System

Abstract

An object of a system according to an embodiment is to analyze an emotion of a baby and take an appropriate action.SOLUTION: A system includes an emotion analysis unit, an alert unit, a log recording unit, a trend prediction unit, and a relaxation content providing unit. The emotion analysis unit analyzes an emotion of the baby. The alert unit transmits an alert when the emotion analyzed by the emotion analysis unit is unstable. The log recording unit records an emotion history analyzed by the emotion analysis unit. The trend prediction unit predicts an emotion trend based on the emotion history recorded by the log recording unit. The relaxation content provider provides relaxation music or video on the basis of the emotion analyzed by the emotion analyzer.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult to analyze a baby's emotions in real time and respond appropriately.

[0005] The system according to the embodiment aims to analyze the emotions of a baby and take appropriate measures. [Means for solving the problem]

[0006] The system according to the embodiment includes an emotion analysis unit, an alert unit, a log recording unit, a trend prediction unit, and a relaxing content provision unit. The emotion analysis unit analyzes the baby's emotions. The alert unit sends an alert when the emotions analyzed by the emotion analysis unit are unstable. The log recording unit records the emotion history analyzed by the emotion analysis unit. The trend prediction unit predicts an emotion trend based on the emotion history recorded by the log recording unit. The relaxing content provision unit provides relaxing music and videos based on the emotions analyzed by the emotion analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the baby's emotions and respond appropriately. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The BabyEmoCare system, an embodiment of the present invention, is a system that solves the problem of corporate employees struggling to balance work and childcare. This system provides appropriate support to employees by utilizing generative AI-based emotion analysis and LINE communication functions. As a result, the BabyEmoCare system reduces the number of employees who need to take sudden leave for childcare and maintains work continuity. Furthermore, it is expected to improve employee satisfaction, increase corporate productivity and efficiency, and promote employee retention.

[0029] The BabyEmoCare system according to the embodiment includes an emotion analysis unit, an alert unit, a log recording unit, a trend prediction unit, and a relaxation content provision unit. The emotion analysis unit analyzes the baby's emotion. For example, the emotion analysis unit analyzes the baby's facial expression data and estimates the emotion. The emotion analysis unit can also analyze the baby's crying data and estimate the emotion. The emotion analysis unit can also analyze the baby's heart rate data and estimate the emotion. The alert unit sends an alert when the emotion analyzed by the emotion analysis unit is unstable. For example, the alert unit sends a notification to the parent's smartphone when the baby starts crying. The alert unit can also send a notification to the parent's smartphone when the baby's heart rate suddenly increases. The alert unit can also send a notification to the parent's smartphone when the baby's facial expression is unstable. The log recording unit records the emotion history analyzed by the emotion analysis unit. For example, the log recording unit records the baby's emotional state by time. The log recording unit can also record the baby's emotional changes by event. The log recording unit can also display the baby's emotional history in a graph. The trend prediction unit predicts an emotional trend based on the emotional history recorded by the log recording unit. For example, the trend prediction unit can predict that the baby will often cry during a specific time period. The trend prediction unit can also predict that the baby will be emotionally unstable on a specific day of the week. The trend prediction unit can also display the baby's emotional trend in a graph. The relaxation content providing unit provides relaxing music and videos based on the emotions analyzed by the emotion analysis unit. For example, the relaxation content providing unit can play relaxing music when the baby is crying. The relaxation content providing unit can also play calming videos when the baby looks sleepy. The relaxation content providing unit can also play cheerful videos when the baby is smiling. As a result, the BabyEmoCare system according to the embodiment can support balancing childcare and work by analyzing the baby's emotions and taking appropriate measures.

[0030] The emotion analysis unit can provide advice tailored to individual parenting styles based on the baby's emotional data. For example, the emotion analysis unit analyzes the baby's emotional data and provides the parent with advice tailored to their individual parenting style, such as, "If your baby is crying, it is effective to hold him / her and comfort him / her." The emotion analysis unit can also provide the parent with specific advice based on the baby's emotional data, such as, "If your baby is crying, giving him / her milk often calms him / her down." The emotion analysis unit can also analyze the baby's emotional data and provide the parent with advice tailored to their individual parenting style, such as, "If your baby is crying, it is effective to let him / her play with toys." In this way, by providing advice tailored to individual parenting styles, the quality of parenting can be improved.

[0031] The emotion analysis unit can link the results of the baby's emotion analysis with other devices in the home and provide advice via voice. For example, the emotion analysis unit can link the results of the baby's emotion analysis with a smart speaker and provide voice advice to the parent, such as, "The baby is crying. Try giving him some milk." The emotion analysis unit can also link the results of the baby's emotion analysis with a smart speaker and provide voice advice to the parent, such as, "The baby is smiling. Try letting him play with a toy." The emotion analysis unit can also link the results of the baby's emotion analysis with a smart speaker and provide voice advice to the parent, such as, "The baby looks sleepy. Try putting him to sleep." In this way, by linking with other devices in the home, advice can be provided via voice.

[0032] The emotion analysis unit can share the baby's emotional data with other childcare apps to provide comprehensive childcare support. For example, the emotion analysis unit can share the baby's emotional data with other childcare apps to provide comprehensive childcare support to parents, such as, "If your baby is crying, please refer to advice from other parents." The emotion analysis unit can also share the baby's emotional data with other childcare apps to provide specific support to parents, such as, "If your baby is smiling, please refer to success stories from other parents." The emotion analysis unit can also share the baby's emotional data with other childcare apps to provide comprehensive childcare support to parents, such as, "If your baby seems sleepy, please refer to advice from other parents." In this way, by sharing with other childcare apps, comprehensive childcare support can be provided.

[0033] The alert unit can analyze the parent's behavioral history and suggest the optimal response method when the baby's emotions become unstable. For example, when the baby's emotions become unstable, the alert unit can analyze the parent's past behavioral history and suggest a specific response method, such as, "Last time, playing with a toy calmed the baby. Try that again this time." The alert unit can also provide advice based on the parent's behavioral history when the baby's emotions become unstable, such as, "Last time, giving milk calmed the baby. Try that again this time." The alert unit can also analyze the parent's behavioral history when the baby's emotions become unstable and suggest a specific response method, such as, "Last time, holding the baby and soothing it was effective. Try that again this time." In this way, by analyzing the parent's behavioral history, the optimal response method can be suggested.

[0034] When an alert is issued, the alert unit can provide specific response procedures in the form of a video according to the baby's emotional state. For example, when a baby's emotions become unstable, the alert unit can provide the parent with specific response procedures in the form of a video, such as "The baby is crying. Please watch the video to see how to feed him / her." When a baby's emotions become unstable, the alert unit can also provide the parent with specific response procedures in the form of a video, such as "The baby is crying. Please watch the video to see how to let him / her play with toys." When a baby's emotions become unstable, the alert unit can also provide the parent with specific response procedures in the form of a video, such as "The baby is crying. Please watch the video to see how to hold him / her and comfort him / her." In this way, by providing specific response procedures in the form of a video, the parent can respond appropriately.

[0035] The alert unit can link the baby's emotional alert with other devices in the home and provide visual notifications. For example, the alert unit can link the baby's emotional alert with a smart light and set the light to turn red if the baby is crying. The alert unit can also link the baby's emotional alert with a smart light and set the light to turn green if the baby is smiling. The alert unit can also link the baby's emotional alert with a smart light and set the light to turn blue if the baby seems sleepy. This allows visual notifications to be provided by linking with other devices in the home.

[0036] The alert unit can link the baby's emotion alert with the parent's workplace system to promote support at work. For example, the alert unit can link the baby's emotion alert with the parent's workplace system to send a notification to the workplace system if the baby is crying. The alert unit can also link the baby's emotion alert with the parent's workplace system to send a notification to the workplace system if the baby is smiling. The alert unit can also link the baby's emotion alert with the parent's workplace system to send a notification to the workplace system if the baby seems sleepy. This can promote support at work and help parents balance childcare and work.

[0037] The log recording unit can record not only the baby's emotion log but also the parent's childcare behavior log and analyze the correlation. For example, the log recording unit can simultaneously record the baby's emotion log and the parent's childcare behavior log and analyze the correlation, such as, "If the baby is crying, it is effective for the parent to hold the baby and soothe it." The log recording unit can also analyze specific correlations, such as, "If the baby is smiling, it is effective for the parent to feed the baby milk." The log recording unit can also simultaneously record the baby's emotion log and the parent's childcare behavior log and analyze the correlation, such as, "If the baby looks sleepy, it is effective for the parent to let the baby play with toys." In this way, by recording the parent's childcare behavior log and analyzing the correlation, the quality of childcare can be improved.

[0038] The log recording unit can provide parenting advice according to the baby's developmental stage based on the emotion log. For example, the log recording unit analyzes the baby's emotion log and provides the parent with parenting advice according to the baby's developmental stage, such as, "When the baby is three months old, he or she will often cry, so it is effective to hold him or her and comfort him or her." The log recording unit can also provide the parent with specific parenting advice based on the baby's emotion log, such as, "When the baby is six months old, giving him or her milk will often calm him or her." The log recording unit can also analyze the baby's emotion log and provide the parent with parenting advice according to the baby's developmental stage, such as, "When the baby is one year old, it is effective to let him or her play with toys." In this way, by providing parenting advice according to the baby's developmental stage, the quality of childcare can be improved.

[0039] The log recording unit can link the baby's emotion log with other devices in the home and visually display it. For example, the log recording unit can link the baby's emotion log with a smart mirror and provide a visual display to the parent, such as "If the baby is crying, the mirror will display the baby's emotional state." The log recording unit can also link the baby's emotion log with a smart mirror and provide a specific visual display to the parent, such as "If the baby is smiling, the mirror will display the baby's emotional state." The log recording unit can also link the baby's emotion log with a smart mirror and provide a visual display to the parent, such as "If the baby looks sleepy, the mirror will display the baby's emotional state." This allows for visual displays by linking with other devices in the home.

[0040] The log recording unit can share the baby's emotion log with other childcare apps to provide comprehensive childcare support. For example, the log recording unit can share the baby's emotion log with other childcare apps to provide comprehensive childcare support to parents, such as, "If your baby is crying, please refer to advice from other parents." The log recording unit can also share the baby's emotion log with other childcare apps to provide specific support to parents, such as, "If your baby is smiling, please refer to success stories from other parents." The log recording unit can also share the baby's emotion log with other childcare apps to provide comprehensive childcare support to parents, such as, "If your baby seems sleepy, please refer to advice from other parents." In this way, sharing with other childcare apps can provide comprehensive childcare support.

[0041] The trend prediction unit can propose a childcare plan according to the baby's developmental stage based on the emotional trend prediction. For example, the trend prediction unit analyzes the baby's emotional trend prediction and proposes to the parent a childcare plan according to the developmental stage, such as, "When the baby is three months old, he / she will often cry, so it is effective to hold him / her and comfort him / her." The trend prediction unit can also propose a specific childcare plan to the parent based on the baby's emotional trend prediction, such as, "When the baby is six months old, giving him / her milk will often calm him / her." The trend prediction unit can also analyze the baby's emotional trend prediction and propose to the parent a childcare plan according to the developmental stage, such as, "When the baby is one year old, it is effective to let him / her play with toys." In this way, by proposing a childcare plan according to the developmental stage, the quality of childcare can be improved.

[0042] The trend prediction unit can link the baby's emotional trend prediction with other devices in the home to automatically generate a childcare plan. For example, the trend prediction unit can link the baby's emotional trend prediction with a smart calendar to automatically generate a childcare plan for parents, such as, "When the baby cries a lot, please set a relaxation time on the calendar." The trend prediction unit can also link the baby's emotional trend prediction with a smart calendar to automatically generate a specific childcare plan for parents, such as, "When the baby laughs a lot, please set a play time on the calendar." The trend prediction unit can also link the baby's emotional trend prediction with a smart calendar to automatically generate a childcare plan for parents, such as, "When the baby seems sleepy, please set a sleep-putting time on the calendar." In this way, by linking with other devices in the home, a childcare plan can be automatically generated.

[0043] The trend prediction unit can share the baby's emotional trend prediction with other childcare apps to provide comprehensive childcare support. For example, the trend prediction unit can share the baby's emotional trend prediction with other childcare apps to provide comprehensive childcare support to parents, such as, "When your baby is crying a lot, please refer to advice from other parents." The trend prediction unit can also share the baby's emotional trend prediction with other childcare apps to provide specific support to parents, such as, "When your baby is laughing a lot, please refer to success stories from other parents." The trend prediction unit can also share the baby's emotional trend prediction with other childcare apps to provide comprehensive childcare support to parents, such as, "When your baby is sleepy, please refer to advice from other parents." In this way, by sharing with other childcare apps, comprehensive childcare support can be provided.

[0044] The relaxation content providing unit can individually customize and provide relaxation content according to the emotional state of the baby. For example, the relaxation content providing unit customizes and provides relaxing music according to the emotional state of the baby. For example, if the baby is crying, calm music is played. The relaxation content providing unit also customizes and provides relaxing videos based on the emotional state of the baby. For example, if the baby is smiling, a fun video can be played. The relaxation content providing unit can also individually customize relaxation content according to the emotional state of the baby and make specific suggestions to the parent, such as "If the baby looks sleepy, please play calm music." In this way, customizing relaxation content according to the emotional state can promote relaxation in the baby.

[0045] The relaxation content providing unit can visually provide the baby's relaxation content in cooperation with other devices in the home. For example, the relaxation content providing unit can link the baby's relaxation content with a smart TV and provide a visual message to the parent, such as "If the baby is crying, please play a relaxing video on the TV." The relaxation content providing unit can also link the baby's relaxation content with a smart TV and provide a specific visual message to the parent, such as "If the baby is smiling, please play a fun video on the TV." The relaxation content providing unit can also link the baby's relaxation content with a smart TV and provide a visual message to the parent, such as "If the baby looks sleepy, please play a calming video on the TV." In this way, by linking with other devices in the home, relaxation content can be visually provided.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The BabyEmoCare system can further include a health management unit that monitors the parent's health condition. The health management unit monitors the parent's heart rate, blood pressure, and sleep patterns and analyzes the parent's health condition. For example, if the parent's heart rate is high, the health management unit can provide advice on how to relax. The health management unit can also analyze the parent's sleep patterns and encourage the parent to get more rest if sleep deprivation continues. Furthermore, the health management unit can monitor the parent's blood pressure and recommend that the parent see a doctor if an abnormality is detected. In this way, by monitoring the parent's health condition and providing appropriate advice, it is possible to reduce parenting stress and maintain the parent's health.

[0048] The BabyEmoCare system can further include a behavior analysis unit that analyzes the parent's behavioral history. The behavior analysis unit records the parent's childcare behavior and analyzes behavioral patterns. For example, the behavior analysis unit can record how often and for how long the parent holds the baby and suggest appropriate childcare methods. The behavior analysis unit can also record the timing and amount of milk the parent feeds the baby and suggest the optimal amount of milk. Furthermore, the behavior analysis unit can record the amount of time the parent plays with the baby and suggest appropriate ways to play. In this way, the quality of childcare can be improved by analyzing the parent's behavioral history and suggesting appropriate childcare methods.

[0049] The BabyEmoCare system can further include a health management unit that monitors the parent's health condition. The health management unit monitors the parent's heart rate, blood pressure, and sleep patterns and analyzes the parent's health condition. For example, if the parent's heart rate is high, the health management unit can provide advice on how to relax. The health management unit can also analyze the parent's sleep patterns and encourage the parent to get more rest if sleep deprivation continues. Furthermore, the health management unit can monitor the parent's blood pressure and recommend that the parent see a doctor if an abnormality is detected. In this way, by monitoring the parent's health condition and providing appropriate advice, it is possible to reduce parenting stress and maintain the parent's health.

[0050] The BabyEmoCare system can further include a behavior analysis unit that analyzes the parent's behavioral history. The behavior analysis unit records the parent's childcare behavior and analyzes behavioral patterns. For example, the behavior analysis unit can record how often and for how long the parent holds the baby and suggest appropriate childcare methods. The behavior analysis unit can also record the timing and amount of milk the parent feeds the baby and suggest the optimal amount of milk. Furthermore, the behavior analysis unit can record the amount of time the parent plays with the baby and suggest appropriate ways to play. In this way, the quality of childcare can be improved by analyzing the parent's behavioral history and suggesting appropriate childcare methods.

[0051] The BabyEmoCare system can further include a health management unit that monitors the parent's health condition. The health management unit monitors the parent's heart rate, blood pressure, and sleep patterns and analyzes the parent's health condition. For example, if the parent's heart rate is high, the health management unit can provide advice on how to relax. The health management unit can also analyze the parent's sleep patterns and encourage the parent to get more rest if sleep deprivation continues. Furthermore, the health management unit can monitor the parent's blood pressure and recommend that the parent see a doctor if an abnormality is detected. In this way, by monitoring the parent's health condition and providing appropriate advice, it is possible to reduce parenting stress and maintain the parent's health.

[0052] The BabyEmoCare system can further include a behavior analysis unit that analyzes the parent's behavioral history. The behavior analysis unit records the parent's childcare behavior and analyzes behavioral patterns. For example, the behavior analysis unit can record how often and for how long the parent holds the baby and suggest appropriate childcare methods. The behavior analysis unit can also record the timing and amount of milk the parent feeds the baby and suggest the optimal amount of milk. Furthermore, the behavior analysis unit can record the amount of time the parent plays with the baby and suggest appropriate ways to play. In this way, the quality of childcare can be improved by analyzing the parent's behavioral history and suggesting appropriate childcare methods.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The emotion analysis unit analyzes the baby's emotions. For example, the emotion analysis unit analyzes the baby's facial expression data, crying data, and heart rate data to estimate the baby's emotions. Step 2: The alert unit sends an alert if the emotion analyzed by the emotion analysis unit is unstable. For example, if the baby starts crying, their heart rate spikes, or their facial expressions become unstable, a notification will be sent to the parent's smartphone. Step 3: The log recorder records the emotion history analyzed by the emotion analyzer. For example, it records the baby's emotional state by time or event, and displays the emotion history in a graph. Step 4: The trend prediction unit predicts emotional trends based on the emotional history recorded by the log recording unit. For example, it predicts that the baby's emotions will be unstable at certain times of the day or on certain days of the week, and displays the emotional trends in a graph. Step 5: The relaxation content provider provides relaxing music and videos based on the emotions analyzed by the emotion analyzer. For example, if the baby is crying, it plays relaxing music, if the baby looks sleepy, it plays calming videos, and if the baby is smiling, it plays happy videos.

[0055] (Example 2) The BabyEmoCare system, an embodiment of the present invention, is a system that solves the problem of corporate employees struggling to balance work and childcare. This system provides appropriate support to employees by utilizing generative AI-based emotion analysis and LINE communication functions. As a result, the BabyEmoCare system reduces the number of employees who need to take sudden leave for childcare and maintains work continuity. Furthermore, it is expected to improve employee satisfaction, increase corporate productivity and efficiency, and promote employee retention.

[0056] The BabyEmoCare system according to the embodiment includes an emotion analysis unit, an alert unit, a log recording unit, a trend prediction unit, and a relaxation content provision unit. The emotion analysis unit analyzes the baby's emotion. For example, the emotion analysis unit analyzes the baby's facial expression data and estimates the emotion. The emotion analysis unit can also analyze the baby's crying data and estimate the emotion. The emotion analysis unit can also analyze the baby's heart rate data and estimate the emotion. The alert unit sends an alert when the emotion analyzed by the emotion analysis unit is unstable. For example, the alert unit sends a notification to the parent's smartphone when the baby starts crying. The alert unit can also send a notification to the parent's smartphone when the baby's heart rate suddenly increases. The alert unit can also send a notification to the parent's smartphone when the baby's facial expression is unstable. The log recording unit records the emotion history analyzed by the emotion analysis unit. For example, the log recording unit records the baby's emotional state by time. The log recording unit can also record the baby's emotional changes by event. The log recording unit can also display the baby's emotional history in a graph. The trend prediction unit predicts an emotional trend based on the emotional history recorded by the log recording unit. For example, the trend prediction unit can predict that the baby will often cry during a specific time period. The trend prediction unit can also predict that the baby will be emotionally unstable on a specific day of the week. The trend prediction unit can also display the baby's emotional trend in a graph. The relaxation content providing unit provides relaxing music and videos based on the emotions analyzed by the emotion analysis unit. For example, the relaxation content providing unit can play relaxing music when the baby is crying. The relaxation content providing unit can also play calming videos when the baby looks sleepy. The relaxation content providing unit can also play cheerful videos when the baby is smiling. As a result, the BabyEmoCare system according to the embodiment can support balancing childcare and work by analyzing the baby's emotions and taking appropriate measures.

[0057] In addition to analyzing the baby's emotions, the emotion analysis unit can also simultaneously analyze the parent's stress level and suggest mental care to the parent. For example, the emotion analysis unit can simultaneously analyze the baby's emotional data and the parent's stress level and provide mental care advice to the parent, such as "Take deep breaths and relax while the baby is crying." The emotion analysis unit can also monitor the baby's emotional state and the parent's stress level in real time and suggest specific mental care to the parent, such as "Take a short break while the baby is crying." The emotion analysis unit can also provide mental care advice to the parent, such as "Listen to relaxing music while the baby is crying," based on the baby's emotional data and the parent's stress level. In this way, suggestions for mental care to the parent can be made, reducing parenting stress.

[0058] The emotion analysis unit can provide advice tailored to individual parenting styles based on the baby's emotional data. For example, the emotion analysis unit analyzes the baby's emotional data and provides the parent with advice tailored to their individual parenting style, such as, "If your baby is crying, it is effective to hold him / her and comfort him / her." The emotion analysis unit can also provide the parent with specific advice based on the baby's emotional data, such as, "If your baby is crying, giving him / her milk often calms him / her down." The emotion analysis unit can also analyze the baby's emotional data and provide the parent with advice tailored to their individual parenting style, such as, "If your baby is crying, it is effective to let him / her play with toys." In this way, by providing advice tailored to individual parenting styles, the quality of parenting can be improved.

[0059] The emotion analysis unit can use the emotion estimation function to analyze the parent's emotional response to the baby's emotions and optimize parent-child interaction. For example, the emotion analysis unit can analyze the baby's emotional data and the parent's emotional response and provide advice to the parent such as, "When the baby is crying, the parent should try to stay relaxed." The emotion analysis unit can also provide specific advice to the parent based on the baby's emotional data and the parent's emotional response, such as, "When the baby is smiling, the parent should try to interact with the baby with a smile." The emotion analysis unit can also analyze the baby's emotional data and the parent's emotional response and provide advice to the parent such as, "When the baby is crying, the parent should try to respond calmly." This can improve the quality of childcare by optimizing parent-child interaction.

[0060] The emotion analysis unit can link the results of the baby's emotion analysis with other devices in the home and provide advice via voice. For example, the emotion analysis unit can link the results of the baby's emotion analysis with a smart speaker and provide voice advice to the parent, such as, "The baby is crying. Try giving him some milk." The emotion analysis unit can also link the results of the baby's emotion analysis with a smart speaker and provide voice advice to the parent, such as, "The baby is smiling. Try letting him play with a toy." The emotion analysis unit can also link the results of the baby's emotion analysis with a smart speaker and provide voice advice to the parent, such as, "The baby looks sleepy. Try putting him to sleep." In this way, by linking with other devices in the home, advice can be provided via voice.

[0061] The emotion analysis unit can share the baby's emotional data with other childcare apps to provide comprehensive childcare support. For example, the emotion analysis unit can share the baby's emotional data with other childcare apps to provide comprehensive childcare support to parents, such as, "If your baby is crying, please refer to advice from other parents." The emotion analysis unit can also share the baby's emotional data with other childcare apps to provide specific support to parents, such as, "If your baby is smiling, please refer to success stories from other parents." The emotion analysis unit can also share the baby's emotional data with other childcare apps to provide comprehensive childcare support to parents, such as, "If your baby seems sleepy, please refer to advice from other parents." In this way, by sharing with other childcare apps, comprehensive childcare support can be provided.

[0062] The emotion analysis unit can use the emotion estimation function to promote the exchange of advice within the childcare community based on the baby's emotions. For example, the emotion analysis unit can use the emotion estimation function to build a system in which baby's emotional data is shared within the childcare community and advice is received from other parents. The emotion analysis unit can also promote the exchange of advice within the childcare community based on the baby's emotional data. For example, if a baby is crying, specific advice can be received from other parents. The emotion analysis unit can also use the emotion estimation function to build a system in which baby's emotional data is shared within the childcare community and best practices from other parents can be used as reference. This can promote the exchange of advice within the childcare community, thereby improving the quality of childcare.

[0063] The alert unit can analyze the parent's behavioral history and suggest the optimal response method when the baby's emotions become unstable. For example, when the baby's emotions become unstable, the alert unit can analyze the parent's past behavioral history and suggest a specific response method, such as, "Last time, playing with a toy calmed the baby. Try that again this time." The alert unit can also provide advice based on the parent's behavioral history when the baby's emotions become unstable, such as, "Last time, giving milk calmed the baby. Try that again this time." The alert unit can also analyze the parent's behavioral history when the baby's emotions become unstable and suggest a specific response method, such as, "Last time, holding the baby and soothing it was effective. Try that again this time." In this way, by analyzing the parent's behavioral history, the optimal response method can be suggested.

[0064] When an alert is issued, the alert unit can provide specific response procedures in the form of a video according to the baby's emotional state. For example, when a baby's emotions become unstable, the alert unit can provide the parent with specific response procedures in the form of a video, such as "The baby is crying. Please watch the video to see how to feed him / her." When a baby's emotions become unstable, the alert unit can also provide the parent with specific response procedures in the form of a video, such as "The baby is crying. Please watch the video to see how to let him / her play with toys." When a baby's emotions become unstable, the alert unit can also provide the parent with specific response procedures in the form of a video, such as "The baby is crying. Please watch the video to see how to hold him / her and comfort him / her." In this way, by providing specific response procedures in the form of a video, the parent can respond appropriately.

[0065] The alert unit can use the emotion estimation function to analyze the parent's emotional response to the baby's emotional changes in real time and suggest stress reduction measures to the parent. For example, the alert unit can analyze the parent's emotional response to the baby's emotional changes in real time and suggest stress reduction measures to the parent, such as "Take deep breaths and relax while the baby is crying." The alert unit can also suggest specific stress reduction measures to the parent, such as "Take a short break while the baby is crying," based on the parent's emotional response to the baby's emotional changes. The alert unit can also analyze the parent's emotional response to the baby's emotional changes in real time and suggest stress reduction measures to the parent, such as "Listen to relaxing music while the baby is crying." In this way, by suggesting stress reduction measures to the parent, it is possible to reduce parenting stress.

[0066] The alert unit can link the baby's emotional alert with other devices in the home and provide visual notifications. For example, the alert unit can link the baby's emotional alert with a smart light and set the light to turn red if the baby is crying. The alert unit can also link the baby's emotional alert with a smart light and set the light to turn green if the baby is smiling. The alert unit can also link the baby's emotional alert with a smart light and set the light to turn blue if the baby seems sleepy. This allows visual notifications to be provided by linking with other devices in the home.

[0067] The alert unit can link the baby's emotion alert with the parent's workplace system to promote support at work. For example, the alert unit can link the baby's emotion alert with the parent's workplace system to send a notification to the workplace system if the baby is crying. The alert unit can also link the baby's emotion alert with the parent's workplace system to send a notification to the workplace system if the baby is smiling. The alert unit can also link the baby's emotion alert with the parent's workplace system to send a notification to the workplace system if the baby seems sleepy. This can promote support at work and help parents balance childcare and work.

[0068] The alert unit can use the emotion estimation function to analyze the parent's emotional response to the baby's emotional alert and customize the optimal alert method. For example, the alert unit can analyze the parent's emotional response to the baby's emotional alert and suggest the optimal alert method to the parent, such as "If the baby is crying, please prioritize using audio alerts." The alert unit can also suggest a specific alert method to the parent, such as "If the baby is smiling, please prioritize using visual alerts," based on the parent's emotional response to the baby's emotional alert. The alert unit can also analyze the parent's emotional response to the baby's emotional alert and suggest the optimal alert method to the parent, such as "If the baby seems sleepy, please prioritize using vibration alerts." This allows the parent to respond appropriately by customizing the optimal alert method.

[0069] The log recording unit can record not only the baby's emotion log but also the parent's childcare behavior log and analyze the correlation. For example, the log recording unit can simultaneously record the baby's emotion log and the parent's childcare behavior log and analyze the correlation, such as, "If the baby is crying, it is effective for the parent to hold the baby and soothe it." The log recording unit can also analyze specific correlations, such as, "If the baby is smiling, it is effective for the parent to feed the baby milk." The log recording unit can also simultaneously record the baby's emotion log and the parent's childcare behavior log and analyze the correlation, such as, "If the baby looks sleepy, it is effective for the parent to let the baby play with toys." In this way, by recording the parent's childcare behavior log and analyzing the correlation, the quality of childcare can be improved.

[0070] The log recording unit can provide parenting advice according to the baby's developmental stage based on the emotion log. For example, the log recording unit analyzes the baby's emotion log and provides the parent with parenting advice according to the baby's developmental stage, such as, "When the baby is three months old, he or she will often cry, so it is effective to hold him or her and comfort him or her." The log recording unit can also provide the parent with specific parenting advice based on the baby's emotion log, such as, "When the baby is six months old, giving him or her milk will often calm him or her." The log recording unit can also analyze the baby's emotion log and provide the parent with parenting advice according to the baby's developmental stage, such as, "When the baby is one year old, it is effective to let him or her play with toys." In this way, by providing parenting advice according to the baby's developmental stage, the quality of childcare can be improved.

[0071] The log recording unit can use the emotion estimation function to record the parent's emotional response to the baby's emotion log and analyze the parenting stress trend. For example, the log recording unit records the parent's emotional response to the baby's emotion log and analyzes the parenting stress trend, such as telling the parent, "When the baby is crying, the parent is also likely to feel stressed. Try to find a way to relax." The log recording unit can also analyze specific parenting stress trends, such as telling the parent, "When the baby is smiling, the parent is also likely to relax. Try to interact with the baby with a smile." The log recording unit can also record the parent's emotional response to the baby's emotion log and analyze the parenting stress trend, such as telling the parent, "When the baby seems sleepy, the parent is also likely to relax. Letting the baby play with toys is effective." By analyzing the parenting stress trend, it is possible to suggest stress reduction measures for the parent.

[0072] The log recording unit can link the baby's emotion log with other devices in the home and visually display it. For example, the log recording unit can link the baby's emotion log with a smart mirror and provide a visual display to the parent, such as "If the baby is crying, the mirror will display the baby's emotional state." The log recording unit can also link the baby's emotion log with a smart mirror and provide a specific visual display to the parent, such as "If the baby is smiling, the mirror will display the baby's emotional state." The log recording unit can also link the baby's emotion log with a smart mirror and provide a visual display to the parent, such as "If the baby looks sleepy, the mirror will display the baby's emotional state." This allows for visual displays by linking with other devices in the home.

[0073] The log recording unit can share the baby's emotion log with other childcare apps to provide comprehensive childcare support. For example, the log recording unit can share the baby's emotion log with other childcare apps to provide comprehensive childcare support to parents, such as, "If your baby is crying, please refer to advice from other parents." The log recording unit can also share the baby's emotion log with other childcare apps to provide specific support to parents, such as, "If your baby is smiling, please refer to success stories from other parents." The log recording unit can also share the baby's emotion log with other childcare apps to provide comprehensive childcare support to parents, such as, "If your baby seems sleepy, please refer to advice from other parents." In this way, sharing with other childcare apps can provide comprehensive childcare support.

[0074] The log recording unit uses the emotion estimation function to analyze parents' emotional reactions to the baby's emotion log and promote information sharing within the childcare community. The log recording unit, for example, uses the emotion estimation function to analyze parents' emotional reactions to the baby's emotion log and promote information sharing within the childcare community. For example, if the baby is crying, parents can receive specific advice from other parents. The log recording unit also promotes information sharing within the childcare community based on parents' emotional reactions to the baby's emotion log. For example, if the baby is smiling, parents can refer to success stories from other parents. The log recording unit also uses the emotion estimation function to analyze parents' emotional reactions to the baby's emotion log and promote information sharing within the childcare community. For example, if the baby seems sleepy, parents can receive specific advice from other parents. This promotes information sharing within the childcare community, thereby improving the quality of childcare.

[0075] The trend prediction unit can propose a childcare plan according to the baby's developmental stage based on the emotional trend prediction. For example, the trend prediction unit analyzes the baby's emotional trend prediction and proposes to the parent a childcare plan according to the developmental stage, such as, "When the baby is three months old, he / she will often cry, so it is effective to hold him / her and comfort him / her." The trend prediction unit can also propose a specific childcare plan to the parent based on the baby's emotional trend prediction, such as, "When the baby is six months old, giving him / her milk will often calm him / her." The trend prediction unit can also analyze the baby's emotional trend prediction and propose to the parent a childcare plan according to the developmental stage, such as, "When the baby is one year old, it is effective to let him / her play with toys." In this way, by proposing a childcare plan according to the developmental stage, the quality of childcare can be improved.

[0076] The trend prediction unit can use the emotion estimation function to predict the parent's emotional response to the baby's emotional trend and optimize parent-child interaction. For example, the trend prediction unit predicts the parent's emotional response to the baby's emotional trend and suggests optimal interactions to the parent, such as, "When the baby cries a lot, try ways to help you relax." The trend prediction unit can also suggest specific interactions to the parent, such as, "When the baby laughs a lot, it's easier for parents to relax. Try to interact with the baby with a smile," based on the parent's emotional response to the baby's emotional trend. The trend prediction unit can also predict the parent's emotional response to the baby's emotional trend and suggest optimal interactions to the parent, such as, "When the baby seems sleepy, it's easier for parents to relax. Letting the baby play with toys is effective." This can improve the quality of childcare by optimizing parent-child interaction.

[0077] The trend prediction unit can link the baby's emotional trend prediction with other devices in the home to automatically generate a childcare plan. For example, the trend prediction unit can link the baby's emotional trend prediction with a smart calendar to automatically generate a childcare plan for parents, such as, "When the baby cries a lot, please set a relaxation time on the calendar." The trend prediction unit can also link the baby's emotional trend prediction with a smart calendar to automatically generate a specific childcare plan for parents, such as, "When the baby laughs a lot, please set a play time on the calendar." The trend prediction unit can also link the baby's emotional trend prediction with a smart calendar to automatically generate a childcare plan for parents, such as, "When the baby seems sleepy, please set a sleep-putting time on the calendar." In this way, by linking with other devices in the home, a childcare plan can be automatically generated.

[0078] The trend prediction unit can share the baby's emotional trend prediction with other childcare apps to provide comprehensive childcare support. For example, the trend prediction unit can share the baby's emotional trend prediction with other childcare apps to provide comprehensive childcare support to parents, such as, "When your baby is crying a lot, please refer to advice from other parents." The trend prediction unit can also share the baby's emotional trend prediction with other childcare apps to provide specific support to parents, such as, "When your baby is laughing a lot, please refer to success stories from other parents." The trend prediction unit can also share the baby's emotional trend prediction with other childcare apps to provide comprehensive childcare support to parents, such as, "When your baby is sleepy, please refer to advice from other parents." In this way, by sharing with other childcare apps, comprehensive childcare support can be provided.

[0079] The trend prediction unit can use the emotion estimation function to analyze parents' emotional reactions to the baby's emotional trend prediction and promote information sharing within the childcare community. The trend prediction unit, for example, uses the emotion estimation function to analyze parents' emotional reactions to the baby's emotional trend prediction and promote information sharing within the childcare community. For example, during periods when the baby cries a lot, parents can receive specific advice from other parents. The trend prediction unit also promotes information sharing within the childcare community based on parents' emotional reactions to the baby's emotional trend prediction. For example, during periods when the baby laughs a lot, parents can refer to success stories from other parents. The trend prediction unit also uses the emotion estimation function to analyze parents' emotional reactions to the baby's emotional trend prediction and promote information sharing within the childcare community. For example, during periods when the baby seems sleepy, parents can receive specific advice from other parents. This promotes information sharing within the childcare community, thereby improving the quality of childcare.

[0080] The relaxation content providing unit can provide not only relaxing content for babies but also content that encourages parental relaxation. For example, in addition to relaxing content for babies, the relaxation content providing unit can provide content that encourages parental relaxation, such as "listen to relaxing music while your baby is crying." In addition to relaxing content for babies, the relaxation content providing unit can also provide specific relaxing content, such as "watch a relaxing video while your baby is smiling." In addition to relaxing content for babies, the relaxation content providing unit can also provide content that encourages parental relaxation, such as "listen to relaxing music if your baby looks sleepy." In this way, by providing content that encourages parental relaxation, it is possible to reduce parenting stress.

[0081] The relaxation content providing unit can individually customize and provide relaxation content according to the emotional state of the baby. For example, the relaxation content providing unit customizes and provides relaxing music according to the emotional state of the baby. For example, if the baby is crying, calm music is played. The relaxation content providing unit also customizes and provides relaxing videos based on the emotional state of the baby. For example, if the baby is smiling, a fun video can be played. The relaxation content providing unit can also individually customize relaxation content according to the emotional state of the baby and make specific suggestions to the parent, such as "If the baby looks sleepy, please play calm music." In this way, customizing relaxation content according to the emotional state can promote relaxation in the baby.

[0082] The relaxation content providing unit can use the emotion estimation function to analyze the parent's emotional response to the baby's relaxation content and provide content that allows both parent and child to relax. For example, the relaxation content providing unit can use the emotion estimation function to analyze the parent's emotional response to the baby's relaxation content and provide the parent with content such as, "Listen to music that will help you relax while your baby is crying." The relaxation content providing unit can also provide the parent with specific content such as, "Watch a video that will help you relax while your baby is smiling," based on the parent's emotional response to the baby's relaxation content. The relaxation content providing unit can also use the emotion estimation function to analyze the parent's emotional response to the baby's relaxation content and provide the parent with content such as, "If your baby seems sleepy, listen to music that will help you relax." This can reduce parenting stress by providing content that will help both parent and child to relax.

[0083] The relaxation content providing unit can visually provide the baby's relaxation content in cooperation with other devices in the home. For example, the relaxation content providing unit can link the baby's relaxation content with a smart TV and provide a visual message to the parent, such as "If the baby is crying, please play a relaxing video on the TV." The relaxation content providing unit can also link the baby's relaxation content with a smart TV and provide a specific visual message to the parent, such as "If the baby is smiling, please play a fun video on the TV." The relaxation content providing unit can also link the baby's relaxation content with a smart TV and provide a visual message to the parent, such as "If the baby looks sleepy, please play a calming video on the TV." In this way, by linking with other devices in the home, relaxation content can be visually provided.

[0084] The relaxation content providing unit can use the emotion estimation function to analyze parents' emotional reactions to baby relaxation content and promote information sharing within the childcare community. The relaxation content providing unit can, for example, use the emotion estimation function to analyze parents' emotional reactions to baby relaxation content and promote information sharing within the childcare community. For example, if a baby is crying, the parent can receive specific advice from other parents. The relaxation content providing unit can also promote information sharing within the childcare community based on the parents' emotional reactions to the baby relaxation content. For example, if a baby is smiling, the parent can refer to success stories from other parents. The relaxation content providing unit can also use the emotion estimation function to analyze parents' emotional reactions to baby relaxation content and promote information sharing within the childcare community. For example, if a baby seems sleepy, the parent can receive specific advice from other parents. This promotes information sharing within the childcare community, thereby improving the quality of childcare.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The BabyEmoCare system can further include a health management unit that monitors the parent's health condition. The health management unit monitors the parent's heart rate, blood pressure, and sleep patterns and analyzes the parent's health condition. For example, if the parent's heart rate is high, the health management unit can provide advice on how to relax. The health management unit can also analyze the parent's sleep patterns and encourage the parent to get more rest if sleep deprivation continues. Furthermore, the health management unit can monitor the parent's blood pressure and recommend that the parent see a doctor if an abnormality is detected. In this way, by monitoring the parent's health condition and providing appropriate advice, it is possible to reduce parenting stress and maintain the parent's health.

[0087] The BabyEmoCare system can further include an emotion analysis unit that analyzes the parent's emotional state. The emotion analysis unit analyzes the parent's facial expression data and voice data to infer their emotions. For example, the emotion analysis unit can analyze the parent's facial expression data and provide relaxation advice if the parent is feeling stressed or tired. The emotion analysis unit can also analyze the parent's voice data to detect changes in emotion. Furthermore, the emotion analysis unit can monitor the parent's emotional state in real time and provide relaxing music or videos if stress levels are high. This allows the system to analyze the parent's emotional state and provide appropriate advice, thereby reducing parenting stress and supporting the parent's mental health.

[0088] The BabyEmoCare system can further include a behavior analysis unit that analyzes the parent's behavioral history. The behavior analysis unit records the parent's childcare behavior and analyzes behavioral patterns. For example, the behavior analysis unit can record how often and for how long the parent holds the baby and suggest appropriate childcare methods. The behavior analysis unit can also record the timing and amount of milk the parent feeds the baby and suggest the optimal amount of milk. Furthermore, the behavior analysis unit can record the amount of time the parent plays with the baby and suggest appropriate ways to play. In this way, the quality of childcare can be improved by analyzing the parent's behavioral history and suggesting appropriate childcare methods.

[0089] The BabyEmoCare system can further include a stress analysis unit that analyzes the parent's stress level. The stress analysis unit analyzes the parent's biometric data and estimates the stress level. For example, the stress analysis unit can analyze the parent's heart rate and blood pressure and provide relaxation advice if stress levels are rising. The stress analysis unit can also analyze the parent's sleep patterns and encourage the parent to take a rest if sleep deprivation continues. Furthermore, the stress analysis unit can monitor the parent's stress level in real time and provide relaxing music or videos if stress levels are rising. In this way, by analyzing the parent's stress level and providing appropriate advice, it is possible to reduce parenting stress and support the parent's mental health.

[0090] The BabyEmoCare system can further include a health management unit that monitors the parent's health condition. The health management unit monitors the parent's heart rate, blood pressure, and sleep patterns and analyzes the parent's health condition. For example, if the parent's heart rate is high, the health management unit can provide advice on how to relax. The health management unit can also analyze the parent's sleep patterns and encourage the parent to get more rest if sleep deprivation continues. Furthermore, the health management unit can monitor the parent's blood pressure and recommend that the parent see a doctor if an abnormality is detected. In this way, by monitoring the parent's health condition and providing appropriate advice, it is possible to reduce parenting stress and maintain the parent's health.

[0091] The BabyEmoCare system can further include a behavior analysis unit that analyzes the parent's behavioral history. The behavior analysis unit records the parent's childcare behavior and analyzes behavioral patterns. For example, the behavior analysis unit can record how often and for how long the parent holds the baby and suggest appropriate childcare methods. The behavior analysis unit can also record the timing and amount of milk the parent feeds the baby and suggest the optimal amount of milk. Furthermore, the behavior analysis unit can record the amount of time the parent plays with the baby and suggest appropriate ways to play. In this way, the quality of childcare can be improved by analyzing the parent's behavioral history and suggesting appropriate childcare methods.

[0092] The BabyEmoCare system can further include a stress analysis unit that analyzes the parent's stress level. The stress analysis unit analyzes the parent's biometric data and estimates the stress level. For example, the stress analysis unit can analyze the parent's heart rate and blood pressure and provide relaxation advice if stress levels are rising. The stress analysis unit can also analyze the parent's sleep patterns and encourage the parent to take a rest if sleep deprivation continues. Furthermore, the stress analysis unit can monitor the parent's stress level in real time and provide relaxing music or videos if stress levels are rising. In this way, by analyzing the parent's stress level and providing appropriate advice, it is possible to reduce parenting stress and support the parent's mental health.

[0093] The BabyEmoCare system can further include a health management unit that monitors the parent's health condition. The health management unit monitors the parent's heart rate, blood pressure, and sleep patterns and analyzes the parent's health condition. For example, if the parent's heart rate is high, the health management unit can provide advice on how to relax. The health management unit can also analyze the parent's sleep patterns and encourage the parent to get more rest if sleep deprivation continues. Furthermore, the health management unit can monitor the parent's blood pressure and recommend that the parent see a doctor if an abnormality is detected. In this way, by monitoring the parent's health condition and providing appropriate advice, it is possible to reduce parenting stress and maintain the parent's health.

[0094] The BabyEmoCare system can further include a behavior analysis unit that analyzes the parent's behavioral history. The behavior analysis unit records the parent's childcare behavior and analyzes behavioral patterns. For example, the behavior analysis unit can record how often and for how long the parent holds the baby and suggest appropriate childcare methods. The behavior analysis unit can also record the timing and amount of milk the parent feeds the baby and suggest the optimal amount of milk. Furthermore, the behavior analysis unit can record the amount of time the parent plays with the baby and suggest appropriate ways to play. In this way, the quality of childcare can be improved by analyzing the parent's behavioral history and suggesting appropriate childcare methods.

[0095] The BabyEmoCare system can further include a stress analysis unit that analyzes the parent's stress level. The stress analysis unit analyzes the parent's biometric data and estimates the stress level. For example, the stress analysis unit can analyze the parent's heart rate and blood pressure and provide relaxation advice if stress levels are rising. The stress analysis unit can also analyze the parent's sleep patterns and encourage the parent to take a rest if sleep deprivation continues. Furthermore, the stress analysis unit can monitor the parent's stress level in real time and provide relaxing music or videos if stress levels are rising. In this way, by analyzing the parent's stress level and providing appropriate advice, it is possible to reduce parenting stress and support the parent's mental health.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The emotion analysis unit analyzes the baby's emotions. For example, the emotion analysis unit analyzes the baby's facial expression data, crying data, and heart rate data to estimate the baby's emotions. Step 2: The alert unit sends an alert if the emotion analyzed by the emotion analysis unit is unstable. For example, if the baby starts crying, their heart rate spikes, or their facial expressions become unstable, a notification will be sent to the parent's smartphone. Step 3: The log recorder records the emotion history analyzed by the emotion analyzer. For example, it records the baby's emotional state by time or event, and displays the emotion history in a graph. Step 4: The trend prediction unit predicts emotional trends based on the emotional history recorded by the log recording unit. For example, it predicts that the baby's emotions will be unstable at certain times of the day or on certain days of the week, and displays the emotional trends in a graph. Step 5: The relaxation content provider provides relaxing music and videos based on the emotions analyzed by the emotion analyzer. For example, if the baby is crying, it plays relaxing music, if the baby looks sleepy, it plays calming videos, and if the baby is smiling, it plays happy videos.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] 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.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0142] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0164] 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]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An emotion analysis unit that analyzes the baby's emotions; an alert unit that sends an alert when the emotion analyzed by the emotion analysis unit is unstable; a log recording unit that records the emotion history analyzed by the emotion analysis unit; a trend prediction unit that predicts an emotion trend based on the emotion history recorded by the log recording unit; a relaxation content providing unit that provides relaxation music and video based on the emotion analyzed by the emotion analysis unit. A system characterized by:

2. The emotion analysis unit In addition to analyzing the baby's emotions, the system also analyzes the parent's stress level and provides mental care suggestions.

2. The system of claim 1.

3. The emotion analysis unit Based on the baby's emotional data, advice tailored to individual parenting styles is provided.

2. The system of claim 1.

4. The emotion analysis unit Analyzing the parent's emotional response to the baby's emotions and optimizing parent-child interaction 2. The system of claim 1.

5. The emotion analysis unit The results of the baby's emotion analysis are linked to other devices in the home to provide voice advice.

2. The system of claim 1.

6. The emotion analysis unit The baby's emotional data will be shared with other parenting apps to provide comprehensive parenting support.

2. The system of claim 1.

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