system

Abstract

The system according to this embodiment aims to analyze vital signs, detect abnormalities, and notify the parent. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a detection unit, and a notification unit. The collection unit collects vital signs. The analysis unit analyzes the vital signs collected by the collection unit. The detection unit detects abnormalities based on the data analyzed by the analysis unit. The notification unit notifies the parent based on the abnormalities detected by the detection unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance. <​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​The system according to this embodiment comprises a data collection unit, an analysis unit, a detection unit, and a notification unit. The data collection unit collects vital signs. The analysis unit analyzes the vital signs collected by the data collection unit. The detection unit detects abnormalities based on the data analyzed by the analysis unit. The notification unit notifies the parent based on the abnormalities detected by the detection unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze vital signs, detect abnormalities, and notify the parent. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F 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 childcare monitoring system according to an embodiment of the present invention is an integrated childcare monitoring system utilizing generative AI. This childcare monitoring system aims to alleviate anxiety about child health management and childcare, particularly for dual-income families and new parents with little childcare experience. By integrating diverse information such as vital signs, behavioral analysis, voice analysis, and lifestyle data, this system provides comprehensive support that goes beyond conventional childcare monitoring. For example, as vital sign analysis, the childcare monitoring system monitors basic health data such as body temperature and heart rate in real time, similar to conventional systems. Next, it performs facial expression, behavior, and skin color analysis, with the generative AI detecting physical changes such as the baby's or toddler's facial expressions, body movements, and skin color, and analyzing changes in physical condition and emotions. This makes it possible to grasp even subtle abnormalities that cannot be detected by vital data alone. Furthermore, it performs voice analysis (crying and speech), analyzing the baby's cries using voice analysis technology to identify needs such as hunger, sleepiness, and poor health. The generative AI estimates the cause and proposes countermeasures to the parents in real time. Furthermore, data can be shared with medical institutions, allowing for the sharing of health data and any detected abnormalities, enabling feedback from experts. This ensures a swift and accurate response in emergencies. In addition, long-term accumulation and prediction of lifestyle data (diet, sleep, activity levels) monitors children's daily diet, sleep, and activity levels, detecting early signs of illness based on long-term trends. This supports parents in taking proactive measures. This is expected to improve peace of mind in childcare, support parents in balancing work and childcare, strengthen collaboration with medical institutions, provide personalized childcare advice, and increase the efficiency of childcare for the entire family. Specifically, the generating AI analyzes crying and behavioral patterns to detect the baby's needs and illnesses in advance, allowing even new parents to respond appropriately and significantly reducing anxiety about childcare. Also, early detection of signs of illness and proactive measures reduce the risk of sudden absences or early departures from work. Furthermore, if an abnormality is detected, data can be smoothly shared with medical institutions, enabling early diagnosis and treatment.By providing childcare content and feedback tailored to a child's developmental stage and health condition based on accumulated data, the quality of childcare improves. Information sharing among family members and those involved in childcare support strengthens cooperation, preventing the burden of childcare from falling solely on the parent and enabling more efficient responses. Therefore, childcare monitoring systems are innovative systems that more accurately support a child's health and growth.

[0029] The childcare monitoring system according to this embodiment comprises a data collection unit, an analysis unit, a detection unit, and a notification unit. The data collection unit collects vital signs. The data collection unit can collect vital signs such as heart rate, body temperature, and blood pressure. The data collection unit can monitor heart rate in real time using a wearable device, for example. The data collection unit can also measure body temperature using a body temperature sensor. Furthermore, the data collection unit can measure blood pressure using a blood pressure monitor. The analysis unit analyzes the vital signs collected by the data collection unit. The analysis unit can, for example, analyze the collected heart rate data and detect abnormal patterns. The analysis unit can also analyze the collected body temperature data and detect signs of fever. Furthermore, the analysis unit can analyze the collected blood pressure data and detect signs of hypertension or hypotension. The detection unit detects abnormalities based on the data analyzed by the analysis unit. The detection unit can, for example, detect abnormal fluctuations in heart rate. The detection unit can also detect a rapid rise in body temperature. Furthermore, the detection unit can also detect abnormal fluctuations in blood pressure. The notification unit notifies the parent based on the abnormality detected by the detection unit. The notification unit can, for example, send a notification to a smartphone. It can also send a notification via email. Furthermore, the notification unit can sound an alarm to indicate an abnormality. This enables the childcare monitoring system according to the embodiment to collect, analyze, detect abnormalities in vital signs, and notify. Some or all of the above-described processes in the collection unit, analysis unit, detection unit, and notification unit may be performed using AI, for example, or without AI. For example, the collection unit can use AI to optimize data collection in order to collect vital signs. The analysis unit can analyze the collected vital sign data using AI and detect abnormalities. The detection unit can detect abnormalities using AI based on the analyzed data. The notification unit can select the optimal notification method using AI based on the detected abnormality. This enables the childcare monitoring system to efficiently collect, analyze, detect abnormalities in vital signs, and notify.

[0030] The data collection unit collects vital signs. For example, it can collect vital signs such as heart rate, body temperature, and blood pressure. Specifically, the unit monitors heart rate in real time using a wearable device. The wearable device is equipped with an optical heart rate sensor that measures heart rate by detecting changes in blood flow through the skin. The unit can also measure body temperature using a body temperature sensor. This sensor uses infrared technology to measure skin surface temperature non-contact. Furthermore, the unit can measure blood pressure using a blood pressure monitor. In addition to the traditional method of measuring blood pressure by wrapping a cuff around the arm and applying pressure, the blood pressure monitor can also continuously monitor blood pressure using a sensor built into the wearable device. This allows the unit to collect a variety of vital signs in real time and transmit them to a central database. Furthermore, the unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and accessed by the analysis and detection units. Adjusting the data collection frequency and accuracy allows for flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes vital signs collected by the data collection unit. For example, the analysis unit analyzes collected heart rate data and detects abnormal patterns. Specifically, it uses AI to analyze heart rate data in real time and detect abnormal fluctuations that exceed the normal range. The AI ​​can quickly identify abnormalities by learning from past data and modeling normal heart rate patterns. The analysis unit can also analyze collected body temperature data and detect signs of fever. The AI ​​monitors fluctuations in body temperature and uses algorithms to detect sudden increases or abnormally high temperatures. Furthermore, the analysis unit can analyze collected blood pressure data and detect signs of hypertension or hypotension. The AI ​​analyzes blood pressure fluctuation patterns and identifies abnormal values, enabling early detection of abnormalities. This allows the analysis unit to quickly and accurately analyze collected data and enable early detection of abnormalities. In addition, the analysis unit can utilize past data and statistical information to perform long-term health trend analysis. For example, it can predict fluctuations in health status over a specific period based on past vital sign data and assess future risks. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only monitor the situation in real time but also to handle long-term health management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The detection unit detects anomalies based on data analyzed by the analysis unit. For example, the detection unit can detect abnormal fluctuations in heart rate. Specifically, it uses AI to analyze heart rate data and identify abnormal fluctuations that exceed the normal range. By learning from past data and modeling normal heart rate patterns, the AI ​​can quickly identify anomalies. The detection unit can also detect rapid increases in body temperature. The AI ​​monitors fluctuations in body temperature and uses an algorithm to detect rapid increases or abnormally high temperatures. Furthermore, the detection unit can detect abnormal fluctuations in blood pressure. By analyzing blood pressure fluctuation patterns and identifying abnormal values, the AI ​​can detect anomalies early. This allows the detection unit to quickly and accurately analyze collected data, enabling early detection of anomalies. In addition, the detection unit can utilize past data and statistical information to perform long-term health trend analysis. For example, based on past vital sign data, it can predict fluctuations in health status over a specific period and assess future risks. The detection unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. This allows the detection unit to not only grasp the situation in real time, but also to handle long-term health management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0033] The notification unit notifies parents based on anomalies detected by the detection unit. For example, the notification unit sends notifications to smartphones. Specifically, it notifies parents of anomalies in real time via a smartphone app, enabling them to respond quickly. The notification unit can also send notifications via email. Email notifications can include detailed anomaly information and recommended actions. Furthermore, the notification unit can sound an alarm to alert parents of anomalies. The alarm uses sound and vibration to immediately notify parents of the anomaly. This allows the notification unit to quickly provide appropriate action instructions to each user, minimizing the risk of disaster. Additionally, the notification unit can collect user feedback and continuously improve the accuracy and effectiveness of its notifications. For example, it can review notification methods and content based on feedback from users who have received anomaly notifications. The notification unit can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the notification unit to provide users with quick and reliable action instructions, minimizing the risk of disaster.

[0034] The data collection unit can collect data on facial expressions, behavior, and skin color. For example, the data collection unit can monitor the baby's facial expressions in real time using a camera. For example, the data collection unit can recognize the baby's facial expressions such as smiles, anger, and sadness. The data collection unit can also monitor the baby's behavior. For example, the data collection unit can recognize the baby's actions such as walking, sitting, and standing. Furthermore, the data collection unit can also monitor the baby's skin color. For example, the data collection unit can detect changes in the baby's skin color using a camera. As a result, the data collection unit can obtain more detailed information by collecting data on facial expressions, behavior, and skin color. 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 image data acquired by the camera into a generating AI and have the generating AI perform the collection of data on facial expressions, behavior, and skin color.

[0035] The analysis unit can analyze facial expression, behavior, and skin color data collected by the collection unit. For example, the analysis unit can analyze the collected facial expression data to estimate the baby's emotions. For example, the analysis unit can analyze the frequency and duration of smiles to estimate the baby's happiness level. The analysis unit can also analyze the collected behavior data to evaluate the baby's activity level. For example, the analysis unit can analyze the frequency and distance of walking to evaluate the baby's exercise level. Furthermore, the analysis unit can analyze the collected skin color data to estimate the baby's health status. For example, the analysis unit can analyze changes in skin color to detect changes in the baby's physical condition. In this way, the analysis unit can grasp changes in physical condition and emotions by analyzing the facial expression, behavior, and skin color data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the analysis of facial expression, behavior, and skin color data.

[0036] The detection unit can detect changes in physical condition and emotions based on data analyzed by the analysis unit. For example, the detection unit can detect changes in the baby's physical condition by detecting fluctuations in heart rate. For example, the detection unit can detect signs of fever by detecting a sudden increase in heart rate. The detection unit can also detect changes in the baby's emotions by detecting changes in facial expressions. For example, the detection unit can detect a decrease in smiles and detect the baby's stress level. Furthermore, the detection unit can detect changes in the baby's health by detecting changes in skin color. For example, the detection unit can detect signs of anemia by detecting paleness of skin color. In this way, the detection unit can detect abnormalities early by detecting changes in physical condition and emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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 processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the analyzed data into a generating AI, which can then perform the detection of changes in physical condition and emotions.

[0037] The data collection unit can collect data on crying and speech. For example, the data collection unit can monitor the baby's crying in real time using a microphone. For example, the data collection unit can analyze the volume and frequency of the baby's crying. The data collection unit can also monitor the baby's speech. For example, the data collection unit can analyze the content and tone of the baby's speech. In this way, the data collection unit can understand the baby's needs by collecting data on crying and speech. 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 audio data acquired by the microphone into a generating AI and have the generating AI perform the collection of crying and speech data.

[0038] The analysis unit can analyze crying and speech data collected by the collection unit. For example, the analysis unit can analyze the collected crying data to identify the baby's needs. For example, the analysis unit can analyze the volume and frequency patterns of the crying to determine if the baby is hungry, sleepy, or unwell. The analysis unit can also analyze the collected speech data to analyze the content and tone of the baby's speech. For example, the analysis unit can analyze the content of the speech to determine what the baby is asking for. In this way, the analysis unit can identify the baby's needs by analyzing crying and speech data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected audio data into a generating AI and have the generating AI perform the analysis of crying and speech data.

[0039] The detection unit can detect the baby's needs based on the data analyzed by the analysis unit. For example, the detection unit can detect crying patterns to determine if the baby is hungry, sleepy, or unwell. For example, the detection unit can detect changes in the volume and frequency of crying to identify the baby's needs. The detection unit can also detect the content of speech to identify what the baby is asking for. For example, the detection unit can analyze the content of speech to identify the baby's needs. This allows the detection unit to take appropriate action by detecting the baby's needs. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the analyzed data into a generating AI and have the generating AI perform the detection of the baby's needs.

[0040] The notification unit can share data with healthcare institutions based on anomalies detected by the detection unit. The notification unit can, for example, send data to healthcare institutions via email. The notification unit can, for example, send reports to healthcare institutions that include details of the detected anomalies. The notification unit can also share data using cloud services. The notification unit can, for example, upload the detected anomaly data to the cloud and make it accessible to healthcare institutions. Furthermore, the notification unit can also share data using a dedicated app. The notification unit can, for example, enable healthcare institutions to access the data through a dedicated app. This allows the notification unit to respond quickly by sharing data with healthcare institutions. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the detected anomaly data into a generating AI and have the generating AI perform data sharing with healthcare institutions.

[0041] The data collection unit can collect data on meals, sleep, and activity levels. For example, the data collection unit can record the content and amount of meals consumed. For example, the data collection unit can save the content of meals entered by parents to a database. The data collection unit can also collect sleep data. For example, the data collection unit can monitor the duration and quality of a baby's sleep. Furthermore, the data collection unit can also collect activity level data. For example, the data collection unit can record the number of steps taken and the amount of exercise a baby takes. As a result, by collecting data on meals, sleep, and activity levels, the data collection unit enables long-term health management. 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 data on meals, sleep, and activity levels into a generating AI and have the generating AI perform the data collection.

[0042] The analysis unit can analyze the diet, sleep, and activity data collected by the collection unit. For example, the analysis unit can analyze the collected diet data to evaluate nutritional balance. For example, the analysis unit can analyze the content and amount of food consumed to evaluate the baby's nutritional status. The analysis unit can also analyze the collected sleep data to evaluate sleep quality. For example, the analysis unit can analyze sleep duration and sleep quality to evaluate the baby's sleep state. Furthermore, the analysis unit can analyze the collected activity data to evaluate the baby's activity level. For example, the analysis unit can analyze the number of steps and exercise time to evaluate the baby's activity level. As a result, by analyzing the diet, sleep, and activity data, the analysis unit can detect early signs of illness. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the analysis of the diet, sleep, and activity data.

[0043] The detection unit can detect signs of poor health based on data analyzed by the analysis unit. For example, the detection unit can detect fluctuations in heart rate to detect signs of poor health. For example, the detection unit can detect rapid fluctuations in heart rate to detect signs of poor health. The detection unit can also detect changes in sleep patterns to detect signs of poor health. For example, the detection unit can detect a decrease in sleep duration or a decline in sleep quality to detect signs of poor health. Furthermore, the detection unit can detect fluctuations in activity levels to detect signs of poor health. For example, the detection unit can detect a decrease in exercise levels to detect signs of poor health. As a result, the detection unit can take countermeasures in advance by detecting signs of poor health. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the analyzed data into a generating AI and have the generating AI perform the detection of signs of poor health.

[0044] The data collection unit can refer to the baby's past health data and select the optimal data collection method. For example, if the baby has experienced illness in the past, the data collection unit can adjust the current data collection method based on that data. For example, the data collection unit can analyze the baby's past health data and optimize the data collection method based on specific patterns. The data collection unit can also refer to the baby's past health data and select a data collection method appropriate for a specific time of day or situation. In this way, the data collection unit can select the optimal data collection method by referring to past health 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 the baby's past health data into a generating AI and have the generating AI select the optimal data collection method.

[0045] The data collection unit can filter data based on the baby's current living environment and activity level. For example, if the baby is sleeping, the data collection unit will minimize the amount of data collected to avoid disturbing sleep. If the baby is active, the data collection unit can prioritize collecting data corresponding to the baby's movement. Furthermore, if the baby is in a specific environment (for example, outdoors), the data collection unit can collect data appropriate to that environment. This allows the data collection unit to collect appropriate data by filtering it based on the living environment and activity level. 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 data on the baby's current living environment and activity level into a generating AI and have the generating AI perform the data filtering.

[0046] The data collection unit can prioritize the collection of highly relevant data by considering the baby's geographical location. For example, if the baby is at home, the data collection unit can prioritize the collection of data related to the indoor environment. If the baby is out, for example, the data collection unit can prioritize the collection of data related to the external environment. Furthermore, if the baby is in a specific location (for example, a daycare center), the data collection unit can prioritize the collection of data related to that location. In this way, the data collection unit can prioritize the collection of highly relevant data by considering geographical location. 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 perform the collection of highly relevant data.

[0047] The data collection unit can analyze a baby's social media activity and collect relevant data. For example, the data collection unit can analyze specific behavioral patterns from a baby's social media activity and collect relevant data. For example, the data collection unit can collect data related to specific time periods from a baby's social media activity. The data collection unit can also collect data related to specific events from a baby's social media activity. In this way, the data collection unit can collect 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 the baby's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0048] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on important data. For example, it can perform a simplified analysis on less important data. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the data. This allows the analysis unit to perform efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0049] The analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a specific analysis algorithm to vital sign data. For example, the analysis unit can apply a different analysis algorithm to behavioral data. Furthermore, the analysis unit can apply a dedicated analysis algorithm to voice data. This improves the accuracy of the analysis by applying the appropriate analysis algorithm according to the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0050] The analysis unit can determine the priority of analysis based on the data collection timing. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may analyze current data while referring to past data. Furthermore, the analysis unit can dynamically adjust the priority of analysis based on the data collection timing. This allows the analysis unit to prioritize the analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI perform the determination of the analysis priority.

[0051] The analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. This allows the analysis unit to perform efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0052] The detection unit can improve detection accuracy by considering the interrelationships between data. For example, the detection unit can detect anomalies by considering the interrelationships between vital sign data and behavioral data. For example, the detection unit can detect anomalies by considering the interrelationships between voice data and facial expression data. The detection unit can also improve detection accuracy by analyzing the interrelationships between data. As a result, the detection unit improves detection accuracy by considering the interrelationships between data. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the interrelationships between data into a generating AI and have the generating AI perform the improvement of detection accuracy.

[0053] The detection unit can perform detection while considering the attribute information of the data submitter. For example, if the data submitter is a parent, the detection unit can perform detection while considering that attribute information. For example, if the data submitter is a medical institution, the detection unit can perform detection while considering that attribute information. The detection unit can also analyze the attribute information of the data submitter to improve the accuracy of detection. As a result, the detection unit improves the accuracy of detection by considering the attribute information of the data submitter. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input the attribute information of the data submitter into a generating AI and have the generating AI perform the detection.

[0054] The detection unit can perform detection while considering the geographical distribution of the data. For example, the detection unit can analyze the geographical distribution of the data and detect anomalies. For example, the detection unit can detect anomalies in a specific region based on the geographical distribution. The detection unit can also improve the accuracy of detection by considering the geographical distribution of the data. As a result, the detection unit improves the accuracy of detection by considering the geographical distribution of the data. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the geographical distribution of the data into a generating AI and have the generating AI perform the detection.

[0055] The detection unit can improve the accuracy of detection by referring to relevant literature for the data. For example, the detection unit can detect anomalies by referring to relevant literature for the data. For example, the detection unit can detect specific anomalies based on relevant literature. The detection unit can also improve the accuracy of detection by analyzing relevant literature for the data. As a result, the detection unit improves the accuracy of detection by referring to relevant literature. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input relevant literature for the data into a generating AI and have the generating AI perform the detection.

[0056] The notification unit can select the optimal notification method by referring to the parent's past response history. For example, the notification unit may prioritize providing notification methods that the parent has preferred in the past. For example, the notification unit may analyze the parent's past response history and select the optimal notification method. The notification unit may also adjust the timing of notifications based on the parent's past response history. In this way, the notification unit can select the optimal notification method by referring to past response 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 may input the parent's past response history into a generating AI and have the generating AI select the optimal notification method.

[0057] The notification unit can select the optimal notification method by considering the parent's device information. For example, if the parent is using a smartphone, the notification unit can provide a notification method that matches the screen size. If the parent is using a tablet, the notification unit can provide a notification method optimized for a larger screen. Furthermore, if the parent is using a smartwatch, the notification unit can provide a concise and highly visible notification method. In this way, the notification unit can select the optimal notification method by considering the 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 parent's device information into a generating AI and have the generating AI select the optimal notification method.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The data collection unit can collect brainwave data from babies while they sleep. For example, it can monitor the baby's brainwaves in real time using a wearable device. The unit can analyze brainwave patterns to assess the quality of the baby's sleep. The unit can also monitor the baby's breathing patterns during sleep. For example, it can analyze breathing rhythms and depths to detect signs of sleep apnea. This allows the data collection unit to evaluate the quality of the baby's sleep in detail by collecting brainwave data and breathing patterns.

[0060] The detection unit can detect early signs of fever based on the baby's body temperature data. For example, the detection unit can detect a rapid rise in body temperature and notify the user of the possibility of fever. The detection unit can also analyze subtle fluctuations in body temperature to detect precursors to fever. Furthermore, the detection unit can analyze body temperature data in combination with other vital sign data (e.g., heart rate and respiratory rate) to comprehensively assess the risk of fever. As a result, the detection unit can detect early signs of fever by analyzing body temperature data in detail.

[0061] The data collection unit can collect infant feeding data and evaluate nutritional balance. For example, the data collection unit can store the details of meals entered by parents in a database and analyze nutrient intake. For example, the data collection unit can monitor the frequency and amount of meals given to infants and evaluate whether there are any excesses or deficiencies. In addition, the data collection unit can accumulate infant feeding data over the long term and track changes in nutritional balance as the infant grows. As a result, the data collection unit can evaluate the infant's nutritional balance by collecting detailed feeding data.

[0062] The detection unit can detect developmental delays early based on the baby's behavioral data. For example, the detection unit analyzes behavioral data such as walking, sitting, and standing to detect signs of developmental delays. The detection unit can also analyze fluctuations in behavioral data to assess the risk of developmental delays. Furthermore, the detection unit can combine behavioral data with other data (e.g., vital sign data and voice data) to comprehensively assess the risk of developmental delays. In this way, the detection unit can detect developmental delays early by analyzing behavioral data in detail.

[0063] The data collection unit collects data on the baby's living environment and can adjust the data collection method according to changes in the environment. For example, if the baby is indoors, the data collection unit can collect data related to the indoor environment (e.g., temperature, humidity, lighting). If the baby is outdoors, the data collection unit can collect data related to the external environment (e.g., temperature, wind speed, UV radiation). Furthermore, if the baby is in a specific location (e.g., a daycare center), the data collection unit can collect data related to that location. This allows the data collection unit to collect detailed data on the baby's living environment, enabling data collection that adapts to changes in the environment.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The data collection unit collects vital signs. The data collection unit can collect vital signs such as heart rate, body temperature, and blood pressure. The data collection unit can monitor heart rate in real time using a wearable device, measure body temperature using a body temperature sensor, and measure blood pressure using a blood pressure monitor. Step 2: The analysis unit analyzes the vital signs collected by the acquisition unit. The analysis unit can analyze the collected heart rate data to detect abnormal patterns, analyze the body temperature data to detect signs of fever, and analyze the blood pressure data to detect signs of hypertension or hypotension. Step 3: The detection unit detects abnormalities based on the data analyzed by the analysis unit. The detection unit can detect abnormal fluctuations in heart rate, a rapid increase in body temperature, and abnormal fluctuations in blood pressure. Step 4: The notification unit notifies the parent based on the anomaly detected by the detection unit. The notification unit can notify the parent of the anomaly via smartphone notification, email notification, or by sounding an alarm.

[0066] (Example of form 2) The childcare monitoring system according to an embodiment of the present invention is an integrated childcare monitoring system utilizing generative AI. This childcare monitoring system aims to alleviate anxiety about child health management and childcare, particularly for dual-income families and new parents with little childcare experience. By integrating diverse information such as vital signs, behavioral analysis, voice analysis, and lifestyle data, this system provides comprehensive support that goes beyond conventional childcare monitoring. For example, as vital sign analysis, the childcare monitoring system monitors basic health data such as body temperature and heart rate in real time, similar to conventional systems. Next, it performs facial expression, behavior, and skin color analysis, with the generative AI detecting physical changes such as the baby's or toddler's facial expressions, body movements, and skin color, and analyzing changes in physical condition and emotions. This makes it possible to grasp even subtle abnormalities that cannot be detected by vital data alone. Furthermore, it performs voice analysis (crying and speech), analyzing the baby's cries using voice analysis technology to identify needs such as hunger, sleepiness, and poor health. The generative AI estimates the cause and proposes countermeasures to the parents in real time. Furthermore, data can be shared with medical institutions, allowing for the sharing of health data and any detected abnormalities, enabling feedback from experts. This ensures a swift and accurate response in emergencies. In addition, long-term accumulation and prediction of lifestyle data (diet, sleep, activity levels) monitors children's daily diet, sleep, and activity levels, detecting early signs of illness based on long-term trends. This supports parents in taking proactive measures. This is expected to improve peace of mind in childcare, support parents in balancing work and childcare, strengthen collaboration with medical institutions, provide personalized childcare advice, and increase the efficiency of childcare for the entire family. Specifically, the generating AI analyzes crying and behavioral patterns to detect the baby's needs and illnesses in advance, allowing even new parents to respond appropriately and significantly reducing anxiety about childcare. Also, early detection of signs of illness and proactive measures reduce the risk of sudden absences or early departures from work. Furthermore, if an abnormality is detected, data can be smoothly shared with medical institutions, enabling early diagnosis and treatment.By providing childcare content and feedback tailored to a child's developmental stage and health condition based on accumulated data, the quality of childcare improves. Information sharing among family members and those involved in childcare support strengthens cooperation, preventing the burden of childcare from falling solely on the parent and enabling more efficient responses. Therefore, childcare monitoring systems are innovative systems that more accurately support a child's health and growth.

[0067] The childcare monitoring system according to this embodiment comprises a data collection unit, an analysis unit, a detection unit, and a notification unit. The data collection unit collects vital signs. The data collection unit can collect vital signs such as heart rate, body temperature, and blood pressure. The data collection unit can monitor heart rate in real time using a wearable device, for example. The data collection unit can also measure body temperature using a body temperature sensor. Furthermore, the data collection unit can measure blood pressure using a blood pressure monitor. The analysis unit analyzes the vital signs collected by the data collection unit. The analysis unit can, for example, analyze the collected heart rate data and detect abnormal patterns. The analysis unit can also analyze the collected body temperature data and detect signs of fever. Furthermore, the analysis unit can analyze the collected blood pressure data and detect signs of hypertension or hypotension. The detection unit detects abnormalities based on the data analyzed by the analysis unit. The detection unit can, for example, detect abnormal fluctuations in heart rate. The detection unit can also detect a rapid rise in body temperature. Furthermore, the detection unit can also detect abnormal fluctuations in blood pressure. The notification unit notifies the parent based on the abnormality detected by the detection unit. The notification unit can, for example, send a notification to a smartphone. It can also send a notification via email. Furthermore, the notification unit can sound an alarm to indicate an abnormality. This enables the childcare monitoring system according to the embodiment to collect, analyze, detect abnormalities in vital signs, and notify. Some or all of the above-described processes in the collection unit, analysis unit, detection unit, and notification unit may be performed using AI, for example, or without AI. For example, the collection unit can use AI to optimize data collection in order to collect vital signs. The analysis unit can analyze the collected vital sign data using AI and detect abnormalities. The detection unit can detect abnormalities using AI based on the analyzed data. The notification unit can select the optimal notification method using AI based on the detected abnormality. This enables the childcare monitoring system to efficiently collect, analyze, detect abnormalities in vital signs, and notify.

[0068] The data collection unit collects vital signs. For example, it can collect vital signs such as heart rate, body temperature, and blood pressure. Specifically, the unit monitors heart rate in real time using a wearable device. The wearable device is equipped with an optical heart rate sensor that measures heart rate by detecting changes in blood flow through the skin. The unit can also measure body temperature using a body temperature sensor. This sensor uses infrared technology to measure skin surface temperature non-contact. Furthermore, the unit can measure blood pressure using a blood pressure monitor. In addition to the traditional method of measuring blood pressure by wrapping a cuff around the arm and applying pressure, the blood pressure monitor can also continuously monitor blood pressure using a sensor built into the wearable device. This allows the unit to collect a variety of vital signs in real time and transmit them to a central database. Furthermore, the unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and accessed by the analysis and detection units. Adjusting the data collection frequency and accuracy allows for flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0069] The analysis unit analyzes vital signs collected by the data collection unit. For example, the analysis unit analyzes collected heart rate data and detects abnormal patterns. Specifically, it uses AI to analyze heart rate data in real time and detect abnormal fluctuations that exceed the normal range. The AI ​​can quickly identify abnormalities by learning from past data and modeling normal heart rate patterns. The analysis unit can also analyze collected body temperature data and detect signs of fever. The AI ​​monitors fluctuations in body temperature and uses algorithms to detect sudden increases or abnormally high temperatures. Furthermore, the analysis unit can analyze collected blood pressure data and detect signs of hypertension or hypotension. The AI ​​analyzes blood pressure fluctuation patterns and identifies abnormal values, enabling early detection of abnormalities. This allows the analysis unit to quickly and accurately analyze collected data and enable early detection of abnormalities. In addition, the analysis unit can utilize past data and statistical information to perform long-term health trend analysis. For example, it can predict fluctuations in health status over a specific period based on past vital sign data and assess future risks. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, enabling it to issue warnings early. This allows the analysis unit to not only monitor the situation in real time but also to handle long-term health management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0070] The detection unit detects anomalies based on data analyzed by the analysis unit. For example, the detection unit can detect abnormal fluctuations in heart rate. Specifically, it uses AI to analyze heart rate data and identify abnormal fluctuations that exceed the normal range. By learning from past data and modeling normal heart rate patterns, the AI ​​can quickly identify anomalies. The detection unit can also detect rapid increases in body temperature. The AI ​​monitors fluctuations in body temperature and uses an algorithm to detect rapid increases or abnormally high temperatures. Furthermore, the detection unit can detect abnormal fluctuations in blood pressure. By analyzing blood pressure fluctuation patterns and identifying abnormal values, the AI ​​can detect anomalies early. This allows the detection unit to quickly and accurately analyze collected data, enabling early detection of anomalies. In addition, the detection unit can utilize past data and statistical information to perform long-term health trend analysis. For example, based on past vital sign data, it can predict fluctuations in health status over a specific period and assess future risks. The detection unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. This allows the detection unit to not only grasp the situation in real time, but also to handle long-term health management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0071] The notification unit notifies parents based on anomalies detected by the detection unit. For example, the notification unit sends notifications to smartphones. Specifically, it notifies parents of anomalies in real time via a smartphone app, enabling them to respond quickly. The notification unit can also send notifications via email. Email notifications can include detailed anomaly information and recommended actions. Furthermore, the notification unit can sound an alarm to alert parents of anomalies. The alarm uses sound and vibration to immediately notify parents of the anomaly. This allows the notification unit to quickly provide appropriate action instructions to each user, minimizing the risk of disaster. Additionally, the notification unit can collect user feedback and continuously improve the accuracy and effectiveness of its notifications. For example, it can review notification methods and content based on feedback from users who have received anomaly notifications. The notification unit can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the notification unit to provide users with quick and reliable action instructions, minimizing the risk of disaster.

[0072] The data collection unit can collect data on facial expressions, behavior, and skin color. For example, the data collection unit can monitor the baby's facial expressions in real time using a camera. For example, the data collection unit can recognize the baby's facial expressions such as smiles, anger, and sadness. The data collection unit can also monitor the baby's behavior. For example, the data collection unit can recognize the baby's actions such as walking, sitting, and standing. Furthermore, the data collection unit can also monitor the baby's skin color. For example, the data collection unit can detect changes in the baby's skin color using a camera. As a result, the data collection unit can obtain more detailed information by collecting data on facial expressions, behavior, and skin color. 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 image data acquired by the camera into a generating AI and have the generating AI perform the collection of data on facial expressions, behavior, and skin color.

[0073] The analysis unit can analyze facial expression, behavior, and skin color data collected by the collection unit. For example, the analysis unit can analyze the collected facial expression data to estimate the baby's emotions. For example, the analysis unit can analyze the frequency and duration of smiles to estimate the baby's happiness level. The analysis unit can also analyze the collected behavior data to evaluate the baby's activity level. For example, the analysis unit can analyze the frequency and distance of walking to evaluate the baby's exercise level. Furthermore, the analysis unit can analyze the collected skin color data to estimate the baby's health status. For example, the analysis unit can analyze changes in skin color to detect changes in the baby's physical condition. In this way, the analysis unit can grasp changes in physical condition and emotions by analyzing the facial expression, behavior, and skin color data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the analysis of facial expression, behavior, and skin color data.

[0074] The detection unit can detect changes in physical condition and emotions based on data analyzed by the analysis unit. For example, the detection unit can detect changes in the baby's physical condition by detecting fluctuations in heart rate. For example, the detection unit can detect signs of fever by detecting a sudden increase in heart rate. The detection unit can also detect changes in the baby's emotions by detecting changes in facial expressions. For example, the detection unit can detect a decrease in smiles and detect the baby's stress level. Furthermore, the detection unit can detect changes in the baby's health by detecting changes in skin color. For example, the detection unit can detect signs of anemia by detecting paleness of skin color. In this way, the detection unit can detect abnormalities early by detecting changes in physical condition and emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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 processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the analyzed data into a generating AI, which can then perform the detection of changes in physical condition and emotions.

[0075] The data collection unit can collect data on crying and speech. For example, the data collection unit can monitor the baby's crying in real time using a microphone. For example, the data collection unit can analyze the volume and frequency of the baby's crying. The data collection unit can also monitor the baby's speech. For example, the data collection unit can analyze the content and tone of the baby's speech. In this way, the data collection unit can understand the baby's needs by collecting data on crying and speech. 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 audio data acquired by the microphone into a generating AI and have the generating AI perform the collection of crying and speech data.

[0076] The analysis unit can analyze crying and speech data collected by the collection unit. For example, the analysis unit can analyze the collected crying data to identify the baby's needs. For example, the analysis unit can analyze the volume and frequency patterns of the crying to determine if the baby is hungry, sleepy, or unwell. The analysis unit can also analyze the collected speech data to analyze the content and tone of the baby's speech. For example, the analysis unit can analyze the content of the speech to determine what the baby is asking for. In this way, the analysis unit can identify the baby's needs by analyzing crying and speech data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected audio data into a generating AI and have the generating AI perform the analysis of crying and speech data.

[0077] The detection unit can detect the baby's needs based on the data analyzed by the analysis unit. For example, the detection unit can detect crying patterns to determine if the baby is hungry, sleepy, or unwell. For example, the detection unit can detect changes in the volume and frequency of crying to identify the baby's needs. The detection unit can also detect the content of speech to identify what the baby is asking for. For example, the detection unit can analyze the content of speech to identify the baby's needs. This allows the detection unit to take appropriate action by detecting the baby's needs. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the analyzed data into a generating AI and have the generating AI perform the detection of the baby's needs.

[0078] The notification unit can share data with healthcare institutions based on anomalies detected by the detection unit. The notification unit can, for example, send data to healthcare institutions via email. The notification unit can, for example, send reports to healthcare institutions that include details of the detected anomalies. The notification unit can also share data using cloud services. The notification unit can, for example, upload the detected anomaly data to the cloud and make it accessible to healthcare institutions. Furthermore, the notification unit can also share data using a dedicated app. The notification unit can, for example, enable healthcare institutions to access the data through a dedicated app. This allows the notification unit to respond quickly by sharing data with healthcare institutions. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the detected anomaly data into a generating AI and have the generating AI perform data sharing with healthcare institutions.

[0079] The data collection unit can collect data on meals, sleep, and activity levels. For example, the data collection unit can record the content and amount of meals consumed. For example, the data collection unit can save the content of meals entered by parents to a database. The data collection unit can also collect sleep data. For example, the data collection unit can monitor the duration and quality of a baby's sleep. Furthermore, the data collection unit can also collect activity level data. For example, the data collection unit can record the number of steps taken and the amount of exercise a baby takes. As a result, by collecting data on meals, sleep, and activity levels, the data collection unit enables long-term health management. 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 data on meals, sleep, and activity levels into a generating AI and have the generating AI perform the data collection.

[0080] The analysis unit can analyze the diet, sleep, and activity data collected by the collection unit. For example, the analysis unit can analyze the collected diet data to evaluate nutritional balance. For example, the analysis unit can analyze the content and amount of food consumed to evaluate the baby's nutritional status. The analysis unit can also analyze the collected sleep data to evaluate sleep quality. For example, the analysis unit can analyze sleep duration and sleep quality to evaluate the baby's sleep state. Furthermore, the analysis unit can analyze the collected activity data to evaluate the baby's activity level. For example, the analysis unit can analyze the number of steps and exercise time to evaluate the baby's activity level. As a result, by analyzing the diet, sleep, and activity data, the analysis unit can detect early signs of illness. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the analysis of the diet, sleep, and activity data.

[0081] The detection unit can detect signs of poor health based on data analyzed by the analysis unit. For example, the detection unit can detect fluctuations in heart rate to detect signs of poor health. For example, the detection unit can detect rapid fluctuations in heart rate to detect signs of poor health. The detection unit can also detect changes in sleep patterns to detect signs of poor health. For example, the detection unit can detect a decrease in sleep duration or a decline in sleep quality to detect signs of poor health. Furthermore, the detection unit can detect fluctuations in activity levels to detect signs of poor health. For example, the detection unit can detect a decrease in exercise levels to detect signs of poor health. As a result, the detection unit can take countermeasures in advance by detecting signs of poor health. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the analyzed data into a generating AI and have the generating AI perform the detection of signs of poor health.

[0082] The data collection unit can estimate the parent's emotions and adjust the types of data collected based on the estimated emotions. For example, if the parent is stressed, the data collection unit can reduce the types of data collected and focus on important data. For example, if the parent is relaxed, the data collection unit can collect detailed data and provide comprehensive information. Also, if the parent is anxious, the data collection unit can increase the types of data collected and provide detailed information to provide reassurance. In this way, the data collection unit can collect more appropriate data by adjusting the types of data collected according to the parent'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 data collection unit may be performed using AI or not using AI. For example, the data collection unit can input the parent's emotion data into the generative AI and have the generative AI adjust the types of data to be collected.

[0083] The data collection unit can refer to the baby's past health data and select the optimal data collection method. For example, if the baby has experienced illness in the past, the data collection unit can adjust the current data collection method based on that data. For example, the data collection unit can analyze the baby's past health data and optimize the data collection method based on specific patterns. The data collection unit can also refer to the baby's past health data and select a data collection method appropriate for a specific time of day or situation. In this way, the data collection unit can select the optimal data collection method by referring to past health 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 the baby's past health data into a generating AI and have the generating AI select the optimal data collection method.

[0084] The data collection unit can filter data based on the baby's current living environment and activity level. For example, if the baby is sleeping, the data collection unit will minimize the amount of data collected to avoid disturbing sleep. If the baby is active, the data collection unit can prioritize collecting data corresponding to the baby's movement. Furthermore, if the baby is in a specific environment (for example, outdoors), the data collection unit can collect data appropriate to that environment. This allows the data collection unit to collect appropriate data by filtering it based on the living environment and activity level. 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 data on the baby's current living environment and activity level into a generating AI and have the generating AI perform the data filtering.

[0085] The data collection unit can estimate the parent's emotions and determine the priority of data to collect based on the estimated parent's emotions. For example, if the parent is stressed, the data collection unit will prioritize collecting the most important data. For example, if the parent is relaxed, the data collection unit can prioritize collecting detailed data. Furthermore, if the parent is anxious, the data collection unit can adjust the priority of data to collect in order to provide reassurance. In this way, the data collection unit can prioritize the collection of important data by determining the priority of data according to the parent'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 data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the parent's emotion data into a generative AI and have the generative AI determine the priority of data to collect.

[0086] The data collection unit can prioritize the collection of highly relevant data by considering the baby's geographical location. For example, if the baby is at home, the data collection unit can prioritize the collection of data related to the indoor environment. If the baby is out, for example, the data collection unit can prioritize the collection of data related to the external environment. Furthermore, if the baby is in a specific location (for example, a daycare center), the data collection unit can prioritize the collection of data related to that location. In this way, the data collection unit can prioritize the collection of highly relevant data by considering geographical location. 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 perform the collection of highly relevant data.

[0087] The data collection unit can analyze a baby's social media activity and collect relevant data. For example, the data collection unit can analyze specific behavioral patterns from a baby's social media activity and collect relevant data. For example, the data collection unit can collect data related to specific time periods from a baby's social media activity. The data collection unit can also collect data related to specific events from a baby's social media activity. In this way, the data collection unit can collect 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 the baby's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0088] The analysis unit can estimate the parent's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the parent is stressed, the analysis unit can provide a simple and easy-to-understand analysis result. For example, if the parent is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the parent is anxious, the analysis unit can adjust the presentation of the analysis result to provide a sense of security. In this way, the analysis unit can provide more appropriate analysis results by adjusting the presentation of the analysis according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the parent's emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0089] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on important data. For example, it can perform a simplified analysis on less important data. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the data. This allows the analysis unit to perform efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0090] The analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a specific analysis algorithm to vital sign data. For example, the analysis unit can apply a different analysis algorithm to behavioral data. Furthermore, the analysis unit can apply a dedicated analysis algorithm to voice data. This improves the accuracy of the analysis by applying the appropriate analysis algorithm according to the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0091] The analysis unit can estimate the parent's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the parent is stressed, the analysis unit can provide a short, concise analysis result. For example, if the parent is relaxed, the analysis unit can provide a detailed analysis result. The analysis unit can also adjust the length of the analysis result to provide reassurance if the parent is anxious. In this way, the analysis unit can provide more appropriate analysis results by adjusting the length of the analysis according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the parent's emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0092] The analysis unit can determine the priority of analysis based on the data collection timing. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may analyze current data while referring to past data. Furthermore, the analysis unit can dynamically adjust the priority of analysis based on the data collection timing. This allows the analysis unit to prioritize the analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI perform the determination of the analysis priority.

[0093] The analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. This allows the analysis unit to perform efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0094] The detection unit can estimate the parent's emotions and adjust the detection criteria based on the estimated parent's emotions. For example, if the parent is stressed, the detection unit can tighten the detection criteria to detect abnormalities early. For example, if the parent is relaxed, the detection unit can loosen the detection criteria to provide more detailed information. The detection unit can also adjust the detection criteria to provide reassurance if the parent is anxious. In this way, the detection unit can perform more appropriate detection by adjusting the detection criteria according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input parent emotion data into the generative AI and have the generative AI perform the adjustment of the detection criteria.

[0095] The detection unit can improve detection accuracy by considering the interrelationships between data. For example, the detection unit can detect anomalies by considering the interrelationships between vital sign data and behavioral data. For example, the detection unit can detect anomalies by considering the interrelationships between voice data and facial expression data. The detection unit can also improve detection accuracy by analyzing the interrelationships between data. As a result, the detection unit improves detection accuracy by considering the interrelationships between data. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the interrelationships between data into a generating AI and have the generating AI perform the improvement of detection accuracy.

[0096] The detection unit can perform detection while considering the attribute information of the data submitter. For example, if the data submitter is a parent, the detection unit can perform detection while considering that attribute information. For example, if the data submitter is a medical institution, the detection unit can perform detection while considering that attribute information. The detection unit can also analyze the attribute information of the data submitter to improve the accuracy of detection. As a result, the detection unit improves the accuracy of detection by considering the attribute information of the data submitter. Some or all of the above processing in the detection unit may be performed using AI, for example, or without using AI. For example, the detection unit can input the attribute information of the data submitter into a generating AI and have the generating AI perform the detection.

[0097] The detection unit can estimate the parent's emotions and adjust the order in which the detection results are displayed based on the estimated parent's emotions. For example, if the parent is stressed, the detection unit can prioritize displaying important results. For example, if the parent is relaxed, the detection unit can prioritize displaying detailed results. The detection unit can also adjust the order in which the results are displayed to provide reassurance if the parent is anxious. In this way, the detection unit can provide more appropriate information by adjusting the order in which the detection results are displayed according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the parent's emotion data into the generative AI and have the generative AI adjust the order in which the detection results are displayed.

[0098] The detection unit can perform detection while considering the geographical distribution of the data. For example, the detection unit can analyze the geographical distribution of the data and detect anomalies. For example, the detection unit can detect anomalies in a specific region based on the geographical distribution. The detection unit can also improve the accuracy of detection by considering the geographical distribution of the data. As a result, the detection unit improves the accuracy of detection by considering the geographical distribution of the data. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the geographical distribution of the data into a generating AI and have the generating AI perform the detection.

[0099] The detection unit can improve the accuracy of detection by referring to relevant literature for the data. For example, the detection unit can detect anomalies by referring to relevant literature for the data. For example, the detection unit can detect specific anomalies based on relevant literature. The detection unit can also improve the accuracy of detection by analyzing relevant literature for the data. As a result, the detection unit improves the accuracy of detection by referring to relevant literature. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input relevant literature for the data into a generating AI and have the generating AI perform the detection.

[0100] The notification unit can estimate the parent's emotions and adjust the notification method based on the estimated emotions. For example, if the parent is stressed, the notification unit can provide a simple and highly visible notification method. For example, if the parent is relaxed, the notification unit can provide a more detailed notification method. The notification unit can also adjust the notification method to provide reassurance if the parent is anxious. In this way, the notification unit can provide more appropriate notifications by adjusting the notification method according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input parent emotion data into the generative AI and have the generative AI perform the adjustment of the notification method.

[0101] The notification unit can select the optimal notification method by referring to the parent's past response history. For example, the notification unit may prioritize providing notification methods that the parent has preferred in the past. For example, the notification unit may analyze the parent's past response history and select the optimal notification method. The notification unit may also adjust the timing of notifications based on the parent's past response history. In this way, the notification unit can select the optimal notification method by referring to past response 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 may input the parent's past response history into a generating AI and have the generating AI select the optimal notification method.

[0102] The notification unit can estimate the parent's emotions and determine the priority of notifications based on the estimated emotions. For example, if the parent is stressed, the notification unit will prioritize important notifications. For example, if the parent is relaxed, the notification unit will prioritize detailed notifications. The notification unit can also adjust the priority of notifications to provide reassurance if the parent is anxious. In this way, the notification unit can prioritize important notifications by determining the priority of notifications according to the parent'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 or not using AI. For example, the notification unit can input parent emotion data into a generative AI and have the generative AI determine the priority of notifications.

[0103] The notification unit can select the optimal notification method by considering the parent's device information. For example, if the parent is using a smartphone, the notification unit can provide a notification method that matches the screen size. If the parent is using a tablet, the notification unit can provide a notification method optimized for a larger screen. Furthermore, if the parent is using a smartwatch, the notification unit can provide a concise and highly visible notification method. In this way, the notification unit can select the optimal notification method by considering the 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 parent's device information into a generating AI and have the generating AI select the optimal notification method.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The data collection unit can collect brainwave data from babies while they sleep. For example, it can monitor the baby's brainwaves in real time using a wearable device. The unit can analyze brainwave patterns to assess the quality of the baby's sleep. The unit can also monitor the baby's breathing patterns during sleep. For example, it can analyze breathing rhythms and depths to detect signs of sleep apnea. This allows the data collection unit to evaluate the quality of the baby's sleep in detail by collecting brainwave data and breathing patterns.

[0106] The analysis unit can estimate the baby's emotions and evaluate the baby's stress level based on the estimated emotions. For example, the analysis unit can analyze the baby's facial expression data and evaluate the frequency and duration of smiles. For example, the analysis unit can analyze the baby's behavioral data and evaluate fluctuations in activity levels. Furthermore, the analysis unit can analyze the baby's crying data and evaluate changes in the volume and frequency of cries. In this way, the analysis unit can comprehensively analyze the baby's emotional data and evaluate the stress level in detail.

[0107] The detection unit can detect early signs of fever based on the baby's body temperature data. For example, the detection unit can detect a rapid rise in body temperature and notify the user of the possibility of fever. The detection unit can also analyze subtle fluctuations in body temperature to detect precursors to fever. Furthermore, the detection unit can analyze body temperature data in combination with other vital sign data (e.g., heart rate and respiratory rate) to comprehensively assess the risk of fever. As a result, the detection unit can detect early signs of fever by analyzing body temperature data in detail.

[0108] The notification unit can estimate the baby's emotions and adjust the content of the notification based on those emotions. For example, if the baby is stressed, the notification unit can send a notification to the parents suggesting ways to relax. For example, if the baby is feeling happy, the notification unit can send a notification to inform the parents of this state. Furthermore, if the baby is feeling anxious, the notification unit can send a notification that includes advice to help the parents feel at ease. In this way, the notification unit can provide more appropriate information by adjusting the content of the notification according to the baby's emotions.

[0109] The data collection unit can collect infant feeding data and evaluate nutritional balance. For example, the data collection unit can store the details of meals entered by parents in a database and analyze nutrient intake. For example, the data collection unit can monitor the frequency and amount of meals given to infants and evaluate whether there are any excesses or deficiencies. In addition, the data collection unit can accumulate infant feeding data over the long term and track changes in nutritional balance as the infant grows. As a result, the data collection unit can evaluate the infant's nutritional balance by collecting detailed feeding data.

[0110] The analysis unit can estimate the baby's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the baby is stressed, the analysis unit can provide the parents with analysis results that include advice on stress reduction. For example, if the baby is feeling happy, the analysis unit can provide analysis results that emphasize that state. Furthermore, if the baby is feeling anxious, the analysis unit can provide analysis results that include information to reassure the parents. In this way, the analysis unit can provide more appropriate information by adjusting the display method of the analysis results according to the baby's emotions.

[0111] The detection unit can detect developmental delays early based on the baby's behavioral data. For example, the detection unit analyzes behavioral data such as walking, sitting, and standing to detect signs of developmental delays. The detection unit can also analyze fluctuations in behavioral data to assess the risk of developmental delays. Furthermore, the detection unit can combine behavioral data with other data (e.g., vital sign data and voice data) to comprehensively assess the risk of developmental delays. In this way, the detection unit can detect developmental delays early by analyzing behavioral data in detail.

[0112] The notification unit can estimate the baby's emotions and adjust the timing of notifications based on those estimates. For example, if the baby is stressed, the notification unit will promptly notify the parents. If the baby is feeling happy, the notification unit can reduce the frequency of notifications to maintain that state. Furthermore, if the baby is feeling anxious, the notification unit can notify the parents at an appropriate time to provide reassurance. This allows the notification unit to provide more relevant information by adjusting the timing of notifications according to the baby's emotions.

[0113] The data collection unit collects data on the baby's living environment and can adjust the data collection method according to changes in the environment. For example, if the baby is indoors, the data collection unit can collect data related to the indoor environment (e.g., temperature, humidity, lighting). If the baby is outdoors, the data collection unit can collect data related to the external environment (e.g., temperature, wind speed, UV radiation). Furthermore, if the baby is in a specific location (e.g., a daycare center), the data collection unit can collect data related to that location. This allows the data collection unit to collect detailed data on the baby's living environment, enabling data collection that adapts to changes in the environment.

[0114] The analysis unit can estimate the baby's emotions and determine the priority of analysis based on the estimated emotions. For example, if the baby is stressed, the analysis unit will prioritize analyzing data related to stress reduction. For example, if the baby is feeling happy, the analysis unit can prioritize analyzing data to maintain that state. Also, if the baby is feeling anxious, the analysis unit can prioritize analyzing data to provide a sense of security. In this way, the analysis unit can provide more appropriate information by determining the priority of analysis according to the baby's emotions.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The data collection unit collects vital signs. The data collection unit can collect vital signs such as heart rate, body temperature, and blood pressure. The data collection unit can monitor heart rate in real time using a wearable device, measure body temperature using a body temperature sensor, and measure blood pressure using a blood pressure monitor. Step 2: The analysis unit analyzes the vital signs collected by the acquisition unit. The analysis unit can analyze the collected heart rate data to detect abnormal patterns, analyze the body temperature data to detect signs of fever, and analyze the blood pressure data to detect signs of hypertension or hypotension. Step 3: The detection unit detects abnormalities based on the data analyzed by the analysis unit. The detection unit can detect abnormal fluctuations in heart rate, a rapid increase in body temperature, and abnormal fluctuations in blood pressure. Step 4: The notification unit notifies the parent based on the anomaly detected by the detection unit. The notification unit can notify the parent of the anomaly via smartphone notification, email notification, or by sounding an alarm.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the baby's facial expressions and vital signs using the camera 42 and body temperature sensor of the smart device 14. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 and detects abnormalities. The detection unit detects the abnormality based on the analysis results, and the notification unit notifies the parent using the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, and notification unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the baby's facial expressions and vital signs using the camera 42 and body temperature sensor of the smart glasses 214. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 and detects abnormalities. The detection unit detects the abnormality based on the analysis results, and the notification unit notifies the parent using the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the baby's facial expressions and vital signs using the camera 42 and body temperature sensor of the headset terminal 314. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 and detects abnormalities. The detection unit detects the abnormality based on the analysis results, and the notification unit notifies the parent using the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[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 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.

[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 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.

[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 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.

[0169] Each of the multiple elements described above, including the collection unit, analysis unit, detection unit, and notification unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the baby's facial expressions and vital signs using the camera 42 and body temperature sensor of the robot 414. The analysis unit analyzes the collected data using the identification processing unit 290 of the data processing unit 12 and detects abnormalities. The detection unit detects the abnormality based on the analysis results, and the notification unit notifies the parent using the control unit 46A of the robot 414. 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] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] (Note 1) A collection unit for collecting vital signs, An analysis unit analyzes the vital signs collected by the aforementioned collection unit, A detection unit that detects anomalies based on the data analyzed by the analysis unit, The system includes a notification unit that notifies the parent based on an abnormality detected by the detection unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data on facial expressions, behavior, and skin color. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The data on facial expressions, behavior, and skin color collected by the aforementioned collection unit is analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 4) The detection unit is Based on the data analyzed by the aforementioned analysis unit, changes in physical condition and emotions are detected. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Collect data on crying and speech. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, The data of crying and speech collected by the aforementioned collection unit is analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 7) The detection unit is The analysis unit detects the baby's needs based on the data it has analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned notification unit, The data is shared with the medical institution based on the abnormality detected by the aforementioned detection unit. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Collect data on diet, sleep, and activity levels. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, The data on diet, sleep, and activity levels collected by the aforementioned collection unit is analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 11) The detection unit is The analysis unit detects signs of poor health based on the data it analyzes. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is We estimate the parents' emotions and adjust the types of data collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is Refer to the baby's past health data to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is Filter the data based on the baby's current living environment and activity level. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is It estimates the parents' emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is Prioritize the collection of highly relevant data, taking into account the baby's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is Analyze babies' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, We estimate the parents' emotions and adjust the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, Adjust the level of detail in the analysis based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, Apply different analysis algorithms depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, The system estimates the parents' emotions and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, Prioritize analysis based on the data collection period. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, Adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The detection unit is We estimate the parent's emotions and adjust the detection criteria based on the estimated parent's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The detection unit is Improve detection accuracy by considering the interrelationships between data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The detection unit is The detection process takes into account the attribute information of the data submitter. The system described in Appendix 1, characterized by the features described herein. (Note 27) The detection unit is It estimates the parent's emotions and adjusts the order in which the detection results are displayed based on the estimated parent's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The detection unit is Perform detection while considering the geographical distribution of the data. The system described in Appendix 1, characterized by the features described herein. (Note 29) The detection unit is Referencing relevant literature for data improves detection accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned notification unit, It estimates the parent's emotions and adjusts the notification method based on the estimated parent's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned notification unit, The optimal notification method is selected by referring to the parents' past response history. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned notification unit, It estimates the parent's emotions and determines the priority of notifications based on the estimated parent's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned notification unit, The optimal notification method is selected considering the parent's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0189] 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 collection unit for collecting vital signs, An analysis unit analyzes the vital signs collected by the aforementioned collection unit, A detection unit that detects anomalies based on the data analyzed by the analysis unit, The system includes a notification unit that notifies the parent based on an abnormality detected by the detection unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect data on facial expressions, behavior, and skin color. The system according to feature 1.

3. The aforementioned analysis unit, The data on facial expressions, behavior, and skin color collected by the aforementioned collection unit is analyzed. The system according to feature 1.

4. The detection unit is Based on the data analyzed by the aforementioned analysis unit, changes in physical condition and emotions are detected. The system according to feature 1.

5. The aforementioned collection unit is Collect data on crying and speech. The system according to feature 1.

6. The aforementioned analysis unit, The data of crying and speech collected by the aforementioned collection unit is analyzed. The system according to feature 1.

7. The detection unit is The analysis unit detects the baby's needs based on the data it has analyzed. The system according to feature 1.

8. The aforementioned notification unit, The data is shared with the medical institution based on the abnormality detected by the aforementioned detection unit. The system according to feature 1.

9. The aforementioned collection unit is Collect data on diet, sleep, and activity levels. The system according to feature 1.

10. The aforementioned analysis unit, The data on diet, sleep, and activity levels collected by the aforementioned collection unit is analyzed. The system according to feature 1.

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