system

Abstract

The system according to this embodiment aims to understand the behavioral patterns of babies and provide appropriate countermeasures. [Solution] The system according to the embodiment comprises a collection unit, a determination unit, a prediction unit, a decision unit, and a notification unit. The collection unit collects the baby's behavior data. The determination unit determines the baby's behavior pattern based on the data collected by the collection unit. The prediction unit predicts the baby's behavior based on the data collected by the collection unit and the determination result by the determination unit. The decision unit determines how to deal with the baby's behavior based on the prediction result by the prediction unit. The notification unit notifies the baby's behavior predicted by the prediction unit and the solution determined by the decision unit.

Description

Technical Field

[0006] , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to accurately grasp the behavior patterns of babies and provide appropriate countermeasures.

[0005] The system according to the embodiment aims to grasp the behavior patterns of babies and provide appropriate countermeasures.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a determination unit, a prediction unit, a decision unit, and a notification unit. The collection unit collects data on the baby's behavior. The determination unit determines the baby's behavior pattern based on the data collected by the collection unit. The prediction unit predicts the baby's behavior based on the data collected by the collection unit and the determination result from the determination unit. The decision unit determines how to deal with the baby's behavior based on the prediction result from the prediction unit. The notification unit notifies the baby of the behavior predicted by the prediction unit and the action determined by the decision unit. [Effects of the Invention]

[0007] The system according to this embodiment can understand the baby's behavioral patterns and provide appropriate countermeasures. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] )]] The smart device 14 comprises a computer 36, a 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 full-support childcare tool according to an embodiment of the present invention is a system aimed at families with no prior childcare experience or families who want to know more about their baby's behavior. This system collects behavioral data such as the baby's "sleep duration," "level of deep sleep," "crying time," "type of crying," and "feeding time / amount," analyzes and predicts it using AI, and notifies the parents of appropriate countermeasures. For example, the full-support childcare tool collects behavioral data of the baby using a baby monitor or sleep tracker (bed pad type). Next, based on the collected data, the AI ​​determines the baby's behavioral pattern. Furthermore, based on the determination result, the AI ​​predicts the baby's next behavior. For example, it predicts when the baby will cry next or when it will be time to feed. Based on the prediction result, the AI ​​determines the optimal countermeasure. For example, it determines how to deal with the baby crying or how to adjust feeding times. Finally, the system notifies the parents of the baby's behavioral predictions and countermeasures through a dedicated app. For example, by predicting when the baby will cry and notifying them of the countermeasures, parents can take appropriate action. This mechanism allows even families with no prior childcare experience to manage their baby's behavior in real time and know appropriate countermeasures. Furthermore, the community within the dedicated app allows parents to consult with other families raising children and to receive free consultations from medical specialists. This enables parents to raise their children with peace of mind and to share childcare responsibilities equally with their spouse. In this way, the full childcare support tool allows parents to monitor their baby's behavior in real time and learn how to deal with appropriate situations.

[0029] The childcare full support tool according to the embodiment comprises a data collection unit, a judgment unit, a prediction unit, a decision unit, and a notification unit. The data collection unit collects behavioral data of the baby. The data collection unit collects data such as the baby's "sleep duration," "level of deep sleep," "crying duration," "type of crying," and "time / amount of meals" using, for example, a baby monitor or sleep tracker (mattress pad type). For example, the data collection unit records the baby's crying duration and type of crying using a baby monitor. The data collection unit can also measure the baby's sleep duration and level of deep sleep using a sleep tracker. Furthermore, the data collection unit can also record the baby's meal times and amounts. The judgment unit determines the baby's behavioral patterns based on the data collected by the data collection unit. The judgment unit analyzes the data using, for example, AI to determine the baby's behavioral patterns. For example, the judgment unit analyzes data on the baby's crying duration and type of crying to determine the baby's crying patterns. The judgment unit can also analyze data on the baby's sleep duration and level of deep sleep to determine the baby's sleep patterns. Furthermore, the judgment unit can analyze data on the baby's feeding times and amounts to determine the baby's eating patterns. The prediction unit predicts the baby's behavior based on the data collected by the collection unit and the judgment results from the judgment unit. The prediction unit predicts the baby's behavior using, for example, AI. For example, the prediction unit predicts the time the baby will cry next based on data on the time and type of crying. The prediction unit can also predict the time the baby will wake up next based on data on the baby's sleep duration and sleep depth. Furthermore, the prediction unit can also predict the time the baby will eat next based on data on the time and amount of meals the baby eats. The decision unit determines how to deal with the baby's behavior based on the prediction results from the prediction unit. The decision unit determines the optimal course of action using, for example, AI. For example, the decision unit determines how to deal with the baby when the baby cries. Furthermore, the decision unit can also determine how to adjust the baby's feeding times. Furthermore, the decision unit can also determine how to adjust the baby's sleep duration. The notification unit notifies the user of the baby's behavior predicted by the prediction unit and the course of action determined by the decision unit. The notification unit, for example, notifies parents of their baby's predicted behavior and how to deal with it through a dedicated app.For example, the notification unit can predict when the baby will cry and notify the parent of how to deal with it. It can also predict when the baby will eat and notify the parent of how to deal with it. Furthermore, it can predict when the baby will sleep and notify the parent of how to deal with it. This allows the full-support childcare tool according to the embodiment to enable parents to manage their baby's behavior in real time and know how to take appropriate action.

[0030] The data collection unit collects behavioral data about the baby. For example, the data collection unit uses a baby monitor or sleep tracker (mattress pad type) to collect data such as the baby's "sleep duration," "sleep depth," "crying time," "crying style," and "feeding time / amount." Specifically, the baby monitor records the baby's crying time and crying style with high precision, collecting audio and video data in real time. This allows for accurate identification of the moment the baby starts crying and how long it takes to stop crying. The sleep tracker monitors the baby's body movements, heart rate, and breathing patterns to measure the baby's sleep duration and sleep depth. This allows for detailed recording of how deep or light the baby's sleep is. Furthermore, the data collection unit can use sensors built into special baby bottles and dishes to record the baby's feeding time and amount. These sensors accurately measure and collect data on how much the baby ingested and at what time of day. In this way, the data collection unit can collect behavioral data about the baby from multiple angles and understand detailed behavioral patterns. The collected data is sent to a cloud server and updated in real time, enabling analysis based on the latest information at all times.

[0031] The judgment unit determines the baby's behavioral patterns based on the data collected by the collection unit. For example, the judgment unit uses AI to analyze the data and determine the baby's behavioral patterns. Specifically, the AI ​​uses machine learning algorithms to analyze data on the timing and type of crying of the baby and identify crying patterns. For example, if a baby tends to cry at a particular time of day, the judgment unit identifies that time and infers the cause of the crying. The AI ​​also analyzes data on the baby's sleep duration and sleep depth to determine sleep patterns. This allows parents to understand when the baby is in deep sleep or light sleep. Furthermore, the AI ​​analyzes data on the timing and amount of meals the baby eats to determine feeding patterns. For example, if a baby tends to eat large amounts at a particular time of day, the judgment unit identifies that time and can adjust the baby's meals accordingly. The judgment unit comprehensively analyzes this data to gain a detailed understanding of the baby's behavioral patterns. This makes it easier for parents to predict their baby's behavior and provides them with the basic information needed to find appropriate responses.

[0032] The prediction unit predicts the baby's behavior based on data collected by the collection unit and the judgment results from the judgment unit. The prediction unit predicts the baby's behavior using, for example, AI. Specifically, the AI ​​predicts the baby's next behavior with high accuracy based on past data. For example, it predicts the time when the baby will cry next based on data on the time and type of crying. If the AI ​​has a tendency to cry during certain times of the day, it identifies those times and predicts the time when the baby is most likely to cry next. The AI ​​can also predict the time the baby will wake up next based on data on the baby's sleep duration and sleep quality. This allows parents to know in advance when their baby will wake up and prepare appropriate responses. Furthermore, the AI ​​can also predict the time the baby will eat next based on data on the time and amount of food the baby eats. This allows parents to prepare meals for their baby more efficiently. The prediction unit updates these prediction results in real time, providing predictions based on the latest information at all times. This allows parents to know in advance what their baby will be doing and take appropriate responses.

[0033] The decision unit determines how to respond to the baby's behavior based on the prediction results from the prediction unit. For example, the decision unit uses AI to determine the optimal response. Specifically, the AI ​​determines the best response when the baby cries based on data on the timing and type of crying. For example, if the baby is crying because of hunger, the AI ​​suggests adjusting meal times. Also, if the baby is having trouble sleeping, the AI ​​can suggest ways to improve the sleep environment. Furthermore, the AI ​​determines how to adjust meals based on data on the baby's meal times and amounts. For example, if the baby tends to eat large amounts at certain times, the AI ​​suggests preparing meals to match those times. The decision unit comprehensively evaluates these responses and can provide the parents with the optimal method. This allows parents to respond appropriately to their baby's behavior and maintain the baby's health and comfort.

[0034] The notification unit notifies parents of the baby's behavior predicted by the prediction unit and the appropriate course of action determined by the decision unit. For example, the notification unit notifies parents of the baby's behavior prediction and appropriate course of action through a dedicated app. Specifically, the notification unit predicts when the baby will cry and notifies parents of how to deal with it. For example, when the time when the baby is likely to cry is approaching, it notifies parents through the dedicated app and suggests how to deal with the crying. The notification unit can also predict the baby's mealtime and notify parents of how to deal with it. For example, when the time for the baby's meal is approaching, it notifies parents through the dedicated app and suggests how to prepare the meal appropriately. Furthermore, the notification unit can also predict the baby's sleep time and notify parents of how to deal with it. For example, when the time for the baby's sleep is approaching, it notifies parents through the dedicated app and suggests how to create an appropriate sleep environment. This allows parents to understand the baby's behavior in real time and know how to deal with it appropriately. In addition, the notification unit can collect feedback from parents and continuously improve the accuracy and effectiveness of the notifications. This allows the notification system to provide parents with quick and accurate information, supporting them in maintaining their baby's health and comfort.

[0035] The data collection unit can collect data including the baby's "sleep duration," "sleep depth," "crying duration," "crying style," and "feeding time / amount" using a baby monitor or sleep tracker. For example, the data collection unit can record the baby's crying duration and crying style using a baby monitor. For example, the data collection unit can record the time the baby started crying and the time they stopped crying, and analyze the crying patterns. The data collection unit can also measure the baby's sleep duration and sleep depth using a sleep tracker. For example, the data collection unit can record the time the baby fell asleep and woke up, and evaluate the sleep depth. Furthermore, the data collection unit can also record the baby's feeding times and amounts. For example, the data collection unit can record the time the baby started eating and finished eating, and measure the amount eaten. This allows for the collection of detailed data on the baby using a baby monitor or sleep tracker. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input video data acquired from a baby monitor into a generating AI, and have the generating AI analyze the baby's crying patterns.

[0036] The judgment unit can analyze the collected data and determine the baby's behavioral patterns. For example, the judgment unit can analyze the collected data using AI to determine the baby's behavioral patterns. For example, the judgment unit can analyze data on the duration and type of crying of the baby to determine the baby's crying patterns. The judgment unit can also analyze data on the duration and depth of sleep of the baby to determine the baby's sleep patterns. Furthermore, the judgment unit can analyze data on the timing and amount of meals the baby eats to determine the baby's eating patterns. In this way, the baby's behavioral patterns can be determined by analyzing the collected data. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the collected data into a generating AI and have the generating AI perform the determination of the baby's behavioral patterns.

[0037] The prediction unit can predict the baby's behavior based on the judgment result. For example, the prediction unit can use AI to predict the baby's behavior based on the judgment result. For example, the prediction unit can predict the time the baby will cry next based on data on the baby's crying time and crying style. The prediction unit can also predict the time the baby will wake up next based on data on the baby's sleep duration and sleep quality. Furthermore, the prediction unit can predict the time the baby will eat next based on data on the baby's meal times and amounts. In this way, by predicting the baby's behavior based on the judgment result, the next behavior can be predicted. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the judgment result into a generating AI and have the generating AI perform the baby's behavior prediction.

[0038] The decision unit can determine how to respond to the baby's behavior based on the prediction results. For example, the decision unit can use AI to determine how to respond to the baby's behavior based on the prediction results. For example, the decision unit can determine how to respond when the baby cries. The decision unit can also determine how to adjust the baby's feeding times. Furthermore, the decision unit can determine how to adjust the baby's sleep times. In this way, by determining how to respond based on the prediction results, appropriate responses can be provided. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input the prediction results into a generating AI and have the generating AI perform the determination of how to respond to the baby's behavior.

[0039] The notification unit can notify parents of their baby's behavior predictions and how to deal with them through an application. For example, the notification unit can notify parents of their baby's behavior predictions and how to deal with them through a dedicated app. For example, the notification unit can predict when the baby will cry and notify parents of how to deal with it. It can also predict when the baby will eat and notify parents of how to deal with it. Furthermore, the notification unit can predict when the baby will sleep and notify parents of how to deal with it. This allows parents to take appropriate action by notifying them through a dedicated app. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the prediction results and how to deal with them into a generating AI and have the generating AI generate the notification content.

[0040] The data collection unit can analyze the baby's past behavioral data and select the optimal data collection method. For example, the data collection unit can identify the times when the baby cries from past data and concentrate data collection during those times. For example, the data collection unit can analyze past data to determine when the baby started crying and when it stopped crying, thereby identifying the crying times. The data collection unit can also understand the baby's sleep patterns from past data and adjust the data collection method during sleep. For example, the data collection unit can analyze past data to determine when the baby fell asleep and when it woke up, thereby identifying the sleep patterns. Furthermore, the data collection unit can identify the baby's meal times from past data and collect data during those times. For example, the data collection unit can analyze past data to determine when the baby started and finished eating, thereby identifying meal times. In this way, the optimal data collection method can be selected by analyzing past behavioral data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past behavioral data into a generating AI and have the generating AI select the optimal data collection method.

[0041] The data collection unit can filter data based on the baby's current health status and environmental conditions during data collection. For example, if the baby has a fever, the data collection unit can increase the frequency of data collection to monitor the baby's health in more detail. For example, the data collection unit can periodically measure the baby's body temperature and record the fever status. The data collection unit can also adjust data collection based on environmental conditions if the baby is outside. For example, the data collection unit can measure the temperature and humidity at the location and adjust the data collection method accordingly. Furthermore, if the baby is in a hospital, the data collection unit can change the data collection method to avoid interference with medical equipment. For example, the data collection unit can consider the environmental conditions within the hospital and adjust the data collection method accordingly. This allows for appropriate data collection by filtering based on health status and environmental conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the baby's health status data into a generating AI and have the generating AI perform filtering of the data collection method.

[0042] The data collection unit can prioritize the collection of highly relevant data based on the baby's geographical location information during data collection. For example, if the baby is at home, the data collection unit prioritizes the collection of data related to the indoor environment. For example, the data collection unit measures the temperature and humidity at home and associates this with the baby's behavioral data. The data collection unit can also prioritize the collection of data related to the external environment if the baby is in a park. For example, the data collection unit measures the temperature and humidity at the park and associates this with the baby's behavioral data. Furthermore, if the baby is in a daycare center, the data collection unit can prioritize the collection of data related to the daycare center environment. For example, the data collection unit measures the temperature and humidity at the daycare center and associates this with the baby's behavioral data. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the baby's geographical location information into a generating AI and have the generating AI determine the priority of highly relevant data.

[0043] The data collection unit can analyze a baby's social media activity and collect relevant data during data collection. For example, if photos or videos of a baby are posted on social media, the data collection unit will collect data based on that information. For example, the data collection unit will analyze photos and videos of babies posted on social media to understand their behavioral patterns. The data collection unit can also analyze the number of followers and reactions on a baby's social media and collect relevant data. For example, the data collection unit will analyze the number of followers and likes to evaluate the baby's popularity. Furthermore, the data collection unit can analyze the content of a baby's social media posts and collect data related to their behavioral patterns. For example, the data collection unit will analyze the content of posts to understand the baby's interests and concerns. This allows for the collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media data into a generating AI and have the generating AI collect relevant data.

[0044] The judgment unit can adjust the level of detail in its judgment based on the importance of the collected data. For example, if high-importance data is collected, the judgment unit will perform a detailed judgment. For example, it will analyze data on the duration and type of crying of a baby in detail to determine the crying pattern. The judgment unit can also perform a simplified judgment if low-importance data is collected. For example, it will analyze data on the duration and depth of sleep of a baby in a simplified manner to determine the sleep pattern. Furthermore, if data of moderate importance is collected, the judgment unit can perform a judgment with an appropriate level of detail. For example, it will analyze data on the timing and amount of meals a baby eats with an appropriate level of detail to determine the meal pattern. By adjusting the level of detail in the judgment based on the importance of the data, efficient judgment becomes possible. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the collected data into a generating AI and have the generating AI perform the adjustment of the level of detail in the judgment.

[0045] The judgment unit can apply different judgment algorithms depending on the baby's category during the judgment process. For example, in the case of a newborn, the judgment unit uses a specific algorithm to determine the behavioral pattern. For instance, based on data on the duration and type of crying of a newborn, the judgment unit applies a newborn-specific algorithm to determine the crying pattern. The judgment unit can also use a different algorithm to determine the behavioral pattern of an infant. For example, based on data on the duration and depth of sleep of an infant, the judgment unit applies an infant-specific algorithm to determine the sleep pattern. Furthermore, in the case of a baby with a specific health condition, the judgment unit can use a dedicated algorithm to determine the behavioral pattern. For example, based on data on the timing and amount of meals given to a baby with a specific health condition, the judgment unit applies a dedicated algorithm to determine the feeding pattern. This allows for more accurate judgments by applying algorithms appropriate to the category. Some or all of the above-described processes in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the baby's category information into a generating AI and have the generating AI apply an appropriate judgment algorithm.

[0046] The judgment unit can determine the priority of judgments based on when the collected data was submitted. For example, if the most recent data is submitted, the judgment unit will prioritize that data for judgment. For instance, the judgment unit will prioritize analyzing the most recent data on the baby's crying time and crying style to determine the crying pattern. The judgment unit can also lower the priority of older data if it is submitted. For example, the judgment unit will analyze older data on the baby's sleep time and sleep depth with a lower priority to determine the sleep pattern. Furthermore, if data of moderate recency is submitted, the judgment unit can also give it a moderate priority for judgment. For example, the judgment unit will analyze data on the baby's meal times and amounts with a moderate priority to determine the feeding pattern. This allows for efficient judgments by determining priorities based on when the data was submitted. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the collected data into a generating AI and have the generating AI determine the priority of judgments.

[0047] The judgment unit can adjust the order of judgments based on the relevance of the collected data during the judgment process. For example, if highly relevant data is collected, the judgment unit will prioritize judging that data. For instance, the judgment unit will prioritize analyzing data on the duration and type of crying of a baby to determine the crying pattern. The judgment unit can also postpone the judgment order of data with low relevance if it is collected. For example, the judgment unit will postpone analyzing data on the duration and depth of sleep of a baby to determine the sleep pattern. Furthermore, if data with moderate relevance is collected, the judgment unit can judge it in an appropriate order. For example, the judgment unit will analyze data on the timing and amount of meals a baby eats in an appropriate order to determine the feeding pattern. This allows for efficient judgments by adjusting the order based on the relevance of the data. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the collected data into a generating AI and have the generating AI adjust the order of judgments.

[0048] The prediction unit can improve the accuracy of its predictions based on the babies' interactions during the prediction process. For example, the prediction unit can improve the accuracy of behavioral predictions by considering the interactions between babies and their parents. For example, the prediction unit can analyze behavioral data of babies and their parents and improve the accuracy of predictions based on their interactions. The prediction unit can also improve the accuracy of behavioral predictions by considering the interactions between babies and their siblings. For example, the prediction unit can analyze behavioral data of babies and their siblings and improve the accuracy of predictions based on their interactions. Furthermore, the prediction unit can also improve the accuracy of behavioral predictions by considering the interactions between babies and their caregivers. For example, the prediction unit can analyze behavioral data of babies and their caregivers and improve the accuracy of predictions based on their interactions. In this way, the accuracy of predictions is improved by considering the interactions. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input behavioral data of babies and their parents into a generating AI and have the generating AI perform accuracy improvements on predictions based on interactions.

[0049] The prediction unit can make predictions based on the baby's attribute information. For example, the prediction unit can make behavioral predictions considering the baby's age. For example, the prediction unit can predict age-appropriate behavioral patterns based on the baby's age data. The prediction unit can also make behavioral predictions considering the baby's gender. For example, the prediction unit can predict gender-appropriate behavioral patterns based on the baby's gender data. Furthermore, the prediction unit can also make behavioral predictions considering the baby's health condition. For example, the prediction unit can predict behavioral patterns appropriate to the baby's health condition based on the baby's health condition data. By considering attribute information, more appropriate predictions become possible. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the baby's attribute information into a generating AI and have the generating AI perform predictions based on the attribute information.

[0050] The prediction unit can make predictions based on the geographical distribution of babies. For example, if a baby is at home, the prediction unit makes predictions based on the home environment. For example, the prediction unit predicts the baby's behavioral patterns based on the temperature and humidity of the home. The prediction unit can also make predictions based on the park environment if the baby is in a park. For example, the prediction unit predicts the baby's behavioral patterns based on the temperature and humidity of the park. Furthermore, if a baby is in a daycare center, the prediction unit can also make predictions based on the daycare center environment. For example, the prediction unit predicts the baby's behavioral patterns based on the temperature and humidity of the daycare center. This allows for more accurate predictions by considering geographical distribution. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input geographical distribution data of babies into a generating AI and have the generating AI perform predictions based on geographical distribution.

[0051] The prediction unit can improve the accuracy of its predictions by referring to relevant literature on babies during the prediction process. For example, the prediction unit can improve the accuracy of its predictions by referring to academic papers on baby behavior. For example, the prediction unit can analyze academic papers on baby behavior to improve the accuracy of its predictions. The prediction unit can also improve the accuracy of its predictions by referring to research data on baby health. For example, the prediction unit can analyze research data on baby health to improve the accuracy of its predictions. Furthermore, the prediction unit can also improve the accuracy of its predictions by referring to literature on baby development. For example, the prediction unit can analyze literature on baby development to improve the accuracy of its predictions. In this way, the accuracy of the predictions is improved by referring to relevant literature. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input relevant literature on babies into a generating AI and have the generating AI perform the prediction accuracy improvement.

[0052] The decision-making unit can select the optimal course of action by referring to the baby's past behavioral data when making a decision. For example, the decision-making unit may prioritize selecting courses of action that have been effective in the past. For example, it may select an effective course of action based on data of the baby's past crying times and crying patterns. The decision-making unit can also avoid selecting courses of action that have failed in the past. For example, it may avoid a course of action based on data of the baby's past sleep duration and sleep quality. Furthermore, the decision-making unit can also select a new course of action based on past behavioral data. For example, it may select a new course of action based on data of the baby's past feeding times and amounts. In this way, the optimal course of action can be selected by referring to past behavioral data. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the baby's past behavioral data into a generating AI and have the generating AI select the optimal course of action.

[0053] The decision-making unit can customize the course of action based on the baby's current health condition at the time of decision-making. For example, if the baby has a fever, the decision-making unit will select a course of action to lower the body temperature. For example, the decision-making unit will select a course of action to lower the body temperature based on the baby's body temperature data. The decision-making unit can also select a standard course of action if the baby is healthy. For example, the decision-making unit will select a standard course of action based on the baby's health condition data. Furthermore, if the baby is unwell, the decision-making unit can select a course of action based on a doctor's advice. For example, the decision-making unit will select a course of action based on a doctor's advice based on the baby's unwell condition data. This allows for more appropriate action by customizing the course of action based on the baby's health condition. Some or all of the above-described processes in the decision-making unit may be performed using AI, for example, or not using AI. For example, the decision-making unit can input the baby's health condition data into a generating AI and have the generating AI perform the customization of the course of action.

[0054] The decision-making unit can select the optimal course of action based on the baby's geographical location information at the time of decision-making. For example, if the baby is at home, the decision-making unit can select a course of action appropriate to the home environment. For example, the decision-making unit can select a course of action based on the temperature and humidity of the home to address the baby's behavior. The decision-making unit can also select a course of action appropriate to the park environment if the baby is in a park. For example, the decision-making unit can select a course of action based on the temperature and humidity of the park to address the baby's behavior. Furthermore, if the baby is in a daycare center, the decision-making unit can select a course of action appropriate to the daycare center environment. For example, the decision-making unit can select a course of action based on the temperature and humidity of the daycare center to address the baby's behavior. This allows for the selection of a more appropriate course of action by considering geographical location information. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the baby's geographical location information into a generating AI and have the generating AI select the optimal course of action.

[0055] The decision-making unit can analyze the baby's social media activity and suggest appropriate actions when making a decision. For example, the decision-making unit can analyze the content of the baby's social media posts and suggest relevant actions. For example, the decision-making unit can analyze the content of the baby's social media posts and suggest actions to take in response to the baby's behavior. The decision-making unit can also analyze the number of followers and reactions on the baby's social media and suggest actions to take. For example, the decision-making unit can analyze the number of followers and likes and suggest actions to take in response to the baby's behavior. Furthermore, the decision-making unit can analyze the photos and videos on the baby's social media and suggest actions to take. For example, the decision-making unit can analyze photos and videos and suggest actions to take in response to the baby's behavior. In this way, by analyzing social media activity, more appropriate actions can be suggested. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the baby's social media data into a generating AI and have the generating AI execute the suggestion of actions.

[0056] The notification unit can select the optimal notification method by referring to the baby's past behavioral history when issuing a notification. For example, the notification unit may prioritize notification methods that have been effective in the past. For example, it may select an effective notification method based on data of the baby's past crying times and crying patterns. The notification unit can also avoid selecting notification methods that have failed in the past. For example, it may avoid selecting a notification method that has failed based on data of the baby's past sleep duration and sleep quality. Furthermore, the notification unit can also select a new notification method based on past behavioral history. For example, it may select a new notification method based on data of the baby's past meal times and amounts. In this way, the optimal notification method can be selected by referring to past behavioral history. 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 baby's past behavioral history into a generating AI and have the generating AI select the optimal notification method.

[0057] The notification unit can adjust the notification content based on the baby's current health condition when it sends a notification. For example, if the baby has a fever, the notification unit will notify the baby of how to lower their temperature. For example, the notification unit will notify the baby of how to lower their temperature based on the baby's temperature data. The notification unit can also notify the baby of the usual course of action if the baby is healthy. For example, the notification unit will notify the baby of the usual course of action based on the baby's health condition data. Furthermore, if the baby is unwell, the notification unit can notify the baby of how to take action based on a doctor's advice. For example, the notification unit will notify the baby of how to take action based on a doctor's advice based on the baby's unwell condition data. This allows for more appropriate notifications by taking the baby's health condition into consideration. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the baby's health condition data into a generating AI and have the generating AI adjust the notification content.

[0058] The notification unit can select the optimal notification method when a notification is sent, taking into account the baby's device information. For example, if the baby is using a smartphone, the notification unit can provide a notification method that matches the screen size. For example, the notification unit can provide a notification method optimized for the smartphone screen size. Also, if the baby is using a tablet, the notification unit can provide a notification method optimized for the larger screen. For example, the notification unit can provide a notification method optimized for the tablet screen size. Furthermore, if the baby is using a smartwatch, the notification unit can provide a concise and highly visible notification method. For example, the notification unit can provide a notification method optimized for the smartwatch screen size. This allows for the selection of a more appropriate notification method by considering device information. 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 baby's device information into a generating AI and have the generating AI select the optimal notification method.

[0059] The notification unit can provide multilingual notifications according to the baby's language settings when a notification is sent. For example, the notification unit can automatically set the notification language based on the language settings of the baby's device. For example, the notification unit can detect the language settings of the baby's device and automatically set the notification content based on that language. The notification unit can also provide a language switching function if the baby uses multiple languages. For example, the notification unit can display the notification content in multiple languages ​​based on the language settings of the baby's device. Furthermore, the notification unit can provide notifications in a specific language if the baby selects that language. For example, the notification unit can display the notification content based on the language selected by the baby. This allows for more appropriate notifications by providing notifications according to language settings. 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 baby's language setting information into a generating AI and have the generating AI execute multilingual notifications.

[0060] The community function can provide optimal advice by referring to past consultation history during consultations within the community. For example, the community function can prioritize providing advice that was effective in the past. For example, the community function can provide effective advice based on past consultation history. The community function can also avoid providing advice that was unsuccessful in the past. For example, the community function can avoid providing advice that was unsuccessful based on past consultation history. Furthermore, the community function can provide new advice based on past consultation history. For example, the community function can provide new advice based on past consultation history. In this way, optimal advice can be provided by referring to past consultation history. Some or all of the above processing in the community function may be performed using AI, or not using AI. For example, the community function can input past consultation history into a generating AI and have the generating AI perform the task of providing optimal advice.

[0061] The community function can provide advice during consultations within the community, taking into account the baby's current health condition. For example, if the baby has a fever, the community function can provide advice on how to lower the temperature. For example, the community function can provide advice on how to lower the temperature based on the baby's temperature data. The community function can also provide standard advice when the baby is healthy. For example, the community function can provide standard advice based on the baby's health status data. Furthermore, if the baby is unwell, the community function can provide advice based on a doctor's advice. For example, the community function can provide advice based on a doctor's advice based on the baby's unwellness data. This allows for more appropriate advice to be provided by considering the baby's health condition. Some or all of the above processing in the community function may be performed using AI, for example, or not using AI. For example, the community function can input the baby's health status data into a generating AI and have the generating AI provide advice.

[0062] The community function can provide optimal advice by considering the user's geographical location during consultations within the community. For example, if the user is at home, the community function can provide advice suitable for the home environment. For example, the community function can provide advice based on the temperature and humidity of the user's home. The community function can also provide advice suitable for the park environment if the user is in a park. For example, the community function can provide advice based on the temperature and humidity of the park. Furthermore, if the user is in a daycare center, the community function can provide advice suitable for the daycare center environment. For example, the community function can provide advice based on the temperature and humidity of the daycare center. In this way, more appropriate advice is provided by considering geographical location information. Some or all of the above processing in the community function may be performed using AI, for example, or without AI. For example, the community function can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing optimal advice.

[0063] The community function can analyze a user's social media activity and provide optimal advice during consultations within the community. For example, the community function can analyze the content of a user's social media posts and provide relevant advice. For example, the community function can analyze the content of a user's social media posts and provide advice on actions. The community function can also analyze the number of followers and likes on a user's social media and provide advice. For example, the community function can analyze the number of followers and likes and provide advice to the user. Furthermore, the community function can analyze photos and videos on a user's social media and provide advice. For example, the community function can analyze photos and videos and provide advice to the user. In this way, more appropriate advice can be provided by analyzing social media activity. Some or all of the above processing in the community function may be performed using AI, for example, or not using AI. For example, the community function can input the user's social media data into a generating AI and have the generating AI perform the provision of advice.

[0064] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0065] The data collection unit can collect biometric data such as the baby's body temperature and heart rate simultaneously with collecting behavioral data from the baby. For example, the data collection unit can periodically measure the baby's body temperature to detect early signs of fever. It can also monitor the baby's heart rate and detect abnormal fluctuations in heart rate. Furthermore, it can measure the baby's respiratory rate and detect respiratory abnormalities. By collecting the baby's biometric data, it becomes possible to detect changes in the baby's health status early and take appropriate action. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the baby's biometric data into a generating AI and have the generating AI perform abnormality detection.

[0066] The data collection unit can monitor the sound environment surrounding the baby when collecting the baby's behavioral data. For example, the data collection unit can measure the noise level around the baby and evaluate the impact of the noise on the baby's sleep. The data collection unit can also analyze the sounds around the baby and evaluate the impact of specific sounds on the baby's behavior. Furthermore, the data collection unit can record music and conversations around the baby and analyze their relationship to the baby's behavioral patterns. This allows for the evaluation of the relationship between the sound environment around the baby and behavioral data, enabling appropriate interventions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input sound data from around the baby into a generating AI and have the generating AI perform the analysis of the sound environment.

[0067] The judgment unit can apply criteria appropriate to the baby's developmental stage when determining the baby's behavioral patterns. For example, for newborns, the judgment unit can determine behavioral patterns by emphasizing frequent feeding and sleep patterns. For infants, the judgment unit can also determine behavioral patterns by emphasizing the timing and amount of solid food intake. Furthermore, for toddlers, the judgment unit can determine behavioral patterns by emphasizing playtime and activity levels. By applying criteria appropriate to the baby's developmental stage, it becomes possible to determine behavioral patterns more appropriately. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input baby developmental stage data into a generating AI and have the generating AI perform the application of criteria.

[0068] The prediction unit can improve prediction accuracy by combining past behavioral data and current environmental data when predicting the baby's behavior. For example, the prediction unit can combine the baby's past sleep patterns and current room temperature data to predict when the baby will wake up next. It can also combine the baby's past eating patterns and current food content data to predict when the baby will eat next. Furthermore, the prediction unit can combine the baby's past crying patterns and current sound environment data to predict when the baby will cry next. By combining past behavioral data and current environmental data, prediction accuracy is improved, enabling more appropriate responses. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input past behavioral data and current environmental data into a generating AI and have the generating AI perform the improvement of prediction accuracy.

[0069] The decision-making unit can provide customized coping strategies that take into account the individual characteristics of the baby when determining how to respond to the baby's behavior. For example, the decision-making unit can consider the baby's allergy information to determine how to feed the baby in a way that will not cause allergies. It can also consider the baby's sleep patterns to determine how to provide an optimal sleep environment. Furthermore, it can consider the baby's activity level to determine appropriate play activities. In this way, more appropriate coping strategies are provided by taking into account the individual characteristics of the baby. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the baby's individual characteristic data into a generating AI and have the generating AI perform the task of providing customized coping strategies.

[0070] The notification unit can adjust the timing of notifications to parents when informing them of the baby's behavior predictions and how to deal with them, taking into account the parents' schedule information. For example, the notification unit can consider the parents' work schedule and refrain from sending notifications while they are at work. It can also consider the parents' sleep schedule and refrain from sending notifications while they are sleeping. Furthermore, it can consider the parents' outings and refrain from sending notifications while they are out. By considering the parents' schedule information, notifications can be sent at a more appropriate time, reducing the burden on the parents. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the parents' schedule information into a generating AI and have the generating AI perform the adjustment of the notification timing.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The data collection unit collects data on the baby's behavior. For example, it uses a baby monitor or sleep tracker (mattress pad type) to collect data such as the baby's "sleep duration," "level of sleep," "time of crying," "type of crying," and "time / amount of feeding." The data collection unit uses the baby monitor to record the time and type of crying, the sleep tracker to measure the baby's sleep duration and level of sleep, and records the time and amount of feeding the baby. Step 2: The judgment unit determines the baby's behavioral patterns based on the data collected by the collection unit. For example, it uses AI to analyze the data, determining crying patterns by analyzing data on the duration and type of crying, determining sleep patterns by analyzing data on sleep duration and sleep quality, and determining feeding patterns by analyzing data on meal times and amounts. Step 3: The prediction unit predicts the baby's behavior based on the data collected by the collection unit and the judgment results from the judgment unit. For example, using AI, it predicts when the baby will cry next based on data on the duration and type of crying, when the baby will wake up next based on data on sleep duration and sleep quality, and when the baby will eat next based on data on meal times and amounts. Step 4: The decision unit determines how to respond to the baby's behavior based on the prediction results from the prediction unit. For example, it uses AI to determine how to respond when the baby cries, how to adjust meal times, and how to adjust sleep times. Step 5: The notification unit notifies parents of the baby's behavior predicted by the prediction unit and the action to take determined by the decision unit. For example, it notifies parents of the baby's behavior predictions and action to take via a dedicated app, predicts when the baby will cry and notifies them of the action to take, predicts meal times and notifies them of the action to take, and predicts sleep times and notifies them of the action to take.

[0073] (Example of form 2) The full-support childcare tool according to an embodiment of the present invention is a system aimed at families with no prior childcare experience or families who want to know more about their baby's behavior. This system collects behavioral data such as the baby's "sleep duration," "level of deep sleep," "crying time," "type of crying," and "feeding time / amount," analyzes and predicts it using AI, and notifies the parents of appropriate countermeasures. For example, the full-support childcare tool collects behavioral data of the baby using a baby monitor or sleep tracker (bed pad type). Next, based on the collected data, the AI ​​determines the baby's behavioral pattern. Furthermore, based on the determination result, the AI ​​predicts the baby's next behavior. For example, it predicts when the baby will cry next or when it will be time to feed. Based on the prediction result, the AI ​​determines the optimal countermeasure. For example, it determines how to deal with the baby crying or how to adjust feeding times. Finally, the system notifies the parents of the baby's behavioral predictions and countermeasures through a dedicated app. For example, by predicting when the baby will cry and notifying them of the countermeasures, parents can take appropriate action. This mechanism allows even families with no prior childcare experience to manage their baby's behavior in real time and know appropriate countermeasures. Furthermore, the community within the dedicated app allows parents to consult with other families raising children and to receive free consultations from medical specialists. This enables parents to raise their children with peace of mind and to share childcare responsibilities equally with their spouse. In this way, the full childcare support tool allows parents to monitor their baby's behavior in real time and learn how to deal with appropriate situations.

[0074] The childcare full support tool according to the embodiment comprises a data collection unit, a judgment unit, a prediction unit, a decision unit, and a notification unit. The data collection unit collects behavioral data of the baby. The data collection unit collects data such as the baby's "sleep duration," "level of deep sleep," "crying duration," "type of crying," and "time / amount of meals" using, for example, a baby monitor or sleep tracker (mattress pad type). For example, the data collection unit records the baby's crying duration and type of crying using a baby monitor. The data collection unit can also measure the baby's sleep duration and level of deep sleep using a sleep tracker. Furthermore, the data collection unit can also record the baby's meal times and amounts. The judgment unit determines the baby's behavioral patterns based on the data collected by the data collection unit. The judgment unit analyzes the data using, for example, AI to determine the baby's behavioral patterns. For example, the judgment unit analyzes data on the baby's crying duration and type of crying to determine the baby's crying patterns. The judgment unit can also analyze data on the baby's sleep duration and level of deep sleep to determine the baby's sleep patterns. Furthermore, the judgment unit can analyze data on the baby's feeding times and amounts to determine the baby's eating patterns. The prediction unit predicts the baby's behavior based on the data collected by the collection unit and the judgment results from the judgment unit. The prediction unit predicts the baby's behavior using, for example, AI. For example, the prediction unit predicts the time the baby will cry next based on data on the time and type of crying. The prediction unit can also predict the time the baby will wake up next based on data on the baby's sleep duration and sleep depth. Furthermore, the prediction unit can also predict the time the baby will eat next based on data on the time and amount of meals the baby eats. The decision unit determines how to deal with the baby's behavior based on the prediction results from the prediction unit. The decision unit determines the optimal course of action using, for example, AI. For example, the decision unit determines how to deal with the baby when the baby cries. Furthermore, the decision unit can also determine how to adjust the baby's feeding times. Furthermore, the decision unit can also determine how to adjust the baby's sleep duration. The notification unit notifies the user of the baby's behavior predicted by the prediction unit and the course of action determined by the decision unit. The notification unit, for example, notifies parents of their baby's predicted behavior and how to deal with it through a dedicated app.For example, the notification unit can predict when the baby will cry and notify the parent of how to deal with it. It can also predict when the baby will eat and notify the parent of how to deal with it. Furthermore, it can predict when the baby will sleep and notify the parent of how to deal with it. This allows the full-support childcare tool according to the embodiment to enable parents to manage their baby's behavior in real time and know how to take appropriate action.

[0075] The data collection unit collects behavioral data about the baby. For example, the data collection unit uses a baby monitor or sleep tracker (mattress pad type) to collect data such as the baby's "sleep duration," "sleep depth," "crying time," "crying style," and "feeding time / amount." Specifically, the baby monitor records the baby's crying time and crying style with high precision, collecting audio and video data in real time. This allows for accurate identification of the moment the baby starts crying and how long it takes to stop crying. The sleep tracker monitors the baby's body movements, heart rate, and breathing patterns to measure the baby's sleep duration and sleep depth. This allows for detailed recording of how deep or light the baby's sleep is. Furthermore, the data collection unit can use sensors built into special baby bottles and dishes to record the baby's feeding time and amount. These sensors accurately measure and collect data on how much the baby ingested and at what time of day. In this way, the data collection unit can collect behavioral data about the baby from multiple angles and understand detailed behavioral patterns. The collected data is sent to a cloud server and updated in real time, enabling analysis based on the latest information at all times.

[0076] The judgment unit determines the baby's behavioral patterns based on the data collected by the collection unit. For example, the judgment unit uses AI to analyze the data and determine the baby's behavioral patterns. Specifically, the AI ​​uses machine learning algorithms to analyze data on the timing and type of crying of the baby and identify crying patterns. For example, if a baby tends to cry at a particular time of day, the judgment unit identifies that time and infers the cause of the crying. The AI ​​also analyzes data on the baby's sleep duration and sleep depth to determine sleep patterns. This allows parents to understand when the baby is in deep sleep or light sleep. Furthermore, the AI ​​analyzes data on the timing and amount of meals the baby eats to determine feeding patterns. For example, if a baby tends to eat large amounts at a particular time of day, the judgment unit identifies that time and can adjust the baby's meals accordingly. The judgment unit comprehensively analyzes this data to gain a detailed understanding of the baby's behavioral patterns. This makes it easier for parents to predict their baby's behavior and provides them with the basic information needed to find appropriate responses.

[0077] The prediction unit predicts the baby's behavior based on data collected by the collection unit and the judgment results from the judgment unit. The prediction unit predicts the baby's behavior using, for example, AI. Specifically, the AI ​​predicts the baby's next behavior with high accuracy based on past data. For example, it predicts the time when the baby will cry next based on data on the time and type of crying. If the AI ​​has a tendency to cry during certain times of the day, it identifies those times and predicts the time when the baby is most likely to cry next. The AI ​​can also predict the time the baby will wake up next based on data on the baby's sleep duration and sleep quality. This allows parents to know in advance when their baby will wake up and prepare appropriate responses. Furthermore, the AI ​​can also predict the time the baby will eat next based on data on the time and amount of food the baby eats. This allows parents to prepare meals for their baby more efficiently. The prediction unit updates these prediction results in real time, providing predictions based on the latest information at all times. This allows parents to know in advance what their baby will be doing and take appropriate responses.

[0078] The decision unit determines how to respond to the baby's behavior based on the prediction results from the prediction unit. For example, the decision unit uses AI to determine the optimal response. Specifically, the AI ​​determines the best response when the baby cries based on data on the timing and type of crying. For example, if the baby is crying because of hunger, the AI ​​suggests adjusting meal times. Also, if the baby is having trouble sleeping, the AI ​​can suggest ways to improve the sleep environment. Furthermore, the AI ​​determines how to adjust meals based on data on the baby's meal times and amounts. For example, if the baby tends to eat large amounts at certain times, the AI ​​suggests preparing meals to match those times. The decision unit comprehensively evaluates these responses and can provide the parents with the optimal method. This allows parents to respond appropriately to their baby's behavior and maintain the baby's health and comfort.

[0079] The notification unit notifies parents of the baby's behavior predicted by the prediction unit and the appropriate course of action determined by the decision unit. For example, the notification unit notifies parents of the baby's behavior prediction and appropriate course of action through a dedicated app. Specifically, the notification unit predicts when the baby will cry and notifies parents of how to deal with it. For example, when the time when the baby is likely to cry is approaching, it notifies parents through the dedicated app and suggests how to deal with the crying. The notification unit can also predict the baby's mealtime and notify parents of how to deal with it. For example, when the time for the baby's meal is approaching, it notifies parents through the dedicated app and suggests how to prepare the meal appropriately. Furthermore, the notification unit can also predict the baby's sleep time and notify parents of how to deal with it. For example, when the time for the baby's sleep is approaching, it notifies parents through the dedicated app and suggests how to create an appropriate sleep environment. This allows parents to understand the baby's behavior in real time and know how to deal with it appropriately. In addition, the notification unit can collect feedback from parents and continuously improve the accuracy and effectiveness of the notifications. This allows the notification system to provide parents with quick and accurate information, supporting them in maintaining their baby's health and comfort.

[0080] The data collection unit can collect data including the baby's "sleep duration," "sleep depth," "crying duration," "crying style," and "feeding time / amount" using a baby monitor or sleep tracker. For example, the data collection unit can record the baby's crying duration and crying style using a baby monitor. For example, the data collection unit can record the time the baby started crying and the time they stopped crying, and analyze the crying patterns. The data collection unit can also measure the baby's sleep duration and sleep depth using a sleep tracker. For example, the data collection unit can record the time the baby fell asleep and woke up, and evaluate the sleep depth. Furthermore, the data collection unit can also record the baby's feeding times and amounts. For example, the data collection unit can record the time the baby started eating and finished eating, and measure the amount eaten. This allows for the collection of detailed data on the baby using a baby monitor or sleep tracker. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input video data acquired from a baby monitor into a generating AI, and have the generating AI analyze the baby's crying patterns.

[0081] The judgment unit can analyze the collected data and determine the baby's behavioral patterns. For example, the judgment unit can analyze the collected data using AI to determine the baby's behavioral patterns. For example, the judgment unit can analyze data on the duration and type of crying of the baby to determine the baby's crying patterns. The judgment unit can also analyze data on the duration and depth of sleep of the baby to determine the baby's sleep patterns. Furthermore, the judgment unit can analyze data on the timing and amount of meals the baby eats to determine the baby's eating patterns. In this way, the baby's behavioral patterns can be determined by analyzing the collected data. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the collected data into a generating AI and have the generating AI perform the determination of the baby's behavioral patterns.

[0082] The prediction unit can predict the baby's behavior based on the judgment result. For example, the prediction unit can use AI to predict the baby's behavior based on the judgment result. For example, the prediction unit can predict the time the baby will cry next based on data on the baby's crying time and crying style. The prediction unit can also predict the time the baby will wake up next based on data on the baby's sleep duration and sleep quality. Furthermore, the prediction unit can predict the time the baby will eat next based on data on the baby's meal times and amounts. In this way, by predicting the baby's behavior based on the judgment result, the next behavior can be predicted. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the judgment result into a generating AI and have the generating AI perform the baby's behavior prediction.

[0083] The decision unit can determine how to respond to the baby's behavior based on the prediction results. For example, the decision unit can use AI to determine how to respond to the baby's behavior based on the prediction results. For example, the decision unit can determine how to respond when the baby cries. The decision unit can also determine how to adjust the baby's feeding times. Furthermore, the decision unit can determine how to adjust the baby's sleep times. In this way, by determining how to respond based on the prediction results, appropriate responses can be provided. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input the prediction results into a generating AI and have the generating AI perform the determination of how to respond to the baby's behavior.

[0084] The notification unit can notify parents of their baby's behavior predictions and how to deal with them through an application. For example, the notification unit can notify parents of their baby's behavior predictions and how to deal with them through a dedicated app. For example, the notification unit can predict when the baby will cry and notify parents of how to deal with it. It can also predict when the baby will eat and notify parents of how to deal with it. Furthermore, the notification unit can predict when the baby will sleep and notify parents of how to deal with it. This allows parents to take appropriate action by notifying them through a dedicated app. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the prediction results and how to deal with them into a generating AI and have the generating AI generate the notification content.

[0085] The data collection unit can estimate the baby's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the baby is crying, the data collection unit increases the frequency of data collection to collect more detailed data. For example, the data collection unit records in detail the time the baby started crying and the time it stopped crying. The data collection unit can also decrease the frequency of data collection if the baby is sleeping soundly to avoid disturbing the baby's sleep. For example, the data collection unit records the time the baby started falling asleep and woke up to assess the level of sleep. Furthermore, if the baby is playing, the data collection unit can set the frequency of data collection to a moderate level to understand the baby's behavioral patterns. For example, the data collection unit records the time the baby started playing and finished playing to analyze the play patterns. This allows for appropriate data collection by adjusting the frequency of data collection based on the baby'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 processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the baby's facial expression data into a generating AI and have the generating AI perform an estimation of the baby's emotions.

[0086] The data collection unit can analyze the baby's past behavioral data and select the optimal data collection method. For example, the data collection unit can identify the times when the baby cries from past data and concentrate data collection during those times. For example, the data collection unit can analyze past data to determine when the baby started crying and when it stopped crying, thereby identifying the crying times. The data collection unit can also understand the baby's sleep patterns from past data and adjust the data collection method during sleep. For example, the data collection unit can analyze past data to determine when the baby fell asleep and when it woke up, thereby identifying the sleep patterns. Furthermore, the data collection unit can identify the baby's meal times from past data and collect data during those times. For example, the data collection unit can analyze past data to determine when the baby started and finished eating, thereby identifying meal times. In this way, the optimal data collection method can be selected by analyzing past behavioral data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past behavioral data into a generating AI and have the generating AI select the optimal data collection method.

[0087] The data collection unit can filter data based on the baby's current health status and environmental conditions during data collection. For example, if the baby has a fever, the data collection unit can increase the frequency of data collection to monitor the baby's health in more detail. For example, the data collection unit can periodically measure the baby's body temperature and record the fever status. The data collection unit can also adjust data collection based on environmental conditions if the baby is outside. For example, the data collection unit can measure the temperature and humidity at the location and adjust the data collection method accordingly. Furthermore, if the baby is in a hospital, the data collection unit can change the data collection method to avoid interference with medical equipment. For example, the data collection unit can consider the environmental conditions within the hospital and adjust the data collection method accordingly. This allows for appropriate data collection by filtering based on health status and environmental conditions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the baby's health status data into a generating AI and have the generating AI perform filtering of the data collection method.

[0088] The data collection unit can estimate the baby's emotions and prioritize the data to collect based on the estimated emotions. For example, if the baby is crying, the unit prioritizes collecting data on the type and duration of crying. For instance, it might meticulously record the time the baby started crying and stopped crying, and analyze the crying patterns. Similarly, if the baby is sleeping soundly, the unit can prioritize collecting data on sleep duration and sleep quality. For example, it might record the time the baby fell asleep and woke up, and evaluate the sleep quality. Furthermore, if the baby is eating, the unit can prioritize collecting data on the time and amount of food consumed. For example, it might record the time the baby started and finished eating, and measure the amount eaten. This allows for the priority collection of important data by prioritizing data based on the baby'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 processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the baby's facial expression data into a generating AI and have the generating AI perform an estimation of the baby's emotions.

[0089] The data collection unit can prioritize the collection of highly relevant data based on the baby's geographical location information during data collection. For example, if the baby is at home, the data collection unit prioritizes the collection of data related to the indoor environment. For example, the data collection unit measures the temperature and humidity at home and associates this with the baby's behavioral data. The data collection unit can also prioritize the collection of data related to the external environment if the baby is in a park. For example, the data collection unit measures the temperature and humidity at the park and associates this with the baby's behavioral data. Furthermore, if the baby is in a daycare center, the data collection unit can prioritize the collection of data related to the daycare center environment. For example, the data collection unit measures the temperature and humidity at the daycare center and associates this with the baby's behavioral data. This allows for the priority collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the baby's geographical location information into a generating AI and have the generating AI determine the priority of highly relevant data.

[0090] The data collection unit can analyze a baby's social media activity and collect relevant data during data collection. For example, if photos or videos of a baby are posted on social media, the data collection unit will collect data based on that information. For example, the data collection unit will analyze photos and videos of babies posted on social media to understand their behavioral patterns. The data collection unit can also analyze the number of followers and reactions on a baby's social media and collect relevant data. For example, the data collection unit will analyze the number of followers and likes to evaluate the baby's popularity. Furthermore, the data collection unit can analyze the content of a baby's social media posts and collect data related to their behavioral patterns. For example, the data collection unit will analyze the content of posts to understand the baby's interests and concerns. This allows for the collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media data into a generating AI and have the generating AI collect relevant data.

[0091] The judgment unit can estimate the baby's emotions and adjust the method for determining behavioral patterns based on the estimated emotions. For example, if the baby is crying, the judgment unit will prioritize data on the crying pattern to determine the behavioral pattern. For example, the judgment unit will meticulously record the time the baby started crying and the time it stopped crying, and analyze the crying pattern. The judgment unit can also prioritize sleep data when the baby is sleeping soundly to determine the behavioral pattern. For example, the judgment unit will record the time the baby started sleeping and the time it woke up, and evaluate the degree of sleep depth. Furthermore, if the baby is playing, the judgment unit can also prioritize play data to determine the behavioral pattern. For example, the judgment unit will record the time the baby started playing and the time it ended playing, and analyze the play pattern. By adjusting the judgment method based on the baby's emotions, it becomes possible to determine behavioral patterns more accurately. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the baby's facial expression data into a generating AI and have the generating AI perform an estimation of the baby's emotions.

[0092] The judgment unit can adjust the level of detail in its judgment based on the importance of the collected data. For example, if high-importance data is collected, the judgment unit will perform a detailed judgment. For example, it will analyze data on the duration and type of crying of a baby in detail to determine the crying pattern. The judgment unit can also perform a simplified judgment if low-importance data is collected. For example, it will analyze data on the duration and depth of sleep of a baby in a simplified manner to determine the sleep pattern. Furthermore, if data of moderate importance is collected, the judgment unit can perform a judgment with an appropriate level of detail. For example, it will analyze data on the timing and amount of meals a baby eats with an appropriate level of detail to determine the meal pattern. By adjusting the level of detail in the judgment based on the importance of the data, efficient judgment becomes possible. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the collected data into a generating AI and have the generating AI perform the adjustment of the level of detail in the judgment.

[0093] The judgment unit can apply different judgment algorithms depending on the baby's category during the judgment process. For example, in the case of a newborn, the judgment unit uses a specific algorithm to determine the behavioral pattern. For instance, based on data on the duration and type of crying of a newborn, the judgment unit applies a newborn-specific algorithm to determine the crying pattern. The judgment unit can also use a different algorithm to determine the behavioral pattern of an infant. For example, based on data on the duration and depth of sleep of an infant, the judgment unit applies an infant-specific algorithm to determine the sleep pattern. Furthermore, in the case of a baby with a specific health condition, the judgment unit can use a dedicated algorithm to determine the behavioral pattern. For example, based on data on the timing and amount of meals given to a baby with a specific health condition, the judgment unit applies a dedicated algorithm to determine the feeding pattern. This allows for more accurate judgments by applying algorithms appropriate to the category. Some or all of the above-described processes in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the baby's category information into a generating AI and have the generating AI apply an appropriate judgment algorithm.

[0094] The judgment unit can estimate the baby's emotions and adjust the display method of the judgment result based on the estimated emotions of the baby. For example, if the baby is crying, the judgment unit can provide a display method that emphasizes urgency. For example, the judgment unit can record in detail the time when the baby started crying and the time when it stopped crying, and display a notification of high urgency. The judgment unit can also provide a calm display method if the baby is sleeping soundly. For example, the judgment unit can record the time when the baby started falling asleep and the time when it woke up, and display a calm notification. Furthermore, the judgment unit can provide a fun display method if the baby is playing. For example, the judgment unit can record the time when the baby started playing and the time when it finished playing, and display a fun notification. In this way, by adjusting the display method based on the baby's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without using AI. For example, the judgment unit can input the baby's facial expression data into a generating AI, allowing the generating AI to estimate the baby's emotions.

[0095] The judgment unit can determine the priority of judgments based on when the collected data was submitted. For example, if the most recent data is submitted, the judgment unit will prioritize that data for judgment. For instance, the judgment unit will prioritize analyzing the most recent data on the baby's crying time and crying style to determine the crying pattern. The judgment unit can also lower the priority of older data if it is submitted. For example, the judgment unit will analyze older data on the baby's sleep time and sleep depth with a lower priority to determine the sleep pattern. Furthermore, if data of moderate recency is submitted, the judgment unit can also give it a moderate priority for judgment. For example, the judgment unit will analyze data on the baby's meal times and amounts with a moderate priority to determine the feeding pattern. This allows for efficient judgments by determining priorities based on when the data was submitted. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the collected data into a generating AI and have the generating AI determine the priority of judgments.

[0096] The judgment unit can adjust the order of judgments based on the relevance of the collected data during the judgment process. For example, if highly relevant data is collected, the judgment unit will prioritize judging that data. For instance, the judgment unit will prioritize analyzing data on the duration and type of crying of a baby to determine the crying pattern. The judgment unit can also postpone the judgment order of data with low relevance if it is collected. For example, the judgment unit will postpone analyzing data on the duration and depth of sleep of a baby to determine the sleep pattern. Furthermore, if data with moderate relevance is collected, the judgment unit can judge it in an appropriate order. For example, the judgment unit will analyze data on the timing and amount of meals a baby eats in an appropriate order to determine the feeding pattern. This allows for efficient judgments by adjusting the order based on the relevance of the data. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the collected data into a generating AI and have the generating AI adjust the order of judgments.

[0097] The prediction unit can estimate the baby's emotions and adjust the criteria for predicting behavior based on the estimated emotions. For example, if the baby is crying, the prediction unit can adjust the criteria for predicting when the baby will cry next. For example, the prediction unit can meticulously record the time the baby started crying and when it stopped crying, and adjust the criteria for predicting when the baby will cry next. The prediction unit can also adjust the criteria for predicting when the baby will wake up next if it is sleeping soundly. For example, the prediction unit can record the time the baby started sleeping and when it woke up, and adjust the criteria for predicting when the baby will wake up next. Furthermore, if the baby is playing, the prediction unit can adjust the criteria for predicting when it will start playing next. For example, the prediction unit can record the time the baby started playing and when it finished playing, and adjust the criteria for predicting when it will start playing next. By adjusting the prediction criteria based on the baby's emotions, more accurate behavior prediction becomes possible. Emotion estimation is achieved using emotion estimation functions, such as emotion engines 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 prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the baby's facial expression data into a generating AI and have the generating AI perform an estimation of the baby's emotions.

[0098] The prediction unit can improve the accuracy of its predictions based on the babies' interactions during the prediction process. For example, the prediction unit can improve the accuracy of behavioral predictions by considering the interactions between babies and their parents. For example, the prediction unit can analyze behavioral data of babies and their parents and improve the accuracy of predictions based on their interactions. The prediction unit can also improve the accuracy of behavioral predictions by considering the interactions between babies and their siblings. For example, the prediction unit can analyze behavioral data of babies and their siblings and improve the accuracy of predictions based on their interactions. Furthermore, the prediction unit can also improve the accuracy of behavioral predictions by considering the interactions between babies and their caregivers. For example, the prediction unit can analyze behavioral data of babies and their caregivers and improve the accuracy of predictions based on their interactions. In this way, the accuracy of predictions is improved by considering the interactions. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input behavioral data of babies and their parents into a generating AI and have the generating AI perform accuracy improvements on predictions based on interactions.

[0099] The prediction unit can make predictions based on the baby's attribute information. For example, the prediction unit can make behavioral predictions considering the baby's age. For example, the prediction unit can predict age-appropriate behavioral patterns based on the baby's age data. The prediction unit can also make behavioral predictions considering the baby's gender. For example, the prediction unit can predict gender-appropriate behavioral patterns based on the baby's gender data. Furthermore, the prediction unit can also make behavioral predictions considering the baby's health condition. For example, the prediction unit can predict behavioral patterns appropriate to the baby's health condition based on the baby's health condition data. By considering attribute information, more appropriate predictions become possible. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the baby's attribute information into a generating AI and have the generating AI perform predictions based on the attribute information.

[0100] The prediction unit can estimate the baby's emotions and adjust the display order of the prediction results based on the estimated emotions. For example, if the baby is crying, the prediction unit will prioritize displaying prediction results that indicate urgency. For instance, the prediction unit will meticulously record the time the baby started crying and the time it stopped crying, and then prioritize displaying the most urgent prediction results. The prediction unit can also prioritize displaying calm prediction results if the baby is sleeping soundly. For example, the prediction unit will record the time the baby fell asleep and woke up, and then prioritize displaying the calm prediction results. Furthermore, if the baby is playing, the prediction unit can also prioritize displaying happy prediction results. For example, the prediction unit will record the time the baby started playing and the time it finished playing, and then prioritize displaying the happy prediction results. By adjusting the display order based on the baby's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, with 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 prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the baby's facial expression data into a generating AI and have the generating AI perform an estimation of the baby's emotions.

[0101] The prediction unit can make predictions based on the geographical distribution of babies. For example, if a baby is at home, the prediction unit makes predictions based on the home environment. For example, the prediction unit predicts the baby's behavioral patterns based on the temperature and humidity of the home. The prediction unit can also make predictions based on the park environment if the baby is in a park. For example, the prediction unit predicts the baby's behavioral patterns based on the temperature and humidity of the park. Furthermore, if a baby is in a daycare center, the prediction unit can also make predictions based on the daycare center environment. For example, the prediction unit predicts the baby's behavioral patterns based on the temperature and humidity of the daycare center. This allows for more accurate predictions by considering geographical distribution. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input geographical distribution data of babies into a generating AI and have the generating AI perform predictions based on geographical distribution.

[0102] The prediction unit can improve the accuracy of its predictions by referring to relevant literature on babies during the prediction process. For example, the prediction unit can improve the accuracy of its predictions by referring to academic papers on baby behavior. For example, the prediction unit can analyze academic papers on baby behavior to improve the accuracy of its predictions. The prediction unit can also improve the accuracy of its predictions by referring to research data on baby health. For example, the prediction unit can analyze research data on baby health to improve the accuracy of its predictions. Furthermore, the prediction unit can also improve the accuracy of its predictions by referring to literature on baby development. For example, the prediction unit can analyze literature on baby development to improve the accuracy of its predictions. In this way, the accuracy of the predictions is improved by referring to relevant literature. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without using AI. For example, the prediction unit can input relevant literature on babies into a generating AI and have the generating AI perform the prediction accuracy improvement.

[0103] The decision-making unit can estimate the baby's emotions and adjust the criteria for determining how to respond based on the estimated emotions. For example, if the baby is crying, the decision-making unit will prioritize deciding on a response to stop the crying. For example, the decision-making unit will meticulously record the time the baby started crying and the time it stopped crying, and then decide on a response to stop the crying. The decision-making unit can also determine a response that does not disturb the baby's sleep if the baby is sleeping soundly. For example, the decision-making unit will record the time the baby fell asleep and woke up, and then decide on a response that does not disturb the sleep. Furthermore, if the baby is playing, the decision-making unit can also determine a response to allow the baby to continue playing. For example, the decision-making unit will record the time the baby started playing and the time it ended, and then decide on a response to allow the baby to continue playing. In this way, by adjusting the decision criteria based on the baby's emotions, a more appropriate response is provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the baby's facial expression data into a generating AI and have the generating AI perform an estimation of the baby's emotions.

[0104] The decision-making unit can select the optimal course of action by referring to the baby's past behavioral data when making a decision. For example, the decision-making unit may prioritize selecting courses of action that have been effective in the past. For example, it may select an effective course of action based on data of the baby's past crying times and crying patterns. The decision-making unit can also avoid selecting courses of action that have failed in the past. For example, it may avoid a course of action based on data of the baby's past sleep duration and sleep quality. Furthermore, the decision-making unit can also select a new course of action based on past behavioral data. For example, it may select a new course of action based on data of the baby's past feeding times and amounts. In this way, the optimal course of action can be selected by referring to past behavioral data. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the baby's past behavioral data into a generating AI and have the generating AI select the optimal course of action.

[0105] The decision-making unit can customize the course of action based on the baby's current health condition at the time of decision-making. For example, if the baby has a fever, the decision-making unit will select a course of action to lower the body temperature. For example, the decision-making unit will select a course of action to lower the body temperature based on the baby's body temperature data. The decision-making unit can also select a standard course of action if the baby is healthy. For example, the decision-making unit will select a standard course of action based on the baby's health condition data. Furthermore, if the baby is unwell, the decision-making unit can select a course of action based on a doctor's advice. For example, the decision-making unit will select a course of action based on a doctor's advice based on the baby's unwell condition data. This allows for more appropriate action by customizing the course of action based on the baby's health condition. Some or all of the above-described processes in the decision-making unit may be performed using AI, for example, or not using AI. For example, the decision-making unit can input the baby's health condition data into a generating AI and have the generating AI perform the customization of the course of action.

[0106] The decision-making unit can estimate the baby's emotions and prioritize appropriate responses based on those emotions. For example, if the baby is crying, the decision-making unit will prioritize responses that soothe the baby. For instance, it might meticulously record the time the baby started crying and the time it stopped crying, and then prioritize responses that soothe the baby. Similarly, if the baby is sleeping soundly, the decision-making unit can prioritize responses that do not disturb sleep. For example, it might record the time the baby fell asleep and woke up, and then prioritize responses that do not disturb sleep. Furthermore, if the baby is playing, the decision-making unit can prioritize responses that allow the baby to continue playing. For example, it might record the time the baby started playing and the time it ended, and then prioritize responses that allow the baby to continue playing. This allows for more appropriate responses by prioritizing responses based on the baby'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 decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the baby's facial expression data into a generating AI and have the generating AI perform an estimation of the baby's emotions.

[0107] The decision-making unit can select the optimal course of action based on the baby's geographical location information at the time of decision-making. For example, if the baby is at home, the decision-making unit can select a course of action appropriate to the home environment. For example, the decision-making unit can select a course of action based on the temperature and humidity of the home to address the baby's behavior. The decision-making unit can also select a course of action appropriate to the park environment if the baby is in a park. For example, the decision-making unit can select a course of action based on the temperature and humidity of the park to address the baby's behavior. Furthermore, if the baby is in a daycare center, the decision-making unit can select a course of action appropriate to the daycare center environment. For example, the decision-making unit can select a course of action based on the temperature and humidity of the daycare center to address the baby's behavior. This allows for the selection of a more appropriate course of action by considering geographical location information. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the baby's geographical location information into a generating AI and have the generating AI select the optimal course of action.

[0108] The decision-making unit can analyze the baby's social media activity and suggest appropriate actions when making a decision. For example, the decision-making unit can analyze the content of the baby's social media posts and suggest relevant actions. For example, the decision-making unit can analyze the content of the baby's social media posts and suggest actions to take in response to the baby's behavior. The decision-making unit can also analyze the number of followers and reactions on the baby's social media and suggest actions to take. For example, the decision-making unit can analyze the number of followers and likes and suggest actions to take in response to the baby's behavior. Furthermore, the decision-making unit can analyze the photos and videos on the baby's social media and suggest actions to take. For example, the decision-making unit can analyze photos and videos and suggest actions to take in response to the baby's behavior. In this way, by analyzing social media activity, more appropriate actions can be suggested. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the baby's social media data into a generating AI and have the generating AI execute the suggestion of actions.

[0109] The notification unit can estimate the baby's emotions and adjust the way notifications are displayed based on the estimated emotions. For example, if the baby is crying, the notification unit can display a notification emphasizing urgency. For example, the notification unit can record in detail the time the baby started crying and the time it stopped crying and display a highly urgent notification. The notification unit can also display a calm notification if the baby is sleeping soundly. For example, the notification unit can record the time the baby started falling asleep and the time it woke up and display a calm notification. Furthermore, the notification unit can display a fun notification if the baby is playing. For example, the notification unit can record the time the baby started playing and the time it finished playing and display a fun notification. This allows for more appropriate notifications by adjusting the display method 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 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 the baby's facial expression data into a generating AI, which can then perform an estimation of the baby's emotions.

[0110] The notification unit can select the optimal notification method by referring to the baby's past behavioral history when issuing a notification. For example, the notification unit may prioritize notification methods that have been effective in the past. For example, it may select an effective notification method based on data of the baby's past crying times and crying patterns. The notification unit can also avoid selecting notification methods that have failed in the past. For example, it may avoid selecting a notification method that has failed based on data of the baby's past sleep duration and sleep quality. Furthermore, the notification unit can also select a new notification method based on past behavioral history. For example, it may select a new notification method based on data of the baby's past meal times and amounts. In this way, the optimal notification method can be selected by referring to past behavioral history. 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 baby's past behavioral history into a generating AI and have the generating AI select the optimal notification method.

[0111] The notification unit can adjust the notification content based on the baby's current health condition when it sends a notification. For example, if the baby has a fever, the notification unit will notify the baby of how to lower their temperature. For example, the notification unit will notify the baby of how to lower their temperature based on the baby's temperature data. The notification unit can also notify the baby of the usual course of action if the baby is healthy. For example, the notification unit will notify the baby of the usual course of action based on the baby's health condition data. Furthermore, if the baby is unwell, the notification unit can notify the baby of how to take action based on a doctor's advice. For example, the notification unit will notify the baby of how to take action based on a doctor's advice based on the baby's unwell condition data. This allows for more appropriate notifications by taking the baby's health condition into consideration. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the baby's health condition data into a generating AI and have the generating AI adjust the notification content.

[0112] The notification unit can estimate the baby's emotions and prioritize notifications based on those emotions. For example, if the baby is crying, the notification unit will prioritize notifications on how to soothe the baby. For instance, it will record the exact time the baby started crying and the time it stopped crying, and then prioritize notifications on how to soothe the baby. Furthermore, if the baby is sleeping soundly, the notification unit can prioritize notifications on how to avoid disturbing sleep. For example, it will record the time the baby fell asleep and woke up, and then prioritize notifications on how to avoid disturbing sleep. Additionally, if the baby is playing, the notification unit can prioritize notifications on how to continue playing. For example, it will record the time the baby started playing and the time it ended, and then prioritize notifications on how to continue playing. This allows for more appropriate notifications by prioritizing based on the baby's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the processing described above in the notification unit may be performed using AI, or not using AI. For example, the notification unit may input baby's facial expression data into the generation AI and have the generation AI perform an estimation of the baby's emotions.

[0113] The notification unit can select the optimal notification method when a notification is sent, taking into account the baby's device information. For example, if the baby is using a smartphone, the notification unit can provide a notification method that matches the screen size. For example, the notification unit can provide a notification method optimized for the smartphone screen size. Also, if the baby is using a tablet, the notification unit can provide a notification method optimized for the larger screen. For example, the notification unit can provide a notification method optimized for the tablet screen size. Furthermore, if the baby is using a smartwatch, the notification unit can provide a concise and highly visible notification method. For example, the notification unit can provide a notification method optimized for the smartwatch screen size. This allows for the selection of a more appropriate notification method by considering device information. 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 baby's device information into a generating AI and have the generating AI select the optimal notification method.

[0114] The notification unit can provide multilingual notifications according to the baby's language settings when a notification is sent. For example, the notification unit can automatically set the notification language based on the language settings of the baby's device. For example, the notification unit can detect the language settings of the baby's device and automatically set the notification content based on that language. The notification unit can also provide a language switching function if the baby uses multiple languages. For example, the notification unit can display the notification content in multiple languages ​​based on the language settings of the baby's device. Furthermore, the notification unit can provide notifications in a specific language if the baby selects that language. For example, the notification unit can display the notification content based on the language selected by the baby. This allows for more appropriate notifications by providing notifications according to language settings. 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 baby's language setting information into a generating AI and have the generating AI execute multilingual notifications.

[0115] The community function can estimate the baby's emotions and adjust how information is displayed within the community based on the estimated emotions. For example, if the baby is crying, the community function can prioritize displaying urgent information. For instance, the community function can meticulously record the time the baby started crying and the time it stopped crying, and then prioritize displaying urgent information. Furthermore, if the baby is sleeping soundly, the community function can prioritize displaying calming information. For example, the community function can record the time the baby fell asleep and woke up, and then prioritize displaying calming information. Additionally, if the baby is playing, the community function can prioritize displaying enjoyable information. For example, the community function can record the time the baby started playing and the time it finished playing, and then prioritize displaying enjoyable information. This allows for more appropriate information display by adjusting the information display method based on the baby's emotions. 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 community function may be performed using AI, or not. For example, the community feature allows users to input baby facial expression data into a generating AI and have the AI ​​estimate the baby's emotions.

[0116] The community function can provide optimal advice by referring to past consultation history during consultations within the community. For example, the community function can prioritize providing advice that was effective in the past. For example, the community function can provide effective advice based on past consultation history. The community function can also avoid providing advice that was unsuccessful in the past. For example, the community function can avoid providing advice that was unsuccessful based on past consultation history. Furthermore, the community function can provide new advice based on past consultation history. For example, the community function can provide new advice based on past consultation history. In this way, optimal advice can be provided by referring to past consultation history. Some or all of the above processing in the community function may be performed using AI, or not using AI. For example, the community function can input past consultation history into a generating AI and have the generating AI perform the task of providing optimal advice.

[0117] The community function can provide advice during consultations within the community, taking into account the baby's current health condition. For example, if the baby has a fever, the community function can provide advice on how to lower the temperature. For example, the community function can provide advice on how to lower the temperature based on the baby's temperature data. The community function can also provide standard advice when the baby is healthy. For example, the community function can provide standard advice based on the baby's health status data. Furthermore, if the baby is unwell, the community function can provide advice based on a doctor's advice. For example, the community function can provide advice based on a doctor's advice based on the baby's unwellness data. This allows for more appropriate advice to be provided by considering the baby's health condition. Some or all of the above processing in the community function may be performed using AI, for example, or not using AI. For example, the community function can input the baby's health status data into a generating AI and have the generating AI provide advice.

[0118] The community function can estimate the baby's emotions and prioritize information within the community based on the estimated emotions. For example, if the baby is crying, the community function will display urgent information first. For instance, it may record the time the baby started crying and the time it stopped crying in detail and display urgent information first. It can also prioritize calming information if the baby is sleeping soundly. For example, it may record the time the baby fell asleep and woke up and display calming information first. Furthermore, it can prioritize fun information if the baby is playing. For example, it may record the time the baby started playing and the time it finished playing and display fun information first. This allows for more appropriate information display by prioritizing information based on the baby's emotions. 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 community function may be performed using AI, or not. For example, the community feature allows users to input baby facial expression data into a generating AI and have the AI ​​estimate the baby's emotions.

[0119] The community function can provide optimal advice by considering the user's geographical location during consultations within the community. For example, if the user is at home, the community function can provide advice suitable for the home environment. For example, the community function can provide advice based on the temperature and humidity of the user's home. The community function can also provide advice suitable for the park environment if the user is in a park. For example, the community function can provide advice based on the temperature and humidity of the park. Furthermore, if the user is in a daycare center, the community function can provide advice suitable for the daycare center environment. For example, the community function can provide advice based on the temperature and humidity of the daycare center. In this way, more appropriate advice is provided by considering geographical location information. Some or all of the above processing in the community function may be performed using AI, for example, or without AI. For example, the community function can input the user's geographical location information into a generating AI and have the generating AI perform the task of providing optimal advice.

[0120] The community function can analyze a user's social media activity and provide optimal advice during consultations within the community. For example, the community function can analyze the content of a user's social media posts and provide relevant advice. For example, the community function can analyze the content of a user's social media posts and provide advice on actions. The community function can also analyze the number of followers and likes on a user's social media and provide advice. For example, the community function can analyze the number of followers and likes and provide advice to the user. Furthermore, the community function can analyze photos and videos on a user's social media and provide advice. For example, the community function can analyze photos and videos and provide advice to the user. In this way, more appropriate advice can be provided by analyzing social media activity. Some or all of the above processing in the community function may be performed using AI, for example, or not using AI. For example, the community function can input the user's social media data into a generating AI and have the generating AI perform the provision of advice.

[0121] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0122] The data collection unit can collect biometric data such as the baby's body temperature and heart rate simultaneously with collecting behavioral data from the baby. For example, the data collection unit can periodically measure the baby's body temperature to detect early signs of fever. It can also monitor the baby's heart rate and detect abnormal fluctuations in heart rate. Furthermore, it can measure the baby's respiratory rate and detect respiratory abnormalities. By collecting the baby's biometric data, it becomes possible to detect changes in the baby's health status early and take appropriate action. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the baby's biometric data into a generating AI and have the generating AI perform abnormality detection.

[0123] The data collection unit can monitor the sound environment surrounding the baby when collecting the baby's behavioral data. For example, the data collection unit can measure the noise level around the baby and evaluate the impact of the noise on the baby's sleep. The data collection unit can also analyze the sounds around the baby and evaluate the impact of specific sounds on the baby's behavior. Furthermore, the data collection unit can record music and conversations around the baby and analyze their relationship to the baby's behavioral patterns. This allows for the evaluation of the relationship between the sound environment around the baby and behavioral data, enabling appropriate interventions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input sound data from around the baby into a generating AI and have the generating AI perform the analysis of the sound environment.

[0124] The judgment unit can apply criteria appropriate to the baby's developmental stage when determining the baby's behavioral patterns. For example, for newborns, the judgment unit can determine behavioral patterns by emphasizing frequent feeding and sleep patterns. For infants, the judgment unit can also determine behavioral patterns by emphasizing the timing and amount of solid food intake. Furthermore, for toddlers, the judgment unit can determine behavioral patterns by emphasizing playtime and activity levels. By applying criteria appropriate to the baby's developmental stage, it becomes possible to determine behavioral patterns more appropriately. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input baby developmental stage data into a generating AI and have the generating AI perform the application of criteria.

[0125] The prediction unit can improve prediction accuracy by combining past behavioral data and current environmental data when predicting the baby's behavior. For example, the prediction unit can combine the baby's past sleep patterns and current room temperature data to predict when the baby will wake up next. It can also combine the baby's past eating patterns and current food content data to predict when the baby will eat next. Furthermore, the prediction unit can combine the baby's past crying patterns and current sound environment data to predict when the baby will cry next. By combining past behavioral data and current environmental data, prediction accuracy is improved, enabling more appropriate responses. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input past behavioral data and current environmental data into a generating AI and have the generating AI perform the improvement of prediction accuracy.

[0126] The decision-making unit can provide customized coping strategies that take into account the individual characteristics of the baby when determining how to respond to the baby's behavior. For example, the decision-making unit can consider the baby's allergy information to determine how to feed the baby in a way that will not cause allergies. It can also consider the baby's sleep patterns to determine how to provide an optimal sleep environment. Furthermore, it can consider the baby's activity level to determine appropriate play activities. In this way, more appropriate coping strategies are provided by taking into account the individual characteristics of the baby. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the baby's individual characteristic data into a generating AI and have the generating AI perform the task of providing customized coping strategies.

[0127] The notification unit can adjust the timing of notifications to parents when informing them of the baby's behavior predictions and how to deal with them, taking into account the parents' schedule information. For example, the notification unit can consider the parents' work schedule and refrain from sending notifications while they are at work. It can also consider the parents' sleep schedule and refrain from sending notifications while they are sleeping. Furthermore, it can consider the parents' outings and refrain from sending notifications while they are out. By considering the parents' schedule information, notifications can be sent at a more appropriate time, reducing the burden on the parents. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the parents' schedule information into a generating AI and have the generating AI perform the adjustment of the notification timing.

[0128] The data collection unit can estimate the baby's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the baby is crying, the data collection unit increases the frequency of data collection to collect more detailed data. For instance, it records in detail the time the baby started crying and the time it stopped crying. The data collection unit can also decrease the frequency of data collection if the baby is sleeping soundly to avoid disturbing the baby's sleep. For example, it records the time the baby fell asleep and woke up to assess the level of sleep. Furthermore, if the baby is playing, the data collection unit can set the frequency of data collection to a moderate level to understand the baby's behavioral patterns. For example, it records the time the baby started playing and finished playing to analyze the play patterns. This allows for appropriate data collection by adjusting the frequency of data collection based on the baby'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 processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the baby's facial expression data into a generating AI and have the generating AI perform an estimation of the baby's emotions.

[0129] The judgment unit can estimate the baby's emotions and adjust the method of determining behavioral patterns based on the estimated emotions. For example, if the baby is crying, the judgment unit will prioritize crying data to determine behavioral patterns. For example, the judgment unit will meticulously record the time the baby started crying and the time it stopped crying, and analyze the crying patterns. The judgment unit can also prioritize sleep data to determine behavioral patterns if the baby is sleeping soundly. For example, the judgment unit will record the time the baby started sleeping and the time it woke up, and evaluate the degree of sleep. Furthermore, if the baby is playing, the judgment unit can also prioritize play data to determine behavioral patterns. For example, the judgment unit will record the time the baby started playing and the time it ended playing, and analyze the play patterns. By adjusting the judgment method based on the baby's emotions, more accurate determination of behavioral patterns becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the baby's facial expression data into a generating AI and have the generating AI perform an estimation of the baby's emotions.

[0130] The prediction unit can estimate the baby's emotions and adjust the criteria for predicting behavior based on the estimated emotions. For example, if the baby is crying, the prediction unit can adjust the criteria for predicting when the baby will cry next. For example, the prediction unit can meticulously record the time the baby started crying and the time it stopped crying, and adjust the criteria for predicting when the baby will cry next. The prediction unit can also adjust the criteria for predicting when the baby will wake up next if the baby is sleeping soundly. For example, the prediction unit can record the time the baby started sleeping and the time it woke up, and adjust the criteria for predicting when the baby will wake up next. Furthermore, if the baby is playing, the prediction unit can adjust the criteria for predicting when the baby will start playing next. For example, the prediction unit can record the time the baby started playing and the time it finished playing, and adjust the criteria for predicting when the baby will start playing next. By adjusting the prediction criteria based on the baby's emotions, more accurate behavior prediction becomes possible. Emotion estimation is achieved using emotion estimation functions, such as emotion engines 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 prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the baby's facial expression data into a generating AI and have the generating AI perform an estimation of the baby's emotions.

[0131] The decision-making unit can estimate the baby's emotions and determine the priority of coping strategies based on the estimated emotions. For example, if the baby is crying, the decision-making unit will prioritize coping strategies to stop the crying. For example, the decision-making unit will consider the time the baby started crying and the crying part

[0132] The following briefly describes the processing flow for example form 2.

[0133] Step 1: The data collection unit collects data on the baby's behavior. For example, it uses a baby monitor or sleep tracker (mattress pad type) to collect data such as the baby's "sleep duration," "level of sleep," "time of crying," "type of crying," and "time / amount of feeding." The data collection unit uses the baby monitor to record the time and type of crying, the sleep tracker to measure the baby's sleep duration and level of sleep, and records the time and amount of feeding the baby. Step 2: The judgment unit determines the baby's behavioral patterns based on the data collected by the collection unit. For example, it uses AI to analyze the data, determining crying patterns by analyzing data on the duration and type of crying, determining sleep patterns by analyzing data on sleep duration and sleep quality, and determining feeding patterns by analyzing data on meal times and amounts. Step 3: The prediction unit predicts the baby's behavior based on the data collected by the collection unit and the judgment results from the judgment unit. For example, using AI, it predicts when the baby will cry next based on data on the duration and type of crying, when the baby will wake up next based on data on sleep duration and sleep quality, and when the baby will eat next based on data on meal times and amounts. Step 4: The decision unit determines how to respond to the baby's behavior based on the prediction results from the prediction unit. For example, it uses AI to determine how to respond when the baby cries, how to adjust meal times, and how to adjust sleep times. Step 5: The notification unit notifies parents of the baby's behavior predicted by the prediction unit and the action to take determined by the decision unit. For example, it notifies parents of the baby's behavior predictions and action to take via a dedicated app, predicts when the baby will cry and notifies them of the action to take, predicts meal times and notifies them of the action to take, and predicts sleep times and notifies them of the action to take.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] For example, the data collection unit can collect baby behavior data using the camera 42 and microphone 38B of the smart device 14. The determination unit is implemented by the identification processing unit 290 of the data processing device 12 and analyzes the collected data to determine the baby's behavior pattern. The prediction unit is implemented by the identification processing unit 290 of the data processing device 12 and predicts the baby's next action based on the determination result. The decision unit is implemented by the identification processing unit 290 of the data processing device 12 and determines the optimal course of action based on the prediction result. The notification unit is implemented by the control unit 46A of the smart device 14 and notifies the parent of the course of action through a dedicated app. 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.

[0138] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] For example, the data collection unit can collect baby behavior data using the camera 42 and microphone 238 of the smart glasses 214. The determination unit is implemented by the identification processing unit 290 of the data processing device 12, which analyzes the collected data to determine the baby's behavior pattern. The prediction unit is implemented by the identification processing unit 290 of the data processing device 12, which predicts the baby's next action based on the determination result. The decision unit is implemented by the identification processing unit 290 of the data processing device 12, which determines the optimal course of action based on the prediction result. The notification unit is implemented by the control unit 46A of the smart glasses 214, which notifies the parent of the course of action through a dedicated app. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0154] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] For example, the data collection unit can collect baby behavior data using the camera 42 and microphone 238 of the headset terminal 314. The determination unit is implemented by the identification processing unit 290 of the data processing device 12, which analyzes the collected data to determine the baby's behavior pattern. The prediction unit is implemented by the identification processing unit 290 of the data processing device 12, which predicts the baby's next action based on the determination result. The decision unit is implemented by the identification processing unit 290 of the data processing device 12, which determines the optimal course of action based on the prediction result. The notification unit is implemented by the control unit 46A of the headset terminal 314, which notifies the parent of the course of action through a dedicated application. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0170] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0175] 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).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.).

[0183] 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.

[0184] 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.

[0185] 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.

[0186] For example, the data collection unit can collect baby behavior data using the camera 42 and microphone 238 of the robot 414. The determination unit is implemented by the identification processing unit 290 of the data processing device 12, which analyzes the collected data to determine the baby's behavior pattern. The prediction unit is implemented by the identification processing unit 290 of the data processing device 12, which predicts the baby's next action based on the determination result. The decision unit is implemented by the identification processing unit 290 of the data processing device 12, which determines the optimal course of action based on the prediction result. The notification unit is implemented by the control unit 46A of the robot 414, which notifies the parent of the course of action through a dedicated app. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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."

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] (Note 1) A data collection unit that collects data on the baby's behavior, A determination unit that determines the baby's behavioral pattern based on the data collected by the collection unit, A prediction unit predicts the baby's behavior based on the data collected by the collection unit and the determination result by the determination unit, Based on the prediction results from the prediction unit, a decision unit determines how to deal with the baby's behavior, The system includes a notification unit that notifies the baby of the baby's behavior predicted by the prediction unit and the course of action determined by the decision unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Use a baby monitor or sleep tracker to collect data including the baby's "sleep duration," "level of sleep," "time of crying," "type of crying," and "time and amount of meals." The system described in Appendix 1, characterized by the features described herein. (Note 3) The determination unit, The collected data is analyzed to determine the baby's behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 4) The prediction unit, Predicting the baby's behavior based on the assessment results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned determination unit, Based on the prediction results, we determine how to respond to the baby's behavior. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned notification unit, The application notifies parents of their baby's behavior predictions and how to respond. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the baby's emotions and adjusts the frequency of data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the baby's past behavioral data to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the baby's current health status and environmental conditions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the baby's emotions and prioritizes the data to collect based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data based on the baby's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, we analyze the baby's social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The determination unit, The system estimates the baby's emotions and adjusts the method for determining behavioral patterns based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The determination unit, When making a decision, adjust the level of detail based on the importance of the collected data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The determination unit, When making a determination, a different determination algorithm is applied depending on the baby's category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The determination unit, The system estimates the baby's emotions and adjusts how the results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The determination unit, When making a decision, the priority of the decision will be determined based on when the collected data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The determination unit, During the decision-making process, the order of decisions is adjusted based on the relevance of the collected data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The prediction unit, We estimate the baby's emotions and adjust the behavioral prediction criteria based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The prediction unit, During prediction, improve prediction accuracy based on the babies' interactions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The prediction unit, When making predictions, the predictions are made based on the baby's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The prediction unit, It estimates the baby's emotions and adjusts the display order of the prediction results based on the estimated emotions of the baby. The system described in Appendix 1, characterized by the features described herein. (Note 23) The prediction unit, When making predictions, the predictions are based on the geographical distribution of babies. The system described in Appendix 1, characterized by the features described herein. (Note 24) The prediction unit, When making predictions, we refer to relevant literature on babies to improve the accuracy of the predictions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned determination unit, We estimate the baby's emotions and adjust the criteria for deciding how to respond based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned determination unit, When making a decision, the optimal course of action is selected by referring to the baby's past behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned determination unit, When making a decision, customize the course of action based on the baby's current health condition. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned determination unit, The system estimates the baby's emotions and prioritizes appropriate responses based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned determination unit, When making a decision, the most appropriate course of action will be selected based on the baby's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned determination unit, When making a decision, we analyze the baby's social media activity and suggest ways to address it. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned notification unit, It estimates the baby's emotions and adjusts how notifications are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned notification unit, When sending a notification, the system will refer to the baby's past behavioral history to select the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned notification unit, When sending a notification, the content of the notification will be adjusted based on the baby's current health condition. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned notification unit, It estimates the baby's emotions and prioritizes notifications based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned notification unit, When sending a notification, the system will select the most suitable notification method, taking into account the baby's device information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned notification unit, When a notification is sent, it provides multilingual notifications according to the baby's language settings. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned Community function is It estimates the baby's emotions and adjusts how information is displayed within the community based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned Community function is When seeking advice within the community, we refer to past consultation history to provide the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned Community function is When providing advice within the community, we take the baby's current health condition into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned Community function is It estimates the baby's emotions and prioritizes information within the community based on the estimated emotions of the baby. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned Community function is When users seek advice within the community, the system provides optimal advice based on their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned Community function is When users seek advice within the community, we analyze their social media activity to provide the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0206] 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 data collection unit that collects data on the baby's behavior, A determination unit that determines the baby's behavioral pattern based on the data collected by the collection unit, A prediction unit predicts the baby's behavior based on the data collected by the collection unit and the determination result by the determination unit, Based on the prediction results from the prediction unit, a decision unit determines how to deal with the baby's behavior, The system includes a notification unit that notifies the baby of the baby's behavior predicted by the prediction unit and the course of action determined by the decision unit. A system characterized by the following features.

2. The aforementioned collection unit is Use a baby monitor or sleep tracker to collect data including the baby's sleep duration, sleep quality, crying time, crying style, and feeding times / amounts. The system according to feature 1.

3. The determination unit, The collected data is analyzed to determine the baby's behavioral patterns. The system according to feature 1.

4. The prediction unit, Predicting the baby's behavior based on the assessment results. The system according to feature 1.

5. The aforementioned determination unit, Based on the prediction results, we determine how to respond to the baby's behavior. The system according to feature 1.

6. The aforementioned notification unit, The application notifies parents of their baby's behavior predictions and how to respond. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the baby's emotions and adjusts the frequency of data collection based on the estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the baby's past behavioral data to select the optimal data collection method. The system according to feature 1.

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