System

Abstract

An object of a system according to an embodiment is to analyze a voice and a life pattern of a child and propose an appropriate response to a parent.SOLUTION: A system includes an analysis unit, an inference unit, a monitoring unit, a notification unit, and an advice unit. The analysis unit analyzes the tone of the child's voice or the pattern of the child's cry. The inference unit infers a health condition or a mood of the child on the basis of the data analyzed by the analysis unit. The monitor may monitor a life pattern of the child based on the sensor data. The notification unit notifies the parent based on the data monitored by the monitoring unit. The advice unit provides personalized advice for each child.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not adequately assess a child's health and mood from their voice and lifestyle patterns, and does not adequately suggest appropriate responses to parents, so there is room for improvement.

[0005] The system according to the embodiment aims to analyze a child's voice and lifestyle patterns and suggest appropriate responses to parents. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, an inference unit, a monitoring unit, a notification unit, and an advice unit. The analysis unit analyzes the tone of a child's voice or crying pattern. The inference unit infers the child's health condition or mood based on the data analyzed by the analysis unit. The monitoring unit monitors the child's lifestyle patterns using sensor data. The notification unit notifies the parent based on the data monitored by the monitoring unit. The advice unit provides personalized advice for each child. [Effects of the Invention]

[0007] The system according to the embodiment can analyze a child's voice and lifestyle patterns and suggest appropriate responses to parents. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A system according to an embodiment of the present invention utilizes voice recognition and sensor data to understand a child's condition and suggest appropriate responses to parents. This system analyzes a child's voice tone and crying patterns to infer the child's health and mood. It also uses sensor data to monitor the child's daily life and notify parents if any abnormalities are detected. It also provides personalized advice for each child to help parents take appropriate measures. For example, it uses voice recognition technology to analyze a child's voice tone and crying patterns. It then analyzes the child's voice pitch, intensity, and crying rhythm in detail to infer the child's health and mood. Next, it uses sensor data to monitor the child's daily life. For example, it measures the child's sleep time, meal timing, and activity level using sensors, and notifies parents if any abnormalities are detected. This allows parents to understand their child's daily rhythm and take appropriate measures. It also provides personalized advice for each child. For example, it provides parenting instructions and information tailored to the child's age, personality, and health. This allows parents to take optimal measures for their children. The system also includes additional features such as providing information on mommy community sites and remote parenting support. For example, parents can exchange information with other parents and receive advice from experts. This allows parents to raise their children with peace of mind, without feeling isolated. The system allows parents to understand their child's condition in detail and take appropriate action. For example, if a child is crying, the cause can be identified and appropriate action can be taken. The system can also understand the child's daily rhythm and receive advice on maintaining their health. Furthermore, by exchanging information with other parents and receiving advice from experts, parents can raise their children with peace of mind.

[0029] A child-rearing support system according to an embodiment includes an analysis unit, an inference unit, a monitoring unit, a notification unit, and an advice unit. The analysis unit analyzes a child's voice tone or crying pattern. For example, the analysis unit analyzes the child's voice pitch, intensity, and crying rhythm in detail. The analysis unit can also analyze the child's voice tone or crying pattern using voice recognition technology. The analysis unit can also analyze the child's voice tone or crying pattern using AI. For example, the analysis unit inputs the child's voice pitch, intensity, and crying rhythm to AI and outputs the analysis results. The inference unit infers the child's health condition or mood based on the data analyzed by the analysis unit. For example, the inference unit infers the child's health condition or mood based on the data analyzed by the analysis unit. The inference unit can also infer the child's health condition or mood based on the data analyzed by the analysis unit using AI. For example, the inference unit inputs the data analyzed by the analysis unit to AI and outputs the health condition or mood. The monitoring unit monitors the child's lifestyle patterns using sensor data. For example, the monitoring unit measures the child's sleep duration, meal timings, and activity level using sensors. The monitoring unit can also monitor the child's lifestyle patterns based on the sensor data using AI. For example, the monitoring unit inputs the sensor data to AI and outputs the lifestyle patterns. The notification unit notifies the parent based on the data monitored by the monitoring unit. For example, the notification unit notifies the parent if an abnormality is detected based on the data monitored by the monitoring unit. The notification unit can also notify the parent based on the data monitored by the monitoring unit using AI. For example, the notification unit inputs the data monitored by the monitoring unit to AI and outputs the notification content. The advice unit provides personalized advice for each child. For example, the advice unit provides parenting instructions and information based on the child's age, personality, and health condition. The advice unit can also provide personalized advice for each child using AI. For example, the advice unit inputs the child's age, personality, and health condition to AI and outputs parenting instructions and information.As a result, the child-rearing support system according to the embodiment allows parents to understand their children's conditions in detail and take appropriate measures.

[0030] The child-rearing support system further includes a community unit that provides information for a mommy friend community or remote child-rearing support. The community unit provides information for a mommy friend community or remote child-rearing support. The community unit supports information exchange between parents, for example, through online forums or chat groups. The community unit can also provide advice from experts through video calls or online consultations. For example, the community unit shares information about child-rearing in online forums and provides a forum for parents to exchange opinions. The community unit can also enable parents to exchange information in real time through chat groups. The community unit can also receive advice from experts through video calls. For example, the community unit can hold video calls with experts such as child-rearing consultants, doctors, and psychologists, allowing parents to receive direct advice. This allows parents to exchange information with other parents and receive advice from experts, allowing them to raise their children with peace of mind without feeling isolated.

[0031] The analysis unit can perform a detailed analysis of the pitch or intensity of the child's voice and the rhythm of the crying. For example, the analysis unit can measure the pitch of the child's voice in terms of frequency and pitch and perform a detailed analysis. The analysis unit can also measure the intensity of the child's voice in decibels (dB) and perform a detailed analysis. The analysis unit can also analyze the rhythm of the crying in terms of periodicity and pattern changes and perform a detailed analysis. For example, the analysis unit can measure the pitch of the child's voice using frequency spectrum analysis and perform a detailed analysis of the pitch changes. The analysis unit can also measure the intensity of the child's voice using a decibel meter and perform a detailed analysis of the intensity changes. The analysis unit can also measure the rhythm of the crying using time domain analysis and perform a detailed analysis of the periodicity and pattern changes. This enables more accurate inference of the child's health and mood through a detailed analysis of the child's voice and crying. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit inputs the pitch, intensity, and rhythm of the child's voice into AI and outputs the analysis results.

[0032] The monitoring unit can measure the child's sleep duration, meal timing, and activity level using sensors. For example, the monitoring unit measures the child's sleep duration based on the amount of sleep at night and the length of naps. The monitoring unit can also measure the child's meal timing based on the number of meals and the time of day. The monitoring unit can also measure the child's activity level based on the number of steps and the amount of exercise time. For example, the monitoring unit measures the child's sleep duration using an acceleration sensor and records the amount of sleep at night and the length of naps. The monitoring unit can also measure the child's meal timing based on the number of meals and the time of day using sensors and record them. The monitoring unit can also measure the child's activity level using a pedometer or an exercise sensor and record the number of steps and the amount of exercise time. This makes it possible to monitor the child's lifestyle patterns in detail. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit inputs sensor data to AI and outputs the lifestyle patterns.

[0033] The notification unit can notify the parent if there is an abnormality. For example, the notification unit can promptly notify the parent if there is an abnormality in the child's lifestyle pattern. The notification unit can also notify the parent if there is an abnormality in the child's health condition or mood. The notification unit can also notify the parent if there is an abnormality in the child's activity level. For example, the notification unit can notify the parent if the child's sleep time exceeds the normal range. The notification unit can also notify the parent if there is a sudden change in the child's meal timing. The notification unit can also notify the parent if there is a sudden increase or decrease in the child's activity level. This makes it possible to promptly notify the parent if there is an abnormality in the child's lifestyle pattern. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit inputs data monitored by the monitoring unit into AI and outputs the notification content.

[0034] The advice unit can provide childcare instructions and information according to the child's age, personality, and health condition. The advice unit can provide childcare instructions according to, for example, the child's age. The advice unit can also provide childcare instructions according to the child's personality. The advice unit can also provide childcare instructions according to the child's health condition. For example, the advice unit can provide childcare instructions according to age, such as infant, toddler, or school-age child. The advice unit can also provide childcare instructions according to personality, such as introversion or extroversion. The advice unit can also provide childcare instructions according to health conditions, such as body temperature, heart rate, and respiratory rate. This makes it possible to provide personalized advice for each child. Some or all of the above-described processing in the advice unit can be performed, for example, using AI, or can be performed without using AI. For example, the advice unit inputs the child's age, personality, and health condition to AI, and outputs childcare instructions and information.

[0035] The community unit can enable parents to exchange information with other parents and provide advice from experts. The community unit supports information exchange with other parents through chats and forums, for example. The community unit can also provide advice from experts through video calls and online consultations. For example, the community unit provides a forum for parents to exchange information in real time through chats. The community unit can also share parenting information and exchange opinions through forums. Furthermore, the community unit can receive advice from experts through video calls. For example, the community unit can hold video calls with experts such as parenting consultants, doctors, and psychologists, allowing parents to receive direct advice. This allows parents to exchange information with other parents and receive advice from experts, allowing them to raise their children with peace of mind without feeling isolated. Some or all of the above-described processing in the community unit may be performed using, or without, AI. For example, the community unit inputs parents' questions and consultation details into AI and outputs appropriate information and advice.

[0036] When analyzing the pitch, intensity, and rhythm of a child's voice, the analysis unit can detect abnormalities by comparing the current data with past data. For example, if the pitch of the voice changes suddenly compared to past data, the analysis unit has the AI ​​detect the change as an abnormality. Furthermore, if the rhythm of the crying differs significantly from past data, the analysis unit can also detect the change in rhythm as an abnormality. Furthermore, if the intensity of the voice becomes extremely strong compared to past data, the analysis unit can also detect the change in intensity as an abnormality. For example, the analysis unit inputs past data and current data into the AI ​​to detect abnormalities. This allows for rapid detection of abnormalities by comparing the current data with past data. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0037] The analysis unit can improve accuracy by removing environmental sounds when analyzing the tone of a child's voice or crying pattern. For example, the analysis unit removes background television sounds and analyzes only the child's voice. The analysis unit can also filter out external traffic sounds to accurately analyze the child's crying pattern. Furthermore, the analysis unit can remove household noise to accurately analyze the tone of a child's voice. For example, the analysis unit applies filtering technology that removes environmental sounds to AI to analyze the tone of a child's voice or crying pattern. In this way, removing environmental sounds improves the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0038] When analyzing a child's vocal tone and crying pattern, the analysis unit can adjust the analysis method depending on the time of day and the situation. For example, the analysis unit may determine that crying at night is more urgent than usual and adjust the analysis method accordingly. The analysis unit may also determine that the vocal tone during mealtime is more relaxed than usual and adjust the analysis method accordingly. Furthermore, the analysis unit may determine that crying during playtime is an excited state and adjust the analysis method accordingly. For example, the analysis unit inputs the time of day and situation into the AI ​​and applies the analysis method accordingly. This allows for more appropriate analysis by adjusting the analysis method depending on the time of day and situation. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI.

[0039] When analyzing a child's voice tone or crying pattern, the analysis unit can detect abnormalities by comparing it with data from other children. For example, if the pitch of the child's voice is abnormally high compared to data from other children, the analysis unit can use the AI ​​to detect the abnormality. The analysis unit can also detect if the rhythm of the child's crying is abnormally fast compared to data from other children. The analysis unit can also detect if the intensity of the child's voice is abnormally strong compared to data from other children. For example, the analysis unit inputs data from other children and the current data into the AI ​​to detect abnormalities. This allows for rapid detection of abnormalities by comparing it with data from other children. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0040] When analyzing the tone of a child's voice and crying pattern, the analysis unit can adjust the analysis results by taking into account the parent's voice and reaction. For example, if the parent speaks to the child in a calm voice, the analysis unit analyzes the child's crying pattern in a way that softens it. The analysis unit can also analyze the child's crying by taking into account the tendency for the child's voice tone to become higher when the parent is excited. Furthermore, the analysis unit can analyze the child by taking into account the tendency for the child's crying pattern to persist when the parent is unresponsive. For example, the analysis unit inputs the parent's voice and reaction into the AI ​​and adjusts the analysis results based on the input. In this way, by taking the parent's voice and reaction into account, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0041] When analyzing the tone of a child's voice or the pattern of their crying, the analysis unit can notify the parent of the analysis results in real time. For example, the analysis unit notifies the parent in real time the moment the child starts crying. The analysis unit can also notify the parent in real time if there is a sudden change in the tone of the child's voice. Furthermore, the analysis unit can also notify the parent in real time if the rhythm of the child's crying becomes abnormal. For example, the analysis unit inputs the tone of a child's voice or the pattern of their crying into AI and notifies the parent of the analysis results in real time. This allows the parent to respond quickly by notifying the analysis results in real time. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or without using AI.

[0042] The inference unit can detect abnormalities by comparing past data when inferring the child's health condition and mood. For example, if the health condition of the child is suddenly worsened by comparing the data with past data, the AI ​​in the inference unit detects the abnormality. The inference unit can also detect abnormalities if the mood is suddenly changed by comparing the data with past data. Furthermore, the inference unit can also detect abnormalities if the activity level is suddenly reduced by comparing the data with past data. For example, the inference unit inputs past data and current data into the AI ​​and detects abnormalities. This allows abnormalities to be quickly detected by comparing the data with past data. Some or all of the above-described processing in the inference unit may be performed, for example, using AI, or may be performed without using AI.

[0043] The estimation unit can take environmental factors into account when estimating the child's health condition and mood. For example, the estimation unit makes an estimation taking into account the possibility that a high room temperature will affect the child's health condition. The estimation unit can also make an estimation taking into account the possibility that a low humidity will affect the child's mood. Furthermore, the estimation unit can make an estimation taking into account the possibility that a loud external noise will affect the child's stress level. For example, the estimation unit inputs environmental factors (temperature, humidity, noise, etc.) into the AI ​​and estimates the health condition and mood based on the environmental factors. In this way, taking environmental factors into account enables more accurate estimation. Some or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI, or may be performed without using AI.

[0044] When inferring the child's health condition or mood, the inference unit can adjust the inference result by reflecting parental feedback. For example, if the parent provides feedback about the child's health condition, the inference unit can adjust the inference result by reflecting that information. Furthermore, if the parent provides feedback about the child's mood, the inference unit can adjust the inference result by reflecting that information. Furthermore, if the parent provides feedback about the child's activity level, the inference unit can adjust the inference result by reflecting that information. For example, the inference unit inputs parental feedback into the AI ​​and adjusts the inference result based on that feedback. In this way, by reflecting parental feedback, more accurate inference results can be provided. Some or all of the above-described processing in the inference unit may be performed, for example, using AI, or may be performed without using AI.

[0045] The inference unit can detect abnormalities by comparing the data of other children when inferring a child's health condition or mood. For example, if the health condition of the child is abnormally deteriorating when compared with the data of other children, the AI ​​detects the abnormality. The inference unit can also detect abnormal changes in mood when compared with the data of other children. Furthermore, the inference unit can also detect abnormalities when the activity level of the child is abnormally low when compared with the data of other children. For example, the inference unit inputs the data of other children and the current data into the AI ​​and detects abnormalities. This allows for rapid detection of abnormalities by comparing with the data of other children. Some or all of the above-described processing in the inference unit may be performed, for example, using AI, or may be performed without using AI.

[0046] When inferring the child's health condition or mood, the inference unit can adjust the inference result by taking into account the parent's voice and reaction. For example, if the parent speaks in a calm voice, the inference unit infers that the child's health condition is stable. The inference unit can also make inference by taking into account the child's tendency for mood to become unstable when the parent is excited. Furthermore, the inference unit can make inference by taking into account the child's tendency for activity level to decrease when the parent is unresponsive. For example, the inference unit inputs the parent's voice and reaction into the AI ​​and adjusts the inference result based on the input. In this way, by taking the parent's voice and reaction into consideration, more accurate inference results can be provided. Some or all of the above-mentioned processing in the inference unit may be performed, for example, using AI or without using AI.

[0047] When the inference unit infers the child's health condition or mood, it can notify the parent of the inference result in real time. For example, if the child's health condition suddenly deteriorates, the inference unit notifies the parent in real time. The inference unit can also notify the parent in real time if the child's mood suddenly changes. Furthermore, the inference unit can also notify the parent in real time if the child's activity level suddenly drops. For example, the inference unit inputs the child's health condition or mood into AI and notifies the parent of the inference result in real time. In this way, notifying the inference result in real time allows the parent to respond quickly. Some or all of the above-mentioned processing in the inference unit may be performed, for example, using AI or without using AI.

[0048] When monitoring a child's sleep time, meal timing, and activity level, the monitoring unit can detect abnormalities by comparing the data with past data. For example, if the sleep time suddenly shortens compared to past data, the AI ​​in the monitoring unit detects the abnormality. The monitoring unit can also detect abnormalities if the meal timing significantly deviates compared to past data. Furthermore, the monitoring unit can also detect abnormalities if the activity level suddenly increases compared to past data. For example, the monitoring unit inputs past data and current data into the AI ​​to detect abnormalities. This allows abnormalities to be quickly detected by comparing the data with past data. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI.

[0049] The monitoring unit can take environmental factors into account when monitoring a child's lifestyle patterns. For example, when room temperature is high, the monitoring unit monitors the child's activity level, taking into account the possibility that this may affect the child's sleep pattern. The monitoring unit can also monitor when humidity is low, taking into account the possibility that this may affect the child's sleep pattern. Furthermore, the monitoring unit can monitor when external noise is loud, taking into account the possibility that this may affect the child's meal timing. For example, the monitoring unit inputs environmental factors (temperature, humidity, noise, etc.) into AI and monitors the lifestyle patterns based on these. By taking environmental factors into account, more accurate monitoring is possible. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI.

[0050] When monitoring a child's lifestyle patterns, the monitoring unit can adjust the monitoring results by reflecting parental feedback. For example, if a parent provides feedback about the child's sleep patterns, the monitoring unit can adjust the monitoring results by reflecting that information. Furthermore, if a parent provides feedback about the child's meal timings, the monitoring unit can also adjust the monitoring results by reflecting that information. Furthermore, if a parent provides feedback about the child's activity level, the monitoring unit can also adjust the monitoring results by reflecting that information. For example, the monitoring unit inputs parental feedback into AI and adjusts the monitoring results based on that feedback. In this way, by reflecting parental feedback, more accurate monitoring results can be provided. Some or all of the above-described processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI.

[0051] When monitoring a child's lifestyle patterns, the monitoring unit can detect abnormalities by comparing them with data from other children. For example, if the child's sleep time is abnormally short compared to data from other children, the monitoring unit uses AI to detect the abnormality. The monitoring unit can also detect abnormalities if the child's meal timing is abnormally off compared to data from other children. The monitoring unit can also detect abnormalities if the child's activity level is abnormally high compared to data from other children. For example, the monitoring unit inputs data from other children and current data into the AI ​​to detect abnormalities. This allows for rapid detection of abnormalities by comparing the data from other children. Some or all of the above-described processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI.

[0052] When monitoring a child's daily life pattern, the monitoring unit can adjust the monitoring results by taking into account the parent's voice and reactions. For example, if the parent speaks to the child in a calm voice, the monitoring unit may monitor that the child's sleep pattern is stable. The monitoring unit may also monitor that the child's meal timing tends to become irregular when the parent is excited. Furthermore, the monitoring unit may monitor that the child's activity level tends to decrease when the parent is unresponsive. For example, the monitoring unit inputs the parent's voice and reactions into AI and adjusts the monitoring results based on the input. By taking the parent's voice and reactions into account, more accurate monitoring results can be provided. Some or all of the above-described processing in the monitoring unit may be performed, for example, using AI or without AI.

[0053] When monitoring a child's lifestyle pattern, the monitoring unit can notify the parent of the monitoring results in real time. For example, if the child's sleep time suddenly shortens, the monitoring unit notifies the parent in real time. The monitoring unit can also notify the parent in real time if the child's meal timings change significantly. Furthermore, the monitoring unit can also notify the parent in real time if the child's activity level suddenly increases. For example, the monitoring unit inputs the child's lifestyle pattern into AI and notifies the parent of the monitoring results in real time. This allows the parent to respond quickly by notifying the monitoring results in real time. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI.

[0054] When notifying a parent of an abnormality, the notification unit can evaluate the severity of the abnormality by comparing it with past data. For example, if the health condition suddenly deteriorates by comparing it with past data, the notification unit evaluates the severity and notifies the parent. The notification unit can also evaluate the severity and notify the parent of a sudden change in mood by comparing it with past data. Furthermore, the notification unit can evaluate the severity and notify the parent of a sudden drop in activity level by comparing it with past data. For example, the notification unit inputs past data and current data into AI and evaluates the severity of the abnormality. This allows the severity of the abnormality to be quickly evaluated by comparing it with past data. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or without AI.

[0055] The notification unit can adjust the timing of notification when notifying a parent if an abnormality is detected. For example, if the child's health condition suddenly deteriorates, the notification unit immediately notifies the parent. The notification unit can also notify the parent at an appropriate time if the child's mood suddenly changes. Furthermore, the notification unit can also notify the parent at an appropriate time if the child's activity level suddenly drops. For example, the notification unit inputs the type and severity of the abnormality into the AI ​​and adjusts the timing of notification based on that. In this way, adjusting the timing of notification allows the parent to respond at an appropriate time. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI.

[0056] When notifying a parent of an abnormality, the notification unit can improve the content of the notification by reflecting the parent's feedback. For example, the notification unit adjusts the content of the notification based on the feedback provided by the parent and reflects the adjustment in the next notification. The notification unit can also adjust the timing of the notification based on the feedback provided by the parent and reflect the adjustment in the next notification. Furthermore, the notification unit can improve the notification method based on the feedback provided by the parent and reflect the adjustment in the next notification. For example, the notification unit inputs the parent's feedback into AI and improves the notification content based on the feedback. In this way, more appropriate notification content can be provided by reflecting the parent's feedback. Some or all of the above-described processing in the notification unit may be performed, for example, using AI or without using AI.

[0057] When notifying a parent of an abnormality, the notification unit can evaluate the severity of the abnormality by comparing it with data from other children. For example, if a child's health condition is abnormally deteriorating compared with data from other children, the notification unit evaluates the severity and notifies the parent. The notification unit can also evaluate the severity and notify the parent of an abnormal change in mood compared with data from other children. Furthermore, the notification unit can evaluate the severity and notify the parent of an abnormal decrease in activity level compared with data from other children. For example, the notification unit inputs the data from other children and the current data into AI and evaluates the severity of the abnormality. This allows the severity of the abnormality to be quickly evaluated by comparing it with data from other children. Some or all of the above-described processing in the notification unit may be performed, for example, using AI or without AI.

[0058] When notifying a parent of an abnormality, the notification unit can adjust the content of the notification taking into account the parent's voice and reaction. For example, if the parent speaks in a calm voice, the notification unit adjusts the content of the notification to be more toned down. Furthermore, if the parent is excited, the notification unit can adjust the content of the notification to be more emphasizing. Furthermore, if the parent is unresponsive, the notification unit can adjust the content of the notification to be more detailed. For example, the notification unit inputs the parent's voice and reaction into AI and adjusts the content of the notification based on that. In this way, more appropriate notification content can be provided by taking the parent's voice and reaction into consideration. Some or all of the above-described processing in the notification unit may be performed, for example, using AI, or may be performed without using AI.

[0059] When notifying a parent of an abnormality, the notification unit can provide the notification content to the parent in real time. For example, if the child's health condition suddenly deteriorates, the notification unit notifies the parent in real time. The notification unit can also notify the parent in real time if the child's mood suddenly changes. Furthermore, the notification unit can also notify the parent in real time if the child's activity level suddenly drops. For example, the notification unit inputs the child's health condition and mood into AI and provides the notification content to the parent in real time. By providing the notification content in real time, the parent can respond quickly. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or without using AI.

[0060] When providing parenting instructions and information according to the child's age, personality, and health condition, the advice unit can select optimal advice by comparing with past data. For example, the advice unit can provide optimal parenting instructions according to the child's age by comparing with past data. The advice unit can also provide optimal parenting instructions according to the child's personality by comparing with past data. Furthermore, the advice unit can provide optimal parenting instructions according to the child's health condition by comparing with past data. For example, the advice unit inputs past data and current data into AI and selects optimal advice. This makes it possible to provide optimal advice by comparing with past data. Some or all of the above-described processing in the advice unit may be performed, for example, using AI, or may be performed without using AI.

[0061] The advice unit can improve the content of the advice by reflecting parental feedback when providing parenting instructions and information according to the child's age, personality, and health condition. For example, the advice unit adjusts the parenting instructions based on the feedback provided by the parent and reflects the adjustment in the next piece of advice. The advice unit can also adjust the parenting information based on the feedback provided by the parent and reflect the adjustment in the next piece of advice. Furthermore, the advice unit can improve parenting methods based on the feedback provided by the parent and reflect the adjustment in the next piece of advice. For example, the advice unit inputs the parent's feedback into AI and improves the content of the advice based on the feedback. In this way, more appropriate advice can be provided by reflecting the parent's feedback. Some or all of the above-described processing in the advice unit may be performed, for example, using AI or without AI.

[0062] The advice unit can adjust the timing of the advice when providing parenting instructions and information according to the child's age, personality, and health condition. The advice unit provides parenting instructions at an appropriate time according to, for example, the child's age. The advice unit can also provide parenting information at an appropriate time according to the child's personality. Furthermore, the advice unit can provide parenting methods at an appropriate time according to the child's health condition. For example, the advice unit inputs the child's age, personality, and health condition into the AI ​​and adjusts the timing of the advice based on that. In this way, adjusting the timing of the advice allows parents to respond at an appropriate time. Some or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI.

[0063] When providing parenting instructions and information according to a child's age, personality, and health condition, the advice unit can select optimal advice by comparing with data of other children. For example, the advice unit can provide optimal parenting instructions according to age by comparing with data of other children. The advice unit can also provide optimal parenting instructions according to personality by comparing with data of other children. Furthermore, the advice unit can provide optimal parenting instructions according to health condition by comparing with data of other children. For example, the advice unit inputs data of other children and current data into AI and selects optimal advice. This makes it possible to provide optimal advice by comparing with data of other children. Some or all of the above-described processing in the advice unit may be performed, for example, using AI, or may be performed without using AI.

[0064] The advice unit can adjust the content of the advice taking into account the parent's voice and reaction when providing parenting instructions and information according to the child's age, personality, and health condition. For example, if the parent speaks in a calm voice, the advice unit adjusts the parenting instructions to be more relaxed. Furthermore, if the parent is excited, the advice unit can adjust the parenting instructions to be more emphasized. Furthermore, if the parent is unresponsive, the advice unit can adjust the parenting instructions to be more detailed. For example, the advice unit inputs the parent's voice and reaction into AI and adjusts the content of the advice based on that. In this way, more appropriate advice can be provided by taking the parent's voice and reaction into consideration. Some or all of the above-mentioned processing in the advice unit may be performed, for example, using AI or without using AI.

[0065] The advice unit can provide advice to parents in real time when providing parenting instructions and information according to the child's age, personality, and health condition. The advice unit provides parenting instructions in real time according to, for example, the child's age. The advice unit can also provide parenting information in real time according to the child's personality. The advice unit can also provide parenting methods in real time according to the child's health condition. For example, the advice unit inputs the child's age, personality, and health condition into AI, and provides advice in real time based on the input. This allows parents to respond quickly by providing advice in real time. Some or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI.

[0066] The community unit can select optimal information by comparing with past data when exchanging information with other parents or providing advice from experts. For example, the community unit can provide optimal child-rearing information by comparing with past data. The community unit can also provide optimal health care information by comparing with past data. Furthermore, the community unit can provide optimal education information by comparing with past data. For example, the community unit inputs past data and current data into AI and selects optimal information. This makes it possible to provide optimal information by comparing with past data. Some or all of the above-mentioned processing in the community unit may be performed, for example, using AI, or may be performed without using AI.

[0067] The community unit can improve the content of information provided by reflecting parental feedback when exchanging information with other parents or providing advice from experts. For example, the community unit adjusts the content of information provided based on parental feedback and reflects it in the next information provided. The community unit can also adjust the timing of information provision based on parental feedback and reflect it in the next information provided. Furthermore, the community unit can improve the method of information provision based on parental feedback and reflect it in the next information provided. For example, the community unit inputs parental feedback into AI and improves the content of information provided based on that feedback. In this way, more appropriate information can be provided by reflecting parental feedback. Some or all of the above-mentioned processing in the community unit may be performed, for example, using AI or without AI.

[0068] The community unit can adjust the timing of information provision when exchanging information with other parents or providing advice from experts. For example, the community unit provides optimal childcare information at the time parents need it. The community unit can also provide optimal health management information at the time parents need it. Furthermore, the community unit can provide optimal educational information at the time parents need it. For example, the community unit inputs parents' needs into AI and adjusts the timing of information provision based on that. In this way, adjusting the timing of information provision allows parents to receive information at an appropriate time. Some or all of the above-mentioned processing in the community unit may be performed, for example, using AI, or may be performed without using AI.

[0069] The community unit can select optimal information by comparing with data of other children when exchanging information with other parents or providing advice from experts. For example, the community unit can provide optimal childcare information by comparing with data of other children. The community unit can also provide optimal health care information by comparing with data of other children. Furthermore, the community unit can provide optimal education information by comparing with data of other children. For example, the community unit inputs data of other children and current data into AI and selects optimal information. This makes it possible to provide optimal information by comparing with data of other children. Some or all of the above-mentioned processing in the community unit may be performed, for example, using AI, or may be performed without using AI.

[0070] The community unit can adjust the content of information provided by taking into account the parent's voice and reactions when exchanging information with other parents or providing advice from experts. For example, if the parent speaks in a calm voice, the community unit adjusts the content of information provided to be more toned down. Furthermore, if the parent is excited, the community unit can adjust the content of information provided to be more emphasizing. Furthermore, if the parent is unresponsive, the community unit can adjust the content of information provided to be more detailed. For example, the community unit inputs the parent's voice and reactions into AI and adjusts the content of information provided based on the input. In this way, more appropriate information can be provided by taking the parent's voice and reactions into consideration. Some or all of the above-described processing in the community unit may be performed, for example, using AI or without using AI.

[0071] The community unit can provide information content to parents in real time when exchanging information with other parents or providing advice from experts. For example, the community unit can provide optimal childcare information in real time when parents need it. The community unit can also provide optimal health management information in real time when parents need it. Furthermore, the community unit can provide optimal education information in real time when parents need it. For example, the community unit inputs parents' needs into AI and provides information content in real time based on that. This allows parents to respond quickly by providing information content in real time. Some or all of the above-mentioned processing in the community unit may be performed, for example, using AI, or may be performed without using AI.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The analysis unit can perform detailed analysis of the frequency components of the voice when analyzing the tone of the child's voice and the pattern of the crying. For example, the analysis unit can analyze the frequency components of the child's voice using spectral analysis to detect changes in specific frequency bands. The analysis unit can also analyze the frequency components of the child's crying in the time domain to analyze the duration and fluctuations of the crying in detail. Furthermore, the analysis unit can analyze the frequency components of the child's voice in real time and immediately notify if there is an abnormality. This allows for more accurate inference of the child's health condition and mood by analyzing the frequency components of the child's voice in detail.

[0074] The monitoring unit can measure the child's body temperature and heart rate when monitoring the child's lifestyle patterns. For example, the monitoring unit can measure the child's body temperature using a non-contact temperature sensor and notify the parent if there is an abnormality. The monitoring unit can also measure the child's heart rate using a wearable device and notify the parent if there is an abnormality. Furthermore, the monitoring unit can monitor the child's body temperature and heart rate in real time and immediately notify the parent if there is an abnormality. This makes it possible to understand the child's health condition in more detail by monitoring the child's body temperature and heart rate.

[0075] The notification unit can customize the content of the notification when notifying the parent of an abnormality. For example, the notification unit can select the notification format according to the parent's preferences. The notification unit can also adjust the timing of the notification according to the parent's schedule. Furthermore, the notification unit can improve the notification content based on the parent's feedback and reflect it in the next notification. This allows the parent to respond more appropriately by customizing the notification content.

[0076] The advice unit can take into account the parent's stress level when providing parenting instructions and information tailored to the child's age, personality, and health condition. For example, the advice unit can provide simple, easy-to-follow parenting instructions when the parent is at a high stress level. The advice unit can also provide detailed, specific parenting instructions when the parent is at a low stress level. Furthermore, the advice unit can monitor the parent's stress level in real time and adjust the parenting instructions accordingly. This allows the provision of more appropriate parenting instructions by taking the parent's stress level into account.

[0077] When exchanging information with other parents or receiving advice from experts, the community department can choose the format of information provision according to parents' needs. For example, if parents prefer information in text format, the community department can provide information through chat or forums. If parents prefer information in video format, the community department can also provide information through video calls or webinars. Furthermore, the community department can improve the format of information provision based on parents' feedback and reflect it in the next information provision. This makes it possible to provide information that meets parents' needs.

[0078] The processing flow of the first embodiment will be briefly explained below.

[0079] Step 1: The analysis unit analyzes the child's vocal tone or crying pattern. The analysis unit analyzes the pitch and intensity of the child's voice and the rhythm of the crying in detail, and performs the analysis using voice recognition technology and AI. Step 2: The inference unit infers the child's health condition or mood based on the data analyzed by the analysis unit. The inference unit inputs the analyzed data using AI and outputs the health condition or mood. Step 3: The monitoring unit uses sensor data to monitor the child's lifestyle patterns. The monitoring unit uses sensors to measure the child's sleep time, meal timings, and activity level, and uses AI to output the lifestyle patterns. Step 4: The notification unit notifies the parent based on the data monitored by the monitoring unit. If there is an abnormality based on the monitoring data, the notification unit notifies the parent and outputs the notification content using AI. Step 5: The advice unit provides personalized advice for each child. The advice unit provides parenting instructions and information based on the child's age, personality, and health condition, and outputs personalized advice using AI.

[0080] (Example 2) A system according to an embodiment of the present invention utilizes voice recognition and sensor data to understand a child's condition and suggest appropriate responses to parents. This system analyzes a child's voice tone and crying patterns to infer the child's health and mood. It also uses sensor data to monitor the child's daily life and notify parents if any abnormalities are detected. It also provides personalized advice for each child to help parents take appropriate measures. For example, it uses voice recognition technology to analyze a child's voice tone and crying patterns. It then analyzes the child's voice pitch, intensity, and crying rhythm in detail to infer the child's health and mood. Next, it uses sensor data to monitor the child's daily life. For example, it measures the child's sleep time, meal timing, and activity level using sensors, and notifies parents if any abnormalities are detected. This allows parents to understand their child's daily rhythm and take appropriate measures. It also provides personalized advice for each child. For example, it provides parenting instructions and information tailored to the child's age, personality, and health. This allows parents to take optimal measures for their children. The system also includes additional features such as providing information on mommy community sites and remote parenting support. For example, parents can exchange information with other parents and receive advice from experts. This allows parents to raise their children with peace of mind, without feeling isolated. The system allows parents to understand their child's condition in detail and take appropriate action. For example, if a child is crying, the cause can be identified and appropriate action can be taken. The system can also understand the child's daily rhythm and receive advice on maintaining their health. Furthermore, by exchanging information with other parents and receiving advice from experts, parents can raise their children with peace of mind.

[0081] A child-rearing support system according to an embodiment includes an analysis unit, an inference unit, a monitoring unit, a notification unit, and an advice unit. The analysis unit analyzes a child's voice tone or crying pattern. For example, the analysis unit analyzes the child's voice pitch, intensity, and crying rhythm in detail. The analysis unit can also analyze the child's voice tone or crying pattern using voice recognition technology. The analysis unit can also analyze the child's voice tone or crying pattern using AI. For example, the analysis unit inputs the child's voice pitch, intensity, and crying rhythm to AI and outputs the analysis results. The inference unit infers the child's health condition or mood based on the data analyzed by the analysis unit. For example, the inference unit infers the child's health condition or mood based on the data analyzed by the analysis unit. The inference unit can also infer the child's health condition or mood based on the data analyzed by the analysis unit using AI. For example, the inference unit inputs the data analyzed by the analysis unit to AI and outputs the health condition or mood. The monitoring unit monitors the child's lifestyle patterns using sensor data. For example, the monitoring unit measures the child's sleep duration, meal timings, and activity level using sensors. The monitoring unit can also monitor the child's lifestyle patterns based on the sensor data using AI. For example, the monitoring unit inputs the sensor data to AI and outputs the lifestyle patterns. The notification unit notifies the parent based on the data monitored by the monitoring unit. For example, the notification unit notifies the parent if an abnormality is detected based on the data monitored by the monitoring unit. The notification unit can also notify the parent based on the data monitored by the monitoring unit using AI. For example, the notification unit inputs the data monitored by the monitoring unit to AI and outputs the notification content. The advice unit provides personalized advice for each child. For example, the advice unit provides parenting instructions and information based on the child's age, personality, and health condition. The advice unit can also provide personalized advice for each child using AI. For example, the advice unit inputs the child's age, personality, and health condition to AI and outputs parenting instructions and information.As a result, the child-rearing support system according to the embodiment allows parents to understand their children's conditions in detail and take appropriate measures.

[0082] The child-rearing support system further includes a community unit that provides information for a mommy friend community or remote child-rearing support. The community unit provides information for a mommy friend community or remote child-rearing support. The community unit supports information exchange between parents, for example, through online forums or chat groups. The community unit can also provide advice from experts through video calls or online consultations. For example, the community unit shares information about child-rearing in online forums and provides a forum for parents to exchange opinions. The community unit can also enable parents to exchange information in real time through chat groups. The community unit can also receive advice from experts through video calls. For example, the community unit can hold video calls with experts such as child-rearing consultants, doctors, and psychologists, allowing parents to receive direct advice. This allows parents to exchange information with other parents and receive advice from experts, allowing them to raise their children with peace of mind without feeling isolated.

[0083] The analysis unit can perform a detailed analysis of the pitch or intensity of the child's voice and the rhythm of the crying. For example, the analysis unit can measure the pitch of the child's voice in terms of frequency and pitch and perform a detailed analysis. The analysis unit can also measure the intensity of the child's voice in decibels (dB) and perform a detailed analysis. The analysis unit can also analyze the rhythm of the crying in terms of periodicity and pattern changes and perform a detailed analysis. For example, the analysis unit can measure the pitch of the child's voice using frequency spectrum analysis and perform a detailed analysis of the pitch changes. The analysis unit can also measure the intensity of the child's voice using a decibel meter and perform a detailed analysis of the intensity changes. The analysis unit can also measure the rhythm of the crying using time domain analysis and perform a detailed analysis of the periodicity and pattern changes. This enables more accurate inference of the child's health and mood through a detailed analysis of the child's voice and crying. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit inputs the pitch, intensity, and rhythm of the child's voice into AI and outputs the analysis results.

[0084] The monitoring unit can measure the child's sleep duration, meal timing, and activity level using sensors. For example, the monitoring unit measures the child's sleep duration based on the amount of sleep at night and the length of naps. The monitoring unit can also measure the child's meal timing based on the number of meals and the time of day. The monitoring unit can also measure the child's activity level based on the number of steps and the amount of exercise time. For example, the monitoring unit measures the child's sleep duration using an acceleration sensor and records the amount of sleep at night and the length of naps. The monitoring unit can also measure the child's meal timing based on the number of meals and the time of day using sensors and record them. The monitoring unit can also measure the child's activity level using a pedometer or an exercise sensor and record the number of steps and the amount of exercise time. This makes it possible to monitor the child's lifestyle patterns in detail. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit inputs sensor data to AI and outputs the lifestyle patterns.

[0085] The notification unit can notify the parent if there is an abnormality. For example, the notification unit can promptly notify the parent if there is an abnormality in the child's lifestyle pattern. The notification unit can also notify the parent if there is an abnormality in the child's health condition or mood. The notification unit can also notify the parent if there is an abnormality in the child's activity level. For example, the notification unit can notify the parent if the child's sleep time exceeds the normal range. The notification unit can also notify the parent if there is a sudden change in the child's meal timing. The notification unit can also notify the parent if there is a sudden increase or decrease in the child's activity level. This makes it possible to promptly notify the parent if there is an abnormality in the child's lifestyle pattern. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit inputs data monitored by the monitoring unit into AI and outputs the notification content.

[0086] The advice unit can provide childcare instructions and information according to the child's age, personality, and health condition. The advice unit can provide childcare instructions according to, for example, the child's age. The advice unit can also provide childcare instructions according to the child's personality. The advice unit can also provide childcare instructions according to the child's health condition. For example, the advice unit can provide childcare instructions according to age, such as infant, toddler, or school-age child. The advice unit can also provide childcare instructions according to personality, such as introversion or extroversion. The advice unit can also provide childcare instructions according to health conditions, such as body temperature, heart rate, and respiratory rate. This makes it possible to provide personalized advice for each child. Some or all of the above-described processing in the advice unit can be performed, for example, using AI, or can be performed without using AI. For example, the advice unit inputs the child's age, personality, and health condition to AI, and outputs childcare instructions and information.

[0087] The community unit can enable parents to exchange information with other parents and provide advice from experts. The community unit supports information exchange with other parents through chats and forums, for example. The community unit can also provide advice from experts through video calls and online consultations. For example, the community unit provides a forum for parents to exchange information in real time through chats. The community unit can also share parenting information and exchange opinions through forums. Furthermore, the community unit can receive advice from experts through video calls. For example, the community unit can hold video calls with experts such as parenting consultants, doctors, and psychologists, allowing parents to receive direct advice. This allows parents to exchange information with other parents and receive advice from experts, allowing them to raise their children with peace of mind without feeling isolated. Some or all of the above-described processing in the community unit may be performed using, or without, AI. For example, the community unit inputs parents' questions and consultation details into AI and outputs appropriate information and advice.

[0088] The analysis unit can estimate a child's emotions and analyze the tone of voice and crying patterns based on the estimated emotions. For example, when a child is angry, the tone of voice tends to be higher, so the analysis unit can identify the emotion of anger by analyzing the tone. Furthermore, when a child is sad, the rhythm of the crying becomes slow and intermittent, so the analysis unit can identify the emotion of sadness by analyzing the rhythm. Furthermore, when a child is excited, the intensity of the voice increases and the crying becomes higher, so the analysis unit can identify the emotion of excitement by analyzing the intensity and pitch. For example, the analysis unit inputs the child's tone of voice and crying patterns into the AI, estimates the emotion, and outputs the analysis result based on the emotion. This enables more accurate analysis by analyzing the tone of voice and crying patterns based on the child's emotions. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0089] When analyzing the pitch, intensity, and rhythm of a child's voice, the analysis unit can detect abnormalities by comparing the current data with past data. For example, if the pitch of the voice changes suddenly compared to past data, the analysis unit has the AI ​​detect the change as an abnormality. Furthermore, if the rhythm of the crying differs significantly from past data, the analysis unit can also detect the change in rhythm as an abnormality. Furthermore, if the intensity of the voice becomes extremely strong compared to past data, the analysis unit can also detect the change in intensity as an abnormality. For example, the analysis unit inputs past data and current data into the AI ​​to detect abnormalities. This allows for rapid detection of abnormalities by comparing the current data with past data. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0090] The analysis unit can improve accuracy by removing environmental sounds when analyzing the tone of a child's voice or crying pattern. For example, the analysis unit removes background television sounds and analyzes only the child's voice. The analysis unit can also filter out external traffic sounds to accurately analyze the child's crying pattern. Furthermore, the analysis unit can remove household noise to accurately analyze the tone of a child's voice. For example, the analysis unit applies filtering technology that removes environmental sounds to AI to analyze the tone of a child's voice or crying pattern. In this way, removing environmental sounds improves the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0091] When analyzing a child's vocal tone and crying pattern, the analysis unit can adjust the analysis method depending on the time of day and the situation. For example, the analysis unit may determine that crying at night is more urgent than usual and adjust the analysis method accordingly. The analysis unit may also determine that the vocal tone during mealtime is more relaxed than usual and adjust the analysis method accordingly. Furthermore, the analysis unit may determine that crying during playtime is an excited state and adjust the analysis method accordingly. For example, the analysis unit inputs the time of day and situation into the AI ​​and applies the analysis method accordingly. This allows for more appropriate analysis by adjusting the analysis method depending on the time of day and situation. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI.

[0092] The analysis unit can estimate the child's emotions and provide the parent with an analysis result based on the estimated child's emotions. For example, if the child is angry, the analysis unit can notify the parent of the child's emotions and suggest an appropriate response. Furthermore, if the child is sad, the analysis unit can notify the parent of the child's emotions and suggest ways to comfort the child. Furthermore, if the child is excited, the analysis unit can notify the parent of the child's emotions and suggest ways to calm the child. For example, the analysis unit can have AI estimate the child's emotions and provide the parent with an analysis result based on the emotions. This allows the parent to take an appropriate response by providing the parent with the analysis result based on the child's emotions. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or without AI.

[0093] When analyzing a child's voice tone or crying pattern, the analysis unit can detect abnormalities by comparing it with data from other children. For example, if the pitch of the child's voice is abnormally high compared to data from other children, the analysis unit can use the AI ​​to detect the abnormality. The analysis unit can also detect if the rhythm of the child's crying is abnormally fast compared to data from other children. The analysis unit can also detect if the intensity of the child's voice is abnormally strong compared to data from other children. For example, the analysis unit inputs data from other children and the current data into the AI ​​to detect abnormalities. This allows for rapid detection of abnormalities by comparing it with data from other children. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0094] When analyzing the tone of a child's voice and crying pattern, the analysis unit can adjust the analysis results by taking into account the parent's voice and reaction. For example, if the parent speaks to the child in a calm voice, the analysis unit analyzes the child's crying pattern in a way that softens it. The analysis unit can also analyze the child's crying by taking into account the tendency for the child's voice tone to become higher when the parent is excited. Furthermore, the analysis unit can analyze the child by taking into account the tendency for the child's crying pattern to persist when the parent is unresponsive. For example, the analysis unit inputs the parent's voice and reaction into the AI ​​and adjusts the analysis results based on the input. In this way, by taking the parent's voice and reaction into account, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0095] When analyzing the tone of a child's voice or the pattern of their crying, the analysis unit can notify the parent of the analysis results in real time. For example, the analysis unit notifies the parent in real time the moment the child starts crying. The analysis unit can also notify the parent in real time if there is a sudden change in the tone of the child's voice. Furthermore, the analysis unit can also notify the parent in real time if the rhythm of the child's crying becomes abnormal. For example, the analysis unit inputs the tone of a child's voice or the pattern of their crying into AI and notifies the parent of the analysis results in real time. This allows the parent to respond quickly by notifying the analysis results in real time. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or without using AI.

[0096] The estimation unit can estimate the child's emotions and estimate the child's health condition and mood based on the estimated emotions. For example, if the child is angry, the estimation unit can estimate the stress level based on the emotion. If the child is sad, the estimation unit can also estimate the child's fatigue level based on the emotion. Furthermore, if the child is excited, the estimation unit can also estimate the activity level based on the emotion. For example, the estimation unit can have AI estimate the child's emotions and infer the child's health condition and mood based on the emotion. This enables more accurate estimation by inferring the child's health condition and mood based on the child's emotions. Some or all of the above-described processing in the estimation unit may be performed, for example, using AI or without using AI.

[0097] The inference unit can detect abnormalities by comparing past data when inferring the child's health condition and mood. For example, if the health condition of the child is suddenly worsened by comparing the data with past data, the AI ​​in the inference unit detects the abnormality. The inference unit can also detect abnormalities if the mood is suddenly changed by comparing the data with past data. Furthermore, the inference unit can also detect abnormalities if the activity level is suddenly reduced by comparing the data with past data. For example, the inference unit inputs past data and current data into the AI ​​and detects abnormalities. This allows abnormalities to be quickly detected by comparing the data with past data. Some or all of the above-described processing in the inference unit may be performed, for example, using AI, or may be performed without using AI.

[0098] The estimation unit can take environmental factors into account when estimating the child's health condition and mood. For example, the estimation unit makes an estimation taking into account the possibility that a high room temperature will affect the child's health condition. The estimation unit can also make an estimation taking into account the possibility that a low humidity will affect the child's mood. Furthermore, the estimation unit can make an estimation taking into account the possibility that a loud external noise will affect the child's stress level. For example, the estimation unit inputs environmental factors (temperature, humidity, noise, etc.) into the AI ​​and estimates the health condition and mood based on the environmental factors. In this way, taking environmental factors into account enables more accurate estimation. Some or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI, or may be performed without using AI.

[0099] When inferring the child's health condition or mood, the inference unit can adjust the inference result by reflecting parental feedback. For example, if the parent provides feedback about the child's health condition, the inference unit can adjust the inference result by reflecting that information. Furthermore, if the parent provides feedback about the child's mood, the inference unit can adjust the inference result by reflecting that information. Furthermore, if the parent provides feedback about the child's activity level, the inference unit can adjust the inference result by reflecting that information. For example, the inference unit inputs parental feedback into the AI ​​and adjusts the inference result based on that feedback. In this way, by reflecting parental feedback, more accurate inference results can be provided. Some or all of the above-described processing in the inference unit may be performed, for example, using AI, or may be performed without using AI.

[0100] The inference unit can infer a child's emotions and provide the parent with an inference result based on the child's estimated emotions. For example, if a child is angry, the inference unit can notify the parent of the child's emotions and suggest ways to manage stress. Furthermore, if a child is sad, the inference unit can notify the parent of the child's emotions and suggest ways to comfort the child. Furthermore, if a child is excited, the inference unit can notify the parent of the child's emotions and suggest ways to calm the child. For example, the inference unit can have an AI infer a child's emotions and provide the parent with an inference result based on the emotions. This allows the parent to take appropriate action by providing the parent with an inference result based on the child's emotions. Some or all of the above-described processing in the inference unit may be performed, for example, using AI or without AI.

[0101] The inference unit can detect abnormalities by comparing the data of other children when inferring a child's health condition or mood. For example, if the health condition of the child is abnormally deteriorating when compared with the data of other children, the AI ​​detects the abnormality. The inference unit can also detect abnormal changes in mood when compared with the data of other children. Furthermore, the inference unit can also detect abnormalities when the activity level of the child is abnormally low when compared with the data of other children. For example, the inference unit inputs the data of other children and the current data into the AI ​​and detects abnormalities. This allows for rapid detection of abnormalities by comparing with the data of other children. Some or all of the above-described processing in the inference unit may be performed, for example, using AI, or may be performed without using AI.

[0102] When inferring the child's health condition or mood, the inference unit can adjust the inference result by taking into account the parent's voice and reaction. For example, if the parent speaks in a calm voice, the inference unit infers that the child's health condition is stable. The inference unit can also make inference by taking into account the child's tendency for mood to become unstable when the parent is excited. Furthermore, the inference unit can make inference by taking into account the child's tendency for activity level to decrease when the parent is unresponsive. For example, the inference unit inputs the parent's voice and reaction into the AI ​​and adjusts the inference result based on the input. In this way, by taking the parent's voice and reaction into consideration, more accurate inference results can be provided. Some or all of the above-mentioned processing in the inference unit may be performed, for example, using AI or without using AI.

[0103] When the inference unit infers the child's health condition or mood, it can notify the parent of the inference result in real time. For example, if the child's health condition suddenly deteriorates, the inference unit notifies the parent in real time. The inference unit can also notify the parent in real time if the child's mood suddenly changes. Furthermore, the inference unit can also notify the parent in real time if the child's activity level suddenly drops. For example, the inference unit inputs the child's health condition or mood into AI and notifies the parent of the inference result in real time. In this way, notifying the inference result in real time allows the parent to respond quickly. Some or all of the above-mentioned processing in the inference unit may be performed, for example, using AI or without using AI.

[0104] The monitoring unit can estimate the child's emotions and monitor the lifestyle patterns based on the estimated emotions. For example, if the child is angry, the monitoring unit can monitor the stress level based on the emotions. If the child is sad, the monitoring unit can also monitor the fatigue level based on the emotions. Furthermore, if the child is excited, the monitoring unit can monitor the activity level based on the emotions. For example, the monitoring unit can have AI estimate the child's emotions and monitor the lifestyle patterns based on the emotions. This enables more accurate monitoring by monitoring the lifestyle patterns based on the child's emotions. Some or all of the above-mentioned processing in the monitoring unit can be performed, for example, using AI or without using AI.

[0105] When monitoring a child's sleep time, meal timing, and activity level, the monitoring unit can detect abnormalities by comparing the data with past data. For example, if the sleep time suddenly shortens compared to past data, the AI ​​in the monitoring unit detects the abnormality. The monitoring unit can also detect abnormalities if the meal timing significantly deviates compared to past data. Furthermore, the monitoring unit can also detect abnormalities if the activity level suddenly increases compared to past data. For example, the monitoring unit inputs past data and current data into the AI ​​to detect abnormalities. This allows abnormalities to be quickly detected by comparing the data with past data. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI.

[0106] The monitoring unit can take environmental factors into account when monitoring a child's lifestyle patterns. For example, when room temperature is high, the monitoring unit monitors the child's activity level, taking into account the possibility that this may affect the child's sleep pattern. The monitoring unit can also monitor when humidity is low, taking into account the possibility that this may affect the child's sleep pattern. Furthermore, the monitoring unit can monitor when external noise is loud, taking into account the possibility that this may affect the child's meal timing. For example, the monitoring unit inputs environmental factors (temperature, humidity, noise, etc.) into AI and monitors the lifestyle patterns based on these. By taking environmental factors into account, more accurate monitoring is possible. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI.

[0107] When monitoring a child's lifestyle patterns, the monitoring unit can adjust the monitoring results by reflecting parental feedback. For example, if a parent provides feedback about the child's sleep patterns, the monitoring unit can adjust the monitoring results by reflecting that information. Furthermore, if a parent provides feedback about the child's meal timings, the monitoring unit can also adjust the monitoring results by reflecting that information. Furthermore, if a parent provides feedback about the child's activity level, the monitoring unit can also adjust the monitoring results by reflecting that information. For example, the monitoring unit inputs parental feedback into AI and adjusts the monitoring results based on that feedback. In this way, by reflecting parental feedback, more accurate monitoring results can be provided. Some or all of the above-described processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI.

[0108] The monitoring unit can estimate the child's emotions and provide the parent with monitoring results based on the estimated child's emotions. For example, if the child is angry, the monitoring unit can notify the parent of the child's emotions and suggest ways to manage stress. Furthermore, if the child is sad, the monitoring unit can notify the parent of the child's emotions and suggest ways to comfort the child. Furthermore, if the child is excited, the monitoring unit can notify the parent of the child's emotions and suggest ways to calm the child. For example, the monitoring unit can use AI to estimate the child's emotions and provide the parent with monitoring results based on the emotions. This allows the parent to take appropriate action by providing the parent with the monitoring results based on the child's emotions. Some or all of the above-described processing in the monitoring unit may be performed, for example, using AI or without AI.

[0109] When monitoring a child's lifestyle patterns, the monitoring unit can detect abnormalities by comparing them with data from other children. For example, if the child's sleep time is abnormally short compared to data from other children, the monitoring unit uses AI to detect the abnormality. The monitoring unit can also detect abnormalities if the child's meal timing is abnormally off compared to data from other children. The monitoring unit can also detect abnormalities if the child's activity level is abnormally high compared to data from other children. For example, the monitoring unit inputs data from other children and current data into the AI ​​to detect abnormalities. This allows for rapid detection of abnormalities by comparing the data from other children. Some or all of the above-described processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI.

[0110] When monitoring a child's daily life pattern, the monitoring unit can adjust the monitoring results by taking into account the parent's voice and reactions. For example, if the parent speaks to the child in a calm voice, the monitoring unit may monitor that the child's sleep pattern is stable. The monitoring unit may also monitor that the child's meal timing tends to become irregular when the parent is excited. Furthermore, the monitoring unit may monitor that the child's activity level tends to decrease when the parent is unresponsive. For example, the monitoring unit inputs the parent's voice and reactions into AI and adjusts the monitoring results based on the input. By taking the parent's voice and reactions into account, more accurate monitoring results can be provided. Some or all of the above-described processing in the monitoring unit may be performed, for example, using AI or without AI.

[0111] When monitoring a child's lifestyle pattern, the monitoring unit can notify the parent of the monitoring results in real time. For example, if the child's sleep time suddenly shortens, the monitoring unit notifies the parent in real time. The monitoring unit can also notify the parent in real time if the child's meal timings change significantly. Furthermore, the monitoring unit can also notify the parent in real time if the child's activity level suddenly increases. For example, the monitoring unit inputs the child's lifestyle pattern into AI and notifies the parent of the monitoring results in real time. This allows the parent to respond quickly by notifying the monitoring results in real time. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI, or may be performed without using AI.

[0112] The notification unit can estimate the child's emotions and adjust the notification content based on the estimated child's emotions. For example, if the child is angry, the notification unit can notify the parent of the child's emotions and suggest ways to manage stress. Furthermore, if the child is sad, the notification unit can notify the parent of the child's emotions and suggest ways to comfort the child. Furthermore, if the child is excited, the notification unit can notify the parent of the child's emotions and suggest ways to calm the child. For example, the notification unit can use AI to estimate the child's emotions and adjust the notification content based on the emotions. By adjusting the notification content based on the child's emotions, the parent can take appropriate action. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or without using AI.

[0113] When notifying a parent of an abnormality, the notification unit can evaluate the severity of the abnormality by comparing it with past data. For example, if the health condition suddenly deteriorates by comparing it with past data, the notification unit evaluates the severity and notifies the parent. The notification unit can also evaluate the severity and notify the parent of a sudden change in mood by comparing it with past data. Furthermore, the notification unit can evaluate the severity and notify the parent of a sudden drop in activity level by comparing it with past data. For example, the notification unit inputs past data and current data into AI and evaluates the severity of the abnormality. This allows the severity of the abnormality to be quickly evaluated by comparing it with past data. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or without AI.

[0114] The notification unit can adjust the timing of notification when notifying a parent if an abnormality is detected. For example, if the child's health condition suddenly deteriorates, the notification unit immediately notifies the parent. The notification unit can also notify the parent at an appropriate time if the child's mood suddenly changes. Furthermore, the notification unit can also notify the parent at an appropriate time if the child's activity level suddenly drops. For example, the notification unit inputs the type and severity of the abnormality into the AI ​​and adjusts the timing of notification based on that. In this way, adjusting the timing of notification allows the parent to respond at an appropriate time. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI.

[0115] When notifying a parent of an abnormality, the notification unit can improve the content of the notification by reflecting the parent's feedback. For example, the notification unit adjusts the content of the notification based on the feedback provided by the parent and reflects the adjustment in the next notification. The notification unit can also adjust the timing of the notification based on the feedback provided by the parent and reflect the adjustment in the next notification. Furthermore, the notification unit can improve the notification method based on the feedback provided by the parent and reflect the adjustment in the next notification. For example, the notification unit inputs the parent's feedback into AI and improves the notification content based on the feedback. In this way, more appropriate notification content can be provided by reflecting the parent's feedback. Some or all of the above-described processing in the notification unit may be performed, for example, using AI or without using AI.

[0116] The notification unit can estimate the child's emotion and determine the priority of notifications based on the estimated child's emotion. For example, if the child is angry, the notification unit can prioritize notifying the parent of the emotion. Also, if the child is sad, the notification unit can prioritize notifying the parent of the emotion. Furthermore, if the child is excited, the notification unit can prioritize notifying the parent of the emotion. For example, the notification unit can have AI estimate the child's emotion and determine the priority of notifications based on the emotion. In this way, by determining the priority of notifications based on the child's emotion, the parent can receive important information preferentially. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or without using AI.

[0117] When notifying a parent of an abnormality, the notification unit can evaluate the severity of the abnormality by comparing it with data from other children. For example, if a child's health condition is abnormally deteriorating compared with data from other children, the notification unit evaluates the severity and notifies the parent. The notification unit can also evaluate the severity and notify the parent of an abnormal change in mood compared with data from other children. Furthermore, the notification unit can evaluate the severity and notify the parent of an abnormal decrease in activity level compared with data from other children. For example, the notification unit inputs the data from other children and the current data into AI and evaluates the severity of the abnormality. This allows the severity of the abnormality to be quickly evaluated by comparing it with data from other children. Some or all of the above-described processing in the notification unit may be performed, for example, using AI or without AI.

[0118] When notifying a parent of an abnormality, the notification unit can adjust the content of the notification taking into account the parent's voice and reaction. For example, if the parent speaks in a calm voice, the notification unit adjusts the content of the notification to be more toned down. Furthermore, if the parent is excited, the notification unit can adjust the content of the notification to be more emphasizing. Furthermore, if the parent is unresponsive, the notification unit can adjust the content of the notification to be more detailed. For example, the notification unit inputs the parent's voice and reaction into AI and adjusts the content of the notification based on that. In this way, more appropriate notification content can be provided by taking the parent's voice and reaction into consideration. Some or all of the above-described processing in the notification unit may be performed, for example, using AI, or may be performed without using AI.

[0119] When notifying a parent of an abnormality, the notification unit can provide the notification content to the parent in real time. For example, if the child's health condition suddenly deteriorates, the notification unit notifies the parent in real time. The notification unit can also notify the parent in real time if the child's mood suddenly changes. Furthermore, the notification unit can also notify the parent in real time if the child's activity level suddenly drops. For example, the notification unit inputs the child's health condition and mood into AI and provides the notification content to the parent in real time. By providing the notification content in real time, the parent can respond quickly. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI or without using AI.

[0120] The advice unit can estimate the child's emotions and adjust the advice content based on the estimated child's emotions. For example, if the child is angry, the advice unit can provide stress management advice based on the child's emotions. Furthermore, if the child is sad, the advice unit can provide advice on how to comfort the child based on the child's emotions. Furthermore, if the child is excited, the advice unit can provide advice on how to calm the child based on the child's emotions. For example, the advice unit can have AI estimate the child's emotions and adjust the advice content based on the emotions. By adjusting the advice content based on the child's emotions, parents can take appropriate action. Some or all of the above-described processing in the advice unit may be performed, for example, using AI or without using AI.

[0121] When providing parenting instructions and information according to the child's age, personality, and health condition, the advice unit can select optimal advice by comparing with past data. For example, the advice unit can provide optimal parenting instructions according to the child's age by comparing with past data. The advice unit can also provide optimal parenting instructions according to the child's personality by comparing with past data. Furthermore, the advice unit can provide optimal parenting instructions according to the child's health condition by comparing with past data. For example, the advice unit inputs past data and current data into AI and selects optimal advice. This makes it possible to provide optimal advice by comparing with past data. Some or all of the above-described processing in the advice unit may be performed, for example, using AI, or may be performed without using AI.

[0122] The advice unit can improve the content of the advice by reflecting parental feedback when providing parenting instructions and information according to the child's age, personality, and health condition. For example, the advice unit adjusts the parenting instructions based on the feedback provided by the parent and reflects the adjustment in the next piece of advice. The advice unit can also adjust the parenting information based on the feedback provided by the parent and reflect the adjustment in the next piece of advice. Furthermore, the advice unit can improve parenting methods based on the feedback provided by the parent and reflect the adjustment in the next piece of advice. For example, the advice unit inputs the parent's feedback into AI and improves the content of the advice based on the feedback. In this way, more appropriate advice can be provided by reflecting the parent's feedback. Some or all of the above-described processing in the advice unit may be performed, for example, using AI or without AI.

[0123] The advice unit can adjust the timing of the advice when providing parenting instructions and information according to the child's age, personality, and health condition. The advice unit provides parenting instructions at an appropriate time according to, for example, the child's age. The advice unit can also provide parenting information at an appropriate time according to the child's personality. Furthermore, the advice unit can provide parenting methods at an appropriate time according to the child's health condition. For example, the advice unit inputs the child's age, personality, and health condition into the AI ​​and adjusts the timing of the advice based on that. In this way, adjusting the timing of the advice allows parents to respond at an appropriate time. Some or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI.

[0124] The advice unit can estimate the child's emotions and determine the priority of advice based on the estimated child's emotions. For example, if the child is angry, the advice unit can prioritize providing stress management advice based on the child's emotions. Furthermore, if the child is sad, the advice unit can prioritize providing advice on how to comfort the child based on the child's emotions. Furthermore, if the child is excited, the advice unit can prioritize providing advice on how to calm the child based on the child's emotions. For example, the advice unit can have AI estimate the child's emotions and determine the priority of advice based on the emotions. In this way, by determining the priority of advice based on the child's emotions, parents can receive important information preferentially. Some or all of the above-mentioned processing in the advice unit may be performed, for example, using AI or without using AI.

[0125] When providing parenting instructions and information according to a child's age, personality, and health condition, the advice unit can select optimal advice by comparing with data of other children. For example, the advice unit can provide optimal parenting instructions according to age by comparing with data of other children. The advice unit can also provide optimal parenting instructions according to personality by comparing with data of other children. Furthermore, the advice unit can provide optimal parenting instructions according to health condition by comparing with data of other children. For example, the advice unit inputs data of other children and current data into AI and selects optimal advice. This makes it possible to provide optimal advice by comparing with data of other children. Some or all of the above-described processing in the advice unit may be performed, for example, using AI, or may be performed without using AI.

[0126] The advice unit can adjust the content of the advice taking into account the parent's voice and reaction when providing parenting instructions and information according to the child's age, personality, and health condition. For example, if the parent speaks in a calm voice, the advice unit adjusts the parenting instructions to be more relaxed. Furthermore, if the parent is excited, the advice unit can adjust the parenting instructions to be more emphasized. Furthermore, if the parent is unresponsive, the advice unit can adjust the parenting instructions to be more detailed. For example, the advice unit inputs the parent's voice and reaction into AI and adjusts the content of the advice based on that. In this way, more appropriate advice can be provided by taking the parent's voice and reaction into consideration. Some or all of the above-mentioned processing in the advice unit may be performed, for example, using AI or without using AI.

[0127] The advice unit can provide advice to parents in real time when providing parenting instructions and information according to the child's age, personality, and health condition. The advice unit provides parenting instructions in real time according to, for example, the child's age. The advice unit can also provide parenting information in real time according to the child's personality. The advice unit can also provide parenting methods in real time according to the child's health condition. For example, the advice unit inputs the child's age, personality, and health condition into AI, and provides advice in real time based on the input. This allows parents to respond quickly by providing advice in real time. Some or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI.

[0128] The community unit can estimate a child's emotions and adjust the content of information provided in the community based on the estimated child's emotions. For example, if a child is angry, the community unit can provide information on stress management based on the child's emotions. Furthermore, if a child is sad, the community unit can provide information on how to comfort the child based on the child's emotions. Furthermore, if a child is excited, the community unit can provide information on how to calm the child based on the child's emotions. For example, the community unit can have an AI estimate a child's emotions and adjust the content of information provided in the community based on the emotions. In this way, by adjusting the content of information provided in the community based on the child's emotions, parents can receive appropriate information. Some or all of the above-mentioned processing in the community unit may be performed, for example, using AI or without using AI.

[0129] The community unit can select optimal information by comparing with past data when exchanging information with other parents or providing advice from experts. For example, the community unit can provide optimal child-rearing information by comparing with past data. The community unit can also provide optimal health care information by comparing with past data. Furthermore, the community unit can provide optimal education information by comparing with past data. For example, the community unit inputs past data and current data into AI and selects optimal information. This makes it possible to provide optimal information by comparing with past data. Some or all of the above-mentioned processing in the community unit may be performed, for example, using AI, or may be performed without using AI.

[0130] The community unit can improve the content of information provided by reflecting parental feedback when exchanging information with other parents or providing advice from experts. For example, the community unit adjusts the content of information provided based on parental feedback and reflects it in the next information provided. The community unit can also adjust the timing of information provision based on parental feedback and reflect it in the next information provided. Furthermore, the community unit can improve the method of information provision based on parental feedback and reflect it in the next information provided. For example, the community unit inputs parental feedback into AI and improves the content of information provided based on that feedback. In this way, more appropriate information can be provided by reflecting parental feedback. Some or all of the above-mentioned processing in the community unit may be performed, for example, using AI or without AI.

[0131] The community unit can adjust the timing of information provision when exchanging information with other parents or providing advice from experts. For example, the community unit provides optimal childcare information at the time parents need it. The community unit can also provide optimal health management information at the time parents need it. Furthermore, the community unit can provide optimal educational information at the time parents need it. For example, the community unit inputs parents' needs into AI and adjusts the timing of information provision based on that. In this way, adjusting the timing of information provision allows parents to receive information at an appropriate time. Some or all of the above-mentioned processing in the community unit may be performed, for example, using AI, or may be performed without using AI.

[0132] The community unit can estimate a child's emotions and determine the priority of information provision in the community based on the estimated child's emotions. For example, if a child is angry, the community unit can prioritize providing information on stress management based on the child's emotions. Furthermore, if a child is sad, the community unit can prioritize providing information on how to comfort the child based on the child's emotions. Furthermore, if a child is excited, the community unit can prioritize providing information on how to calm the child based on the child's emotions. For example, the community unit can have AI estimate a child's emotions and determine the priority of information provision based on the emotions. Thus, by determining the priority of information provision based on the child's emotions, parents can receive important information preferentially. Some or all of the above-described processing in the community unit may be performed, for example, using AI or without AI.

[0133] The community unit can select optimal information by comparing with data of other children when exchanging information with other parents or providing advice from experts. For example, the community unit can provide optimal childcare information by comparing with data of other children. The community unit can also provide optimal health care information by comparing with data of other children. Furthermore, the community unit can provide optimal education information by comparing with data of other children. For example, the community unit inputs data of other children and current data into AI and selects optimal information. This makes it possible to provide optimal information by comparing with data of other children. Some or all of the above-mentioned processing in the community unit may be performed, for example, using AI, or may be performed without using AI.

[0134] The community unit can adjust the content of information provided by taking into account the parent's voice and reactions when exchanging information with other parents or providing advice from experts. For example, if the parent speaks in a calm voice, the community unit adjusts the content of information provided to be more toned down. Furthermore, if the parent is excited, the community unit can adjust the content of information provided to be more emphasizing. Furthermore, if the parent is unresponsive, the community unit can adjust the content of information provided to be more detailed. For example, the community unit inputs the parent's voice and reactions into AI and adjusts the content of information provided based on the input. In this way, more appropriate information can be provided by taking the parent's voice and reactions into consideration. Some or all of the above-described processing in the community unit may be performed, for example, using AI or without using AI.

[0135] The community unit can provide information content to parents in real time when exchanging information with other parents or providing advice from experts. For example, the community unit can provide optimal childcare information in real time when parents need it. The community unit can also provide optimal health management information in real time when parents need it. Furthermore, the community unit can provide optimal education information in real time when parents need it. For example, the community unit inputs parents' needs into AI and provides information content in real time based on that. This allows parents to respond quickly by providing information content in real time. Some or all of the above-mentioned processing in the community unit may be performed, for example, using AI, or may be performed without using AI. === Hard Collateral 1-1 === Each of the multiple elements, including the analysis unit, inference unit, monitoring unit, notification unit, advice unit, and community unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart device 14 and analyzes the child's tone of voice and crying pattern. The inference unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and infers the child's health condition and mood based on the analyzed data. The monitoring unit, for example, monitors the child's lifestyle patterns using sensors in the smart device 14. The notification unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and notifies the parent based on the monitored data. The advice unit, for example, is realized by the control unit 46A of the smart device 14 and provides personalized advice for each child. The community unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and provides information on mommy friend communities and remote parenting support. === Hard Collateral 1-2 === Each of the multiple elements, including the analysis unit, inference unit, monitoring unit, notification unit, advice unit, and community unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart glasses 214 and analyzes a child's tone of voice and crying pattern. The inference unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and infers the child's health condition and mood based on the analyzed data. The monitoring unit, for example, monitors the child's lifestyle patterns using sensors in the smart glasses 214. The notification unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and notifies parents based on the monitored data. The advice unit, for example, is realized by the control unit 46A of the smart glasses 214 and provides personalized advice for each child. The community unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and provides information on mommy friend communities and remote parenting support. === Hard Collateral 1-3 === Each of the multiple elements, including the analysis unit, inference unit, monitoring unit, notification unit, advice unit, and community unit, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the analysis unit is implemented by the control unit 46A of the headset-type terminal 314 and analyzes the child's tone of voice and crying pattern. The inference unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and infers the child's health condition and mood based on the analyzed data. The monitoring unit, for example, monitors the child's lifestyle patterns using a sensor in the headset-type terminal 314. The notification unit, for example, is implemented by the specific processing unit 290 of the data processing device 12 and notifies the parent based on the monitored data. The advice unit, for example, is implemented by the control unit 46A of the headset-type terminal 314 and provides personalized advice for each child. The community unit, for example, is implemented by the specific processing unit 290 of the data processing device 12 and provides information on mommy friend communities and remote child-rearing support. === Hard Collateral 1-4 === Each of the multiple elements, including the analysis unit, inference unit, monitoring unit, notification unit, advice unit, and community unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the robot 414 and analyzes the child's tone of voice and crying pattern. The inference unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and infers the child's health condition and mood based on the analyzed data. The monitoring unit, for example, monitors the child's daily life pattern using a sensor of the robot 414. The notification unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and notifies the parent based on the monitored data. The advice unit, for example, is realized by the control unit 46A of the robot 414 and provides personalized advice for each child. The community unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and provides information on mommy friend communities and remote child-rearing support.

[0136] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0137] The analysis unit can perform detailed analysis of the frequency components of the voice when analyzing the tone of the child's voice and the pattern of the crying. For example, the analysis unit can analyze the frequency components of the child's voice using spectral analysis to detect changes in specific frequency bands. The analysis unit can also analyze the frequency components of the child's crying in the time domain to analyze the duration and fluctuations of the crying in detail. Furthermore, the analysis unit can analyze the frequency components of the child's voice in real time and immediately notify if there is an abnormality. This allows for more accurate inference of the child's health condition and mood by analyzing the frequency components of the child's voice in detail.

[0138] The monitoring unit can measure the child's body temperature and heart rate when monitoring the child's lifestyle patterns. For example, the monitoring unit can measure the child's body temperature using a non-contact temperature sensor and notify the parent if there is an abnormality. The monitoring unit can also measure the child's heart rate using a wearable device and notify the parent if there is an abnormality. Furthermore, the monitoring unit can monitor the child's body temperature and heart rate in real time and immediately notify the parent if there is an abnormality. This makes it possible to understand the child's health condition in more detail by monitoring the child's body temperature and heart rate.

[0139] The notification unit can customize the content of the notification when notifying the parent of an abnormality. For example, the notification unit can select the notification format according to the parent's preferences. The notification unit can also adjust the timing of the notification according to the parent's schedule. Furthermore, the notification unit can improve the notification content based on the parent's feedback and reflect it in the next notification. This allows the parent to respond more appropriately by customizing the notification content.

[0140] The advice unit can take into account the parent's stress level when providing parenting instructions and information tailored to the child's age, personality, and health condition. For example, the advice unit can provide simple, easy-to-follow parenting instructions when the parent is at a high stress level. The advice unit can also provide detailed, specific parenting instructions when the parent is at a low stress level. Furthermore, the advice unit can monitor the parent's stress level in real time and adjust the parenting instructions accordingly. This allows the provision of more appropriate parenting instructions by taking the parent's stress level into account.

[0141] When exchanging information with other parents or receiving advice from experts, the community department can choose the format of information provision according to parents' needs. For example, if parents prefer information in text format, the community department can provide information through chat or forums. If parents prefer information in video format, the community department can also provide information through video calls or webinars. Furthermore, the community department can improve the format of information provision based on parents' feedback and reflect it in the next information provision. This makes it possible to provide information that meets parents' needs.

[0142] The analysis unit can estimate the child's emotions when analyzing the child's tone of voice and crying pattern, and provide the analysis result to the parent based on the estimated child's emotions. For example, if the child is angry, the analysis unit can notify the parent of the child's emotions and suggest an appropriate response. Also, if the child is sad, the analysis unit can notify the parent of the child's emotions and suggest ways to comfort the child. Furthermore, if the child is excited, the analysis unit can notify the parent of the child's emotions and suggest ways to calm the child. In this way, by providing the parent with the analysis result based on the child's emotions, the parent can take appropriate action.

[0143] The estimation unit can estimate the child's emotions and estimate the child's health condition and mood based on the estimated emotions. For example, if the child is angry, the estimation unit can estimate the stress level based on the emotion. If the child is sad, the estimation unit can also estimate the child's fatigue level based on the emotion. Furthermore, if the child is excited, the estimation unit can also estimate the child's activity level based on the emotion. This allows for more accurate estimation by estimating the child's health condition and mood based on the child's emotions.

[0144] The monitoring unit can estimate the child's emotions and monitor the lifestyle patterns based on the estimated emotions. For example, if the child is angry, the monitoring unit can monitor the stress level based on the emotion. If the child is sad, the monitoring unit can also monitor the fatigue level based on the emotion. Furthermore, if the child is excited, the monitoring unit can also monitor the activity level based on the emotion. This allows for more accurate monitoring by monitoring the lifestyle patterns based on the child's emotions.

[0145] The notification unit can estimate the child's emotion and adjust the notification content based on the estimated child's emotion. For example, if the child is angry, the notification unit can notify the parent of the emotion and suggest ways to manage stress. If the child is sad, the notification unit can also notify the parent of the emotion and suggest ways to comfort the child. Furthermore, if the child is excited, the notification unit can also notify the parent of the emotion and suggest ways to calm the child. In this way, by adjusting the notification content based on the child's emotion, the parent can take appropriate action.

[0146] The advice unit can estimate the child's emotions and adjust the advice content based on the estimated child's emotions. For example, if the child is angry, the advice unit can provide stress management advice based on the child's emotions. Also, if the child is sad, the advice unit can provide advice on how to comfort the child based on the child's emotions. Furthermore, if the child is excited, the advice unit can provide advice on how to calm the child based on the child's emotions. In this way, by adjusting the advice content based on the child's emotions, parents can take appropriate action.

[0147] The processing flow of the second embodiment will be briefly explained below.

[0148] Step 1: The analysis unit analyzes the child's vocal tone or crying pattern. The analysis unit analyzes the pitch and intensity of the child's voice and the rhythm of the crying in detail, and performs the analysis using voice recognition technology and AI. Step 2: The inference unit infers the child's health condition or mood based on the data analyzed by the analysis unit. The inference unit inputs the analyzed data using AI and outputs the health condition or mood. Step 3: The monitoring unit uses sensor data to monitor the child's lifestyle patterns. The monitoring unit uses sensors to measure the child's sleep time, meal timings, and activity level, and uses AI to output the lifestyle patterns. Step 4: The notification unit notifies the parent based on the data monitored by the monitoring unit. If there is an abnormality based on the monitoring data, the notification unit notifies the parent and outputs the notification content using AI. Step 5: The advice unit provides personalized advice for each child. The advice unit provides parenting instructions and information based on the child's age, personality, and health condition, and outputs personalized advice using AI.

[0149] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0150] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0154] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0155] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to 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 imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0165] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0170] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0171] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0172] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0173] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0175] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0176] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0177] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0178] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0179] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0180] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0181] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0182] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0183] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0184] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0185] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0186] 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.

[0187] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0188] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0189] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0190] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0191] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0192] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0193] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0194] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0195] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0196] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0197] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0198] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0199] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0200] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0201] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0202] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0203] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0204] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0205] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0206] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0207] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0208] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0209] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0210] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0211] 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.

[0212] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0213] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0214] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0215] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0216] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0217] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0218] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0219] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0220] [Explanation of symbols]

[0221] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit for analyzing the tone of a child's voice or crying pattern; an inference unit that infers the child's health condition or mood based on the data analyzed by the analysis unit; a monitoring unit that monitors the child's lifestyle patterns using sensor data; a notification unit that notifies a parent based on the data monitored by the monitoring unit; An advice department that provides personalized advice for each child; Equipped with A system characterized by:

2. Have a community department that provides information on mommy friend communities and remote childcare support 2. The system of claim 1.

3. The analysis unit A detailed analysis of the pitch or intensity of the child's voice and the rhythm of the cry 2. The system of claim 1.

4. The monitoring unit Sensors measure children's sleep times, meal times, and activity levels 2. The system of claim 1.

5. The notification unit Notify parents if there is any abnormality 2. The system of claim 1.

6. The advice unit Providing childcare instructions and information appropriate to the child's age, personality, and health condition 2. The system of claim 1.

7. The community section Provides access to other parents and expert advice 3. The system of claim 2.

8. The analysis unit Inferring a child's emotions and analyzing their tone of voice and crying patterns based on the inferred emotions 2. The system of claim 1.

9. The analysis unit When analyzing the pitch, strength, and rhythm of a child's voice, it compares it with past data to detect abnormalities.

2. The system of claim 1.

10. The analysis unit Filters out ambient noise to improve accuracy when analyzing a child's tone of voice and crying patterns 2. The system of claim 1.

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