system

Abstract

The system according to the embodiment aims to grasp the baby's mood and health condition in real time and suggest appropriate actions to the parents. [Solution] The system according to the embodiment includes an acquisition unit, an analysis unit, and a provision unit. The acquisition unit uses a camera or sensor to record the baby's movements and facial expressions. The analysis unit uses AI to analyze the data obtained from the acquisition unit and determine the baby's mood and health condition. The provision unit suggests actions to the parent based on the results of the analysis unit.

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 has the drawback of making it difficult to accurately grasp a baby's mood and health status in real time and suggest appropriate actions.

[0005] The system according to the embodiment aims to grasp the baby's mood and health condition in real time and suggest appropriate actions to the parents. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, and a provision unit. The acquisition unit uses a camera or sensor to record the baby's movements and facial expressions. The analysis unit uses AI to analyze the data obtained from the acquisition unit and determine the baby's mood and health condition. The provision unit suggests actions to the parent based on the results of the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the baby's mood and health condition in real time and suggest appropriate actions to the 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 of form 1) A baby growth monitoring system according to an embodiment of the present invention uses cameras and sensors to record a baby's movements and facial expressions, analyzes the data using AI, and recommends appropriate actions to parents. This system monitors a baby's growth and development in real time and provides appropriate care. For example, a camera and sensors can record a baby's movements and facial expressions in real time, and an AI analyzes the input data to determine the baby's mood and health. The AI ​​then recommends appropriate actions to parents based on the analysis results. This service allows parents to monitor their baby's growth and development in real time and provide appropriate care. For example, playing with a baby when they're in a good mood can deepen the bond between parent and child. Furthermore, understanding a baby's health status allows for early detection of abnormalities and appropriate treatment. Furthermore, combining and analyzing this data with other health data can provide more accurate advice. This allows parents to monitor their baby's growth and development in real time and provide appropriate care.

[0029] The baby growth monitoring system according to this embodiment comprises an acquisition unit, an analysis unit, and a provision unit. The acquisition unit records the baby's movements and facial expressions using a camera or sensor. For example, the acquisition unit can record the baby's smiles, crying faces, and hand and foot movements in real time. The acquisition unit can, for example, use a high-resolution camera to record the baby's facial expressions in detail. The acquisition unit can also use a motion sensor to detect the baby's movements. Furthermore, the acquisition unit can also use a voice sensor to record the baby's voice. The analysis unit uses AI to analyze the data obtained from the acquisition unit and determine the baby's mood and health. For example, the analysis unit uses deep learning to analyze the baby's facial expressions and estimate the baby's mood. The analysis unit can also use machine learning algorithms to analyze the baby's movement patterns and determine the baby's health. Furthermore, the analysis unit can also analyze the baby's voice data and estimate the baby's emotions. The provision unit suggests actions to the parents based on the results of the analysis unit. For example, the provision unit suggests playtime when the baby is in a good mood. The provision unit can also suggest meals based on the baby's health. Furthermore, the providing unit can provide sleep advice based on the baby's sleep state. As a result, the baby growth monitoring system according to the embodiment allows parents to understand the growth and development of their baby in real time and provide appropriate care.

[0030] The acquisition unit can record the baby's movements and facial expressions using a camera or sensor. For example, the acquisition unit can use a high-resolution camera to record the baby's facial expressions in detail. For example, the acquisition unit can record the baby's smiles and crying faces in high resolution. The acquisition unit can also use a motion sensor to detect the baby's movements. For example, the acquisition unit can detect and record the baby's arm and leg movements in real time. Furthermore, the acquisition unit can use a sound sensor to record the baby's voice. For example, the acquisition unit can record the baby's cries and laughter with high sensitivity. This allows for the acquisition of accurate data by recording the baby's movements and facial expressions in real time.

[0031] The analysis unit uses AI to analyze data obtained from the acquisition unit and determine the baby's mood and health. For example, the analysis unit can use deep learning to analyze the baby's facial expressions and estimate the baby's mood. For instance, it can analyze the baby's smiles and crying faces to determine the baby's mood. The analysis unit can also use machine learning algorithms to analyze the baby's movement patterns and determine the baby's health. For example, it can analyze the baby's arm and leg movement patterns to determine the baby's health. Furthermore, the analysis unit can analyze the baby's voice data and estimate the baby's emotions. For example, it can analyze the baby's cries and laughter to determine the baby's emotions. In this way, by using AI, the baby's mood and health can be accurately determined.

[0032] The providing unit can suggest an action to the parent based on the results of the analysis unit. For example, the providing unit suggests a game to play when the baby is in a good mood. For example, the providing unit can suggest a game to the parent when the baby smiles. The providing unit can also suggest a meal based on the baby's health condition. For example, the providing unit can suggest a meal to the parent when the baby's health condition is good. Furthermore, the providing unit can provide sleep advice based on the baby's sleeping condition. For example, the providing unit can provide sleep advice to the parent when the baby's sleeping condition is good. This improves baby care by suggesting an appropriate action to the parent based on the analysis results.

[0033] The acquisition unit can estimate the baby's emotions and adjust the timing of recording based on the estimated baby's emotions. The acquisition unit, for example, detects the moment the baby smiles and records a high-resolution photo at that timing. For example, the acquisition unit can detect the moment the baby smiles and record that moment in high resolution. The acquisition unit can also detect the moment the baby starts to cry and record a video at that timing so that the parent can review it later. For example, the acquisition unit can detect the moment the baby starts to cry and record that moment in video so that the parent can review it later. The acquisition unit can also detect the moment the baby falls asleep and record environmental sound and lighting data at that timing. For example, the acquisition unit can detect the moment the baby falls asleep and record that moment together with environmental sound and lighting data. In this way, by adjusting the recording timing based on the baby's emotions, important moments can be recorded without missing.

[0034] The acquisition unit can analyze data on the baby's past movements and facial expressions and select an optimal recording method. The acquisition unit can, for example, analyze time periods when the baby smiled in the past and focus on recording during those time periods. For example, the acquisition unit can analyze time periods when the baby smiled in the past and focus on recording during those time periods. The acquisition unit can also analyze time periods when the baby cried in the past and focus on recording during those time periods. For example, the acquisition unit can analyze time periods when the baby cried in the past and focus on recording during those time periods. The acquisition unit can also analyze time periods when the baby was active in the past and prioritize video recording during those time periods. For example, the acquisition unit can analyze time periods when the baby was active in the past and prioritize video recording during those time periods. In this way, an optimal recording method can be selected by analyzing past data.

[0035] The acquisition unit can perform filtering based on the baby's current activity and environment when recording. For example, when the baby is playing, the acquisition unit can prioritize recording scenes with a lot of movement. For example, when the baby is playing, the acquisition unit can prioritize recording scenes with a lot of movement. Furthermore, when the baby is sleeping, the acquisition unit can filter and record quiet environmental sounds. For example, when the baby is sleeping, the acquisition unit can filter and record quiet environmental sounds. Furthermore, when the baby is eating, the acquisition unit can mainly record the baby's eating behavior. For example, when the baby is eating, the acquisition unit can mainly record the baby's eating behavior. In this way, important data can be preferentially recorded by filtering based on the current activity and environment.

[0036] The acquisition unit can estimate the baby's emotions and determine the priority of data to be recorded based on the estimated baby's emotions. The acquisition unit, for example, prioritizes recording of moments when the baby smiles. For example, the acquisition unit can prioritize recording of moments when the baby smiles. The acquisition unit can also prioritize recording of moments when the baby starts to cry. For example, the acquisition unit can prioritize recording of moments when the baby starts to cry. Furthermore, the acquisition unit can also prioritize recording of moments when the baby is excited. For example, the acquisition unit can prioritize recording of moments when the baby is excited. In this way, by determining the priority of data based on the baby's emotions, important data can be preferentially recorded.

[0037] When recording, the acquisition unit can prioritize recording highly relevant data by taking into account the geographical location information of the baby. For example, when the baby is in a park, the acquisition unit can prioritize recording scenes of the baby playing on playground equipment. For example, when the baby is in a park, the acquisition unit can prioritize recording scenes of the baby playing on playground equipment. Furthermore, when the baby is at home, the acquisition unit can prioritize recording scenes of the baby interacting with family. For example, when the baby is at home, the acquisition unit can prioritize recording scenes of the baby interacting with family. Furthermore, when the baby is in a hospital, the acquisition unit can prioritize recording details of the baby's examination and treatment. For example, when the baby is in a hospital, the acquisition unit can prioritize recording details of the baby's examination and treatment. In this way, highly relevant data can be prioritized by taking into account the geographical location information.

[0038] The acquisition unit can analyze the baby's social media activity during recording and record related data. For example, the acquisition unit can analyze time periods when many baby photos are posted and record related data. For example, the acquisition unit can analyze time periods when many baby photos are posted and record related data. The acquisition unit can also analyze time periods when many baby videos are shared and prioritize video recording during those time periods. For example, the acquisition unit can analyze time periods when many baby videos are shared and prioritize video recording during those time periods. Furthermore, the acquisition unit can analyze time periods when many baby videos are shared and prioritize video recording during those time periods. For example, the acquisition unit can analyze time periods when many baby growth records are commented on and record related data during those time periods. In this way, related data can be recorded by analyzing social media activity.

[0039] The analysis unit can estimate the baby's emotions and adjust the way the analysis is presented based on the estimated baby's emotions. For example, if the baby smiles, the analysis unit can display the analysis result using positive expressions. For example, if the baby smiles, the analysis unit can display the analysis result using positive expressions. Furthermore, if the baby is crying, the analysis unit can display the analysis result including comforting advice to the parent. For example, if the baby is crying, the analysis unit can display the analysis result including comforting advice to the parent. Furthermore, if the baby is excited, the analysis unit can display the analysis result including play suggestions to the parent. For example, if the baby is excited, the analysis unit can display the analysis result including play suggestions to the parent. In this way, by adjusting the way the analysis is presented based on the baby's emotions, appropriate information can be provided to the parent.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can analyze data related to the baby's health condition in detail and provide specific advice to the parent. For example, the analysis unit can analyze data related to the baby's health condition in detail and provide specific advice to the parent. The analysis unit can also analyze data related to the baby's mood briefly and provide simple advice to the parent. For example, the analysis unit can analyze data related to the baby's mood briefly and provide simple advice to the parent. Furthermore, the analysis unit can analyze data related to the baby's activity with a medium level of detail and provide appropriate advice to the parent. For example, the analysis unit can analyze data related to the baby's activity with a medium level of detail and provide appropriate advice to the parent. In this way, by adjusting the level of detail of the analysis based on the importance of the data, appropriate information can be provided to the parent.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit can apply a medical analysis algorithm to baby's health data. For example, the analysis unit can apply a medical analysis algorithm to baby's health data. The analysis unit can also apply an emotion analysis algorithm to baby's mood data. For example, the analysis unit can apply an emotion analysis algorithm to baby's mood data. Furthermore, the analysis unit can also apply a movement analysis algorithm to baby's activity data. For example, the analysis unit can apply a movement analysis algorithm to baby's activity data. In this way, by applying different analysis algorithms depending on the category of data, accurate analysis results can be obtained.

[0042] The analysis unit can estimate the baby's emotions and adjust the length of the analysis based on the estimated baby's emotions. For example, if the baby smiles, the analysis unit can display a short analysis result. For example, if the baby smiles, the analysis unit can display a short analysis result. Furthermore, if the baby is crying, the analysis unit can display a detailed analysis result. For example, if the baby is crying, the analysis unit can display a detailed analysis result. Furthermore, if the baby is excited, the analysis unit can display a medium-length analysis result. For example, if the baby is excited, the analysis unit can display a medium-length analysis result. In this way, by adjusting the length of the analysis based on the baby's emotions, appropriate information can be provided to parents.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the time of data acquisition. For example, the analysis unit can prioritize the most recent data in analysis and provide prompt advice to the parent. For example, the analysis unit can prioritize the most recent data in analysis and provide prompt advice to the parent. The analysis unit can also analyze current data while referring to past data. For example, the analysis unit can analyze current data while referring to past data. Furthermore, the analysis unit can prioritize analysis of data acquired intensively during a specific period. For example, the analysis unit can prioritize analysis of data acquired intensively during a specific period. In this way, by determining the priority of analysis based on the time of data acquisition, prompt advice can be provided.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit can associate and analyze the baby's health data and mood data, and provide comprehensive advice to the parent. For example, the analysis unit can associate and analyze the baby's health data and mood data, and provide comprehensive advice to the parent. The analysis unit can also associate and analyze the baby's activity data and sleep data, and suggest appropriate care to the parent. For example, the analysis unit can associate and analyze the baby's activity data and sleep data, and suggest appropriate care to the parent. Furthermore, the analysis unit can associate and analyze the baby's dietary data and growth data, and provide nutritional management advice to the parent. For example, the analysis unit can associate and analyze the baby's dietary data and growth data, and provide nutritional management advice to the parent. In this way, comprehensive advice can be provided by adjusting the order of analysis based on the relevance of the data.

[0045] The providing unit can estimate the baby's emotions and adjust the way in which the suggestion is expressed based on the estimated baby's emotions. For example, when the baby smiles, the providing unit makes a play suggestion to the parent using positive expression. For example, when the baby smiles, the providing unit can make a play suggestion to the parent using positive expression. Furthermore, when the baby is crying, the providing unit can make a suggestion to the parent that includes comforting advice. For example, when the baby is crying, the providing unit can make a suggestion to the parent that includes comforting advice. Furthermore, when the baby is excited, the providing unit can make a play suggestion to the parent. For example, when the baby is excited, the providing unit can make a play suggestion to the parent. In this way, by adjusting the way in which the suggestion is expressed based on the baby's emotions, it is possible to suggest an appropriate action to the parent.

[0046] The providing unit may adjust the level of detail of the suggestion based on the importance of the action when suggesting the action. For example, the providing unit may suggest an action related to the baby's health in detail and provide specific advice to the parent. For example, the providing unit may suggest an action related to the baby's health in detail and provide specific advice to the parent. The providing unit may also briefly suggest an action related to the baby's mood and provide simple advice to the parent. For example, the providing unit may briefly suggest an action related to the baby's mood and provide simple advice to the parent. Furthermore, the providing unit may suggest an action related to the baby's activity with a medium level of detail and provide moderate advice to the parent. For example, the providing unit may suggest an action related to the baby's activity with a medium level of detail and provide moderate advice to the parent. In this way, by adjusting the level of detail of the suggestion based on the importance of the action, appropriate advice can be provided to the parent.

[0047] The providing unit can apply different suggestion algorithms depending on the category of the action when making a suggestion. For example, the providing unit applies a medical suggestion algorithm to an action related to the baby's health. For example, the providing unit can apply a medical suggestion algorithm to an action related to the baby's health. The providing unit can also apply an emotion analysis algorithm to an action related to the baby's mood. For example, the providing unit can apply an emotion analysis algorithm to an action related to the baby's mood. The providing unit can also apply a motion analysis algorithm to an action related to the baby's activity. For example, the providing unit can apply a motion analysis algorithm to an action related to the baby's activity. In this way, by applying different suggestion algorithms depending on the category of the action, it is possible to suggest an appropriate action to the parent.

[0048] The providing unit can estimate the baby's emotion and adjust the length of the suggestion based on the estimated baby's emotion. The providing unit, for example, makes a short suggestion when the baby smiles. For example, the providing unit can make a short suggestion when the baby smiles. The providing unit can also make a detailed suggestion when the baby is crying. For example, the providing unit can make a detailed suggestion when the baby is crying. Furthermore, the providing unit can also make a medium-length suggestion when the baby is excited. For example, the providing unit can make a medium-length suggestion when the baby is excited. In this way, by adjusting the length of the suggestion based on the baby's emotion, it is possible to suggest an appropriate action to the parent.

[0049] When making suggestions, the providing unit can determine the priority of the suggestions based on the implementation time of the actions. The providing unit, for example, gives priority to suggesting actions with a high urgency. For example, the providing unit can give priority to suggesting actions with a high urgency. The providing unit can also give priority to suggesting actions that should be performed periodically. For example, the providing unit can give priority to suggesting actions that should be performed periodically. Furthermore, the providing unit can also give priority to suggesting actions that should be performed from a long-term perspective. For example, the providing unit can give priority to suggesting actions that should be performed from a long-term perspective. In this way, by determining the priority of the suggestions based on the implementation time of the actions, it is possible to suggest appropriate actions to the parent.

[0050] When making suggestions, the providing unit can adjust the order of suggestions based on the relevance of the actions. For example, the providing unit gives the highest priority to suggesting actions related to the baby's health. For example, the providing unit can suggest actions related to the baby's health as the highest priority. The providing unit can also suggest actions related to the baby's mood next. For example, the providing unit can suggest actions related to the baby's mood next. The providing unit can also suggest actions related to the baby's activity last. For example, the providing unit can suggest actions related to the baby's activity last. In this way, by adjusting the order of suggestions based on the relevance of the actions, appropriate actions can be suggested to the parent.

[0051] The camera installation method can estimate the baby's emotions and adjust the installation position of the camera based on the estimated baby's emotions. The camera installation method can, for example, identify a location where the baby smiles and install a camera at that location. For example, the camera installation method can identify a location where the baby smiles and install a camera at that location. The camera installation method can also identify a location where the baby cries and install a camera at that location. For example, the camera installation method can identify a location where the baby often plays and install a camera at that location. For example, the camera installation method can identify a location where the baby often plays and install a camera at that location. In this way, by adjusting the installation position of the camera based on the baby's emotions, it is possible to record important moments without missing any.

[0052] The camera placement method can be determined by analyzing the baby's past movement patterns. For example, the camera can be placed in locations where the baby frequently moved in the past. It can also be placed in locations where the baby stayed for extended periods in the past. Furthermore, the camera can be placed in locations where the baby played in the past. By analyzing past movement patterns, the optimal camera placement method can be selected.

[0053] The camera's placement method can estimate the baby's emotions and adjust the camera's angle based on those emotions. For example, the camera can be pointed in the direction the baby smiles. It can also be pointed in the direction the baby cries. Furthermore, the camera can be pointed in the direction the baby plays. By adjusting the camera's angle based on the baby's emotions, important moments can be recorded without being missed.

[0054] When installing the camera, the optimal installation method can be selected by taking into consideration the area where the baby is active. For example, the camera can be installed in an area where the baby often plays. For example, the camera can be installed in an area where the baby often plays. Furthermore, the camera can also be installed in an area where the baby often sleeps. For example, the camera can be installed in an area where the baby often sleeps. Furthermore, the camera can also be installed in an area where the baby often eats. For example, the camera can be installed in an area where the baby often eats. In this way, the optimal camera installation method can be selected by taking into consideration the area where the baby is active.

[0055] The specific type of sensor can be used to estimate the baby's emotion and select the type of sensor to be used based on the estimated baby's emotion. For example, a face recognition sensor can be used to detect the baby's smile. For example, a face recognition sensor can be used to detect the baby's smile. Furthermore, a voice sensor can be used to detect the baby's cry. For example, a voice sensor can be used to detect the baby's cry. Furthermore, a motion sensor can be used to detect the baby's movement. For example, a motion sensor can be used to detect the baby's movement. By selecting the type of sensor to be used based on the baby's emotion, accurate data can be obtained.

[0056] When selecting a specific sensor type, the optimal sensor can be selected by analyzing past data of the baby. For example, a face recognition sensor can be selected by analyzing data of past smiling faces of the baby. Furthermore, a voice sensor can be selected by analyzing data of past crying faces of the baby. For example, a voice sensor can be selected by analyzing data of past crying faces of the baby. Furthermore, a movement sensor can be selected by analyzing data of past active movements of the baby. For example, a movement sensor can be selected by analyzing data of past active movements of the baby. In this way, the optimal sensor can be selected by analyzing past data.

[0057] A specific type of sensor can estimate the baby's emotions and adjust the sensitivity of the sensor based on the estimated baby's emotions. A specific type of sensor can, for example, increase the sensitivity of a face recognition sensor when the baby smiles. For example, a specific type of sensor can increase the sensitivity of a face recognition sensor when the baby smiles. A specific type of sensor can also increase the sensitivity of a sound sensor when the baby is crying. For example, a specific type of sensor can increase the sensitivity of a sound sensor when the baby is crying. A specific type of sensor can also increase the sensitivity of a movement sensor when the baby is excited. For example, a specific type of sensor can increase the sensitivity of a movement sensor when the baby is excited. This allows accurate data to be obtained by adjusting the sensitivity of the sensor based on the baby's emotions.

[0058] The type of sensor to choose from can be determined by considering the baby's activity environment. For example, if the baby spends a lot of time indoors, an indoor sensor can be selected. Similarly, if the baby spends a lot of time outdoors, an outdoor sensor can be selected. Furthermore, if the baby plays with water, a waterproof sensor can be selected. This allows for the selection of the most suitable sensor by considering the baby's activity environment.

[0059] The AI ​​analysis method can estimate the baby's emotions and adjust the analysis method based on the estimated emotions. For example, if the baby smiles, the AI ​​analysis method can apply a positive emotion analysis method. For example, if the baby smiles, the AI ​​analysis method can apply a positive emotion analysis method. For example, if the baby cries, the AI ​​analysis method can apply a negative emotion analysis method. For example, if the baby cries, the AI ​​analysis method can apply a negative emotion analysis method. For example, if the baby is excited, the AI ​​analysis method can apply a method to analyze the excited state. For example, if the baby is excited, the AI ​​analysis method can apply a method to analyze the excited state. In this way, by adjusting the analysis method based on the baby's emotions, accurate analysis results can be obtained.

[0060] When selecting an analysis method, an AI analysis method can refer to past analysis data to select the optimal method. For example, an AI analysis method can select a method to analyze a baby's smile based on past analysis data. For example, an AI analysis method can select a method to analyze a baby's smile based on past analysis data. Furthermore, an AI analysis method can also select a method to analyze a baby's cry based on past analysis data. For example, an AI analysis method can select a method to analyze a baby's cry based on past analysis data. Furthermore, an AI analysis method can also select a method to analyze a baby's movements based on past analysis data. For example, an AI analysis method can select a method to analyze a baby's movements based on past analysis data. This allows the optimal analysis method to be selected by referring to past analysis data.

[0061] The AI ​​analysis method can estimate the baby's emotions and adjust the frequency of analysis based on the estimated baby's emotions. The AI ​​analysis method can, for example, perform analysis more frequently when the baby smiles. The AI ​​analysis method can also perform analysis more frequently when the baby is crying. For example, the AI ​​analysis method can perform analysis more frequently when the baby is crying. The AI ​​analysis method can also perform analysis more frequently when the baby is excited. For example, the AI ​​analysis method can perform analysis more frequently when the baby is excited. This makes it possible to obtain accurate analysis results by adjusting the frequency of analysis based on the baby's emotions.

[0062] AI analysis methods can be selected based on the data acquisition date. For example, an AI analysis method can be selected to analyze a baby's smile based on the latest data. Similarly, an AI analysis method can be selected to analyze a baby's crying based on the latest data. Furthermore, an AI analysis method can be selected to analyze a baby's movements based on the latest data. By selecting an analysis method based on the data acquisition date, accurate analysis results can be obtained.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The baby growth monitoring system can also be equipped with an environmental monitoring unit. This unit acquires environmental data such as temperature, humidity, and illuminance around the baby and provides it to the analysis unit. For example, if the temperature in the baby's room is inappropriate, the environmental monitoring unit can suggest temperature adjustments to the parents. It can also suggest the use of a humidifier if the humidity is low. Furthermore, if the illuminance is inappropriate, it can suggest adjusting the lighting. In this way, the baby growth monitoring system can provide advice on maintaining an appropriate environment around the baby.

[0065] The baby growth monitoring system may further include a movement prediction unit. The movement prediction unit analyzes the baby's movement data obtained from the acquisition unit and predicts the next possible movement. For example, the movement prediction unit can detect the baby's movement of reaching out and predict the baby's subsequent movement of grabbing a toy. It can also detect the baby's movement of trying to roll over and predict the baby's subsequent movement of completing the rollover. It can also detect the baby's movement of trying to stand up and predict the baby's subsequent movement of starting to walk. This allows the baby growth monitoring system to predict the baby's next movement and provide appropriate support to parents.

[0066] The baby growth monitoring system may further include a health monitoring unit. The health monitoring unit acquires biological data such as the baby's body temperature, heart rate, and respiratory rate, and provides the data to the analysis unit. For example, the health monitoring unit may suggest to the parents that they see a doctor if the baby's body temperature is high. It may also suggest that the baby rest if the heart rate is abnormally high. Furthermore, it may prompt emergency treatment if the respiratory rate is abnormally low. This enables the baby growth monitoring system to monitor the baby's health condition in real time, detect abnormalities early, and take appropriate measures.

[0067] The baby growth monitoring system can further include a learning support unit. The learning support unit suggests appropriate learning activities to parents based on the baby's growth data. For example, if the baby repeats a specific action, the learning support unit can suggest games to encourage that action. Also, if the baby begins to learn a new word, the learning support unit can suggest communication using that word. Furthermore, if the baby responds to a specific sound, the learning support unit can suggest games using that sound. In this way, the baby growth monitoring system suggests learning activities according to the baby's growth, allowing parents to support their baby's growth.

[0068] The baby growth monitoring system may further include a sleep monitoring unit. The sleep monitoring unit records the baby's sleep patterns and provides them to the analysis unit. For example, the sleep monitoring unit may record the baby's sleep duration and sleep quality and suggest an appropriate sleep environment to the parent. If the baby wakes up in the middle of the night, the sleep monitoring unit may analyze the cause and suggest a solution to the parent. Furthermore, the sleep monitoring unit may record the baby's sleep patterns over the long term and provide sleep advice according to the baby's growth. This allows the baby growth monitoring system to properly manage the baby's sleep and support healthy growth.

[0069] The baby growth monitoring system can further include a nutrition management unit. The nutrition management unit records the baby's dietary data and provides it to the analysis unit. For example, the nutrition management unit can record the baby's food preferences and suggest nutritionally balanced meals to the parents. If the baby has an allergic reaction to a particular food, it can also suggest that the baby avoid that food. It can also suggest the amount and type of food to eat according to the baby's growth. In this way, the baby growth monitoring system can support the baby's nutritional management and promote healthy growth.

[0070] The processing flow of the first embodiment will be briefly explained below.

[0071] Step 1: The acquisition unit uses a camera or a sensor to record the baby's movements and facial expressions. For example, the acquisition unit can record the baby's smiling and crying faces, and the movements of its limbs in real time. It is also possible to use a high-resolution camera to record the baby's facial expressions in detail, or to use a motion sensor to detect the baby's movements. It is also possible to use an audio sensor to record the baby's voice. Step 2: The analysis unit uses AI to analyze the data obtained from the acquisition unit and determine the baby's mood and health condition. For example, deep learning can be used to analyze the baby's facial expressions to estimate the baby's mood. Machine learning algorithms can also be used to analyze the baby's movement patterns to determine the baby's health condition. Furthermore, the baby's voice data can be analyzed to estimate the baby's emotions. Step 3: The provider suggests actions to the parent based on the results of the analysis unit. For example, it suggests playtime when the baby is in a good mood. It can also suggest meals based on the baby's health condition. It can also provide sleep advice based on the baby's sleep condition.

[0072] (Example 2) A baby growth monitoring system according to an embodiment of the present invention uses cameras and sensors to record a baby's movements and facial expressions, analyzes the data using AI, and recommends appropriate actions to parents. This system monitors a baby's growth and development in real time and provides appropriate care. For example, a camera and sensors can record a baby's movements and facial expressions in real time, and an AI analyzes the input data to determine the baby's mood and health. The AI ​​then recommends appropriate actions to parents based on the analysis results. This service allows parents to monitor their baby's growth and development in real time and provide appropriate care. For example, playing with a baby when they're in a good mood can deepen the bond between parent and child. Furthermore, understanding a baby's health status allows for early detection of abnormalities and appropriate treatment. Furthermore, combining and analyzing this data with other health data can provide more accurate advice. This allows parents to monitor their baby's growth and development in real time and provide appropriate care.

[0073] The baby growth monitoring system according to this embodiment comprises an acquisition unit, an analysis unit, and a provision unit. The acquisition unit records the baby's movements and facial expressions using a camera or sensor. For example, the acquisition unit can record the baby's smiles, crying faces, and hand and foot movements in real time. The acquisition unit can, for example, use a high-resolution camera to record the baby's facial expressions in detail. The acquisition unit can also use a motion sensor to detect the baby's movements. Furthermore, the acquisition unit can also use a voice sensor to record the baby's voice. The analysis unit uses AI to analyze the data obtained from the acquisition unit and determine the baby's mood and health. For example, the analysis unit uses deep learning to analyze the baby's facial expressions and estimate the baby's mood. The analysis unit can also use machine learning algorithms to analyze the baby's movement patterns and determine the baby's health. Furthermore, the analysis unit can also analyze the baby's voice data and estimate the baby's emotions. The provision unit suggests actions to the parents based on the results of the analysis unit. For example, the provision unit suggests playtime when the baby is in a good mood. The provision unit can also suggest meals based on the baby's health. Furthermore, the providing unit can provide sleep advice based on the baby's sleep state. As a result, the baby growth monitoring system according to the embodiment allows parents to understand the growth and development of their baby in real time and provide appropriate care.

[0074] The acquisition unit can record the baby's movements and facial expressions using a camera or sensor. For example, the acquisition unit can use a high-resolution camera to record the baby's facial expressions in detail. For example, the acquisition unit can record the baby's smiles and crying faces in high resolution. The acquisition unit can also use a motion sensor to detect the baby's movements. For example, the acquisition unit can detect and record the baby's arm and leg movements in real time. Furthermore, the acquisition unit can use a sound sensor to record the baby's voice. For example, the acquisition unit can record the baby's cries and laughter with high sensitivity. This allows for the acquisition of accurate data by recording the baby's movements and facial expressions in real time.

[0075] The analysis unit uses AI to analyze data obtained from the acquisition unit and determine the baby's mood and health. For example, the analysis unit can use deep learning to analyze the baby's facial expressions and estimate the baby's mood. For instance, it can analyze the baby's smiles and crying faces to determine the baby's mood. The analysis unit can also use machine learning algorithms to analyze the baby's movement patterns and determine the baby's health. For example, it can analyze the baby's arm and leg movement patterns to determine the baby's health. Furthermore, the analysis unit can analyze the baby's voice data and estimate the baby's emotions. For example, it can analyze the baby's cries and laughter to determine the baby's emotions. In this way, by using AI, the baby's mood and health can be accurately determined.

[0076] The providing unit can suggest an action to the parent based on the results of the analysis unit. For example, the providing unit suggests a game to play when the baby is in a good mood. For example, the providing unit can suggest a game to the parent when the baby smiles. The providing unit can also suggest a meal based on the baby's health condition. For example, the providing unit can suggest a meal to the parent when the baby's health condition is good. Furthermore, the providing unit can provide sleep advice based on the baby's sleeping condition. For example, the providing unit can provide sleep advice to the parent when the baby's sleeping condition is good. This improves baby care by suggesting an appropriate action to the parent based on the analysis results.

[0077] The acquisition unit can estimate the baby's emotions and adjust the timing of recording based on the estimated baby's emotions. The acquisition unit, for example, detects the moment the baby smiles and records a high-resolution photo at that timing. For example, the acquisition unit can detect the moment the baby smiles and record that moment in high resolution. The acquisition unit can also detect the moment the baby starts to cry and record a video at that timing so that the parent can review it later. For example, the acquisition unit can detect the moment the baby starts to cry and record that moment in video so that the parent can review it later. The acquisition unit can also detect the moment the baby falls asleep and record environmental sound and lighting data at that timing. For example, the acquisition unit can detect the moment the baby falls asleep and record that moment together with environmental sound and lighting data. In this way, by adjusting the recording timing based on the baby's emotions, important moments can be recorded without missing.

[0078] The acquisition unit can analyze data on the baby's past movements and facial expressions and select an optimal recording method. The acquisition unit can, for example, analyze time periods when the baby smiled in the past and focus on recording during those time periods. For example, the acquisition unit can analyze time periods when the baby smiled in the past and focus on recording during those time periods. The acquisition unit can also analyze time periods when the baby cried in the past and focus on recording during those time periods. For example, the acquisition unit can analyze time periods when the baby cried in the past and focus on recording during those time periods. The acquisition unit can also analyze time periods when the baby was active in the past and prioritize video recording during those time periods. For example, the acquisition unit can analyze time periods when the baby was active in the past and prioritize video recording during those time periods. In this way, an optimal recording method can be selected by analyzing past data.

[0079] The acquisition unit can perform filtering based on the baby's current activity and environment when recording. For example, when the baby is playing, the acquisition unit can prioritize recording scenes with a lot of movement. For example, when the baby is playing, the acquisition unit can prioritize recording scenes with a lot of movement. Furthermore, when the baby is sleeping, the acquisition unit can filter and record quiet environmental sounds. For example, when the baby is sleeping, the acquisition unit can filter and record quiet environmental sounds. Furthermore, when the baby is eating, the acquisition unit can mainly record the baby's eating behavior. For example, when the baby is eating, the acquisition unit can mainly record the baby's eating behavior. In this way, important data can be preferentially recorded by filtering based on the current activity and environment.

[0080] The acquisition unit can estimate the baby's emotions and determine the priority of data to be recorded based on the estimated baby's emotions. The acquisition unit, for example, prioritizes recording of moments when the baby smiles. For example, the acquisition unit can prioritize recording of moments when the baby smiles. The acquisition unit can also prioritize recording of moments when the baby starts to cry. For example, the acquisition unit can prioritize recording of moments when the baby starts to cry. Furthermore, the acquisition unit can also prioritize recording of moments when the baby is excited. For example, the acquisition unit can prioritize recording of moments when the baby is excited. In this way, by determining the priority of data based on the baby's emotions, important data can be preferentially recorded.

[0081] When recording, the acquisition unit can prioritize recording highly relevant data by taking into account the geographical location information of the baby. For example, when the baby is in a park, the acquisition unit can prioritize recording scenes of the baby playing on playground equipment. For example, when the baby is in a park, the acquisition unit can prioritize recording scenes of the baby playing on playground equipment. Furthermore, when the baby is at home, the acquisition unit can prioritize recording scenes of the baby interacting with family. For example, when the baby is at home, the acquisition unit can prioritize recording scenes of the baby interacting with family. Furthermore, when the baby is in a hospital, the acquisition unit can prioritize recording details of the baby's examination and treatment. For example, when the baby is in a hospital, the acquisition unit can prioritize recording details of the baby's examination and treatment. In this way, highly relevant data can be prioritized by taking into account the geographical location information.

[0082] The acquisition unit can analyze the baby's social media activity during recording and record related data. For example, the acquisition unit can analyze time periods when many baby photos are posted and record related data. For example, the acquisition unit can analyze time periods when many baby photos are posted and record related data. The acquisition unit can also analyze time periods when many baby videos are shared and prioritize video recording during those time periods. For example, the acquisition unit can analyze time periods when many baby videos are shared and prioritize video recording during those time periods. Furthermore, the acquisition unit can analyze time periods when many baby videos are shared and prioritize video recording during those time periods. For example, the acquisition unit can analyze time periods when many baby growth records are commented on and record related data during those time periods. In this way, related data can be recorded by analyzing social media activity.

[0083] The analysis unit can estimate the baby's emotions and adjust the way the analysis is presented based on the estimated baby's emotions. For example, if the baby smiles, the analysis unit can display the analysis result using positive expressions. For example, if the baby smiles, the analysis unit can display the analysis result using positive expressions. Furthermore, if the baby is crying, the analysis unit can display the analysis result including comforting advice to the parent. For example, if the baby is crying, the analysis unit can display the analysis result including comforting advice to the parent. Furthermore, if the baby is excited, the analysis unit can display the analysis result including play suggestions to the parent. For example, if the baby is excited, the analysis unit can display the analysis result including play suggestions to the parent. In this way, by adjusting the way the analysis is presented based on the baby's emotions, appropriate information can be provided to the parent.

[0084] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can analyze data related to the baby's health condition in detail and provide specific advice to the parent. For example, the analysis unit can analyze data related to the baby's health condition in detail and provide specific advice to the parent. The analysis unit can also analyze data related to the baby's mood briefly and provide simple advice to the parent. For example, the analysis unit can analyze data related to the baby's mood briefly and provide simple advice to the parent. Furthermore, the analysis unit can analyze data related to the baby's activity with a medium level of detail and provide appropriate advice to the parent. For example, the analysis unit can analyze data related to the baby's activity with a medium level of detail and provide appropriate advice to the parent. In this way, by adjusting the level of detail of the analysis based on the importance of the data, appropriate information can be provided to the parent.

[0085] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit can apply a medical analysis algorithm to baby's health data. For example, the analysis unit can apply a medical analysis algorithm to baby's health data. The analysis unit can also apply an emotion analysis algorithm to baby's mood data. For example, the analysis unit can apply an emotion analysis algorithm to baby's mood data. Furthermore, the analysis unit can also apply a movement analysis algorithm to baby's activity data. For example, the analysis unit can apply a movement analysis algorithm to baby's activity data. In this way, by applying different analysis algorithms depending on the category of data, accurate analysis results can be obtained.

[0086] The analysis unit can estimate the baby's emotions and adjust the length of the analysis based on the estimated baby's emotions. For example, if the baby smiles, the analysis unit can display a short analysis result. For example, if the baby smiles, the analysis unit can display a short analysis result. Furthermore, if the baby is crying, the analysis unit can display a detailed analysis result. For example, if the baby is crying, the analysis unit can display a detailed analysis result. Furthermore, if the baby is excited, the analysis unit can display a medium-length analysis result. For example, if the baby is excited, the analysis unit can display a medium-length analysis result. In this way, by adjusting the length of the analysis based on the baby's emotions, appropriate information can be provided to parents.

[0087] During analysis, the analysis unit can determine the priority of analysis based on the time of data acquisition. For example, the analysis unit can prioritize the most recent data in analysis and provide prompt advice to the parent. For example, the analysis unit can prioritize the most recent data in analysis and provide prompt advice to the parent. The analysis unit can also analyze current data while referring to past data. For example, the analysis unit can analyze current data while referring to past data. Furthermore, the analysis unit can prioritize analysis of data acquired intensively during a specific period. For example, the analysis unit can prioritize analysis of data acquired intensively during a specific period. In this way, by determining the priority of analysis based on the time of data acquisition, prompt advice can be provided.

[0088] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit can associate and analyze the baby's health data and mood data, and provide comprehensive advice to the parent. For example, the analysis unit can associate and analyze the baby's health data and mood data, and provide comprehensive advice to the parent. The analysis unit can also associate and analyze the baby's activity data and sleep data, and suggest appropriate care to the parent. For example, the analysis unit can associate and analyze the baby's activity data and sleep data, and suggest appropriate care to the parent. Furthermore, the analysis unit can associate and analyze the baby's dietary data and growth data, and provide nutritional management advice to the parent. For example, the analysis unit can associate and analyze the baby's dietary data and growth data, and provide nutritional management advice to the parent. In this way, comprehensive advice can be provided by adjusting the order of analysis based on the relevance of the data.

[0089] The providing unit can estimate the baby's emotions and adjust the way in which the suggestion is expressed based on the estimated baby's emotions. For example, when the baby smiles, the providing unit makes a play suggestion to the parent using positive expression. For example, when the baby smiles, the providing unit can make a play suggestion to the parent using positive expression. Furthermore, when the baby is crying, the providing unit can make a suggestion to the parent that includes comforting advice. For example, when the baby is crying, the providing unit can make a suggestion to the parent that includes comforting advice. Furthermore, when the baby is excited, the providing unit can make a play suggestion to the parent. For example, when the baby is excited, the providing unit can make a play suggestion to the parent. In this way, by adjusting the way in which the suggestion is expressed based on the baby's emotions, it is possible to suggest an appropriate action to the parent.

[0090] The providing unit may adjust the level of detail of the suggestion based on the importance of the action when suggesting the action. For example, the providing unit may suggest an action related to the baby's health in detail and provide specific advice to the parent. For example, the providing unit may suggest an action related to the baby's health in detail and provide specific advice to the parent. The providing unit may also briefly suggest an action related to the baby's mood and provide simple advice to the parent. For example, the providing unit may briefly suggest an action related to the baby's mood and provide simple advice to the parent. Furthermore, the providing unit may suggest an action related to the baby's activity with a medium level of detail and provide moderate advice to the parent. For example, the providing unit may suggest an action related to the baby's activity with a medium level of detail and provide moderate advice to the parent. In this way, by adjusting the level of detail of the suggestion based on the importance of the action, appropriate advice can be provided to the parent.

[0091] The providing unit can apply different suggestion algorithms depending on the category of the action when making a suggestion. For example, the providing unit applies a medical suggestion algorithm to an action related to the baby's health. For example, the providing unit can apply a medical suggestion algorithm to an action related to the baby's health. The providing unit can also apply an emotion analysis algorithm to an action related to the baby's mood. For example, the providing unit can apply an emotion analysis algorithm to an action related to the baby's mood. The providing unit can also apply a motion analysis algorithm to an action related to the baby's activity. For example, the providing unit can apply a motion analysis algorithm to an action related to the baby's activity. In this way, by applying different suggestion algorithms depending on the category of the action, it is possible to suggest an appropriate action to the parent.

[0092] The providing unit can estimate the baby's emotion and adjust the length of the suggestion based on the estimated baby's emotion. The providing unit, for example, makes a short suggestion when the baby smiles. For example, the providing unit can make a short suggestion when the baby smiles. The providing unit can also make a detailed suggestion when the baby is crying. For example, the providing unit can make a detailed suggestion when the baby is crying. Furthermore, the providing unit can also make a medium-length suggestion when the baby is excited. For example, the providing unit can make a medium-length suggestion when the baby is excited. In this way, by adjusting the length of the suggestion based on the baby's emotion, it is possible to suggest an appropriate action to the parent.

[0093] When making suggestions, the providing unit can determine the priority of the suggestions based on the implementation time of the actions. The providing unit, for example, gives priority to suggesting actions with a high urgency. For example, the providing unit can give priority to suggesting actions with a high urgency. The providing unit can also give priority to suggesting actions that should be performed periodically. For example, the providing unit can give priority to suggesting actions that should be performed periodically. Furthermore, the providing unit can also give priority to suggesting actions that should be performed from a long-term perspective. For example, the providing unit can give priority to suggesting actions that should be performed from a long-term perspective. In this way, by determining the priority of the suggestions based on the implementation time of the actions, it is possible to suggest appropriate actions to the parent.

[0094] When making suggestions, the providing unit can adjust the order of suggestions based on the relevance of the actions. For example, the providing unit gives the highest priority to suggesting actions related to the baby's health. For example, the providing unit can suggest actions related to the baby's health as the highest priority. The providing unit can also suggest actions related to the baby's mood next. For example, the providing unit can suggest actions related to the baby's mood next. The providing unit can also suggest actions related to the baby's activity last. For example, the providing unit can suggest actions related to the baby's activity last. In this way, by adjusting the order of suggestions based on the relevance of the actions, appropriate actions can be suggested to the parent.

[0095] The camera installation method can estimate the baby's emotions and adjust the installation position of the camera based on the estimated baby's emotions. The camera installation method can, for example, identify a location where the baby smiles and install a camera at that location. For example, the camera installation method can identify a location where the baby smiles and install a camera at that location. The camera installation method can also identify a location where the baby cries and install a camera at that location. For example, the camera installation method can identify a location where the baby often plays and install a camera at that location. For example, the camera installation method can identify a location where the baby often plays and install a camera at that location. In this way, by adjusting the installation position of the camera based on the baby's emotions, it is possible to record important moments without missing any.

[0096] The camera placement method can be determined by analyzing the baby's past movement patterns. For example, the camera can be placed in locations where the baby frequently moved in the past. It can also be placed in locations where the baby stayed for extended periods in the past. Furthermore, the camera can be placed in locations where the baby played in the past. By analyzing past movement patterns, the optimal camera placement method can be selected.

[0097] The camera's placement method can estimate the baby's emotions and adjust the camera's angle based on those emotions. For example, the camera can be pointed in the direction the baby smiles. It can also be pointed in the direction the baby cries. Furthermore, the camera can be pointed in the direction the baby plays. By adjusting the camera's angle based on the baby's emotions, important moments can be recorded without being missed.

[0098] When installing the camera, the optimal installation method can be selected by taking into consideration the area where the baby is active. For example, the camera can be installed in an area where the baby often plays. For example, the camera can be installed in an area where the baby often plays. Furthermore, the camera can also be installed in an area where the baby often sleeps. For example, the camera can be installed in an area where the baby often sleeps. Furthermore, the camera can also be installed in an area where the baby often eats. For example, the camera can be installed in an area where the baby often eats. In this way, the optimal camera installation method can be selected by taking into consideration the area where the baby is active.

[0099] The specific type of sensor can be used to estimate the baby's emotion and select the type of sensor to be used based on the estimated baby's emotion. For example, a face recognition sensor can be used to detect the baby's smile. For example, a face recognition sensor can be used to detect the baby's smile. Furthermore, a voice sensor can be used to detect the baby's cry. For example, a voice sensor can be used to detect the baby's cry. Furthermore, a motion sensor can be used to detect the baby's movement. For example, a motion sensor can be used to detect the baby's movement. By selecting the type of sensor to be used based on the baby's emotion, accurate data can be obtained.

[0100] When selecting a specific sensor type, the optimal sensor can be selected by analyzing past data of the baby. For example, a face recognition sensor can be selected by analyzing data of past smiling faces of the baby. Furthermore, a voice sensor can be selected by analyzing data of past crying faces of the baby. For example, a voice sensor can be selected by analyzing data of past crying faces of the baby. Furthermore, a movement sensor can be selected by analyzing data of past active movements of the baby. For example, a movement sensor can be selected by analyzing data of past active movements of the baby. In this way, the optimal sensor can be selected by analyzing past data.

[0101] A specific type of sensor can estimate the baby's emotions and adjust the sensitivity of the sensor based on the estimated baby's emotions. A specific type of sensor can, for example, increase the sensitivity of a face recognition sensor when the baby smiles. For example, a specific type of sensor can increase the sensitivity of a face recognition sensor when the baby smiles. A specific type of sensor can also increase the sensitivity of a sound sensor when the baby is crying. For example, a specific type of sensor can increase the sensitivity of a sound sensor when the baby is crying. A specific type of sensor can also increase the sensitivity of a movement sensor when the baby is excited. For example, a specific type of sensor can increase the sensitivity of a movement sensor when the baby is excited. This allows accurate data to be obtained by adjusting the sensitivity of the sensor based on the baby's emotions.

[0102] The type of sensor to choose from can be determined by considering the baby's activity environment. For example, if the baby spends a lot of time indoors, an indoor sensor can be selected. Similarly, if the baby spends a lot of time outdoors, an outdoor sensor can be selected. Furthermore, if the baby plays with water, a waterproof sensor can be selected. This allows for the selection of the most suitable sensor by considering the baby's activity environment.

[0103] The AI ​​analysis method can estimate the baby's emotions and adjust the analysis method based on the estimated emotions. For example, if the baby smiles, the AI ​​analysis method can apply a positive emotion analysis method. For example, if the baby smiles, the AI ​​analysis method can apply a positive emotion analysis method. For example, if the baby cries, the AI ​​analysis method can apply a negative emotion analysis method. For example, if the baby cries, the AI ​​analysis method can apply a negative emotion analysis method. For example, if the baby is excited, the AI ​​analysis method can apply a method to analyze the excited state. For example, if the baby is excited, the AI ​​analysis method can apply a method to analyze the excited state. In this way, by adjusting the analysis method based on the baby's emotions, accurate analysis results can be obtained.

[0104] When selecting an analysis method, an AI analysis method can refer to past analysis data to select the optimal method. For example, an AI analysis method can select a method to analyze a baby's smile based on past analysis data. For example, an AI analysis method can select a method to analyze a baby's smile based on past analysis data. Furthermore, an AI analysis method can also select a method to analyze a baby's cry based on past analysis data. For example, an AI analysis method can select a method to analyze a baby's cry based on past analysis data. Furthermore, an AI analysis method can also select a method to analyze a baby's movements based on past analysis data. For example, an AI analysis method can select a method to analyze a baby's movements based on past analysis data. This allows the optimal analysis method to be selected by referring to past analysis data.

[0105] The AI ​​analysis method can estimate the baby's emotions and adjust the frequency of analysis based on the estimated baby's emotions. The AI ​​analysis method can, for example, perform analysis more frequently when the baby smiles. The AI ​​analysis method can also perform analysis more frequently when the baby is crying. For example, the AI ​​analysis method can perform analysis more frequently when the baby is crying. The AI ​​analysis method can also perform analysis more frequently when the baby is excited. For example, the AI ​​analysis method can perform analysis more frequently when the baby is excited. This makes it possible to obtain accurate analysis results by adjusting the frequency of analysis based on the baby's emotions.

[0106] AI analysis methods can be selected based on the data acquisition date. For example, an AI analysis method can be selected to analyze a baby's smile based on the latest data. Similarly, an AI analysis method can be selected to analyze a baby's crying based on the latest data. Furthermore, an AI analysis method can be selected to analyze a baby's movements based on the latest data. By selecting an analysis method based on the data acquisition date, accurate analysis results can be obtained. === Hard Collateral 1-1 === Each of the multiple elements including the acquisition unit, analysis unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit records the baby's movements and facial expressions using the camera 42 and audio sensor of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and AI analyzes the data obtained from the acquisition unit to determine the baby's mood and health condition. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and suggests appropriate actions to the parent based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements, including the acquisition unit, analysis unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit records the baby's movements and facial expressions using the camera 42 and audio sensor of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and AI analyzes the data obtained from the acquisition unit to determine the baby's mood and health condition. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and suggests appropriate actions to the parent based on the analysis results. === Hard Collateral 1-3 === Each of the multiple elements described above, including the acquisition unit, analysis unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit records the baby's movements and facial expressions using the camera 42 and voice sensor of the headset terminal 314. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, where AI analyzes the data obtained from the acquisition unit to determine the baby's mood and health condition. The provision unit is implemented in the control unit 46A of the headset terminal 314, where it suggests appropriate actions to the parents based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements described above, including the acquisition unit, analysis unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit records the baby's movements and facial expressions using the camera 42 and sound sensor of the robot 414. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, where an AI analyzes the data obtained from the acquisition unit to determine the baby's mood and health condition. The provision unit is implemented, for example, by the control unit 46A of the robot 414, and proposes appropriate actions to the parents based on the analysis results.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The baby growth monitoring system can also be equipped with an environmental monitoring unit. This unit acquires environmental data such as temperature, humidity, and illuminance around the baby and provides it to the analysis unit. For example, if the temperature in the baby's room is inappropriate, the environmental monitoring unit can suggest temperature adjustments to the parents. It can also suggest the use of a humidifier if the humidity is low. Furthermore, if the illuminance is inappropriate, it can suggest adjusting the lighting. In this way, the baby growth monitoring system can provide advice on maintaining an appropriate environment around the baby.

[0109] The baby growth monitoring system may further include a voice recognition unit. The voice recognition unit analyzes the baby's crying and laughter and provides the resulting voice data to the analysis unit. For example, the voice recognition unit may analyze the baby's crying pattern to determine whether the baby is crying because he or she is hungry or because he or she is sleepy. It may also analyze the baby's laughter pattern to determine the circumstances in which the baby is laughing. The voice recognition unit may also analyze the tone and rhythm of the baby's voice to estimate the baby's emotional state. This allows the baby growth monitoring system to more accurately grasp the baby's emotions and state from the baby's voice.

[0110] The baby growth monitoring system may further include a movement prediction unit. The movement prediction unit analyzes the baby's movement data obtained from the acquisition unit and predicts the next possible movement. For example, the movement prediction unit can detect the baby's movement of reaching out and predict the baby's subsequent movement of grabbing a toy. It can also detect the baby's movement of trying to roll over and predict the baby's subsequent movement of completing the rollover. It can also detect the baby's movement of trying to stand up and predict the baby's subsequent movement of starting to walk. This allows the baby growth monitoring system to predict the baby's next movement and provide appropriate support to parents.

[0111] The baby growth monitoring system may further include a health monitoring unit. The health monitoring unit acquires biological data such as the baby's body temperature, heart rate, and respiratory rate, and provides the data to the analysis unit. For example, the health monitoring unit may suggest to the parents that they see a doctor if the baby's body temperature is high. It may also suggest that the baby rest if the heart rate is abnormally high. Furthermore, it may prompt emergency treatment if the respiratory rate is abnormally low. This enables the baby growth monitoring system to monitor the baby's health condition in real time, detect abnormalities early, and take appropriate measures.

[0112] The baby growth monitoring system may further include an emotion feedback unit. The emotion feedback unit feeds back the baby's emotion data obtained from the analysis unit to the parent, thereby supporting the parent in taking appropriate action according to the baby's emotions. For example, if the baby smiles, the emotion feedback unit may suggest to the parent that they should enjoy the moment. If the baby is crying, the emotion feedback unit may also suggest to the parent how to comfort the baby. Furthermore, if the baby is excited, the emotion feedback unit may also suggest a play activity to the parent. In this way, the baby growth monitoring system can support the parent in taking appropriate action according to the baby's emotions.

[0113] The baby growth monitoring system can further include a learning support unit. The learning support unit suggests appropriate learning activities to parents based on the baby's growth data. For example, if the baby repeats a specific action, the learning support unit can suggest games to encourage that action. Also, if the baby begins to learn a new word, the learning support unit can suggest communication using that word. Furthermore, if the baby responds to a specific sound, the learning support unit can suggest games using that sound. In this way, the baby growth monitoring system suggests learning activities according to the baby's growth, allowing parents to support their baby's growth.

[0114] The baby growth monitoring system may further include a communication support unit. The communication support unit supports communication between parents and babies based on the baby's emotional data. For example, if the baby smiles, the communication support unit may suggest to the parent that they share that moment. If the baby is crying, the communication support unit may suggest to the parent how to comfort the baby. Furthermore, if the baby is excited, the communication support unit may suggest a play activity to the parent. In this way, the baby growth monitoring system can facilitate communication between parents and babies and deepen the bond between parents and children.

[0115] The baby growth monitoring system may further include a sleep monitoring unit. The sleep monitoring unit records the baby's sleep patterns and provides them to the analysis unit. For example, the sleep monitoring unit may record the baby's sleep duration and sleep quality and suggest an appropriate sleep environment to the parent. If the baby wakes up in the middle of the night, the sleep monitoring unit may analyze the cause and suggest a solution to the parent. Furthermore, the sleep monitoring unit may record the baby's sleep patterns over the long term and provide sleep advice according to the baby's growth. This allows the baby growth monitoring system to properly manage the baby's sleep and support healthy growth.

[0116] The baby growth monitoring system can further include a nutrition management unit. The nutrition management unit records the baby's dietary data and provides it to the analysis unit. For example, the nutrition management unit can record the baby's food preferences and suggest nutritionally balanced meals to the parents. If the baby has an allergic reaction to a particular food, it can also suggest that the baby avoid that food. It can also suggest the amount and type of food to eat according to the baby's growth. In this way, the baby growth monitoring system can support the baby's nutritional management and promote healthy growth.

[0117] The baby growth monitoring system can also be equipped with an emotion sharing unit. This unit shares the baby's emotional data with the parents, helping them to respond appropriately to the baby's emotions. For example, the unit can notify parents when the baby smiles. It can also analyze the cause of crying and suggest appropriate responses to the parents. Furthermore, it can notify parents of the baby's agitated state, prompting them to take appropriate action. In this way, the baby growth monitoring system can help parents respond appropriately to their baby's emotions.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The acquisition unit uses a camera or a sensor to record the baby's movements and facial expressions. For example, the acquisition unit can record the baby's smiling and crying faces, and the movements of its limbs in real time. It is also possible to use a high-resolution camera to record the baby's facial expressions in detail, or to use a motion sensor to detect the baby's movements. It is also possible to use an audio sensor to record the baby's voice. Step 2: The analysis unit uses AI to analyze the data obtained from the acquisition unit and determine the baby's mood and health condition. For example, deep learning can be used to analyze the baby's facial expressions to estimate the baby's mood. Machine learning algorithms can also be used to analyze the baby's movement patterns to determine the baby's health condition. Furthermore, the baby's voice data can be analyzed to estimate the baby's emotions. Step 3: The provider suggests actions to the parent based on the results of the analysis unit. For example, it suggests playtime when the baby is in a good mood. It can also suggest meals based on the baby's health condition. It can also provide sleep advice based on the baby's sleep condition.

[0120] 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.

[0121] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 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. A system characterized by comprising: an acquisition unit that uses a camera or sensor to record the baby's movements and facial expressions; an analysis unit that uses AI to analyze the data obtained from the acquisition unit and determine the baby's mood and health condition; and a provision unit that suggests actions to the parent based on the results of the analysis unit.

2. The system according to claim 1 , wherein the acquisition unit records the baby's movements and facial expressions using a camera or a sensor.

3. The system according to claim 1, wherein the analysis unit uses AI to analyze the data obtained from the acquisition unit and determine the baby's mood and health condition.

4. The system of claim 1 , wherein the provider suggests an action to the parent based on the results of the analyzer.

5. The acquisition unit Estimates the baby's emotions and adjusts the timing of recording based on the estimated emotions 2. The system of claim 1.

6. The acquisition unit Analyze your baby's past movements and facial expressions to select the best recording method 2. The system of claim 1.

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