System

Abstract

An object of the system according to the embodiment is to identify the request from the facial expression of the baby and notify the mother of the request.SOLUTION: A system includes a face photographing part, an expression analysis part, a data comparison part, and a notification part. The face picture taking unit takes a face picture of a baby. The expression analysis unit analyzes the face photograph taken by the face photograph taking unit. A data comparison part compares the expression analyzed by the expression analysis part with stored data to specify the request of the baby. The notification unit notifies the mother of the request from the baby identified by the data comparison unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to identify a baby's needs from its facial expressions, and it took a long time for the mother to understand what the baby wanted.

[0005] The system according to the embodiment aims to identify the baby's needs from his / her facial expressions and notify the mother. [Means for solving the problem]

[0006] The system according to the embodiment includes a face photographing unit, an expression analysis unit, a data comparison unit, and a notification unit. The face photographing unit takes a face photograph of the baby. The expression analysis unit analyzes the face photograph taken by the face photographing unit. The data comparison unit compares the expression analyzed by the expression analysis unit with accumulated data to identify the baby's needs. The notification unit notifies the mother of the baby's needs identified by the data comparison unit. [Effects of the Invention]

[0007] The system according to the embodiment can identify the baby's needs from his / her facial expressions and notify the mother. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The baby's need identification system according to the embodiment of the present invention is a system that takes a photograph of the baby's face, analyzes the facial expression to identify the baby's needs, and notifies the mother of the baby's needs. This allows the baby's need identification system to accurately identify the baby's needs and notify the mother of the needs.

[0029] A system for identifying a baby's needs according to an embodiment includes a face photographing unit, an expression analysis unit, a data comparison unit, and a notification unit. The face photographing unit takes a face photograph of the baby. For example, the face photographing unit may take a face photograph of the baby using a high-resolution camera. Alternatively, the face photographing unit may take a face photograph of the baby using a smartphone camera. Alternatively, the face photographing unit may take a face photograph of the baby even in the dark using an infrared camera. The expression analysis unit analyzes the face photograph taken by the face photographing unit. For example, the expression analysis unit may analyze the baby's expression using a generative AI (e.g., a text generation AI or a multimodal generation AI). Alternatively, the expression analysis unit may estimate emotions from the baby's expression using an emotion estimation function. Alternatively, the expression analysis unit may detect subtle changes in expression in real time and instantly update the analysis results. The data comparison unit compares the expression analyzed by the expression analysis unit with accumulated data to identify the baby's needs. For example, the data comparison unit may compare the expression analyzed by the expression analysis unit with past expression data to identify the baby's needs. The data comparison unit can also perform time series analysis to analyze the baby's request patterns over the long term. The data comparison unit can also perform clustering to group data of similar babies to identify requests. The notification unit notifies the mother of the baby's request identified by the data comparison unit. For example, the notification unit can send a notification to the mother's smartphone. The notification unit can also send a notification to devices such as a smartwatch or a smart speaker. The notification unit can also share the content of the notification to the mother with other family members and caregivers. In this way, the baby request identification system according to the embodiment can accurately identify the baby's request and notify the mother. For example, the mother can respond quickly to the baby's request. The mother can accurately understand the baby's request. The mother can also take appropriate action according to the baby's request.

[0030] The facial photography unit takes multiple photos at different angles and lighting conditions, and the generation AI integrates and analyzes them, enabling more accurate facial expression analysis. For example, when taking a photo of a baby's face, the facial photography unit takes multiple photos from different angles, such as from the front, side, and diagonal. This allows the generation AI to integrate information from each angle and analyze the baby's facial expressions more accurately. The facial photography unit also takes photos of the baby's face under different lighting conditions. For example, it takes photos of the baby's face using natural light and artificial light. This allows the generation AI to analyze facial expressions under different lighting conditions and perform more accurate facial expression analysis. This allows for more accurate facial expression analysis by integrating and analyzing photos taken at different angles and lighting conditions.

[0031] The facial expression analysis unit can detect subtle changes in facial expressions in real time and instantly update the analysis results. For example, the facial expression analysis unit takes a photo of a baby's face in real time, and the generation AI analyzes the facial expression on the spot. For example, it captures the moment when a baby changes from smiling to crying and instantly analyzes that change. The facial expression analysis unit also detects the movement of the baby's facial muscles in real time and analyzes those changes. For example, it detects the movement of the baby's eyes and mouth and analyzes those changes. The facial expression analysis unit also monitors changes in the baby's facial expression in real time and analyzes those changes. For example, it captures the moment when a baby's facial expression changes and analyzes those changes. This makes it possible to detect subtle changes in facial expressions in real time and instantly update the analysis results, quickly identifying the baby's needs.

[0032] The facial photographing unit uses an infrared camera to perform accurate facial expression analysis even in dark places. For example, when taking a photo of a baby's face, the facial photographing unit uses an infrared camera to perform accurate facial expression analysis even in dark places. For example, it can accurately capture a baby's facial expression even at night or in a dark room. The facial photographing unit also uses an infrared camera to take a photo of the baby's face and analyzes the photo. For example, by using an infrared camera, it can accurately analyze a baby's facial expression even in dark places. The facial photographing unit also uses an infrared camera to take a photo of the baby's face, and the generation AI analyzes the photo. For example, by using an infrared camera, it can accurately analyze a baby's facial expression even in dark places. As a result, by using an infrared camera, accurate facial expression analysis is possible even in dark places.

[0033] The facial expression analysis unit analyzes the baby's facial expressions in combination with audio analysis, and can also use audio data such as crying and laughter for analysis. For example, the facial expression analysis unit takes a photo of the baby's face and simultaneously collects audio data such as crying and laughter. The generative AI analyzes the audio data and combines it with facial expression analysis to identify the baby's needs. The facial expression analysis unit also uses an audio analysis algorithm to analyze the baby's crying and laughter. For example, it analyzes the volume and frequency of the crying and combines that data with facial expression analysis. The facial expression analysis unit also combines the baby's facial expression analysis and audio analysis to identify needs. For example, if a baby is crying, it analyzes the crying data and combines it with facial expression analysis to identify needs such as "I'm hungry" or "My diaper is wet." This, combined with audio analysis, allows for more accurate identification of the baby's needs.

[0034] The data comparison unit performs time series analysis on the accumulated data to analyze the baby's request pattern over the long term. The data comparison unit, for example, performs time series analysis on the accumulated data to analyze the baby's request pattern over the long term. For example, if the baby often cries at a particular time of day, it determines that the baby is likely hungry at that time. The data comparison unit also analyzes the baby's request pattern using a time series analysis algorithm. For example, the baby's request pattern is identified based on past data. The data comparison unit also analyzes long-term data to identify the baby's request pattern. For example, the data comparison unit analyzes data over several months to identify the baby's request pattern. In this way, the time series analysis makes it possible to analyze the baby's request pattern over the long term.

[0035] The data comparison unit can cluster data of similar babies from the accumulated data and apply a different request identification algorithm to each cluster. The data comparison unit, for example, clusters the accumulated data and groups data of similar babies. For example, babies who cry in a similar manner are classified into the same cluster. The data comparison unit also groups the baby data using a clustering algorithm. For example, clustering is performed based on data of the babies' facial expressions. The data comparison unit also applies a different request identification algorithm to each cluster. For example, an algorithm is applied to identify the requests of babies belonging to a specific cluster. In this way, clustering allows data of similar babies to be grouped, enabling more accurate request identification.

[0036] The data comparison unit can integrate the accumulated data with other baby databases and perform comparative analysis using a wider data set. For example, the data comparison unit integrates the accumulated data with other baby databases and performs comparative analysis using a wider data set. For example, data on babies from different regions or countries is integrated. The data comparison unit also integrates data using a database integration algorithm. For example, data in different formats is converted into a unified format and integrated. The data comparison unit also identifies the baby's needs using a wider data set. For example, data from different cultural regions is used to identify the baby's needs. This allows for more accurate comparative analysis by using a wider data set.

[0037] The data comparison unit can compare the accumulated data with data from different regions or cultural spheres to identify region-specific request patterns. The data comparison unit, for example, compares the accumulated data with data from different regions or cultural spheres to identify region-specific request patterns. For example, if the causes of babies crying are different in a particular region, the request patterns specific to that region can be identified. The data comparison unit also collects region-specific data and performs comparative analysis. For example, it collects and compares data on babies from different regions. The data comparison unit also collects and compares data specific to cultural spheres. For example, it collects and compares data on babies from different cultural spheres. In this way, by comparing with data from different regions or cultural spheres, it is possible to identify region-specific request patterns.

[0038] The notification unit can prioritize and transmit notification content to the mother according to the urgency of the baby's request. The notification unit prioritizes and transmits notification content to the mother according to, for example, the urgency of the baby's request. For example, it gives priority to notifications of highly urgent requests such as "I'm hungry" or "My diaper is wet." The notification unit also determines the urgency of the baby's request using an urgency determination algorithm. For example, it determines the urgency based on data on the baby's facial expression and crying. The notification unit also customizes the notification content according to the urgency. For example, it sends a message urging a prompt response to a highly urgent request. In this way, by prioritizing and transmitting notification content according to the urgency of the request, the mother can respond promptly.

[0039] The notification unit includes specific methods of response and advice in the notification to the mother, thereby enabling the mother to respond quickly. The notification unit, for example, includes specific methods of response and advice in the notification to the mother. For example, it notifies specific instructions such as, "The baby seems hungry. Please give him milk." The notification unit also creates notification content based on expert advice. For example, it notifies advice that incorporates the opinions of pediatricians and childcare experts. The notification unit also suggests specific methods of response based on past data. For example, it notifies how to respond if a similar request had been made in the past. In this way, by including specific methods of response and advice, the mother can respond quickly.

[0040] The notification unit can also send notifications to the mother to devices such as a smartwatch or smart speaker, allowing the mother to receive notifications on multiple devices. For example, the notification unit can send notifications to the mother to a smartwatch, allowing the mother to check the notifications at her fingertips. For example, the baby's requests can be displayed on the smartwatch. The notification unit can also send notifications to a smart speaker, allowing the mother to receive the notifications by voice. For example, the smart speaker can notify the baby's requests by voice. The notification unit can also send notifications to multiple devices, allowing the mother to check the notifications on any device. For example, notifications can be sent to multiple devices such as a smartphone, smartwatch, and smart speaker. This allows notifications to be received on multiple devices, allowing the mother to check the notifications on any device.

[0041] The notification unit can share the content of the notification sent to the mother with other family members and caregivers, allowing them to respond to the baby's requests together. For example, the notification unit shares the content of the notification sent to the mother with other family members and caregivers. For example, the baby's requests are notified to all family members, and they respond together. The notification unit also sends notifications to family members and caregivers, allowing them to respond to the baby's requests together. For example, the baby's requests are shared with all family members, and they respond together. The notification unit also uses an algorithm for sharing the content of the notification. For example, notifications are sent to and shared with devices of family members and caregivers. In this way, by sharing the content of the notification, the baby's requests can be responded to together by all family members.

[0042] The facial expression analysis unit can take into account facial expression changes according to different ages and growth stages when learning a baby's unique facial expressions. For example, the facial expression analysis unit takes into account facial expression changes according to different ages and growth stages when learning a baby's unique facial expressions. For example, it learns facial expression changes during the newborn, infancy, and toddler stages. The facial expression analysis unit also collects and learns facial expression data according to age and growth stage. For example, it collects and learns facial expression data for each month of age. The facial expression analysis unit also uses an algorithm that analyzes facial expression changes according to growth stages. For example, it analyzes and learns facial expression changes for each developmental stage. This allows for more accurate facial expression analysis by taking into account facial expression changes according to different ages and growth stages.

[0043] When learning a baby's unique facial expressions, the facial expression analysis unit can collect facial expression data in different environments (for example, indoors and outdoors, day and night) and perform learning according to the environment. For example, when learning a baby's unique facial expressions, the facial expression analysis unit collects facial expression data in different environments. For example, it analyzes facial expression data in indoor and outdoor and day and night environments. In addition, the facial expression analysis unit uses an facial expression analysis algorithm according to the environment. For example, it performs facial expression analysis according to the indoor and outdoor and day and night environments. In addition, the facial expression analysis unit collects and learns facial expression data in different environments. For example, it collects facial expression data that takes environmental conditions such as temperature and humidity into consideration. In this way, by collecting facial expression data in different environments, learning according to the environment becomes possible.

[0044] The facial expression analysis unit can compare a baby's unique facial expression with data on other babies and identify common facial expression patterns. For example, when learning a baby's unique facial expression, the facial expression analysis unit compares it with data on other babies. For example, it analyzes data on babies with similar facial expressions and identifies common facial expression patterns. The facial expression analysis unit also uses an algorithm to identify common facial expression patterns. For example, it analyzes facial expression data on multiple babies and extracts common patterns. The facial expression analysis unit also compares it with data on other babies and identifies common facial expression patterns. For example, it groups data on babies with specific facial expression patterns and identifies common patterns. This makes it possible to identify common facial expression patterns by comparing it with data on other babies.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The baby's need identification system can further include an audio analysis unit. The audio analysis unit collects and analyzes audio data such as the baby's crying and laughter. For example, by analyzing the volume and frequency of the baby's crying and combining that data with facial expression analysis, the baby's needs can be identified more accurately. The audio analysis unit can also analyze the tone and rhythm of the baby's voice to estimate the baby's emotional state. This makes it possible to more accurately identify the baby's needs by analyzing the audio data.

[0047] The baby's need identification system can further include a temperature sensor unit. The temperature sensor unit measures the baby's body temperature and the ambient temperature and analyzes the data. For example, if the baby's body temperature is high, it can identify a need such as "it's hot." The temperature sensor unit can also monitor changes in the baby's body temperature in real time and notify the mother if there is an abnormality. This allows the baby's needs to be identified more accurately by analyzing the temperature data.

[0048] The system for identifying a baby's needs can further include a motion analysis unit. The motion analysis unit detects the baby's movements and analyzes the data. For example, it can analyze the baby's movements such as waving its hands or kicking its legs and identify the baby's needs from those movements. The motion analysis unit can also learn the baby's movement patterns and identify a request such as "I want to play" when a specific movement is repeated. This makes it possible to more accurately identify the baby's needs by analyzing the movement data.

[0049] The baby's need identification system can further include a location information acquisition unit. The location information acquisition unit acquires the baby's location information and analyzes the data. For example, if the baby is in a specific location, it can identify a request such as "I need to change my diaper." The location information acquisition unit can also analyze the baby's movement patterns and identify a request such as "I want to play" if the baby frequently moves to a specific location. In this way, by analyzing the location information, it is possible to more accurately identify the baby's needs.

[0050] The baby's need identification system can further include a vibration sensor unit. The vibration sensor unit detects vibrations caused by the baby's movements and analyzes the data. For example, if the baby moves vigorously, it can identify a request such as "I feel uncomfortable." The vibration sensor unit can also analyze the strength and frequency of the baby's movements, and if a specific pattern is repeated, it can identify a request such as "I want to play." This makes it possible to more accurately identify the baby's needs by analyzing the vibration data.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The face photographing unit takes a face photograph of the baby. For example, the face photographing unit can take a face photograph of the baby using a high-resolution camera, a smartphone camera, or an infrared camera. Step 2: The facial expression analysis unit analyzes the facial photograph taken by the facial photography unit. For example, it can analyze a baby's facial expression using generative AI and emotion estimation functions, detect subtle changes in facial expression in real time, and instantly update the analysis results. Step 3: The data comparison unit compares the facial expressions analyzed by the facial expression analysis unit with the accumulated data to identify the baby's needs. For example, it can identify the baby's needs by comparing with past facial expression data, or by performing time series analysis or clustering. Step 4: The notification unit notifies the mother of the baby's needs identified by the data comparison unit, for example by sending a notification to the mother's smartphone, smartwatch, smart speaker, or other device, which can also be shared with other family members or caregivers.

[0053] (Example 2) The baby's need identification system according to the embodiment of the present invention is a system that takes a photograph of the baby's face, analyzes the facial expression to identify the baby's needs, and notifies the mother of the baby's needs. This allows the baby's need identification system to accurately identify the baby's needs and notify the mother of the needs.

[0054] A system for identifying a baby's needs according to an embodiment includes a face photographing unit, an expression analysis unit, a data comparison unit, and a notification unit. The face photographing unit takes a face photograph of the baby. For example, the face photographing unit may take a face photograph of the baby using a high-resolution camera. Alternatively, the face photographing unit may take a face photograph of the baby using a smartphone camera. Alternatively, the face photographing unit may take a face photograph of the baby even in the dark using an infrared camera. The expression analysis unit analyzes the face photograph taken by the face photographing unit. For example, the expression analysis unit may analyze the baby's expression using a generative AI (e.g., a text generation AI or a multimodal generation AI). Alternatively, the expression analysis unit may estimate emotions from the baby's expression using an emotion estimation function. Alternatively, the expression analysis unit may detect subtle changes in expression in real time and instantly update the analysis results. The data comparison unit compares the expression analyzed by the expression analysis unit with accumulated data to identify the baby's needs. For example, the data comparison unit may compare the expression analyzed by the expression analysis unit with past expression data to identify the baby's needs. The data comparison unit can also perform time series analysis to analyze the baby's request patterns over the long term. The data comparison unit can also perform clustering to group data of similar babies to identify requests. The notification unit notifies the mother of the baby's request identified by the data comparison unit. For example, the notification unit can send a notification to the mother's smartphone. The notification unit can also send a notification to devices such as a smartwatch or a smart speaker. The notification unit can also share the content of the notification to the mother with other family members and caregivers. In this way, the baby request identification system according to the embodiment can accurately identify the baby's request and notify the mother. For example, the mother can respond quickly to the baby's request. The mother can accurately understand the baby's request. The mother can also take appropriate action according to the baby's request.

[0055] The facial photography unit takes multiple photos at different angles and lighting conditions, and the generation AI integrates and analyzes them, enabling more accurate facial expression analysis. For example, when taking a photo of a baby's face, the facial photography unit takes multiple photos from different angles, such as from the front, side, and diagonal. This allows the generation AI to integrate information from each angle and analyze the baby's facial expressions more accurately. The facial photography unit also takes photos of the baby's face under different lighting conditions. For example, it takes photos of the baby's face using natural light and artificial light. This allows the generation AI to analyze facial expressions under different lighting conditions and perform more accurate facial expression analysis. This allows for more accurate facial expression analysis by integrating and analyzing photos taken at different angles and lighting conditions.

[0056] The facial expression analysis unit can detect subtle changes in facial expressions in real time and instantly update the analysis results. For example, the facial expression analysis unit takes a photo of a baby's face in real time, and the generation AI analyzes the facial expression on the spot. For example, it captures the moment when a baby changes from smiling to crying and instantly analyzes that change. The facial expression analysis unit also detects the movement of the baby's facial muscles in real time and analyzes those changes. For example, it detects the movement of the baby's eyes and mouth and analyzes those changes. The facial expression analysis unit also monitors changes in the baby's facial expression in real time and analyzes those changes. For example, it captures the moment when a baby's facial expression changes and analyzes those changes. This makes it possible to detect subtle changes in facial expressions in real time and instantly update the analysis results, quickly identifying the baby's needs.

[0057] The facial expression analysis unit uses an emotion estimation function to estimate emotions from the baby's facial expression and can identify requests based on those emotions. For example, the facial expression analysis unit takes a photo of the baby's face, and the generation AI estimates the emotion from that expression. For example, a smiling face may be estimated as "happy," and a crying face may be estimated as "sad." The facial expression analysis unit also estimates emotions from the baby's facial expression using an emotion estimation algorithm. For example, it calculates an emotion score based on changes in facial expression. The facial expression analysis unit also estimates emotions from the baby's facial expression and identifies requests based on those emotions. For example, if a baby is crying, it may identify requests such as "I'm hungry" or "My diaper is wet." As a result, the emotion estimation function can be used to identify requests based on the baby's emotions.

[0058] The facial photographing unit uses an infrared camera to perform accurate facial expression analysis even in dark places. For example, when taking a photo of a baby's face, the facial photographing unit uses an infrared camera to perform accurate facial expression analysis even in dark places. For example, it can accurately capture a baby's facial expression even at night or in a dark room. The facial photographing unit also uses an infrared camera to take a photo of the baby's face and analyzes the photo. For example, by using an infrared camera, it can accurately analyze a baby's facial expression even in dark places. The facial photographing unit also uses an infrared camera to take a photo of the baby's face, and the generation AI analyzes the photo. For example, by using an infrared camera, it can accurately analyze a baby's facial expression even in dark places. As a result, by using an infrared camera, accurate facial expression analysis is possible even in dark places.

[0059] The facial expression analysis unit analyzes the baby's facial expressions in combination with audio analysis, and can also use audio data such as crying and laughter for analysis. For example, the facial expression analysis unit takes a photo of the baby's face and simultaneously collects audio data such as crying and laughter. The generative AI analyzes the audio data and combines it with facial expression analysis to identify the baby's needs. The facial expression analysis unit also uses an audio analysis algorithm to analyze the baby's crying and laughter. For example, it analyzes the volume and frequency of the crying and combines that data with facial expression analysis. The facial expression analysis unit also combines the baby's facial expression analysis and audio analysis to identify needs. For example, if a baby is crying, it analyzes the crying data and combines it with facial expression analysis to identify needs such as "I'm hungry" or "My diaper is wet." This, combined with audio analysis, allows for more accurate identification of the baby's needs.

[0060] The facial expression analysis unit uses an emotion estimation function to link the results of analyzing the baby's facial expressions with the mother's emotions, allowing it to customize notification content according to the mother's emotions. For example, the facial expression analysis unit takes a photo of the baby's face, and the generation AI estimates the emotion from the facial expression. The facial expression analysis unit monitors the mother's emotional state in real time and customizes notification content according to the mother's emotions. For example, if the mother is feeling stressed, it sends a notification suggesting ways to relax. The facial expression analysis unit also analyzes the mother's emotional state and customizes notification content according to that emotion. For example, if the mother is tired, it sends an encouraging message. The facial expression analysis unit also customizes notification content according to the mother's emotions, sending notifications that elicit positive emotions. For example, if the mother is feeling anxious, it sends a reassuring message. This customization of notification content according to the mother's emotions reduces the burden on the mother.

[0061] The data comparison unit performs time series analysis on the accumulated data to analyze the baby's request pattern over the long term. The data comparison unit, for example, performs time series analysis on the accumulated data to analyze the baby's request pattern over the long term. For example, if the baby often cries at a particular time of day, it determines that the baby is likely hungry at that time. The data comparison unit also analyzes the baby's request pattern using a time series analysis algorithm. For example, the baby's request pattern is identified based on past data. The data comparison unit also analyzes long-term data to identify the baby's request pattern. For example, the data comparison unit analyzes data over several months to identify the baby's request pattern. In this way, the time series analysis makes it possible to analyze the baby's request pattern over the long term.

[0062] The data comparison unit can cluster data of similar babies from the accumulated data and apply a different request identification algorithm to each cluster. The data comparison unit, for example, clusters the accumulated data and groups data of similar babies. For example, babies who cry in a similar manner are classified into the same cluster. The data comparison unit also groups the baby data using a clustering algorithm. For example, clustering is performed based on data of the babies' facial expressions. The data comparison unit also applies a different request identification algorithm to each cluster. For example, an algorithm is applied to identify the requests of babies belonging to a specific cluster. In this way, clustering allows data of similar babies to be grouped, enabling more accurate request identification.

[0063] The data comparison unit can integrate the accumulated data with other baby databases and perform comparative analysis using a wider data set. For example, the data comparison unit integrates the accumulated data with other baby databases and performs comparative analysis using a wider data set. For example, data on babies from different regions or countries is integrated. The data comparison unit also integrates data using a database integration algorithm. For example, data in different formats is converted into a unified format and integrated. The data comparison unit also identifies the baby's needs using a wider data set. For example, data from different cultural regions is used to identify the baby's needs. This allows for more accurate comparative analysis by using a wider data set.

[0064] The data comparison unit can compare the accumulated data with data from different regions or cultural spheres to identify region-specific request patterns. The data comparison unit, for example, compares the accumulated data with data from different regions or cultural spheres to identify region-specific request patterns. For example, if the causes of babies crying are different in a particular region, the request patterns specific to that region can be identified. The data comparison unit also collects region-specific data and performs comparative analysis. For example, it collects and compares data on babies from different regions. The data comparison unit also collects and compares data specific to cultural spheres. For example, it collects and compares data on babies from different cultural spheres. In this way, by comparing with data from different regions or cultural spheres, it is possible to identify region-specific request patterns.

[0065] The data comparison unit can also use the emotion estimation function to collect the mother's emotional responses from the stored data and analyze the relationship between the mother's emotions and the baby's requests. For example, the data comparison unit uses the emotion estimation function to collect the mother's emotional responses from the stored data and analyze the relationship between the mother's emotions and the baby's requests. For example, when the mother is stressed, the baby's requests often increase. The data comparison unit also collects the mother's emotional response data and compares it with the baby's requests. For example, the data comparison unit monitors the mother's emotional state in real time and compares the data with the baby's requests. The data comparison unit also uses an algorithm to analyze the relationship between the mother's emotions and the baby's requests. For example, the data comparison unit analyzes the mother's emotional data and the baby's request data and identifies the relationship. This allows for a more appropriate response by analyzing the relationship between the mother's emotions and the baby's requests.

[0066] The notification unit can prioritize and transmit notification content to the mother according to the urgency of the baby's request. The notification unit prioritizes and transmits notification content to the mother according to, for example, the urgency of the baby's request. For example, it gives priority to notifications of highly urgent requests such as "I'm hungry" or "My diaper is wet." The notification unit also determines the urgency of the baby's request using an urgency determination algorithm. For example, it determines the urgency based on data on the baby's facial expression and crying. The notification unit also customizes the notification content according to the urgency. For example, it sends a message urging a prompt response to a highly urgent request. In this way, by prioritizing and transmitting notification content according to the urgency of the request, the mother can respond promptly.

[0067] The notification unit includes specific methods of response and advice in the notification to the mother, thereby enabling the mother to respond quickly. The notification unit, for example, includes specific methods of response and advice in the notification to the mother. For example, it notifies specific instructions such as, "The baby seems hungry. Please give him milk." The notification unit also creates notification content based on expert advice. For example, it notifies advice that incorporates the opinions of pediatricians and childcare experts. The notification unit also suggests specific methods of response based on past data. For example, it notifies how to respond if a similar request had been made in the past. In this way, by including specific methods of response and advice, the mother can respond quickly.

[0068] The notification unit can monitor the mother's emotional state in real time using the emotion estimation function and suggest relaxation methods if stress is rising. The notification unit, for example, monitors the mother's emotional state in real time using the emotion estimation function. For example, if the mother is feeling stressed, it sends a notification suggesting relaxation methods. The notification unit also analyzes the mother's emotional state and suggests relaxation methods if stress is rising. For example, it suggests methods such as deep breathing or listening to relaxing music. The notification unit also monitors the mother's emotional state and uses an algorithm that suggests relaxation methods if stress is rising. For example, it determines the stress level based on the mother's heart rate and facial expression data and suggests relaxation methods. In this way, the burden on the mother can be reduced by monitoring the mother's emotional state and suggesting relaxation methods if stress is rising.

[0069] The notification unit can also send notifications to the mother to devices such as a smartwatch or smart speaker, allowing the mother to receive notifications on multiple devices. For example, the notification unit can send notifications to the mother to a smartwatch, allowing the mother to check the notifications at her fingertips. For example, the baby's requests can be displayed on the smartwatch. The notification unit can also send notifications to a smart speaker, allowing the mother to receive the notifications by voice. For example, the smart speaker can notify the baby's requests by voice. The notification unit can also send notifications to multiple devices, allowing the mother to check the notifications on any device. For example, notifications can be sent to multiple devices such as a smartphone, smartwatch, and smart speaker. This allows notifications to be received on multiple devices, allowing the mother to check the notifications on any device.

[0070] The notification unit can share the content of the notification sent to the mother with other family members and caregivers, allowing them to respond to the baby's requests together. For example, the notification unit shares the content of the notification sent to the mother with other family members and caregivers. For example, the baby's requests are notified to all family members, and they respond together. The notification unit also sends notifications to family members and caregivers, allowing them to respond to the baby's requests together. For example, the baby's requests are shared with all family members, and they respond together. The notification unit also uses an algorithm for sharing the content of the notification. For example, notifications are sent to and shared with devices of family members and caregivers. In this way, by sharing the content of the notification, the baby's requests can be responded to together by all family members.

[0071] The notification unit can use the emotion estimation function to customize the notification content according to the mother's emotions and provide a notification that elicits positive emotions. The notification unit, for example, uses the emotion estimation function to customize the notification content according to the mother's emotions. For example, if the mother is feeling stressed, it sends an encouraging message. The notification unit also analyzes the mother's emotional state and provides a notification that elicits positive emotions. For example, if the mother is feeling anxious, it sends a reassuring message. The notification unit also customizes the notification content according to the mother's emotions and uses an algorithm that elicits positive emotions. For example, it customizes the notification content based on the mother's emotion data and elicits positive emotions. In this way, positive emotions can be elicited by customizing the notification content according to the mother's emotions.

[0072] The facial expression analysis unit can take into account facial expression changes according to different ages and growth stages when learning a baby's unique facial expressions. For example, the facial expression analysis unit takes into account facial expression changes according to different ages and growth stages when learning a baby's unique facial expressions. For example, it learns facial expression changes during the newborn, infancy, and toddler stages. The facial expression analysis unit also collects and learns facial expression data according to age and growth stage. For example, it collects and learns facial expression data for each month of age. The facial expression analysis unit also uses an algorithm that analyzes facial expression changes according to growth stages. For example, it analyzes and learns facial expression changes for each developmental stage. This allows for more accurate facial expression analysis by taking into account facial expression changes according to different ages and growth stages.

[0073] The facial expression analysis unit can use the emotion estimation function to estimate an emotion from the baby's unique facial expression and identify a request based on that emotion. The facial expression analysis unit, for example, uses the emotion estimation function to estimate an emotion from the baby's unique facial expression. For example, when the baby makes a specific facial expression, it estimates an emotion such as "I'm hungry." The facial expression analysis unit also uses an emotion estimation algorithm to estimate an emotion from the baby's unique facial expression. For example, it estimates an emotion based on a specific facial expression pattern. The facial expression analysis unit also estimates an emotion from the baby's unique facial expression and identifies a request based on that emotion. For example, when the baby makes a specific facial expression, it identifies a request such as "My diaper is wet." In this way, by using the emotion estimation function, it is possible to estimate an emotion from the baby's unique facial expression and identify a request.

[0074] When learning a baby's unique facial expressions, the facial expression analysis unit can collect facial expression data in different environments (for example, indoors and outdoors, day and night) and perform learning according to the environment. For example, when learning a baby's unique facial expressions, the facial expression analysis unit collects facial expression data in different environments. For example, it analyzes facial expression data in indoor and outdoor and day and night environments. In addition, the facial expression analysis unit uses an facial expression analysis algorithm according to the environment. For example, it performs facial expression analysis according to the indoor and outdoor and day and night environments. In addition, the facial expression analysis unit collects and learns facial expression data in different environments. For example, it collects facial expression data that takes environmental conditions such as temperature and humidity into consideration. In this way, by collecting facial expression data in different environments, learning according to the environment becomes possible.

[0075] The facial expression analysis unit can compare a baby's unique facial expression with data on other babies and identify common facial expression patterns. For example, when learning a baby's unique facial expression, the facial expression analysis unit compares it with data on other babies. For example, it analyzes data on babies with similar facial expressions and identifies common facial expression patterns. The facial expression analysis unit also uses an algorithm to identify common facial expression patterns. For example, it analyzes facial expression data on multiple babies and extracts common patterns. The facial expression analysis unit also compares it with data on other babies and identifies common facial expression patterns. For example, it groups data on babies with specific facial expression patterns and identifies common patterns. This makes it possible to identify common facial expression patterns by comparing it with data on other babies.

[0076] The facial expression analysis unit can use the emotion estimation function to link the baby's unique facial expression with the mother's emotional response and suggest a response method according to the mother's emotions. The facial expression analysis unit, for example, uses the emotion estimation function to link the baby's unique facial expression with the mother's emotional response. For example, if the mother is feeling stressed when the baby makes a specific facial expression, the unit suggests a way to relax. The facial expression analysis unit also uses an algorithm to suggest a response method according to the mother's emotions. For example, the unit suggests a response method based on the mother's emotional data. The facial expression analysis unit also links the baby's unique facial expression with the mother's emotional response and suggests a response method according to the mother's emotions. For example, if the mother is tired, the unit suggests that she should take a rest. In this way, by suggesting a response method according to the mother's emotions, the burden on the mother can be reduced.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The baby's need identification system can further include an audio analysis unit. The audio analysis unit collects and analyzes audio data such as the baby's crying and laughter. For example, by analyzing the volume and frequency of the baby's crying and combining that data with facial expression analysis, the baby's needs can be identified more accurately. The audio analysis unit can also analyze the tone and rhythm of the baby's voice to estimate the baby's emotional state. This makes it possible to more accurately identify the baby's needs by analyzing the audio data.

[0079] The baby's need identification system can further include a temperature sensor unit. The temperature sensor unit measures the baby's body temperature and the ambient temperature and analyzes the data. For example, if the baby's body temperature is high, it can identify a need such as "it's hot." The temperature sensor unit can also monitor changes in the baby's body temperature in real time and notify the mother if there is an abnormality. This allows the baby's needs to be identified more accurately by analyzing the temperature data.

[0080] The system for identifying a baby's needs can further include a motion analysis unit. The motion analysis unit detects the baby's movements and analyzes the data. For example, it can analyze the baby's movements such as waving its hands or kicking its legs and identify the baby's needs from those movements. The motion analysis unit can also learn the baby's movement patterns and identify a request such as "I want to play" when a specific movement is repeated. This makes it possible to more accurately identify the baby's needs by analyzing the movement data.

[0081] The baby's need identification system can further include a location information acquisition unit. The location information acquisition unit acquires the baby's location information and analyzes the data. For example, if the baby is in a specific location, it can identify a request such as "I need to change my diaper." The location information acquisition unit can also analyze the baby's movement patterns and identify a request such as "I want to play" if the baby frequently moves to a specific location. In this way, by analyzing the location information, it is possible to more accurately identify the baby's needs.

[0082] The baby's need identification system can further include a vibration sensor unit. The vibration sensor unit detects vibrations caused by the baby's movements and analyzes the data. For example, if the baby moves vigorously, it can identify a request such as "I feel uncomfortable." The vibration sensor unit can also analyze the strength and frequency of the baby's movements, and if a specific pattern is repeated, it can identify a request such as "I want to play." This makes it possible to more accurately identify the baby's needs by analyzing the vibration data.

[0083] The facial expression analysis unit uses an emotion estimation function to estimate emotions from the baby's facial expression and can identify requests based on those emotions. For example, if the baby is smiling, it estimates the emotion as "happy," and if the baby is crying, it estimates the emotion as "sad." The facial expression analysis unit also estimates emotions from the baby's facial expression using an emotion estimation algorithm. For example, it calculates an emotion score based on changes in facial expression. The facial expression analysis unit also estimates emotions from the baby's facial expression and identifies requests based on those emotions. For example, if the baby is crying, it identifies requests such as "I'm hungry" or "My diaper is wet." As a result, by using the emotion estimation function, it is possible to identify requests based on the baby's emotions.

[0084] The facial expression analysis unit uses the emotion estimation function to link the baby's facial expression analysis results with the mother's emotions, allowing it to customize notification content according to the mother's emotions. For example, a photo of the baby's face is taken, and the generation AI estimates the emotion from the facial expression. The mother's emotional state is monitored in real time, and notification content is customized according to the mother's emotions. For example, if the mother is feeling stressed, a notification suggesting relaxation methods is sent. The facial expression analysis unit also analyzes the mother's emotional state and customizes notification content according to that emotion. For example, if the mother is tired, an encouraging message is sent. The facial expression analysis unit also customizes notification content according to the mother's emotions, sending notifications that elicit positive emotions. For example, if the mother is feeling anxious, a reassuring message is sent. This customization of notification content according to the mother's emotions reduces the burden on the mother.

[0085] The data comparison unit can also use the emotion estimation function to collect the mother's emotional reactions from the accumulated data and analyze the relationship between the mother's emotions and the baby's requests. For example, when the mother is stressed, the baby's requests often increase. The data comparison unit also collects the mother's emotional reaction data and compares it with the baby's requests. For example, it monitors the mother's emotional state in real time and compares that data with the baby's requests. The data comparison unit also uses an algorithm to analyze the relationship between the mother's emotions and the baby's requests. For example, it analyzes the mother's emotional data and the baby's request data and identifies the relationship. This allows for a more appropriate response by analyzing the relationship between the mother's emotions and the baby's requests.

[0086] The notification unit can use the emotion estimation function to monitor the mother's emotional state in real time and suggest relaxation methods if stress is rising. For example, the emotion estimation function is used to monitor the mother's emotional state in real time. For example, if the mother is feeling stressed, a notification suggesting relaxation methods is sent. The notification unit also analyzes the mother's emotional state and suggests relaxation methods if stress is rising. For example, it suggests methods such as deep breathing or listening to relaxing music. The notification unit also monitors the mother's emotional state and uses an algorithm to suggest relaxation methods if stress is rising. For example, it determines the stress level based on the mother's heart rate and facial expression data and suggests relaxation methods. In this way, the burden on the mother can be reduced by monitoring the mother's emotional state and suggesting relaxation methods if stress is rising.

[0087] The notification unit can use the emotion estimation function to customize the notification content according to the mother's emotions and provide a notification that elicits positive emotions. For example, the emotion estimation function is used to customize the notification content according to the mother's emotions. For example, if the mother is feeling stressed, an encouraging message is sent. The notification unit also analyzes the mother's emotional state and provides a notification that elicits positive emotions. For example, if the mother is feeling anxious, a reassuring message is sent. The notification unit also customizes the notification content according to the mother's emotions and uses an algorithm that elicits positive emotions. For example, the notification content is customized based on the mother's emotion data to elicit positive emotions. In this way, positive emotions can be elicited by customizing the notification content according to the mother's emotions.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The face photographing unit takes a face photograph of the baby. For example, the face photographing unit can take a face photograph of the baby using a high-resolution camera, a smartphone camera, or an infrared camera. Step 2: The facial expression analysis unit analyzes the facial photograph taken by the facial photography unit. For example, it can analyze a baby's facial expression using generative AI and emotion estimation functions, detect subtle changes in facial expression in real time, and instantly update the analysis results. Step 3: The data comparison unit compares the facial expressions analyzed by the facial expression analysis unit with the accumulated data to identify the baby's needs. For example, it can identify the baby's needs by comparing with past facial expression data, or by performing time series analysis or clustering. Step 4: The notification unit notifies the mother of the baby's needs identified by the data comparison unit, for example by sending a notification to the mother's smartphone, smartwatch, smart speaker, or other device, which can also be shared with other family members or caregivers.

[0090] 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.

[0091] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0092] 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.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0103] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0104] 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.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0116] 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.

[0117] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0118] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0119] 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.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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.

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 7, a 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0134] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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.

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

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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."

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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, in order to avoid confusion and to 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.

[0156] 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. [Explanation of symbols]

[0157] 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 face photography department takes pictures of babies' faces, an expression analysis unit that analyzes the facial photograph taken by the facial photographing unit; a data comparison unit that compares the facial expression analyzed by the facial expression analysis unit with stored data to identify the baby's needs; a notification unit that notifies the mother of the baby's request identified by the data comparison unit. A system characterized by:

2. The face photographing unit By taking multiple photos at different angles and lighting conditions and then integrating and analyzing them, generative AI can perform more accurate facial expression analysis.

2. The system of claim 1.

3. The data comparison unit Perform time series analysis on the accumulated data to analyze demand patterns over the long term.

2. The system of claim 1.

4. The notification unit The notification contents to the mother are sent with priority according to the urgency of the request.

2. The system of claim 1.

5. The facial expression analysis unit The emotion estimation function is used to estimate the emotion from the baby's facial expression, and the request is identified based on the emotion.

2. The system of claim 1.

6. The data comparison unit Using an emotion estimation function, the emotional changes of the baby are analyzed from past data, and the request is identified based on the emotional changes.

2. The system of claim 1.

7. The notification unit Using an emotion estimation function, the system monitors the mother's emotional state in real time and suggests relaxation methods if stress levels increase.

2. The system of claim 1.

8. The facial expression analysis unit An emotion estimation function is used to estimate an emotion from the baby's unique facial expression, and the request is identified based on the emotion.

2. The system of claim 1.

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