes into account changes due to season and time of day. In this way, by learning the parent's walking patterns, it can apply the optimal tracking algorithm.

[0111] The MerryCart AI agent can monitor the parent's health and stop tracking if an abnormality is detected. For example, it can monitor the parent's heart rate and body temperature and stop tracking if an abnormality is detected. The parent's heart rate and body temperature are obtained in real time using sensors and wearable devices. It can also stop tracking to ensure the parent's safety if an abnormality is detected in the parent's walking pattern. For example, it can analyze the parent's walking pattern in real time and stop tracking if an abnormality is detected. Furthermore, it can analyze the parent's health status in real time and issue an alert if an abnormality is detected. This allows the agent to monitor the parent's health status and stop tracking if an abnormality is detected.

[0112] The Merry Cart AI agent can estimate the parent's emotions and adjust tracking accuracy based on those emotions. For example, it can capture the parent's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. If the parent is stressed, it increases tracking accuracy to respond quickly to the parent's movements. It can also record the parent's voice and estimate their emotions using voice analysis technology. If the parent is relaxed, it slightly loosens tracking accuracy to allow for more natural movements. Furthermore, it can collect the parent's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. If the parent is in a hurry, it maximizes tracking accuracy to respond to sudden movements. In this way, by adjusting tracking accuracy based on the parent's emotions, it can respond quickly to the parent's movements.

[0113] The MerryCart AI agent can share location information by linking with the parent's smartphone. For example, it can link with the parent's smartphone and share location information in real time. The parent's smartphone has GPS functionality, allowing it to accurately obtain the parent's location information. Furthermore, the tracking accuracy can be improved by utilizing the parent's smartphone's GPS functionality. For example, it can link with the parent's smartphone and save location information to the cloud for later review. This allows for real-time sharing of location information by linking with the parent's smartphone.

[0114] The Merry Cart AI agent can estimate the parent's emotions and adjust its tracking speed based on those emotions. For example, it can capture the parent's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. If the parent is in a hurry, it increases the tracking speed to keep up with their movements. It can also record the parent's voice and estimate their emotions using voice analysis technology. If the parent is relaxed, it slows down the tracking speed to allow for natural movement. Furthermore, it can collect the parent's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. If the parent is stressed, it adjusts the tracking speed appropriately to respond quickly to the parent's movements. In this way, by adjusting the tracking speed based on the parent's emotions, it can respond quickly to the parent's movements.

[0115] The Merry Cart AI agent can estimate the parent's emotions and adjust the timing of obstacle avoidance based on those emotions. For example, it can capture the parent's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. If the parent is tense, it will avoid obstacles earlier. It can also record the parent's voice and estimate their emotions using voice analysis technology. If the parent is relaxed, it will avoid obstacles at a natural timing. Furthermore, it can collect the parent's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. If the parent is in a hurry, it will avoid obstacles quickly. In this way, by adjusting the timing of avoidance based on the parent's emotions, it can respond quickly to the parent's movements.

[0116] The MerryCart AI agent can estimate a parent's emotions and adjust the content of notifications based on those emotions. For example, it can capture a parent's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. If the parent is stressed, it will provide a concise and clear notification. It can also record the parent's voice and estimate their emotions using voice analysis technology. If the parent is relaxed, it will provide a detailed notification. Furthermore, it can collect the parent's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. If the parent is in a hurry, it will provide a quick and to-the-point notification. By adjusting the content of notifications based on the parent's emotions, it is possible to provide notifications that are optimal for the parent.

[0117] The MerryCart AI agent can estimate the parent's emotions and adjust the charging timing based on those emotions. For example, it can capture the parent's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. If the parent is tired, it will speed up the charging time to conserve energy. It can also record the parent's voice and estimate their emotions using voice analysis technology. If the parent is relaxed, it will delay the charging time for a more natural charging experience. Furthermore, it can collect the parent's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. If the parent is in a hurry, it will optimize the charging timing for faster charging. In this way, by adjusting the charging timing based on the parent's emotions, it can charge at the optimal time.

[0118] The Merry Cart AI agent can estimate the parent's emotions and determine charging priorities based on those emotions. For example, it can capture the parent's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. If the parent is tired, it sets a higher charging priority. It can also record the parent's voice and estimate their emotions using voice analysis technology. If the parent is relaxed, it sets a lower charging priority. Furthermore, it can collect the parent's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. If the parent is in a hurry, it sets the charging priority to the highest level. By determining charging priorities based on the parent's emotions, it can charge the cart at the optimal time.

[0119] The MerryCart AI agent can work with the parent's smart home system to alert it to anomalies. For example, it can send an anomaly notification to the parent's smart home system. The smart home system can work with the MerryCart AI agent using IoT protocols and communication technologies. It can also work with the parent's smart home system to sound an alarm in case of an anomaly. The alarm will alert it to the anomaly using voice and vibration. Furthermore, it can work with the parent's smart home system to make the lights flash in case of an anomaly. This allows for rapid notification of anomalies by working with the parent's smart home system.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The tracking unit tracks the parent. The tracking unit can track the parent's location using technologies such as GPS, RFID, and camera recognition. For example, the tracking unit can use GPS to obtain the parent's location information in real time and transmit it to the stroller. The tracking unit can also identify the parent's location using RFID tags. Furthermore, the tracking unit can use camera recognition technology to recognize and track the parent's face and posture. Step 2: The avoidance unit avoids obstacles based on the information tracked by the tracking unit. The avoidance unit can, for example, use an AI camera and sensors to detect obstacles and automatically avoid them. For example, the avoidance unit can use an AI camera to detect an obstacle in front of the stroller and take avoidance action. The avoidance unit can also use ultrasonic sensors or infrared sensors to detect and avoid obstacles. Furthermore, the avoidance unit can use an AI algorithm to predict the position and movement of obstacles and calculate the optimal avoidance path. Step 3: The monitoring unit monitors the baby's body temperature and heart rate. The monitoring unit can monitor the baby's body temperature and heart rate in real time, for example, using sensors or wearable devices. For example, the monitoring unit is equipped with a temperature sensor to measure the baby's body temperature. The monitoring unit is also equipped with a heart rate sensor to measure the baby's heart rate. The monitoring unit can transmit the data acquired from these sensors to the parent's smartphone in real time. Step 4: The notification unit notifies of abnormalities based on the information monitored by the monitoring unit. The notification unit can, for example, notify parents of abnormalities using smartphone notifications or alarms. For example, the notification unit sends a notification to the parent's smartphone if the baby's body temperature is abnormally high or their heart rate is abnormally low. The notification unit can also sound an alarm to notify parents of abnormalities. Furthermore, the notification unit can apply different notification methods depending on the type of abnormality. Step 5: The charging unit self-charges using the rotation of the wheels and sunlight. For example, the charging unit can recover energy generated by the rotation of the wheels and store it in a battery. The charging unit can also generate electricity using solar panels and charge the battery using sunlight. By using these energy harvesting technologies in combination, the stroller can be used continuously.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the tracking unit, avoidance unit, monitoring unit, notification unit, and charging unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the tracking unit tracks the parent's location using GPS, RFID, and camera recognition technology of the smart device 14. The avoidance unit detects obstacles using the AI ​​camera and sensors of the smart device 14 and automatically avoids them. The monitoring unit monitors the baby's body temperature and heart rate in real time using the sensors of the smart device 14. The notification unit notifies the parent of any abnormalities using smartphone notifications and alarms from the smart device 14. The charging unit self-charges the smart device 14 using the rotation of its wheels and sunlight. 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.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the tracking unit, avoidance unit, monitoring unit, notification unit, and charging unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the tracking unit tracks the parent's location using the GPS, RFID, and camera recognition technologies of the smart glasses 214. The avoidance unit detects obstacles using the AI ​​camera and sensors of the smart glasses 214 and automatically avoids them. The monitoring unit monitors the baby's body temperature and heart rate in real time using the sensors of the smart glasses 214. The notification unit notifies the parent of any abnormalities using smartphone notifications and alarms from the smart glasses 214. The charging unit self-charges using the rotation of the wheels of the smart glasses 214 and sunlight. 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.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the tracking unit, avoidance unit, monitoring unit, notification unit, and charging unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the tracking unit tracks the parent's location using GPS, RFID, and camera recognition technology of the headset terminal 314. The avoidance unit detects obstacles using the AI ​​camera and sensors of the headset terminal 314 and automatically avoids them. The monitoring unit monitors the baby's body temperature and heart rate in real time using the sensors of the headset terminal 314. The notification unit notifies the parent of any abnormalities using smartphone notifications and alarms from the headset terminal 314. The charging unit self-charges using the rotation of the wheels of the headset terminal 314 and sunlight. 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.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] Each of the multiple elements described above, including the tracking unit, avoidance unit, monitoring unit, notification unit, and charging unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the tracking unit tracks the parent's location using the robot 414's GPS, RFID, and camera recognition technology. The avoidance unit detects obstacles using the robot 414's AI camera and sensors and automatically avoids them. The monitoring unit monitors the baby's body temperature and heart rate in real time using the robot 414's sensors. The notification unit notifies the parent of any abnormalities using the robot 414's smartphone notifications and alarms. The charging unit self-charges using the rotation of the robot 414's wheels and sunlight. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] (Note 1) A tracking unit that tracks the parent, An avoidance unit that avoids obstacles based on information tracked by the aforementioned tracking unit, A monitoring unit that monitors the baby's body temperature and heart rate, A notification unit that notifies of an anomaly based on the information monitored by the aforementioned monitoring unit, It includes a charging unit that self-charges using the rotation of the wheels and sunlight. A system characterized by the following features. (Note 2) The aforementioned tracking unit is Track your parents using a smart wristband The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned avoidance section is It uses AI cameras and sensors to detect obstacles and automatically avoid them. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned monitoring unit, Monitor the baby's body temperature and heart rate in real time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned notification unit, Notify the parent if an abnormality is detected. The system described in Appendix 1, characterized by the features described herein. (Note 6) The charging unit is It self-charges using the rotation of its wheels and sunlight. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned tracking unit is It estimates the parent's emotions and adjusts the tracking accuracy based on the estimated parent's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned tracking unit is It learns the parent's walking patterns and applies the optimal tracking algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned tracking unit is During tracking, the parent's location information and surrounding environment information are integrated to improve tracking accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned tracking unit is It estimates the parent's emotions and adjusts the tracking speed based on the estimated parent's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned tracking unit is During tracking, the system monitors the parent's health status and stops tracking if an abnormality is detected. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned tracking unit is During tracking, location information is shared by linking with the parent's smartphone. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned avoidance section is It estimates the parent's emotions and adjusts the timing of avoidance based on the estimated parent's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned avoidance section is Apply different avoidance algorithms depending on the type of obstacle. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned avoidance section is During avoidance, the system analyzes surrounding environmental information in real time and selects the optimal avoidance route. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned avoidance section is It estimates the parent's emotions and determines the priority of avoidance based on the estimated parent's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned avoidance section is During evasion, the movement of the obstacle is predicted, and evasive action is taken based on that prediction. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned avoidance section is During evasion, coordinated actions are performed to avoid collisions with other moving objects. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned monitoring unit, The system estimates the baby's emotions and adjusts the monitoring frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned monitoring unit, It learns the variability patterns of the baby's body temperature and heart rate to detect abnormalities early. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned monitoring unit, During monitoring, the baby's sleep patterns are analyzed to select the optimal monitoring timing. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned monitoring unit, The system estimates the baby's emotions and determines monitoring priorities based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned monitoring unit, During monitoring, additionally monitor the baby's breathing status. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned monitoring unit, During monitoring, it detects the baby's movements and issues an alert if any abnormal movements are detected. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned notification unit, The system estimates the parent's emotions and adjusts the content of the notification based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned notification unit, When sending a notification, different notification methods will be applied depending on the type of anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned notification unit, When sending a notification, the system will select the optimal timing for the notification, taking into account the parent's current activity status. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned notification unit, It estimates the parent's emotions and determines the priority of notifications based on the estimated parent's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification unit, When a notification is sent, it will be sent in conjunction with the parent's smartwatch. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned notification unit, When a notification is sent, it will coordinate with the parent's smart home system to alert them of any abnormalities. The system described in Appendix 1, characterized by the features described herein. (Note 31) The charging unit is It estimates the parent's emotions and adjusts the charging timing based on the estimated parent's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The charging unit is During charging, optimization is performed to efficiently recover the rotational energy of the wheels. The system described in Appendix 1, characterized by the features described herein. (Note 33) The charging unit is During charging, adjust the angle of sunlight exposure to improve charging efficiency. The system described in Appendix 1, characterized by the features described herein. (Note 34) The charging unit is It estimates the parent's emotions and determines charging priorities based on the estimated parent's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The charging unit is During charging, other energy harvesting technologies are used in conjunction to improve charging efficiency. The system described in Appendix 1, characterized by the features described herein. (Note 36) The charging unit is During charging, it monitors the charging status in conjunction with the charging station. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0194] 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 tracking unit that tracks the parent, An avoidance unit that avoids obstacles based on information tracked by the aforementioned tracking unit, A monitoring unit that monitors the baby's body temperature and heart rate, A notification unit that notifies of an anomaly based on the information monitored by the aforementioned monitoring unit, It includes a charging unit that self-charges using the rotation of the wheels and sunlight. A system characterized by the following features.

2. The aforementioned tracking unit is Track your parents using a smart wristband The system according to feature 1.

3. The aforementioned avoidance part is, It uses AI cameras and sensors to detect obstacles and automatically avoid them. The system according to feature 1.

4. The aforementioned monitoring unit, Monitor the baby's body temperature and heart rate in real time. The system according to feature 1.

5. The aforementioned notification unit, Notify the parent if an abnormality is detected. The system according to feature 1.

6. The charging unit is It self-charges using the rotation of its wheels and sunlight. The system according to feature 1.

7. The aforementioned tracking unit is It estimates the parent's emotions and adjusts the tracking accuracy based on the estimated parent's emotions. The system according to feature 1.

8. The aforementioned tracking unit is It learns the parent's walking patterns and applies the optimal tracking algorithm. The system according to feature 1.

Images