system

Abstract

The system according to this embodiment aims to identify the reason for a baby's crying and to take appropriate action. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a presentation unit, and a sleep-inducing unit. The collection unit collects information on the baby's crying and surrounding environment. The analysis unit analyzes the information collected by the collection unit and identifies the reason for the crying. The presentation unit presents a solution based on the reason identified by the analysis unit. The sleep-inducing unit uses a combination of generative AI and robotics to induce sleep.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

[0005] , , , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to identify the reason for a baby's crying sound and take appropriate measures. [[ID=​​​​​​​​​The system according to this embodiment comprises a data collection unit, an analysis unit, a presentation unit, and a sleep-inducing unit. The data collection unit collects information on the baby's crying and surrounding environment. The analysis unit analyzes the information collected by the data collection unit and identifies the reason for the crying. The presentation unit presents solutions based on the reason identified by the analysis unit. The sleep-inducing unit uses a combination of generative AI and robotics to induce sleep. [Effects of the Invention]

[0007] The system according to this embodiment can identify the reason for a baby's crying and take appropriate action. [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 such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The childcare support system according to an embodiment of the present invention is a system for reducing the burden of nighttime crying and getting babies to sleep during postpartum childcare. This childcare support system collects information about the baby's crying, body temperature, time, sounds, temperature, and other surrounding environmental information, and an AI analyzes this information to identify the reason for the crying and suggests solutions. It can also perform sleep training by combining generative AI and robotics. For example, the childcare support system collects information about the baby's crying, body temperature, time, sounds, temperature, and other surrounding environmental information in real time using sensors and microphones. Next, the collected information is analyzed by an AI. The AI ​​has learned past nighttime crying patterns and the baby's individual characteristics, and identifies the reason for the crying based on this. Furthermore, the AI ​​suggests solutions based on the identified reason. For example, if the baby is crying because they are hungry, it will encourage breastfeeding, and if the crying is due to the room temperature, it will suggest adjusting the temperature. In addition, by combining generative AI and robotics, it can also handle some aspects of getting babies to sleep. For example, it can reproduce pre-learned voices of the mother or father talking to the baby or lullabies, and can even get the baby to sleep through physical interactions such as holding them. This allows parents to sleep peacefully. This system reduces the burden of postpartum childcare and alleviates sleep deprivation and stress for parents. Furthermore, it is expected to make it possible to balance work and childcare, contributing to measures against the declining birthrate. In this way, childcare support systems can reduce the burden of postpartum childcare and alleviate sleep deprivation and stress for parents.

[0029] The childcare support system according to this embodiment comprises a data collection unit, an analysis unit, a presentation unit, and a sleep-inducing unit. The data collection unit collects information on the baby's cries and surrounding environment. For example, the data collection unit collects information on the surrounding environment, such as the baby's cries, body temperature, time, sound, and temperature, in real time using sensors and microphones. For example, the data collection unit collects the baby's cries with a high-sensitivity microphone and stores it as audio data. The data collection unit can also measure the baby's body temperature with a non-contact temperature sensor and store it as body temperature data. Furthermore, the data collection unit can measure the temperature and humidity around the baby with an environmental sensor and store it as environmental data. The analysis unit analyzes the information collected by the data collection unit to identify the reason for the crying. For example, the analysis unit analyzes the crying pattern using an audio analysis algorithm to identify the reason for the crying. The analysis unit can also analyze environmental data to identify the reason for the crying. For example, the analysis unit analyzes the volume and frequency of the baby's cries to identify the reason for the crying. The analysis unit can analyze the baby's body temperature data and identify whether fluctuations in body temperature are the reason for the crying. The presentation unit then suggests solutions based on the reason identified by the analysis unit. For example, if the baby is crying because it is hungry, the presentation unit will display a message encouraging feeding. It can also display a message suggesting temperature adjustment if the crying is due to the room temperature. For example, if the baby is crying because it is hungry, the presentation unit will display the message "Please feed the baby." It can also display a message "Please lower the room temperature" if the room temperature is too high. The sleep-inducing unit uses a combination of generative AI and robotics to help the baby fall asleep. For example, the sleep-inducing unit can reproduce pre-learned voices of the mother or father speaking to the baby or singing lullabies. It can also help the baby fall asleep through physical interaction such as holding. For example, the sleep-inducing unit can play the voices of the mother or father to soothe the baby. The sleep-inducing unit can use robotics to hold the baby and reproduce rocking patterns. As a result, the childcare support system according to this embodiment can reduce the burden of postpartum childcare and alleviate sleep deprivation and stress for parents.

[0030] The data collection unit collects information about the baby's cries and surrounding environment. For example, it collects information about the surrounding environment, such as the baby's cries, body temperature, time, sound, and temperature, in real time using sensors and microphones. Specifically, it uses a high-sensitivity microphone to collect the baby's cries and saves the audio data. This provides detailed data such as the crying pattern, volume, and frequency. It can also measure the baby's body temperature using a non-contact temperature sensor and save the temperature data. This allows for real-time monitoring of the baby's body temperature fluctuations and immediate response if an abnormality is detected. Furthermore, it can measure the temperature and humidity around the baby using environmental sensors and save this environmental data. This provides data to maintain a comfortable environment for the baby. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, the collected data is stored on a cloud server, making it accessible to the analysis and presentation units. Adjusting the data collection frequency and accuracy allows for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the information collected by the data collection unit to identify the reason for the crying. For example, the analysis unit uses a voice analysis algorithm to analyze the crying pattern and identify the reason for the crying. Specifically, it uses AI to analyze the volume, frequency, and duration of the crying to determine whether the baby is crying because of hunger, inability to sleep, or illness. The analysis unit can also analyze environmental data to identify the reason for crying. For example, it can analyze the baby's body temperature data to determine if fluctuations in body temperature are the reason for the crying. Furthermore, it can analyze ambient temperature and humidity data to determine if the environment is influencing the baby's crying. The analysis unit comprehensively analyzes this data to quickly and accurately identify the cause of the baby's crying. In addition, the analysis unit can utilize past data and statistical information to analyze long-term trends and patterns. For example, based on past crying data, it can predict the frequency of crying at specific times of day or under specific environmental conditions and formulate future countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns or abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The display unit presents solutions based on the reasons identified by the analysis unit. For example, if the baby is crying because it is hungry, the display unit will display a message prompting feeding. Specifically, it will display a message to the parent via a smartphone or tablet application saying, "Your baby is hungry. Please feed them." The display unit can also display a message suggesting temperature adjustment if the baby is crying due to the room temperature. For example, it will display a message saying, "The room temperature is too high. Please lower the temperature," prompting the parent to take specific action. Furthermore, if the baby's body temperature is abnormally high or low, the display unit can display a message prompting contact with a medical institution. For example, it will display a message saying, "Your baby's body temperature is too high. Please consult a doctor," prompting the parent to take prompt action. The display unit can not only visually display these messages but also use voice and vibration notifications to alert the parent. This allows the display unit to help parents respond quickly and appropriately, ensuring the baby's comfort and safety. In addition, the display unit can collect user feedback and continuously improve the accuracy and effectiveness of its presentations. For example, the system records the parents' reactions and actions to the messages presented and incorporates this information into future presentations. This allows the system to consistently provide highly accurate solutions based on the latest information, thereby reducing the burden of childcare on parents.

[0033] The sleep-inducing unit combines generative AI and robotics to help babies fall asleep. For example, it can reproduce pre-learned voices of mothers and fathers, such as soothing speech and lullabies. Specifically, it uses generative AI to analyze the voices of mothers and fathers and learns their characteristics. This allows the generative AI to reproduce the tone and rhythm of the mother's or father's voice, providing the baby with a sense of security. The sleep-inducing unit can also help babies fall asleep through physical interaction, such as holding them. For example, it can use robotics to hold the baby and reproduce rocking patterns. This gives the baby the feeling of being held by their mother or father, allowing them to fall asleep peacefully. Furthermore, the sleep-inducing unit can monitor the baby's condition in real time and adjust the sleep-inducing method as needed. For example, if the baby won't stop crying, the generative AI can generate a new lullaby, and the robotics can change the rocking pattern. This allows the sleep-inducing unit to provide the optimal sleep-inducing method tailored to the individual needs of each baby. Furthermore, the sleep-inducing unit can learn the baby's sleep patterns based on past data, improving the efficiency of future sleep-inducing efforts. For example, it can identify patterns in which babies tend to fall asleep at specific times and adjust sleep-inducing techniques accordingly. This allows the sleep-inducing unit to improve the quality of the baby's sleep and reduce the burden on parents.

[0034] The data collection unit can collect information about the baby's crying, body temperature, time, sounds, temperature, and other environmental factors. For example, the data collection unit can collect the baby's crying using a high-sensitivity microphone and save it as audio data. The data collection unit can also measure the baby's body temperature using a non-contact temperature sensor and save it as body temperature data. Furthermore, the data collection unit can measure the temperature and humidity around the baby using an environmental sensor and save it as environmental data. By collecting information about the baby's crying, body temperature, time, sounds, temperature, and other environmental factors, it becomes easier to identify the reason for the crying. 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 crying data into a generating AI and have the generating AI perform an analysis of the crying.

[0035] The analysis unit can analyze the collected information and identify the reason for crying. For example, the analysis unit can use a voice analysis algorithm to analyze the crying pattern and identify the reason for crying. The analysis unit can also analyze environmental data to identify the reason for crying. For example, the analysis unit can analyze the volume and frequency of the baby's cries to identify the reason for crying. The analysis unit can also analyze the baby's body temperature data to determine if fluctuations in body temperature are the reason for crying. By analyzing the collected information and identifying the reason for crying, appropriate solutions can be suggested. 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 baby's crying data into a generating AI and have the generating AI perform the crying analysis.

[0036] The display unit can suggest solutions based on the identified reason. For example, if the baby is crying because it is hungry, the display unit will display a message encouraging feeding. The display unit can also display a message suggesting temperature adjustment if the baby is crying because of the room temperature. For example, if the baby is crying because it is hungry, the display unit will display the message "Please feed the baby." The display unit can also display the message "Please lower the room temperature" if the room temperature is too high. In this way, by suggesting solutions based on the identified reason, the baby's crying can be effectively resolved. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input baby crying data into a generating AI and have the generating AI perform the task of suggesting solutions.

[0037] The lullaby unit can soothe a baby to sleep by reproducing pre-learned voices of the mother or father speaking to the baby and lullabies, and by using physical interaction such as holding the baby. For example, the lullaby unit can play the voices of the mother or father to reassure the baby. The lullaby unit can also use robotics to hold the baby and reproduce rocking patterns. For example, the lullaby unit can play the voices of the mother or father to reassure the baby. The lullaby unit can also use robotics to hold the baby and reproduce rocking patterns. This allows the baby to be effectively soothed to sleep by reproducing pre-learned voices of the mother or father and lullabies, and by using physical interaction such as holding the baby. Some or all of the above processing in the lullaby unit may be performed using AI, for example, or without AI. For example, the lullaby unit can input voice data of the mother or father into a generating AI and have the generating AI play the voices.

[0038] The data collection unit can estimate the baby's emotions and adjust the type of information collected based on the estimated emotions. For example, if the baby is anxious, the data collection unit may focus on collecting heart rate and body temperature fluctuations. If the baby is excited, the data collection unit may focus on collecting ambient volume and light intensity. Furthermore, if the baby is sleepy, the data collection unit may focus on collecting room temperature and humidity. By adjusting the type of information collected based on the baby's emotions, more appropriate information can be collected. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input the baby's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0039] The data collection unit can analyze the baby's past crying patterns and select the optimal timing for data collection. For example, based on past crying data, the data collection unit can detect signs before crying begins and collect information early. The data collection unit can also focus on collecting information during specific time periods if the baby tends to cry during those times. Furthermore, the data collection unit can analyze the intensity and frequency of the baby's cries and collect information before the crying becomes stronger. This enables effective information collection by analyzing the baby's past crying patterns and selecting the optimal timing for data collection. 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 crying data into a generating AI and have the generating AI select the optimal timing for data collection.

[0040] The data collection unit can simultaneously collect biometric information such as the baby's body movements and heart rate during data collection. For example, when the baby starts crying, the data collection unit can collect heart rate fluctuations in real time. The data collection unit can also use the baby's body movement sensor to collect the relationship between crying and body movements. Furthermore, the data collection unit can monitor the baby's breathing patterns and simultaneously collect changes in crying and breathing. This allows for the simultaneous collection of biometric information such as the baby's body movements and heart rate, enabling the acquisition of more detailed information. Some or all of the above-described processes 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 body movement data into a generating AI and have the generating AI perform the body movement analysis.

[0041] The data collection unit can estimate the baby's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the baby is anxious, the data collection unit will prioritize collecting heart rate and body temperature fluctuations. It can also prioritize collecting ambient volume and light intensity if the baby is excited. Furthermore, if the baby is sleepy, it can prioritize collecting room temperature and humidity. This allows for the priority collection of important information based on the baby's emotions. 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 emotional data into a generating AI and have the generating AI determine the priority of information.

[0042] The data collection unit can collect information while considering the light environment and humidity surrounding the baby. For example, the data collection unit can measure the light intensity in the baby's room with a sensor and collect its correlation with the baby's crying. The data collection unit can also monitor the humidity in the room and collect its correlation with the baby's crying. Furthermore, the data collection unit can collect the sound environment surrounding the baby and analyze its correlation with the crying. By collecting information while considering the light environment and humidity surrounding the baby, more accurate information can be obtained. 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 light environment data around the baby into a generating AI and have the generating AI perform an analysis of the light environment.

[0043] The data collection unit can analyze the baby's sleep cycle during collection and adjust the optimal collection timing. For example, the data collection unit can monitor the baby's sleep cycle, predict when crying is likely to occur, and collect information. The data collection unit can also collect body temperature and heart rate before the baby enters deep sleep. Furthermore, based on the baby's sleep cycle, the data collection unit can collect information before crying occurs. This allows for effective information collection by analyzing the baby's sleep cycle and adjusting the optimal collection timing. 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 sleep cycle data into a generating AI and have the generating AI adjust the optimal collection timing.

[0044] The analysis unit can estimate the baby's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the baby is feeling anxious, the analysis unit will focus on changes in heart rate and body temperature. If the baby is excited, the analysis unit can also focus on ambient noise levels and light intensity. Furthermore, if the baby is sleepy, the analysis unit can also focus on room temperature and humidity. By adjusting the analysis algorithm based on the baby's emotions, a more accurate analysis becomes possible. 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 baby's emotion data into a generating AI and have the generating AI adjust the analysis algorithm.

[0045] The analysis unit can improve the accuracy of its analysis by referring to the baby's past health status and medical history during the analysis. For example, the analysis unit can refer to the baby's past health checkup data to identify the cause of the crying. It can also analyze whether a specific health problem is the cause of the crying based on the baby's medical history. Furthermore, the analysis unit can refer to the baby's past medical history to more accurately identify the cause of the crying. In this way, the accuracy of the analysis is improved by referring to the baby's past health status and medical history. 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 baby's past health data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0046] The analysis unit can apply different analysis methods depending on the individual characteristics of the baby during the analysis. For example, the analysis unit can identify the cause of crying based on the baby's constitution and allergy information. The analysis unit can also analyze the cause of crying by considering the baby's personality and behavioral patterns. Furthermore, the analysis unit can apply different analysis methods depending on the baby's developmental stage. This allows for more accurate analysis by applying different analysis methods according to the individual characteristics of the baby. 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 baby's individual characteristic data into a generating AI and have the generating AI execute the application of the analysis method.

[0047] 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 feeling anxious, the analysis unit will display the results simply. If the baby is excited, the analysis unit can also display detailed results. Furthermore, if the baby is sleepy, the analysis unit can display the results in a visually easy-to-understand manner. By adjusting the display method of the analysis results based on the baby's emotions, a more easily understandable display becomes possible. 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 baby's emotion data into a generating AI and have the generating AI adjust the display method of the analysis results.

[0048] The analysis unit can perform analysis while considering the baby's family structure and living environment. For example, the analysis unit can identify the cause of crying by considering the baby's family structure. The analysis unit can also perform analysis while considering the baby's living environment (for example, the size of the residence and the noise level). Furthermore, the analysis unit can identify the cause of crying by considering the lifestyle of the baby's family. This allows for a more accurate analysis by considering the baby's family structure and living environment. 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 baby's family structure data into a generating AI and have the generating AI perform the analysis.

[0049] The analysis unit can improve the accuracy of its analysis by referring to the baby's dietary history and allergy information during the analysis. For example, the analysis unit can refer to the baby's dietary history to determine if a specific food is causing the crying. The analysis unit can also identify the cause of the crying based on the baby's allergy information. Furthermore, the analysis unit can analyze the baby's eating patterns to more accurately identify the cause of the crying. This improves the accuracy of the analysis by referring to the baby's dietary history and allergy information. 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 baby's dietary history data into a generating AI and have the generating AI perform the task of improving the analysis accuracy.

[0050] The presentation unit can estimate the baby's emotions and adjust the way it presents solutions based on the estimated emotions. For example, if the baby is feeling anxious, the presentation unit will present solutions in a calm voice. If the baby is excited, the presentation unit can also present solutions in a cheerful voice. Furthermore, if the baby is sleepy, the presentation unit can also present solutions in a gentle voice. By adjusting the way solutions are presented based on the baby's emotions, more effective solutions can be presented. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the baby's emotional data into a generating AI and have the generating AI adjust the way solutions are presented.

[0051] The presentation unit can select the optimal solution by referring to the baby's past response data at the time of presentation. For example, the presentation unit can present the optimal solution based on the solutions the baby has preferred in the past. The presentation unit can also analyze the baby's past response data and select the most effective solution. Furthermore, the presentation unit can refer to the baby's past response patterns and present the optimal solution. In this way, the optimal solution can be selected by referring to the baby's past response data. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the baby's past response data into a generating AI and have the generating AI perform the selection of the optimal solution.

[0052] The presentation unit can present different solutions depending on the baby's age and developmental stage. For example, the presentation unit can present an appropriate solution depending on the baby's age. It can also present different solutions depending on the baby's developmental stage. Furthermore, the presentation unit can present the optimal solution based on the baby's developmental stage. This enables effective solutions by presenting appropriate solutions according to the baby's age and developmental stage. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the baby's age data into a generating AI and have the generating AI perform the task of presenting solutions.

[0053] The presentation unit can estimate the baby's emotions and determine the priority of coping methods based on the estimated emotions. For example, if the baby is anxious, the presentation unit will prioritize collecting heart rate and body temperature fluctuations. It can also prioritize collecting ambient volume and light intensity if the baby is agitated. Furthermore, if the baby is sleepy, it can prioritize collecting room temperature and humidity. This allows the presentation unit to prioritize and present important coping methods based on the baby's emotions. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the baby's emotional data into a generating AI and have the generating AI determine the priority of coping methods.

[0054] The presentation unit can present solutions while considering the lifestyle and daily routines of the baby's family. For example, the presentation unit can consider the lifestyle of the baby's family and present the optimal solution. The presentation unit can also consider the daily routines of the baby's family and present solutions. Furthermore, the presentation unit can consider the lifestyle patterns of the baby's family and present the optimal solution. This allows for the presentation of more appropriate solutions by considering the lifestyle and daily routines of the baby's family. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input lifestyle data of the baby's family into a generating AI and have the generating AI perform the task of presenting solutions.

[0055] The presentation unit can adjust the solution method by referring to the baby's past sleep patterns at the time of presentation. For example, the presentation unit can refer to the baby's past sleep patterns and present the optimal solution method. The presentation unit can also analyze the baby's sleep patterns and adjust the solution method. Furthermore, the presentation unit can present the optimal solution method based on the baby's past sleep data. This allows for the presentation of more effective solutions by referring to the baby's past sleep patterns. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the baby's past sleep data into a generating AI and have the generating AI perform the adjustment of the solution method.

[0056] The sleep-inducing unit can estimate the baby's emotions and adjust the sleep-inducing method based on the estimated emotions. For example, if the baby is feeling anxious, the sleep-inducing unit can play calming music to help them fall asleep. It can also play soothing music if the baby is excited. Furthermore, if the baby appears sleepy, the sleep-inducing unit can create a quiet environment to help them fall asleep. This allows for more effective sleep-inducing by adjusting the method based on the baby's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the sleep-inducing unit may be performed using AI, or not. For example, the sleep-inducing unit can input the baby's emotion data into a generative AI and have the generative AI adjust the sleep-inducing method.

[0057] The sleep-inducing unit can select the optimal method by referring to the baby's past sleep-inducing patterns during the sleep-inducing process. For example, the sleep-inducing unit can select the optimal method based on the baby's past preferred sleep-inducing methods. It can also analyze the baby's past sleep-inducing patterns and select the most effective method. Furthermore, the sleep-inducing unit can refer to the baby's past sleep-inducing data to select the optimal method. This allows the optimal sleep-inducing method to be selected by referring to the baby's past sleep-inducing patterns. Some or all of the above-described processes in the sleep-inducing unit may be performed using AI, for example, or without AI. For example, the sleep-inducing unit can input the baby's past sleep-inducing data into a generating AI and have the generating AI select the optimal sleep-inducing method.

[0058] The sleep-inducing unit can help put a baby to sleep while monitoring the baby's body movements and heart rate. For example, the sleep-inducing unit can monitor the baby's heart rate and continue putting the baby to sleep until the heart rate stabilizes. It can also detect the baby's body movements with a sensor and continue putting the baby to sleep until the movements decrease. Furthermore, the sleep-inducing unit can monitor the baby's breathing pattern and continue putting the baby to sleep until the breathing stabilizes. By monitoring the baby's body movements and heart rate, more effective sleep-inducing becomes possible. Some or all of the above processes in the sleep-inducing unit may be performed using AI, for example, or without AI. For example, the sleep-inducing unit can input the baby's heart rate data into a generating AI and have the generating AI perform heart rate monitoring.

[0059] The sleep-inducing unit can estimate the baby's emotions and determine sleep-inducing priorities based on those emotions. For example, if the baby is feeling anxious, the sleep-inducing unit will prioritize collecting data on heart rate and body temperature fluctuations. It can also prioritize collecting data on ambient volume and light intensity if the baby is agitated. Furthermore, if the baby appears sleepy, it can prioritize collecting data on room temperature and humidity. This allows for the prioritization of important sleep-inducing methods based on the baby's emotions. Some or all of the above-described processes in the sleep-inducing unit may be performed using AI, for example, or without AI. For instance, the sleep-inducing unit can input the baby's emotional data into a generating AI, which can then determine the sleep-inducing priorities.

[0060] The sleep-inducing unit can adjust the sound and light environment around the baby to help it fall asleep. For example, the sleep-inducing unit can adjust the volume of the baby's room to create a quiet environment for sleep. It can also adjust the intensity of the light in the baby's room to create a calm environment for sleep. Furthermore, the sleep-inducing unit can adjust the temperature of the baby's room to create a comfortable environment for sleep. By adjusting the sound and light environment around the baby, more effective sleep-inducing becomes possible. Some or all of the above processes in the sleep-inducing unit may be performed using AI, for example, or without AI. For example, the sleep-inducing unit can input sound environment data around the baby into a generating AI and have the generating AI perform the sound environment adjustments.

[0061] The sleep-inducing unit can play music or sounds that the baby likes to help them fall asleep. For example, the sleep-inducing unit can play music that the baby likes to help them relax and fall asleep. It can also play sounds that the baby likes (for example, the mother's voice) to help them feel secure and fall asleep. Furthermore, the sleep-inducing unit can play lullabies that the baby likes to create a calm environment and help them fall asleep. In this way, playing music or sounds that the baby likes makes it possible to put the baby to sleep more effectively. Some or all of the above processes in the sleep-inducing unit may be performed using AI, for example, or not. For example, the sleep-inducing unit can input data on the baby's favorite music into a generating AI and have the generating AI play the music.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] Childcare support systems not only collect information about the baby's crying, body temperature, time, sounds, and surrounding environment, but can also monitor the baby's sleep cycle and adjust the optimal data collection timing. For example, by monitoring the baby's sleep cycle, it can predict when crying is likely to occur and collect that information. It can also collect body temperature and heart rate before the baby enters deep sleep. Furthermore, based on the baby's sleep cycle, it can collect information even before crying occurs. This allows for effective information collection by analyzing the baby's sleep cycle and adjusting the optimal data collection timing.

[0064] Childcare support systems can not only analyze a baby's cries but also simultaneously collect and analyze biometric information such as the baby's body movements and heart rate. For example, when a baby starts crying, it can collect heart rate fluctuations in real time. It can also use a baby movement sensor to collect the relationship between crying and body movements. Furthermore, it can monitor the baby's breathing patterns and collect changes in crying and breathing simultaneously. By simultaneously collecting biometric information such as the baby's body movements and heart rate, more detailed information can be obtained.

[0065] Childcare support systems can improve the accuracy of their analysis by referencing the baby's past health status and medical history based on the results of their analysis of the baby's cries. For example, they can identify the cause of the crying by referring to the baby's past health check data. They can also analyze whether a specific health problem is the cause of the crying based on the baby's medical history. Furthermore, they can more accurately identify the cause of the crying by referring to the baby's past medical history. In this way, the accuracy of the analysis is improved by referring to the baby's past health status and medical history.

[0066] The childcare support system can apply different analysis methods to each baby based on the results of analyzing their cries, taking into account the baby's individual characteristics. For example, it can identify the cause of crying based on the baby's constitution and allergy information. It can also analyze the cause of crying by considering the baby's personality and behavioral patterns. Furthermore, different analysis methods can be applied depending on the baby's developmental stage. This allows for more accurate analysis by applying different analysis methods according to each baby's individual characteristics.

[0067] The childcare support system can analyze a baby's crying based on the results of the analysis, taking into account the baby's family structure and living environment. For example, it can identify the cause of the crying by considering the baby's family structure. It can also analyze the baby's living environment (e.g., the size of the residence and noise levels). Furthermore, it can identify the cause of the crying by considering the lifestyle of the baby's family. By considering the baby's family structure and living environment during the analysis, a more accurate analysis becomes possible.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The data collection unit collects information about the baby's cries and surrounding environment. The data collection unit collects information about the surrounding environment, such as the baby's cries, body temperature, time, sounds, and temperature, in real time using sensors and microphones. For example, the data collection unit can collect the baby's cries with a high-sensitivity microphone and save it as audio data. The data collection unit can also measure the baby's body temperature with a non-contact temperature sensor and save it as body temperature data. Furthermore, the data collection unit can measure the temperature and humidity around the baby with environmental sensors and save it as environmental data. Step 2: The analysis unit analyzes the information collected by the collection unit to identify the reason for the crying. For example, the analysis unit may use a voice analysis algorithm to analyze the crying pattern and identify the reason for the crying. The analysis unit can also analyze environmental data to identify the reason for the crying. For example, the analysis unit may analyze the volume and frequency of the baby's crying to identify the reason for the crying. The analysis unit may also analyze the baby's body temperature data to determine if fluctuations in body temperature are the reason for the crying. Step 3: The presentation unit presents solutions based on the reasons identified by the analysis unit. For example, if the baby is crying because it is hungry, the presentation unit will display a message prompting feeding. The presentation unit can also display a message suggesting temperature adjustment if the baby is crying because of the room temperature. For example, if the baby is crying because it is hungry, the presentation unit will display the message "Please feed the baby." The presentation unit can also display the message "Please lower the room temperature" if the room temperature is too high. Step 4: The sleep-inducing unit combines generative AI and robotics to help the baby fall asleep. For example, the sleep-inducing unit can reproduce pre-learned voices of the mother or father speaking to the baby or singing lullabies. The sleep-inducing unit can also soothe the baby through physical interaction such as holding. For example, the sleep-inducing unit can play the voices of the mother or father to comfort the baby. The sleep-inducing unit can also use robotics to hold the baby and reproduce rocking patterns.

[0070] (Example of form 2) The childcare support system according to an embodiment of the present invention is a system for reducing the burden of nighttime crying and getting babies to sleep during postpartum childcare. This childcare support system collects information about the baby's crying, body temperature, time, sounds, temperature, and other surrounding environmental information, and an AI analyzes this information to identify the reason for the crying and suggests solutions. It can also perform sleep training by combining generative AI and robotics. For example, the childcare support system collects information about the baby's crying, body temperature, time, sounds, temperature, and other surrounding environmental information in real time using sensors and microphones. Next, the collected information is analyzed by an AI. The AI ​​has learned past nighttime crying patterns and the baby's individual characteristics, and identifies the reason for the crying based on this. Furthermore, the AI ​​suggests solutions based on the identified reason. For example, if the baby is crying because they are hungry, it will encourage breastfeeding, and if the crying is due to the room temperature, it will suggest adjusting the temperature. In addition, by combining generative AI and robotics, it can also handle some aspects of getting babies to sleep. For example, it can reproduce pre-learned voices of the mother or father talking to the baby or lullabies, and can even get the baby to sleep through physical interactions such as holding them. This allows parents to sleep peacefully. This system reduces the burden of postpartum childcare and alleviates sleep deprivation and stress for parents. Furthermore, it is expected to make it possible to balance work and childcare, contributing to measures against the declining birthrate. In this way, childcare support systems can reduce the burden of postpartum childcare and alleviate sleep deprivation and stress for parents.

[0071] The childcare support system according to this embodiment comprises a data collection unit, an analysis unit, a presentation unit, and a sleep-inducing unit. The data collection unit collects information on the baby's cries and surrounding environment. For example, the data collection unit collects information on the surrounding environment, such as the baby's cries, body temperature, time, sound, and temperature, in real time using sensors and microphones. For example, the data collection unit collects the baby's cries with a high-sensitivity microphone and stores it as audio data. The data collection unit can also measure the baby's body temperature with a non-contact temperature sensor and store it as body temperature data. Furthermore, the data collection unit can measure the temperature and humidity around the baby with an environmental sensor and store it as environmental data. The analysis unit analyzes the information collected by the data collection unit to identify the reason for the crying. For example, the analysis unit analyzes the crying pattern using an audio analysis algorithm to identify the reason for the crying. The analysis unit can also analyze environmental data to identify the reason for the crying. For example, the analysis unit analyzes the volume and frequency of the baby's cries to identify the reason for the crying. The analysis unit can analyze the baby's body temperature data and identify whether fluctuations in body temperature are the reason for the crying. The presentation unit then suggests solutions based on the reason identified by the analysis unit. For example, if the baby is crying because it is hungry, the presentation unit will display a message encouraging feeding. It can also display a message suggesting temperature adjustment if the crying is due to the room temperature. For example, if the baby is crying because it is hungry, the presentation unit will display the message "Please feed the baby." It can also display a message "Please lower the room temperature" if the room temperature is too high. The sleep-inducing unit uses a combination of generative AI and robotics to help the baby fall asleep. For example, the sleep-inducing unit can reproduce pre-learned voices of the mother or father speaking to the baby or singing lullabies. It can also help the baby fall asleep through physical interaction such as holding. For example, the sleep-inducing unit can play the voices of the mother or father to soothe the baby. The sleep-inducing unit can use robotics to hold the baby and reproduce rocking patterns. As a result, the childcare support system according to this embodiment can reduce the burden of postpartum childcare and alleviate sleep deprivation and stress for parents.

[0072] The data collection unit collects information about the baby's cries and surrounding environment. For example, it collects information about the surrounding environment, such as the baby's cries, body temperature, time, sound, and temperature, in real time using sensors and microphones. Specifically, it uses a high-sensitivity microphone to collect the baby's cries and saves the audio data. This provides detailed data such as the crying pattern, volume, and frequency. It can also measure the baby's body temperature using a non-contact temperature sensor and save the temperature data. This allows for real-time monitoring of the baby's body temperature fluctuations and immediate response if an abnormality is detected. Furthermore, it can measure the temperature and humidity around the baby using environmental sensors and save this environmental data. This provides data to maintain a comfortable environment for the baby. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, the collected data is stored on a cloud server, making it accessible to the analysis and presentation units. Adjusting the data collection frequency and accuracy allows for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0073] The analysis unit analyzes the information collected by the data collection unit to identify the reason for the crying. For example, the analysis unit uses a voice analysis algorithm to analyze the crying pattern and identify the reason for the crying. Specifically, it uses AI to analyze the volume, frequency, and duration of the crying to determine whether the baby is crying because of hunger, inability to sleep, or illness. The analysis unit can also analyze environmental data to identify the reason for crying. For example, it can analyze the baby's body temperature data to determine if fluctuations in body temperature are the reason for the crying. Furthermore, it can analyze ambient temperature and humidity data to determine if the environment is influencing the baby's crying. The analysis unit comprehensively analyzes this data to quickly and accurately identify the cause of the baby's crying. In addition, the analysis unit can utilize past data and statistical information to analyze long-term trends and patterns. For example, based on past crying data, it can predict the frequency of crying at specific times of day or under specific environmental conditions and formulate future countermeasures. The analysis unit can also use anomaly detection algorithms to detect unusual patterns or abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0074] The display unit presents solutions based on the reasons identified by the analysis unit. For example, if the baby is crying because it is hungry, the display unit will display a message prompting feeding. Specifically, it will display a message to the parent via a smartphone or tablet application saying, "Your baby is hungry. Please feed them." The display unit can also display a message suggesting temperature adjustment if the baby is crying due to the room temperature. For example, it will display a message saying, "The room temperature is too high. Please lower the temperature," prompting the parent to take specific action. Furthermore, if the baby's body temperature is abnormally high or low, the display unit can display a message prompting contact with a medical institution. For example, it will display a message saying, "Your baby's body temperature is too high. Please consult a doctor," prompting the parent to take prompt action. The display unit can not only visually display these messages but also use voice and vibration notifications to alert the parent. This allows the display unit to help parents respond quickly and appropriately, ensuring the baby's comfort and safety. In addition, the display unit can collect user feedback and continuously improve the accuracy and effectiveness of its presentations. For example, the system records the parents' reactions and actions to the messages presented and incorporates this information into future presentations. This allows the system to consistently provide highly accurate solutions based on the latest information, thereby reducing the burden of childcare on parents.

[0075] The sleep-inducing unit combines generative AI and robotics to help babies fall asleep. For example, it can reproduce pre-learned voices of mothers and fathers, such as soothing speech and lullabies. Specifically, it uses generative AI to analyze the voices of mothers and fathers and learns their characteristics. This allows the generative AI to reproduce the tone and rhythm of the mother's or father's voice, providing the baby with a sense of security. The sleep-inducing unit can also help babies fall asleep through physical interaction, such as holding them. For example, it can use robotics to hold the baby and reproduce rocking patterns. This gives the baby the feeling of being held by their mother or father, allowing them to fall asleep peacefully. Furthermore, the sleep-inducing unit can monitor the baby's condition in real time and adjust the sleep-inducing method as needed. For example, if the baby won't stop crying, the generative AI can generate a new lullaby, and the robotics can change the rocking pattern. This allows the sleep-inducing unit to provide the optimal sleep-inducing method tailored to the individual needs of each baby. Furthermore, the sleep-inducing unit can learn the baby's sleep patterns based on past data, improving the efficiency of future sleep-inducing efforts. For example, it can identify patterns in which babies tend to fall asleep at specific times and adjust sleep-inducing techniques accordingly. This allows the sleep-inducing unit to improve the quality of the baby's sleep and reduce the burden on parents.

[0076] The data collection unit can collect information about the baby's crying, body temperature, time, sounds, temperature, and other environmental factors. For example, the data collection unit can collect the baby's crying using a high-sensitivity microphone and save it as audio data. The data collection unit can also measure the baby's body temperature using a non-contact temperature sensor and save it as body temperature data. Furthermore, the data collection unit can measure the temperature and humidity around the baby using an environmental sensor and save it as environmental data. By collecting information about the baby's crying, body temperature, time, sounds, temperature, and other environmental factors, it becomes easier to identify the reason for the crying. 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 crying data into a generating AI and have the generating AI perform an analysis of the crying.

[0077] The analysis unit can analyze the collected information and identify the reason for crying. For example, the analysis unit can use a voice analysis algorithm to analyze the crying pattern and identify the reason for crying. The analysis unit can also analyze environmental data to identify the reason for crying. For example, the analysis unit can analyze the volume and frequency of the baby's cries to identify the reason for crying. The analysis unit can also analyze the baby's body temperature data to determine if fluctuations in body temperature are the reason for crying. By analyzing the collected information and identifying the reason for crying, appropriate solutions can be suggested. 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 baby's crying data into a generating AI and have the generating AI perform the crying analysis.

[0078] The display unit can suggest solutions based on the identified reason. For example, if the baby is crying because it is hungry, the display unit will display a message encouraging feeding. The display unit can also display a message suggesting temperature adjustment if the baby is crying because of the room temperature. For example, if the baby is crying because it is hungry, the display unit will display the message "Please feed the baby." The display unit can also display the message "Please lower the room temperature" if the room temperature is too high. In this way, by suggesting solutions based on the identified reason, the baby's crying can be effectively resolved. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input baby crying data into a generating AI and have the generating AI perform the task of suggesting solutions.

[0079] The lullaby unit can soothe a baby to sleep by reproducing pre-learned voices of the mother or father speaking to the baby and lullabies, and by using physical interaction such as holding the baby. For example, the lullaby unit can play the voices of the mother or father to reassure the baby. The lullaby unit can also use robotics to hold the baby and reproduce rocking patterns. For example, the lullaby unit can play the voices of the mother or father to reassure the baby. The lullaby unit can also use robotics to hold the baby and reproduce rocking patterns. This allows the baby to be effectively soothed to sleep by reproducing pre-learned voices of the mother or father and lullabies, and by using physical interaction such as holding the baby. Some or all of the above processing in the lullaby unit may be performed using AI, for example, or without AI. For example, the lullaby unit can input voice data of the mother or father into a generating AI and have the generating AI play the voices.

[0080] The data collection unit can estimate the baby's emotions and adjust the type of information collected based on the estimated emotions. For example, if the baby is anxious, the data collection unit may focus on collecting heart rate and body temperature fluctuations. If the baby is excited, the data collection unit may focus on collecting ambient volume and light intensity. Furthermore, if the baby is sleepy, the data collection unit may focus on collecting room temperature and humidity. By adjusting the type of information collected based on the baby's emotions, more appropriate information can be collected. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input the baby's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0081] The data collection unit can analyze the baby's past crying patterns and select the optimal timing for data collection. For example, based on past crying data, the data collection unit can detect signs before crying begins and collect information early. The data collection unit can also focus on collecting information during specific time periods if the baby tends to cry during those times. Furthermore, the data collection unit can analyze the intensity and frequency of the baby's cries and collect information before the crying becomes stronger. This enables effective information collection by analyzing the baby's past crying patterns and selecting the optimal timing for data collection. 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 crying data into a generating AI and have the generating AI select the optimal timing for data collection.

[0082] The data collection unit can simultaneously collect biometric information such as the baby's body movements and heart rate during data collection. For example, when the baby starts crying, the data collection unit can collect heart rate fluctuations in real time. The data collection unit can also use the baby's body movement sensor to collect the relationship between crying and body movements. Furthermore, the data collection unit can monitor the baby's breathing patterns and simultaneously collect changes in crying and breathing. This allows for the simultaneous collection of biometric information such as the baby's body movements and heart rate, enabling the acquisition of more detailed information. Some or all of the above-described processes 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 body movement data into a generating AI and have the generating AI perform the body movement analysis.

[0083] The data collection unit can estimate the baby's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the baby is anxious, the data collection unit will prioritize collecting heart rate and body temperature fluctuations. It can also prioritize collecting ambient volume and light intensity if the baby is excited. Furthermore, if the baby is sleepy, it can prioritize collecting room temperature and humidity. This allows for the priority collection of important information based on the baby's emotions. 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 emotional data into a generating AI and have the generating AI determine the priority of information.

[0084] The data collection unit can collect information while considering the light environment and humidity surrounding the baby. For example, the data collection unit can measure the light intensity in the baby's room with a sensor and collect its correlation with the baby's crying. The data collection unit can also monitor the humidity in the room and collect its correlation with the baby's crying. Furthermore, the data collection unit can collect the sound environment surrounding the baby and analyze its correlation with the crying. By collecting information while considering the light environment and humidity surrounding the baby, more accurate information can be obtained. 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 light environment data around the baby into a generating AI and have the generating AI perform an analysis of the light environment.

[0085] The data collection unit can analyze the baby's sleep cycle during collection and adjust the optimal collection timing. For example, the data collection unit can monitor the baby's sleep cycle, predict when crying is likely to occur, and collect information. The data collection unit can also collect body temperature and heart rate before the baby enters deep sleep. Furthermore, based on the baby's sleep cycle, the data collection unit can collect information before crying occurs. This allows for effective information collection by analyzing the baby's sleep cycle and adjusting the optimal collection timing. 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 sleep cycle data into a generating AI and have the generating AI adjust the optimal collection timing.

[0086] The analysis unit can estimate the baby's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the baby is feeling anxious, the analysis unit will focus on changes in heart rate and body temperature. If the baby is excited, the analysis unit can also focus on ambient noise levels and light intensity. Furthermore, if the baby is sleepy, the analysis unit can also focus on room temperature and humidity. By adjusting the analysis algorithm based on the baby's emotions, a more accurate analysis becomes possible. 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 baby's emotion data into a generating AI and have the generating AI adjust the analysis algorithm.

[0087] The analysis unit can improve the accuracy of its analysis by referring to the baby's past health status and medical history during the analysis. For example, the analysis unit can refer to the baby's past health checkup data to identify the cause of the crying. It can also analyze whether a specific health problem is the cause of the crying based on the baby's medical history. Furthermore, the analysis unit can refer to the baby's past medical history to more accurately identify the cause of the crying. In this way, the accuracy of the analysis is improved by referring to the baby's past health status and medical history. 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 baby's past health data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0088] The analysis unit can apply different analysis methods depending on the individual characteristics of the baby during the analysis. For example, the analysis unit can identify the cause of crying based on the baby's constitution and allergy information. The analysis unit can also analyze the cause of crying by considering the baby's personality and behavioral patterns. Furthermore, the analysis unit can apply different analysis methods depending on the baby's developmental stage. This allows for more accurate analysis by applying different analysis methods according to the individual characteristics of the baby. 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 baby's individual characteristic data into a generating AI and have the generating AI execute the application of the analysis method.

[0089] 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 feeling anxious, the analysis unit will display the results simply. If the baby is excited, the analysis unit can also display detailed results. Furthermore, if the baby is sleepy, the analysis unit can display the results in a visually easy-to-understand manner. By adjusting the display method of the analysis results based on the baby's emotions, a more easily understandable display becomes possible. 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 baby's emotion data into a generating AI and have the generating AI adjust the display method of the analysis results.

[0090] The analysis unit can perform analysis while considering the baby's family structure and living environment. For example, the analysis unit can identify the cause of crying by considering the baby's family structure. The analysis unit can also perform analysis while considering the baby's living environment (for example, the size of the residence and the noise level). Furthermore, the analysis unit can identify the cause of crying by considering the lifestyle of the baby's family. This allows for a more accurate analysis by considering the baby's family structure and living environment. 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 baby's family structure data into a generating AI and have the generating AI perform the analysis.

[0091] The analysis unit can improve the accuracy of its analysis by referring to the baby's dietary history and allergy information during the analysis. For example, the analysis unit can refer to the baby's dietary history to determine if a specific food is causing the crying. The analysis unit can also identify the cause of the crying based on the baby's allergy information. Furthermore, the analysis unit can analyze the baby's eating patterns to more accurately identify the cause of the crying. This improves the accuracy of the analysis by referring to the baby's dietary history and allergy information. 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 baby's dietary history data into a generating AI and have the generating AI perform the task of improving the analysis accuracy.

[0092] The presentation unit can estimate the baby's emotions and adjust the way it presents solutions based on the estimated emotions. For example, if the baby is feeling anxious, the presentation unit will present solutions in a calm voice. If the baby is excited, the presentation unit can also present solutions in a cheerful voice. Furthermore, if the baby is sleepy, the presentation unit can also present solutions in a gentle voice. By adjusting the way solutions are presented based on the baby's emotions, more effective solutions can be presented. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the baby's emotional data into a generating AI and have the generating AI adjust the way solutions are presented.

[0093] The presentation unit can select the optimal solution by referring to the baby's past response data at the time of presentation. For example, the presentation unit can present the optimal solution based on the solutions the baby has preferred in the past. The presentation unit can also analyze the baby's past response data and select the most effective solution. Furthermore, the presentation unit can refer to the baby's past response patterns and present the optimal solution. In this way, the optimal solution can be selected by referring to the baby's past response data. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the baby's past response data into a generating AI and have the generating AI perform the selection of the optimal solution.

[0094] The presentation unit can present different solutions depending on the baby's age and developmental stage. For example, the presentation unit can present an appropriate solution depending on the baby's age. It can also present different solutions depending on the baby's developmental stage. Furthermore, the presentation unit can present the optimal solution based on the baby's developmental stage. This enables effective solutions by presenting appropriate solutions according to the baby's age and developmental stage. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the baby's age data into a generating AI and have the generating AI perform the task of presenting solutions.

[0095] The presentation unit can estimate the baby's emotions and determine the priority of coping methods based on the estimated emotions. For example, if the baby is anxious, the presentation unit will prioritize collecting heart rate and body temperature fluctuations. It can also prioritize collecting ambient volume and light intensity if the baby is agitated. Furthermore, if the baby is sleepy, it can prioritize collecting room temperature and humidity. This allows the presentation unit to prioritize and present important coping methods based on the baby's emotions. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the baby's emotional data into a generating AI and have the generating AI determine the priority of coping methods.

[0096] The presentation unit can present solutions while considering the lifestyle and daily routines of the baby's family. For example, the presentation unit can consider the lifestyle of the baby's family and present the optimal solution. The presentation unit can also consider the daily routines of the baby's family and present solutions. Furthermore, the presentation unit can consider the lifestyle patterns of the baby's family and present the optimal solution. This allows for the presentation of more appropriate solutions by considering the lifestyle and daily routines of the baby's family. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input lifestyle data of the baby's family into a generating AI and have the generating AI perform the task of presenting solutions.

[0097] The presentation unit can adjust the solution method by referring to the baby's past sleep patterns at the time of presentation. For example, the presentation unit can refer to the baby's past sleep patterns and present the optimal solution method. The presentation unit can also analyze the baby's sleep patterns and adjust the solution method. Furthermore, the presentation unit can present the optimal solution method based on the baby's past sleep data. This allows for the presentation of more effective solutions by referring to the baby's past sleep patterns. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the baby's past sleep data into a generating AI and have the generating AI perform the adjustment of the solution method.

[0098] The sleep-inducing unit can estimate the baby's emotions and adjust the sleep-inducing method based on the estimated emotions. For example, if the baby is feeling anxious, the sleep-inducing unit can play calming music to help them fall asleep. It can also play soothing music if the baby is excited. Furthermore, if the baby appears sleepy, the sleep-inducing unit can create a quiet environment to help them fall asleep. This allows for more effective sleep-inducing by adjusting the method based on the baby's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the sleep-inducing unit may be performed using AI, or not. For example, the sleep-inducing unit can input the baby's emotion data into a generative AI and have the generative AI adjust the sleep-inducing method.

[0099] The sleep-inducing unit can select the optimal method by referring to the baby's past sleep-inducing patterns during the sleep-inducing process. For example, the sleep-inducing unit can select the optimal method based on the baby's past preferred sleep-inducing methods. It can also analyze the baby's past sleep-inducing patterns and select the most effective method. Furthermore, the sleep-inducing unit can refer to the baby's past sleep-inducing data to select the optimal method. This allows the optimal sleep-inducing method to be selected by referring to the baby's past sleep-inducing patterns. Some or all of the above-described processes in the sleep-inducing unit may be performed using AI, for example, or without AI. For example, the sleep-inducing unit can input the baby's past sleep-inducing data into a generating AI and have the generating AI select the optimal sleep-inducing method.

[0100] The sleep-inducing unit can help put a baby to sleep while monitoring the baby's body movements and heart rate. For example, the sleep-inducing unit can monitor the baby's heart rate and continue putting the baby to sleep until the heart rate stabilizes. It can also detect the baby's body movements with a sensor and continue putting the baby to sleep until the movements decrease. Furthermore, the sleep-inducing unit can monitor the baby's breathing pattern and continue putting the baby to sleep until the breathing stabilizes. By monitoring the baby's body movements and heart rate, more effective sleep-inducing becomes possible. Some or all of the above processes in the sleep-inducing unit may be performed using AI, for example, or without AI. For example, the sleep-inducing unit can input the baby's heart rate data into a generating AI and have the generating AI perform heart rate monitoring.

[0101] The sleep-inducing unit can estimate the baby's emotions and determine sleep-inducing priorities based on those emotions. For example, if the baby is feeling anxious, the sleep-inducing unit will prioritize collecting data on heart rate and body temperature fluctuations. It can also prioritize collecting data on ambient volume and light intensity if the baby is agitated. Furthermore, if the baby appears sleepy, it can prioritize collecting data on room temperature and humidity. This allows for the prioritization of important sleep-inducing methods based on the baby's emotions. Some or all of the above-described processes in the sleep-inducing unit may be performed using AI, for example, or without AI. For instance, the sleep-inducing unit can input the baby's emotional data into a generating AI, which can then determine the sleep-inducing priorities.

[0102] The sleep-inducing unit can adjust the sound and light environment around the baby to help it fall asleep. For example, the sleep-inducing unit can adjust the volume of the baby's room to create a quiet environment for sleep. It can also adjust the intensity of the light in the baby's room to create a calm environment for sleep. Furthermore, the sleep-inducing unit can adjust the temperature of the baby's room to create a comfortable environment for sleep. By adjusting the sound and light environment around the baby, more effective sleep-inducing becomes possible. Some or all of the above processes in the sleep-inducing unit may be performed using AI, for example, or without AI. For example, the sleep-inducing unit can input sound environment data around the baby into a generating AI and have the generating AI perform the sound environment adjustments.

[0103] The sleep-inducing unit can play music or sounds that the baby likes to help them fall asleep. For example, the sleep-inducing unit can play music that the baby likes to help them relax and fall asleep. It can also play sounds that the baby likes (for example, the mother's voice) to help them feel secure and fall asleep. Furthermore, the sleep-inducing unit can play lullabies that the baby likes to create a calm environment and help them fall asleep. In this way, playing music or sounds that the baby likes makes it possible to put the baby to sleep more effectively. Some or all of the above processes in the sleep-inducing unit may be performed using AI, for example, or not. For example, the sleep-inducing unit can input data on the baby's favorite music into a generating AI and have the generating AI play the music.

[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 childcare support system not only analyzes the patterns of a baby's cries, but also captures the baby's facial expressions with a camera and estimates their emotions from those expressions. For example, if the baby is smiling, it is assumed that they are feeling secure and no special action is needed. If the baby is frowning, it is assumed that they are feeling uncomfortable, and additional information can be collected to identify the cause. Furthermore, if the baby is crying, the system can immediately analyze the cries and quickly suggest solutions. This allows for more accurate responses by estimating the baby's emotions from their facial expressions.

[0106] Childcare support systems not only collect information about the baby's crying, body temperature, time, sounds, and surrounding environment, but can also monitor the baby's sleep cycle and adjust the optimal data collection timing. For example, by monitoring the baby's sleep cycle, it can predict when crying is likely to occur and collect that information. It can also collect body temperature and heart rate before the baby enters deep sleep. Furthermore, based on the baby's sleep cycle, it can collect information even before crying occurs. This allows for effective information collection by analyzing the baby's sleep cycle and adjusting the optimal data collection timing.

[0107] Childcare support systems can not only analyze a baby's cries but also simultaneously collect and analyze biometric information such as the baby's body movements and heart rate. For example, when a baby starts crying, it can collect heart rate fluctuations in real time. It can also use a baby movement sensor to collect the relationship between crying and body movements. Furthermore, it can monitor the baby's breathing patterns and collect changes in crying and breathing simultaneously. By simultaneously collecting biometric information such as the baby's body movements and heart rate, more detailed information can be obtained.

[0108] The childcare support system can estimate the baby's emotions based on an analysis of their cries and suggest solutions based on those emotions. For example, if the baby is feeling anxious, it can suggest solutions in a calm voice. If the baby is excited, it can suggest solutions in a cheerful voice. Furthermore, if the baby is sleepy, it can suggest solutions in a gentle voice. By adjusting the way solutions are presented based on the baby's emotions, the system can offer more effective solutions.

[0109] Childcare support systems can improve the accuracy of their analysis by referencing the baby's past health status and medical history based on the results of their analysis of the baby's cries. For example, they can identify the cause of the crying by referring to the baby's past health check data. They can also analyze whether a specific health problem is the cause of the crying based on the baby's medical history. Furthermore, they can more accurately identify the cause of the crying by referring to the baby's past medical history. In this way, the accuracy of the analysis is improved by referring to the baby's past health status and medical history.

[0110] The childcare support system can apply different analysis methods to each baby based on the results of analyzing their cries, taking into account the baby's individual characteristics. For example, it can identify the cause of crying based on the baby's constitution and allergy information. It can also analyze the cause of crying by considering the baby's personality and behavioral patterns. Furthermore, different analysis methods can be applied depending on the baby's developmental stage. This allows for more accurate analysis by applying different analysis methods according to each baby's individual characteristics.

[0111] The childcare support system can estimate the baby's emotions based on the analysis of their cries and adjust the analysis algorithm accordingly. For example, if the baby is anxious, the system will prioritize changes in heart rate and body temperature. If the baby is excited, the system can prioritize ambient noise levels and light intensity. Furthermore, if the baby is sleepy, the system can prioritize room temperature and humidity. By adjusting the analysis algorithm based on the baby's emotions, a more accurate analysis becomes possible.

[0112] The childcare support system can analyze a baby's crying based on the results of the analysis, taking into account the baby's family structure and living environment. For example, it can identify the cause of the crying by considering the baby's family structure. It can also analyze the baby's living environment (e.g., the size of the residence and noise levels). Furthermore, it can identify the cause of the crying by considering the lifestyle of the baby's family. By considering the baby's family structure and living environment during the analysis, a more accurate analysis becomes possible.

[0113] The childcare support system can estimate the baby's emotions based on the analysis of their cries and adjust the display of the analysis results accordingly. For example, if the baby is anxious, the analysis results will be displayed simply. If the baby is excited, a more detailed analysis result can be displayed. Furthermore, if the baby is sleepy, the analysis results can be displayed in a visually easy-to-understand manner. In this way, by adjusting the display method of the analysis results based on the baby's emotions, a more easily understandable display becomes possible.

[0114] The childcare support system can estimate the baby's emotions based on an analysis of their cries and adjust the sleep-inducing method accordingly. For example, if the baby is feeling anxious, it can play calming music to help them fall asleep. If the baby is excited, it can play soothing music to help them fall asleep. Furthermore, if the baby seems sleepy, it can create a quiet environment to help them fall asleep. By adjusting the sleep-inducing method based on the baby's emotions, it becomes possible to induce sleep more effectively.

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

[0116] Step 1: The data collection unit collects information about the baby's cries and surrounding environment. The data collection unit collects information about the surrounding environment, such as the baby's cries, body temperature, time, sounds, and temperature, in real time using sensors and microphones. For example, the data collection unit can collect the baby's cries with a high-sensitivity microphone and save it as audio data. The data collection unit can also measure the baby's body temperature with a non-contact temperature sensor and save it as body temperature data. Furthermore, the data collection unit can measure the temperature and humidity around the baby with environmental sensors and save it as environmental data. Step 2: The analysis unit analyzes the information collected by the collection unit to identify the reason for the crying. For example, the analysis unit may use a voice analysis algorithm to analyze the crying pattern and identify the reason for the crying. The analysis unit can also analyze environmental data to identify the reason for the crying. For example, the analysis unit may analyze the volume and frequency of the baby's crying to identify the reason for the crying. The analysis unit may also analyze the baby's body temperature data to determine if fluctuations in body temperature are the reason for the crying. Step 3: The presentation unit presents solutions based on the reasons identified by the analysis unit. For example, if the baby is crying because it is hungry, the presentation unit will display a message prompting feeding. The presentation unit can also display a message suggesting temperature adjustment if the baby is crying because of the room temperature. For example, if the baby is crying because it is hungry, the presentation unit will display the message "Please feed the baby." The presentation unit can also display the message "Please lower the room temperature" if the room temperature is too high. Step 4: The sleep-inducing unit combines generative AI and robotics to help the baby fall asleep. For example, the sleep-inducing unit can reproduce pre-learned voices of the mother or father speaking to the baby or singing lullabies. The sleep-inducing unit can also soothe the baby through physical interaction such as holding. For example, the sleep-inducing unit can play the voices of the mother or father to comfort the baby. The sleep-inducing unit can also use robotics to hold the baby and reproduce rocking patterns.

[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, presentation unit, and lullaby unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the sensors and microphone of the smart device 14 to collect information on the baby's crying, body temperature, and surrounding environment in real time. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected information to identify the reason for the crying. The presentation unit uses, for example, the display 40A and speaker 40B of the smart device 14 to present solutions. The lullaby unit is implemented, for example, by the control unit 46A of the smart device 14, which plays the voices of the mother or father and uses robotics to lull the baby to sleep. 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, presentation unit, and lullaby 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 uses the sensors and microphones of the smart glasses 214 to collect information on the baby's crying, body temperature, and surrounding environment in real time. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected information to identify the reason for the crying. The presentation unit uses, for example, the display and speaker of the smart glasses 214 to present solutions. The lullaby unit is implemented, for example, by the control unit 46A of the smart glasses 214, which plays the voices of the mother or father and uses robotics to lull the baby to sleep. 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, presentation unit, and lullaby unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the sensors and microphone of the headset terminal 314 to collect information on the baby's crying, body temperature, and surrounding environment in real time. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected information to identify the reason for the crying. The presentation unit uses, for example, the display and speaker of the headset terminal 314 to present solutions. The lullaby unit is implemented, for example, by the control unit 46A of the headset terminal 314, which plays the voices of the mother or father and uses robotics to lull the baby to sleep. 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, presentation unit, and lullaby unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the robot 414's sensors and microphones to collect information on the baby's crying, body temperature, and surrounding environment in real time. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected information to identify the reason for the crying. The presentation unit uses, for example, the robot 414's display and speaker to present solutions. The lullaby unit is implemented, for example, by the control unit 46A of the robot 414, which plays the voices of the mother or father and uses robotics to lull the baby to sleep. 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 that collects information on the baby's crying and surrounding conditions, An analysis unit analyzes the information collected by the aforementioned collection unit to identify the reason for crying, A presentation unit that presents a solution based on the reason identified by the analysis unit, It includes a sleep-inducing unit that combines generative AI and robotics to help put the baby to sleep. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect information about the baby's crying, body temperature, time, sounds, temperature, and other environmental factors. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected information is analyzed to identify the reason for crying. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned display unit is, Based on the identified reasons, we will propose solutions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned sleep-inducing section is, The system reproduces pre-learned voice recordings of mothers and fathers speaking to the baby and singing lullabies, and uses physical interaction such as holding the baby to help it fall asleep. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the baby's emotions and adjust the type of information we collect based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the baby's past crying patterns to select the optimal timing for data collection. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During the collection process, biometric information such as the baby's body movements and heart rate will also be collected. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system estimates the baby's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, we also take into account the light environment and humidity surrounding the baby. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the baby's sleep cycle is analyzed to adjust the optimal collection timing. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the baby's emotions and adjusts the analysis algorithm based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the baby's past health status and medical history are referenced to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During the analysis, different analytical methods are applied according to the individual characteristics of each baby. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the baby's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During the analysis, the baby's family structure and living environment will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the baby's dietary history and allergy information are referenced to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned display unit is, The system estimates the baby's emotions and adjusts the method of suggesting solutions based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned display unit is, When presenting the issue, the optimal solution is selected by referring to the baby's past response data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is, When presenting the solution, different methods should be offered depending on the baby's age and developmental stage. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is, The system estimates the baby's emotions and prioritizes solutions based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is, When presenting solutions, consider the baby's family's lifestyle and daily routines. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is, When presenting the solution, refer to the baby's past sleep patterns to adjust the method of resolving the issue. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned sleep-inducing section is, It estimates the baby's emotions and adjusts the sleep-inducing method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned sleep-inducing section is, When putting your baby to sleep, refer to their past sleep patterns to select the most suitable method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned sleep-inducing section is, When putting the baby to sleep, monitor their body movements and heart rate. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned sleep-inducing section is, It estimates the baby's emotions and determines the priority of sleep-inducing activities based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned sleep-inducing section is, When putting the baby to sleep, adjust the sound and light environment around the baby to help them fall asleep. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned sleep-inducing section is, When putting the baby to sleep, play their favorite music or sounds to help them fall asleep. 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 that collects information on the baby's crying and surrounding conditions, An analysis unit analyzes the information collected by the aforementioned collection unit to identify the reason for crying, A presentation unit that presents a solution based on the reason identified by the analysis unit, It includes a sleep-inducing unit that combines generative AI and robotics to put babies to sleep. A system characterized by the following features.

2. The aforementioned collection unit is Collect information about the baby's crying, body temperature, time, sounds, temperature, and other environmental factors. The system according to feature 1.

3. The aforementioned analysis unit, The collected information is analyzed to identify the reason for crying. The system according to feature 1.

4. The aforementioned display unit is, Based on the identified reasons, we will propose solutions. The system according to feature 1.

5. The aforementioned sleep-inducing section is, The system reproduces pre-learned voice recordings of mothers and fathers speaking to the baby and singing lullabies, and uses physical interaction such as holding the baby to help it fall asleep. The system according to feature 1.

6. The aforementioned collection unit is We estimate the baby's emotions and adjust the type of information we collect based on the estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the baby's past crying patterns to select the optimal timing for data collection. The system according to feature 1.

8. The aforementioned collection unit is During the collection process, biometric information such as the baby's body movements and heart rate will also be collected. The system according to feature 1.

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